## What changes were proposed in this pull request?
These are no longer needed after https://issues.apache.org/jira/browse/SPARK-17183
cc cloud-fan
## How was this patch tested?
Existing parquet and orc tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15799 from ericl/sc-4929.
## What changes were proposed in this pull request?
This PR port RDD API to use commit protocol, the changes made here:
1. Add new internal helper class that saves an RDD using a Hadoop OutputFormat named `SparkNewHadoopWriter`, it's similar with `SparkHadoopWriter` but uses commit protocol. This class supports the newer `mapreduce` API, instead of the old `mapred` API which is supported by `SparkHadoopWriter`;
2. Rewrite `PairRDDFunctions.saveAsNewAPIHadoopDataset` function, so it uses commit protocol now.
## How was this patch tested?
Exsiting test cases.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15769 from jiangxb1987/rdd-commit.
## What changes were proposed in this pull request?
a follow up of https://github.com/apache/spark/pull/15688
## How was this patch tested?
updated test in `DDLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15805 from cloud-fan/truncate.
## What changes were proposed in this pull request?
In RewriteDistinctAggregates rewrite funtion,after the UDAF's childs are mapped to AttributeRefference, If the UDAF(such as ApproximatePercentile) has a foldable TypeCheck for the input, It will failed because the AttributeRefference is not foldable,then the UDAF is not resolved, and then nullify on the unresolved object will throw a Exception.
In this PR, only map Unfoldable child to AttributeRefference, this can avoid the UDAF's foldable TypeCheck. and then only Expand Unfoldable child, there is no need to Expand a static value(foldable value).
**Before sql result**
> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> org.apache.spark.sql.catalyst.analysis.UnresolvedException: Invalid call to dataType on unresolved object, tree: 'percentile_approx(CAST(src.`key` AS DOUBLE), CAST(0.99999BD AS DOUBLE), 10000)
> at org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute.dataType(unresolved.scala:92)
> at org.apache.spark.sql.catalyst.optimizer.RewriteDistinctAggregates$.org$apache$spark$sql$catalyst$optimizer$RewriteDistinctAggregates$$nullify(RewriteDistinctAggregates.scala:261)
**After sql result**
> select percentile_approxy(key,0.99999),count(distinct key),sume(distinc key) from src limit 1
> [498.0,309,79136]
## How was this patch tested?
Add a test case in HiveUDFSuit.
Author: root <root@iZbp1gsnrlfzjxh82cz80vZ.(none)>
Closes#15668 from windpiger/RewriteDistinctUDAFUnresolveExcep.
### What changes were proposed in this pull request?
Based on the discussion in [SPARK-18209](https://issues.apache.org/jira/browse/SPARK-18209). It doesn't really make sense to create permanent views based on temporary views or temporary UDFs.
To disallow the supports and issue the exceptions, this PR needs to detect whether a temporary view/UDF is being used when defining a permanent view. Basically, this PR can be split to two sub-tasks:
**Task 1:** detecting a temporary view from the query plan of view definition.
When finding an unresolved temporary view, Analyzer replaces it by a `SubqueryAlias` with the corresponding logical plan, which is stored in an in-memory HashMap. After replacement, it is impossible to detect whether the `SubqueryAlias` is added/generated from a temporary view. Thus, to detect the usage of a temporary view in view definition, this PR traverses the unresolved logical plan and uses the name of an `UnresolvedRelation` to detect whether it is a (global) temporary view.
**Task 2:** detecting a temporary UDF from the query plan of view definition.
Detecting usage of a temporary UDF in view definition is not straightfoward.
First, in the analyzed plan, we are having different forms to represent the functions. More importantly, some classes (e.g., `HiveGenericUDF`) are not accessible from `CreateViewCommand`, which is part of `sql/core`. Thus, we used the unanalyzed plan `child` of `CreateViewCommand` to detect the usage of a temporary UDF. Because the plan has already been successfully analyzed, we can assume the functions have been defined/registered.
Second, in Spark, the functions have four forms: Spark built-in functions, built-in hash functions, permanent UDFs and temporary UDFs. We do not have any direct way to determine whether a function is temporary or not. Thus, we introduced a function `isTemporaryFunction` in `SessionCatalog`. This function contains the detailed logics to determine whether a function is temporary or not.
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15764 from gatorsmile/blockTempFromPermViewCreation.
## What changes were proposed in this pull request?
This adds support for Hive variables:
* Makes values set via `spark-sql --hivevar name=value` accessible
* Adds `getHiveVar` and `setHiveVar` to the `HiveClient` interface
* Adds a SessionVariables trait for sessions like Hive that support variables (including Hive vars)
* Adds SessionVariables support to variable substitution
* Adds SessionVariables support to the SET command
## How was this patch tested?
* Adds a test to all supported Hive versions for accessing Hive variables
* Adds HiveVariableSubstitutionSuite
Author: Ryan Blue <blue@apache.org>
Closes#15738 from rdblue/SPARK-18086-add-hivevar-support.
## What changes were proposed in this pull request?
Add a function to check if two integers are compatible when invoking `acceptsType()` in `DataType`.
## How was this patch tested?
Manually.
E.g.
```
spark.sql("create table t3(a map<bigint, array<string>>)")
spark.sql("select * from t3 where a[1] is not null")
```
Before:
```
cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
org.apache.spark.sql.AnalysisException: cannot resolve 't.`a`[1]' due to data type mismatch: argument 2 requires bigint type, however, '1' is of int type.; line 1 pos 22
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:82)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:307)
```
After:
Run the sql queries above. No errors.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15448 from weiqingy/SPARK_17108.
### What changes were proposed in this pull request?
Currently, the Hive built-in `hash` function is not being used in Spark since Spark 2.0. The public interface does not allow users to unregister the Spark built-in functions. Thus, users will never use Hive's built-in `hash` function.
The only exception here is `TestHiveFunctionRegistry`, which allows users to unregister the built-in functions. Thus, we can load Hive's hash function in the test cases. If we disable it, 10+ test cases will fail because the results are different from the Hive golden answer files.
This PR is to remove `hash` from the list of `hiveFunctions` in `HiveSessionCatalog`. It will also remove `TestHiveFunctionRegistry`. This removal makes us easier to remove `TestHiveSessionState` in the future.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14498 from gatorsmile/removeHash.
## What changes were proposed in this pull request?
Previously `TRUNCATE TABLE ... PARTITION` will always truncate the whole table for data source tables, this PR fixes it and improve `InMemoryCatalog` to make this command work with it.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15688 from cloud-fan/truncate.
## What changes were proposed in this pull request?
For data source tables, we will put its table schema, partition columns, etc. to table properties, to work around some hive metastore issues, e.g. not case-preserving, bad decimal type support, etc.
We should also do this for hive serde tables, to reduce the difference between hive serde tables and data source tables, e.g. column names should be case preserving.
## How was this patch tested?
existing tests, and a new test in `HiveExternalCatalog`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14750 from cloud-fan/minor1.
## What changes were proposed in this pull request?
It seems the proximate cause of the test failures is that `cast(str as decimal)` in derby will raise an exception instead of returning NULL. This is a problem since Hive sometimes inserts `__HIVE_DEFAULT_PARTITION__` entries into the partition table as documented here: https://github.com/apache/hive/blob/trunk/metastore/src/java/org/apache/hadoop/hive/metastore/MetaStoreDirectSql.java#L1034
Basically, when these special default partitions are present, partition pruning pushdown using the SQL-direct mode will fail due this cast exception. As commented on in `MetaStoreDirectSql.java` above, this is normally fine since Hive falls back to JDO pruning, however when the pruning predicate contains an unsupported operator such as `>`, that will fail as well.
The only remaining question is why this behavior is nondeterministic. We know that when the test flakes, retries do not help, therefore the cause must be environmental. The current best hypothesis is that some config is different between different jenkins runs, which is why this PR prints out the Spark SQL and Hive confs for the test. The hope is that by comparing the config state for failure vs success we can isolate the root cause of the flakiness.
**Update:** we could not isolate the issue. It does not seem to be due to configuration differences. As such, I'm going to enable the non-flaky parts of the test since we are fairly confident these issues only occur with Derby (which is not used in production).
## How was this patch tested?
N/A
Author: Eric Liang <ekl@databricks.com>
Closes#15725 from ericl/print-confs-out.
hive.exec.stagingdir have no effect in spark2.0.1，
Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf`
Author: 福星 <fuxing@wacai.com>
Closes#15744 from ClassNotFoundExp/master.
## What changes were proposed in this pull request?
This patch renames partitionProviderIsHive to tracksPartitionsInCatalog, as the old name was too Hive specific.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15750 from rxin/SPARK-18244.
## What changes were proposed in this pull request?
This PR adds a new hash-based aggregate operator named `ObjectHashAggregateExec` that supports `TypedImperativeAggregate`, which may use arbitrary Java objects as aggregation states. Please refer to the [design doc](https://issues.apache.org/jira/secure/attachment/12834260/%5BDesign%20Doc%5D%20Support%20for%20Arbitrary%20Aggregation%20States.pdf) attached in [SPARK-17949](https://issues.apache.org/jira/browse/SPARK-17949) for more details about it.
The major benefit of this operator is better performance when evaluating `TypedImperativeAggregate` functions, especially when there are relatively few distinct groups. Functions like Hive UDAFs, `collect_list`, and `collect_set` may also benefit from this after being migrated to `TypedImperativeAggregate`.
The following feature flag is introduced to enable or disable the new aggregate operator:
- Name: `spark.sql.execution.useObjectHashAggregateExec`
- Default value: `true`
We can also configure the fallback threshold using the following SQL operation:
- Name: `spark.sql.objectHashAggregate.sortBased.fallbackThreshold`
- Default value: 128
Fallback to sort-based aggregation when more than 128 distinct groups are accumulated in the aggregation hash map. This number is intentionally made small to avoid GC problems since aggregation buffers of this operator may contain arbitrary Java objects.
This may be improved by implementing size tracking for this operator, but that can be done in a separate PR.
Code generation and size tracking are planned to be implemented in follow-up PRs.
## Benchmark results
### `ObjectHashAggregateExec` vs `SortAggregateExec`
The first benchmark compares `ObjectHashAggregateExec` and `SortAggregateExec` by evaluating `typed_count`, a testing `TypedImperativeAggregate` version of the SQL `count` function.
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 31251 / 31908 3.4 298.0 1.0X
object agg w/ group by w/o fallback 6903 / 7141 15.2 65.8 4.5X
object agg w/ group by w/ fallback 20945 / 21613 5.0 199.7 1.5X
sort agg w/o group by 4734 / 5463 22.1 45.2 6.6X
object agg w/o group by w/o fallback 4310 / 4529 24.3 41.1 7.3X
```
The next benchmark compares `ObjectHashAggregateExec` and `SortAggregateExec` by evaluating the Spark native version of `percentile_approx`.
Note that `percentile_approx` is so heavy an aggregate function that the bottleneck of the benchmark is evaluating the aggregate function itself rather than the aggregate operator since I couldn't run a large scale benchmark on my laptop. That's why the results are so close and looks counter-intuitive (aggregation with grouping is even faster than that aggregation without grouping).
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 3418 / 3530 0.6 1630.0 1.0X
object agg w/ group by w/o fallback 3210 / 3314 0.7 1530.7 1.1X
object agg w/ group by w/ fallback 3419 / 3511 0.6 1630.1 1.0X
sort agg w/o group by 4336 / 4499 0.5 2067.3 0.8X
object agg w/o group by w/o fallback 4271 / 4372 0.5 2036.7 0.8X
```
### Hive UDAF vs Spark AF
This benchmark compares the following two kinds of aggregate functions:
- "hive udaf": Hive implementation of `percentile_approx`, without partial aggregation supports, evaluated using `SortAggregateExec`.
- "spark af": Spark native implementation of `percentile_approx`, with partial aggregation support, evaluated using `ObjectHashAggregateExec`
The performance differences are mostly due to faster implementation and partial aggregation support in the Spark native version of `percentile_approx`.
This benchmark basically shows the performance differences between the worst case, where an aggregate function without partial aggregation support is evaluated using `SortAggregateExec`, and the best case, where a `TypedImperativeAggregate` with partial aggregation support is evaluated using `ObjectHashAggregateExec`.
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hive udaf w/o group by 5326 / 5408 0.0 81264.2 1.0X
spark af w/o group by 93 / 111 0.7 1415.6 57.4X
hive udaf w/ group by 3804 / 3946 0.0 58050.1 1.4X
spark af w/ group by w/o fallback 71 / 90 0.9 1085.7 74.8X
spark af w/ group by w/ fallback 98 / 111 0.7 1501.6 54.1X
```
### Real world benchmark
We also did a relatively large benchmark using a real world query involving `percentile_approx`:
- Hive UDAF implementation, sort-based aggregation, w/o partial aggregation support
24.77 minutes
- Native implementation, sort-based aggregation, w/ partial aggregation support
4.64 minutes
- Native implementation, object hash aggregator, w/ partial aggregation support
1.80 minutes
## How was this patch tested?
New unit tests and randomized test cases are added in `ObjectAggregateFunctionSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#15590 from liancheng/obj-hash-agg.
## What changes were proposed in this pull request?
I was reading this part of the code and was really confused by the "partition" parameter. This patch adds some documentation for it to reduce confusion in the future.
I also looked around other logical plans but most of them are either already documented, or pretty self-evident to people that know Spark SQL.
## How was this patch tested?
N/A - doc change only.
Author: Reynold Xin <rxin@databricks.com>
Closes#15749 from rxin/doc-improvement.
## What changes were proposed in this pull request?
This PR proposes to change the documentation for functions. Please refer the discussion from https://github.com/apache/spark/pull/15513
The changes include
- Re-indent the documentation
- Add examples/arguments in `extended` where the arguments are multiple or specific format (e.g. xml/ json).
For examples, the documentation was updated as below:
### Functions with single line usage
**Before**
- `pow`
``` sql
Usage: pow(x1, x2) - Raise x1 to the power of x2.
Extended Usage:
> SELECT pow(2, 3);
8.0
```
- `current_timestamp`
``` sql
Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.
Extended Usage:
No example for current_timestamp.
```
**After**
- `pow`
``` sql
Usage: pow(expr1, expr2) - Raises `expr1` to the power of `expr2`.
Extended Usage:
Examples:
> SELECT pow(2, 3);
8.0
```
- `current_timestamp`
``` sql
Usage: current_timestamp() - Returns the current timestamp at the start of query evaluation.
Extended Usage:
No example/argument for current_timestamp.
```
### Functions with (already) multiple line usage
**Before**
- `approx_count_distinct`
``` sql
Usage: approx_count_distinct(expr) - Returns the estimated cardinality by HyperLogLog++.
approx_count_distinct(expr, relativeSD=0.05) - Returns the estimated cardinality by HyperLogLog++
with relativeSD, the maximum estimation error allowed.
Extended Usage:
No example for approx_count_distinct.
```
- `percentile_approx`
``` sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive integer literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy]) - Returns the approximate
percentile array of column `col` at the given percentage array. Each value of the
percentage array must be between 0.0 and 1.0. The `accuracy` parameter (default: 10000) is
a positive integer literal which controls approximation accuracy at the cost of memory.
Higher value of `accuracy` yields better accuracy, `1.0/accuracy` is the relative error of
the approximation.
Extended Usage:
No example for percentile_approx.
```
**After**
- `approx_count_distinct`
``` sql
Usage:
approx_count_distinct(expr[, relativeSD]) - Returns the estimated cardinality by HyperLogLog++.
`relativeSD` defines the maximum estimation error allowed.
Extended Usage:
No example/argument for approx_count_distinct.
```
- `percentile_approx`
``` sql
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column `col` at the given
percentage array.
Extended Usage:
Examples:
> SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);
[10.0,10.0,10.0]
> SELECT percentile_approx(10.0, 0.5, 100);
10.0
```
## How was this patch tested?
Manually tested
**When examples are multiple**
``` sql
spark-sql> describe function extended reflect;
Function: reflect
Class: org.apache.spark.sql.catalyst.expressions.CallMethodViaReflection
Usage: reflect(class, method[, arg1[, arg2 ..]]) - Calls a method with reflection.
Extended Usage:
Examples:
> SELECT reflect('java.util.UUID', 'randomUUID');
c33fb387-8500-4bfa-81d2-6e0e3e930df2
> SELECT reflect('java.util.UUID', 'fromString', 'a5cf6c42-0c85-418f-af6c-3e4e5b1328f2');
a5cf6c42-0c85-418f-af6c-3e4e5b1328f2
```
**When `Usage` is in single line**
``` sql
spark-sql> describe function extended min;
Function: min
Class: org.apache.spark.sql.catalyst.expressions.aggregate.Min
Usage: min(expr) - Returns the minimum value of `expr`.
Extended Usage:
No example/argument for min.
```
**When `Usage` is already in multiple lines**
``` sql
spark-sql> describe function extended percentile_approx;
Function: percentile_approx
Class: org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile
Usage:
percentile_approx(col, percentage [, accuracy]) - Returns the approximate percentile value of numeric
column `col` at the given percentage. The value of percentage must be between 0.0
and 1.0. The `accuracy` parameter (default: 10000) is a positive numeric literal which
controls approximation accuracy at the cost of memory. Higher value of `accuracy` yields
better accuracy, `1.0/accuracy` is the relative error of the approximation.
When `percentage` is an array, each value of the percentage array must be between 0.0 and 1.0.
In this case, returns the approximate percentile array of column `col` at the given
percentage array.
Extended Usage:
Examples:
> SELECT percentile_approx(10.0, array(0.5, 0.4, 0.1), 100);
[10.0,10.0,10.0]
> SELECT percentile_approx(10.0, 0.5, 100);
10.0
```
**When example/argument is missing**
``` sql
spark-sql> describe function extended rank;
Function: rank
Class: org.apache.spark.sql.catalyst.expressions.Rank
Usage:
rank() - Computes the rank of a value in a group of values. The result is one plus the number
of rows preceding or equal to the current row in the ordering of the partition. The values
will produce gaps in the sequence.
Extended Usage:
No example/argument for rank.
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15677 from HyukjinKwon/SPARK-17963-1.
## What changes were proposed in this pull request?
Due to a limitation of hive metastore(table location must be directory path, not file path), we always store `path` for data source table in storage properties, instead of the `locationUri` field. However, we should not expose this difference to `CatalogTable` level, but just treat it as a hack in `HiveExternalCatalog`, like we store table schema of data source table in table properties.
This PR unifies `path` and `locationUri` outside of `HiveExternalCatalog`, both data source table and hive serde table should use the `locationUri` field.
This PR also unifies the way we handle default table location for managed table. Previously, the default table location of hive serde managed table is set by external catalog, but the one of data source table is set by command. After this PR, we follow the hive way and the default table location is always set by external catalog.
For managed non-file-based tables, we will assign a default table location and create an empty directory for it, the table location will be removed when the table is dropped. This is reasonable as metastore doesn't care about whether a table is file-based or not, and an empty table directory has no harm.
For external non-file-based tables, ideally we can omit the table location, but due to a hive metastore issue, we will assign a random location to it, and remove it right after the table is created. See SPARK-15269 for more details. This is fine as it's well isolated in `HiveExternalCatalog`.
To keep the existing behaviour of the `path` option, in this PR we always add the `locationUri` to storage properties using key `path`, before passing storage properties to `DataSource` as data source options.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15024 from cloud-fan/path.
## What changes were proposed in this pull request?
When a user appended a column using a "nondeterministic" function to a DataFrame, e.g., `rand`, `randn`, and `monotonically_increasing_id`, the expected semantic is the following:
- The value in each row should remain unchanged, as if we materialize the column immediately, regardless of later DataFrame operations.
However, since we use `TaskContext.getPartitionId` to get the partition index from the current thread, the values from nondeterministic columns might change if we call `union` or `coalesce` after. `TaskContext.getPartitionId` returns the partition index of the current Spark task, which might not be the corresponding partition index of the DataFrame where we defined the column.
See the unit tests below or JIRA for examples.
This PR uses the partition index from `RDD.mapPartitionWithIndex` instead of `TaskContext` and fixes the partition initialization logic in whole-stage codegen, normal codegen, and codegen fallback. `initializeStatesForPartition(partitionIndex: Int)` was added to `Projection`, `Nondeterministic`, and `Predicate` (codegen) and initialized right after object creation in `mapPartitionWithIndex`. `newPredicate` now returns a `Predicate` instance rather than a function for proper initialization.
## How was this patch tested?
Unit tests. (Actually I'm not very confident that this PR fixed all issues without introducing new ones ...)
cc: rxin davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#15567 from mengxr/SPARK-14393.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
Running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
Modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Author: eyal farago <eyal farago>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: eyal farago <eyal.farago@gmail.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#15718 from hvanhovell/SPARK-16839-2.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
There are a couple issues with the current 2.1 behavior when inserting into Datasource tables with partitions managed by Hive.
(1) OVERWRITE TABLE ... PARTITION will actually overwrite the entire table instead of just the specified partition.
(2) INSERT|OVERWRITE does not work with partitions that have custom locations.
This PR fixes both of these issues for Datasource tables managed by Hive. The behavior for legacy tables or when `manageFilesourcePartitions = false` is unchanged.
There is one other issue in that INSERT OVERWRITE with dynamic partitions will overwrite the entire table instead of just the updated partitions, but this behavior is pretty complicated to implement for Datasource tables. We should address that in a future release.
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes#15705 from ericl/sc-4942.
(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-17992)
## What changes were proposed in this pull request?
We recently added table partition pruning for partitioned Hive tables converted to using `TableFileCatalog`. When the Hive configuration option `hive.metastore.try.direct.sql` is set to `false`, Hive will throw an exception for unsupported filter expressions. For example, attempting to filter on an integer partition column will throw a `org.apache.hadoop.hive.metastore.api.MetaException`.
I discovered this behavior because VideoAmp uses the CDH version of Hive with a Postgresql metastore DB. In this configuration, CDH sets `hive.metastore.try.direct.sql` to `false` by default, and queries that filter on a non-string partition column will fail.
Rather than throw an exception in query planning, this patch catches this exception, logs a warning and returns all table partitions instead. Clients of this method are already expected to handle the possibility that the filters will not be honored.
## How was this patch tested?
A unit test was added.
Author: Michael Allman <michael@videoamp.com>
Closes#15673 from mallman/spark-17992-catch_hive_partition_filter_exception.
We now know it's a persistent environmental issue that is causing this test to sometimes fail. One hypothesis is that some configuration is leaked from another suite, and depending on suite ordering this can cause this test to fail.
I am planning on mining the jenkins logs to try to narrow down which suite could be causing this. For now, disable the test.
Author: Eric Liang <ekl@databricks.com>
Closes#15720 from ericl/disable-flaky-test.
## What changes were proposed in this pull request?
Simplify struct creation, especially the aspect of `CleanupAliases` which missed some aliases when handling trees created by `CreateStruct`.
This PR includes:
1. A failing test (create struct with nested aliases, some of the aliases survive `CleanupAliases`).
2. A fix that transforms `CreateStruct` into a `CreateNamedStruct` constructor, effectively eliminating `CreateStruct` from all expression trees.
3. A `NamePlaceHolder` used by `CreateStruct` when column names cannot be extracted from unresolved `NamedExpression`.
4. A new Analyzer rule that resolves `NamePlaceHolder` into a string literal once the `NamedExpression` is resolved.
5. `CleanupAliases` code was simplified as it no longer has to deal with `CreateStruct`'s top level columns.
## How was this patch tested?
running all tests-suits in package org.apache.spark.sql, especially including the analysis suite, making sure added test initially fails, after applying suggested fix rerun the entire analysis package successfully.
modified few tests that expected `CreateStruct` which is now transformed into `CreateNamedStruct`.
Credit goes to hvanhovell for assisting with this PR.
Author: eyal farago <eyal farago>
Author: eyal farago <eyal.farago@gmail.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Eyal Farago <eyal.farago@actimize.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: eyalfa <eyal.farago@gmail.com>
Closes#14444 from eyalfa/SPARK-16839_redundant_aliases_after_cleanupAliases.
## What changes were proposed in this pull request?
As reported on the jira, insert overwrite statement runs much slower in Spark, compared with hive-client.
It seems there is a patch [HIVE-11940](ba21806b77) which largely improves insert overwrite performance on Hive. HIVE-11940 is patched after Hive 2.0.0.
Because Spark SQL uses older Hive library, we can not benefit from such improvement.
The reporter verified that there is also a big performance gap between Hive 1.2.1 (520.037 secs) and Hive 2.0.1 (35.975 secs) on insert overwrite execution.
Instead of upgrading to Hive 2.0 in Spark SQL, which might not be a trivial task, this patch provides an approach to delete the partition before asking Hive to load data files into the partition.
Note: The case reported on the jira is insert overwrite to partition. Since `Hive.loadTable` also uses the function to replace files, insert overwrite to table should has the same issue. We can take the same approach to delete the table first. I will upgrade this to include this.
## How was this patch tested?
Jenkins tests.
There are existing tests using insert overwrite statement. Those tests should be passed. I added a new test to specially test insert overwrite into partition.
For performance issue, as I don't have Hive 2.0 environment, this needs the reporter to verify it. Please refer to the jira.
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#15667 from viirya/improve-hive-insertoverwrite.
## What changes were proposed in this pull request?
This patch introduces an internal commit protocol API that is used by the batch data source to do write commits. It currently has only one implementation that uses Hadoop MapReduce's OutputCommitter API. In the future, this commit API can be used to unify streaming and batch commits.
## How was this patch tested?
Should be covered by existing write tests.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#15707 from rxin/SPARK-18024-2.
## What changes were proposed in this pull request?
This will re-run the flaky test a few times after it fails. This will help determine if it's due to nondeterministic test setup, or because of some environment issue (e.g. leaked config from another test).
cc yhuai
Author: Eric Liang <ekl@databricks.com>
Closes#15708 from ericl/spark-18167-3.
## What changes were proposed in this pull request?
One possibility for this test flaking is that we have corrupted the partition schema somehow in the tests, which causes the cast to decimal to fail in the call. This should at least show us the actual partition values.
## How was this patch tested?
Run it locally, it prints out something like `ArrayBuffer(test(partcol=0), test(partcol=1), test(partcol=2), test(partcol=3), test(partcol=4))`.
Author: Eric Liang <ekl@databricks.com>
Closes#15701 from ericl/print-more-info.
## What changes were proposed in this pull request?
To reduce the number of components in SQL named *Catalog, rename *FileCatalog to *FileIndex. A FileIndex is responsible for returning the list of partitions / files to scan given a filtering expression.
```
TableFileCatalog => CatalogFileIndex
FileCatalog => FileIndex
ListingFileCatalog => InMemoryFileIndex
MetadataLogFileCatalog => MetadataLogFileIndex
PrunedTableFileCatalog => PrunedInMemoryFileIndex
```
cc yhuai marmbrus
## How was this patch tested?
N/A
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15634 from ericl/rename-file-provider.
## What changes were proposed in this pull request?
org.apache.spark.sql.hive.execution.SQLQuerySuite is flaking when hive partition pruning is enabled.
Based on the stack traces, it seems to be an old issue where Hive fails to cast a numeric partition column ("Invalid character string format for type DECIMAL"). There are two possibilities here: either we are somehow corrupting the partition table to have non-decimal values in that column, or there is a transient issue with Derby.
This PR logs the result of the retry when this exception is encountered, so we can confirm what is going on.
## How was this patch tested?
n/a
cc yhuai
Author: Eric Liang <ekl@databricks.com>
Closes#15676 from ericl/spark-18167.
## What changes were proposed in this pull request?
Issue:
Querying on a global temp view throws Table or view not found exception.
Fix:
Update the lookupRelation in HiveSessionCatalog to check for global temp views similar to the SessionCatalog.lookupRelation.
Before fix:
Querying on a global temp view ( for. e.g.: select * from global_temp.v1) throws Table or view not found exception
After fix:
Query succeeds and returns the right result.
## How was this patch tested?
- Two unit tests are added to check for global temp view for the code path when hive support is enabled.
- Regression unit tests were run successfully. ( build/sbt -Phive hive/test, build/sbt sql/test, build/sbt catalyst/test)
Author: Sunitha Kambhampati <skambha@us.ibm.com>
Closes#15649 from skambha/lookuprelationChanges.
## What changes were proposed in this pull request?
We should follow hive table and also store partition spec in metastore for data source table.
This brings 2 benefits:
1. It's more flexible to manage the table data files, as users can use `ADD PARTITION`, `DROP PARTITION` and `RENAME PARTITION`
2. We don't need to cache all file status for data source table anymore.
## How was this patch tested?
existing tests.
Author: Eric Liang <ekl@databricks.com>
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekhliang@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15515 from cloud-fan/partition.
## What changes were proposed in this pull request?
Currently we always lowercase the partition columns of partition spec in parser, with the assumption that table partition columns are always lowercased.
However, this is not true for data source tables, which are case preserving. It's safe for now because data source tables don't store partition spec in metastore and don't support `ADD PARTITION`, `DROP PARTITION`, `RENAME PARTITION`, but we should make our code future-proof.
This PR makes partition spec case preserving at parser, and improve the `PreprocessTableInsertion` analyzer rule to normalize the partition columns in partition spec, w.r.t. the table partition columns.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15566 from cloud-fan/partition-spec.
### What changes were proposed in this pull request?
This follow-up PR is for addressing the [comment](https://github.com/apache/spark/pull/15048).
We added two test cases based on the suggestion from yhuai . One is a new test case using the `saveAsTable` API to create a data source table. Another is for CTAS on Hive serde table.
Note: No need to backport this PR to 2.0. Will submit a new PR to backport the whole fix with new test cases to Spark 2.0
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15459 from gatorsmile/ctasOptimizedTestCases.
## What changes were proposed in this pull request?
Simplify/cleanup TableFileCatalog:
1. pass a `CatalogTable` instead of `databaseName` and `tableName` into `TableFileCatalog`, so that we don't need to fetch table metadata from metastore again
2. In `TableFileCatalog.filterPartitions0`, DO NOT set `PartitioningAwareFileCatalog.BASE_PATH_PARAM`. According to the [classdoc](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L189-L209), the default value of `basePath` already satisfies our need. What's more, if we set this parameter, we may break the case 2 which is metioned in the classdoc.
3. add `equals` and `hashCode` to `TableFileCatalog`
4. add `SessionCatalog.listPartitionsByFilter` which handles case sensitivity.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15568 from cloud-fan/table-file-catalog.
## What changes were proposed in this pull request?
Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15382 from srowen/SPARK-17810.
## What changes were proposed in this pull request?
The testsuite `HiveDataFrameAnalyticsSuite` has nothing to do with HIVE, we should move it to package `sql`.
The original test cases in that suite are splited into two existing testsuites: `DataFrameAggregateSuite` tests for the functions and ~~`SQLQuerySuite`~~`SQLQueryTestSuite` tests for the SQL statements.
## How was this patch tested?
~~Modified `SQLQuerySuite` in package `sql`.~~
Add query file for `SQLQueryTestSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15582 from jiangxb1987/group-analytics-test.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-18038
This was a suggestion by rxin over one of the dev list discussion : http://apache-spark-developers-list.1001551.n3.nabble.com/Project-not-preserving-child-partitioning-td19417.html
His words:
>> It would be better (safer) to move the output partitioning definition into each of the operator and remove it from UnaryExecNode.
With this PR, following is the output partitioning and ordering for all the impls of `UnaryExecNode`.
UnaryExecNode's impl | outputPartitioning | outputOrdering | comment
------------ | ------------- | ------------ | ------------
AppendColumnsExec | child's | Nil | child's ordering can be used
AppendColumnsWithObjectExec | child's | Nil | child's ordering can be used
BroadcastExchangeExec | BroadcastPartitioning | Nil | -
CoalesceExec | UnknownPartitioning | Nil | -
CollectLimitExec | SinglePartition | Nil | -
DebugExec | child's | Nil | child's ordering can be used
DeserializeToObjectExec | child's | Nil | child's ordering can be used
ExpandExec | UnknownPartitioning | Nil | -
FilterExec | child's | child's | -
FlatMapGroupsInRExec | child's | Nil | child's ordering can be used
GenerateExec | child's | Nil | need to dig more
GlobalLimitExec | child's | child's | -
HashAggregateExec | child's | Nil | -
InputAdapter | child's | child's | -
InsertIntoHiveTable | child's | Nil | terminal node, doesn't need partitioning
LocalLimitExec | child's | child's | -
MapElementsExec | child's | child's | -
MapGroupsExec | child's | Nil | child's ordering can be used
MapPartitionsExec | child's | Nil | child's ordering can be used
ProjectExec | child's | child's | -
SampleExec | child's | Nil | child's ordering can be used
ScriptTransformation | child's | Nil | child's ordering can be used
SerializeFromObjectExec | child's | Nil | child's ordering can be used
ShuffleExchange | custom | Nil | -
SortAggregateExec | child's | sort over grouped exprs | -
SortExec | child's | custom | -
StateStoreRestoreExec | child's | Nil | child's ordering can be used
StateStoreSaveExec | child's | Nil | child's ordering can be used
SubqueryExec | child's | child's | -
TakeOrderedAndProjectExec | SinglePartition | custom | -
WholeStageCodegenExec | child's | child's | -
WindowExec | child's | child's | -
## How was this patch tested?
This does NOT change any existing functionality so relying on existing tests
Author: Tejas Patil <tejasp@fb.com>
Closes#15575 from tejasapatil/SPARK-18038_UnaryNodeExec_output_partitioning.
## What changes were proposed in this pull request?
Jira: https://issues.apache.org/jira/browse/SPARK-18035
In HiveInspectors, I saw that converting Java map to Spark's `ArrayBasedMapData` spent quite sometime in buffer copying : https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L658
The reason being `map.toSeq` allocates a new buffer and copies the map entries to it: https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/MapLike.scala#L323
This copy is not needed as we get rid of it once we extract the key and value arrays.
Here is the call trace:
```
org.apache.spark.sql.hive.HiveInspectors$$anonfun$unwrapperFor$41.apply(HiveInspectors.scala:664)
scala.collection.AbstractMap.toSeq(Map.scala:59)
scala.collection.MapLike$class.toSeq(MapLike.scala:323)
scala.collection.AbstractMap.toBuffer(Map.scala:59)
scala.collection.MapLike$class.toBuffer(MapLike.scala:326)
scala.collection.AbstractTraversable.copyToBuffer(Traversable.scala:104)
scala.collection.TraversableOnce$class.copyToBuffer(TraversableOnce.scala:275)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
scala.collection.AbstractIterable.foreach(Iterable.scala:54)
scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
scala.collection.Iterator$class.foreach(Iterator.scala:893)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
```
Also, earlier code was populating keys and values arrays separately by iterating twice. The PR avoids double iteration of the map and does it in one iteration.
EDIT: During code review, there were several more places in the code which were found to do similar thing. The PR dedupes those instances and introduces convenient APIs which are performant and memory efficient
## Performance gains
The number is subjective and depends on how many map columns are accessed in the query and average entries per map. For one the queries that I tried out, I saw 3% CPU savings (end-to-end) for the query.
## How was this patch tested?
This does not change the end result produced so relying on existing tests.
Author: Tejas Patil <tejasp@fb.com>
Closes#15573 from tejasapatil/SPARK-18035_avoid_toSeq.
## What changes were proposed in this pull request?
In SPARK-16980, we removed the full in-memory cache of table partitions in favor of loading only needed partitions from the metastore. This greatly improves the initial latency of queries that only read a small fraction of table partitions.
However, since the metastore does not store file statistics, we need to discover those from remote storage. With the loss of the in-memory file status cache this has to happen on each query, increasing the latency of repeated queries over the same partitions.
The proposal is to add back a per-table cache of partition contents, i.e. Map[Path, Array[FileStatus]]. This cache would be retained per-table, and can be invalidated through refreshTable() and refreshByPath(). Unlike the prior cache, it can be incrementally updated as new partitions are read.
## How was this patch tested?
Existing tests and new tests in `HiveTablePerfStatsSuite`.
cc mallman
Author: Eric Liang <ekl@databricks.com>
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#15539 from ericl/meta-cache.
## What changes were proposed in this pull request?
This patch adds a new "path" method on OutputWriter that returns the path of the file written by the OutputWriter. This is part of the necessary work to consolidate structured streaming and batch write paths.
The batch write path has a nice feature that each data source can define the extension of the files, and allow Spark to specify the staging directory and the prefix for the files. However, in the streaming path we need to collect the list of files written, and there is no interface right now to do that.
## How was this patch tested?
N/A - there is no behavior change and this should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15580 from rxin/SPARK-18042.
## What changes were proposed in this pull request?
In `PruneFileSourcePartitions`, we will replace the `LogicalRelation` with a pruned one. However, this replacement may change the output of the `LogicalRelation` if it doesn't have `expectedOutputAttributes`. This PR fixes it.
## How was this patch tested?
the new `PruneFileSourcePartitionsSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15569 from cloud-fan/partition-bug.
## What changes were proposed in this pull request?
Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing.
On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions.
This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically.
There are also some other minor cleanups:
- Removed the UUID passed through generic Configuration string
- Some minor rewrites for better clarity
- Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning
## How was this patch tested?
This should be covered by existing data source tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#15562 from rxin/SPARK-18021.
## What changes were proposed in this pull request?
Jira : https://issues.apache.org/jira/browse/SPARK-17698
`ExtractEquiJoinKeys` is incorrectly using filter predicates as the join condition for joins. `canEvaluate` [0] tries to see if the an `Expression` can be evaluated using output of a given `Plan`. In case of filter predicates (eg. `a.id='1'`), the `Expression` passed for the right hand side (ie. '1' ) is a `Literal` which does not have any attribute references. Thus `expr.references` is an empty set which theoretically is a subset of any set. This leads to `canEvaluate` returning `true` and `a.id='1'` is treated as a join predicate. While this does not lead to incorrect results but in case of bucketed + sorted tables, we might miss out on avoiding un-necessary shuffle + sort. See example below:
[0] : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala#L91
eg.
```
val df = (1 until 10).toDF("id").coalesce(1)
hc.sql("DROP TABLE IF EXISTS table1").collect
df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table1")
hc.sql("DROP TABLE IF EXISTS table2").collect
df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table2")
sqlContext.sql("""
SELECT a.id, b.id
FROM table1 a
FULL OUTER JOIN table2 b
ON a.id = b.id AND a.id='1' AND b.id='1'
""").explain(true)
```
BEFORE: This is doing shuffle + sort over table scan outputs which is not needed as both tables are bucketed and sorted on the same columns and have same number of buckets. This should be a single stage job.
```
SortMergeJoin [id#38, cast(id#38 as double), 1.0], [id#39, 1.0, cast(id#39 as double)], FullOuter
:- *Sort [id#38 ASC NULLS FIRST, cast(id#38 as double) ASC NULLS FIRST, 1.0 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#38, cast(id#38 as double), 1.0, 200)
: +- *FileScan parquet default.table1[id#38] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
+- *Sort [id#39 ASC NULLS FIRST, 1.0 ASC NULLS FIRST, cast(id#39 as double) ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#39, 1.0, cast(id#39 as double), 200)
+- *FileScan parquet default.table2[id#39] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
```
AFTER :
```
SortMergeJoin [id#32], [id#33], FullOuter, ((cast(id#32 as double) = 1.0) && (cast(id#33 as double) = 1.0))
:- *FileScan parquet default.table1[id#32] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
+- *FileScan parquet default.table2[id#33] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int>
```
## How was this patch tested?
- Added a new test case for this scenario : `SPARK-17698 Join predicates should not contain filter clauses`
- Ran all the tests in `BucketedReadSuite`
Author: Tejas Patil <tejasp@fb.com>
Closes#15272 from tejasapatil/SPARK-17698_join_predicate_filter_clause.
## What changes were proposed in this pull request?
SHOW COLUMNS command validates the user supplied database
name with database name from qualified table name name to make
sure both of them are consistent. This comparison should respect
case sensitivity.
## How was this patch tested?
Added tests in DDLSuite and existing tests were moved to use new sql based test infrastructure.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15423 from dilipbiswal/dkb_show_column_fix.
## What changes were proposed in this pull request?
Currently, Spark 2.0 raises an `input path does not exist` AnalysisException if the file name contains '*'. It is misleading since it occurs when there exist some matched files. Also, it was a supported feature in Spark 1.6.2. This PR aims to support wildcard characters in filename for `LOAD DATA LOCAL INPATH` SQL command like Spark 1.6.2.
**Reported Error Scenario**
```scala
scala> sql("CREATE TABLE t(a string)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("LOAD DATA LOCAL INPATH '/tmp/x*' INTO TABLE t")
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /tmp/x*;
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15376 from dongjoon-hyun/SPARK-17796.
## What changes were proposed in this pull request?
There was a bug introduced in https://github.com/apache/spark/pull/14690 which broke refreshByPath with converted hive tables (though, it turns out it was very difficult to refresh converted hive tables anyways, since you had to specify the exact path of one of the partitions).
This changes refreshByPath to invalidate by prefix instead of exact match, and fixes the issue.
cc sameeragarwal for refreshByPath changes
mallman
## How was this patch tested?
Extended unit test.
Author: Eric Liang <ekl@databricks.com>
Closes#15521 from ericl/fix-caching.
## What changes were proposed in this pull request?
This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait.
In summary,
```
MetadataLogFileCatalog extends PartitioningAwareFileCatalog
ListingFileCatalog extends PartitioningAwareFileCatalog
PartitioningAwareFileCatalog extends FileCatalog
TableFileCatalog extends FileCatalog
```
(note that this is a re-submission of https://github.com/apache/spark/pull/15518 which got reverted)
## How was this patch tested?
Existing tests
Author: Eric Liang <ekl@databricks.com>
Closes#15533 from ericl/fix-scalastyle-revert.
## What changes were proposed in this pull request?
Debug mode should work for corrupted table, so that we can really debug
## How was this patch tested?
new test in `MetastoreDataSourcesSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15528 from cloud-fan/debug.
## What changes were proposed in this pull request?
This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait.
In summary,
```
MetadataLogFileCatalog extends PartitioningAwareFileCatalog
ListingFileCatalog extends PartitioningAwareFileCatalog
PartitioningAwareFileCatalog extends FileCatalog
TableFileCatalog extends FileCatalog
```
cc cloud-fan mallman
## How was this patch tested?
Existing tests
Author: Eric Liang <ekl@databricks.com>
Closes#15518 from ericl/refactor-session-file-catalog.
## What changes were proposed in this pull request?
Reopens the closed PR https://github.com/apache/spark/pull/15190
(Please refer to the above link for review comments on the PR)
Make sure the hive.default.fileformat is used to when creating the storage format metadata.
Output
``` SQL
scala> spark.sql("SET hive.default.fileformat=orc")
res1: org.apache.spark.sql.DataFrame = [key: string, value: string]
scala> spark.sql("CREATE TABLE tmp_default(id INT)")
res2: org.apache.spark.sql.DataFrame = []
```
Before
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
After
```SQL
scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println)
..
[# Storage Information,,]
[SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,]
[InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,]
[OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,]
[Compressed:,No,]
[Storage Desc Parameters:,,]
[ serialization.format,1,]
```
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Added new tests to HiveDDLCommandSuite, SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#15495 from dilipbiswal/orc2.
(This PR addresses https://issues.apache.org/jira/browse/SPARK-16980.)
## What changes were proposed in this pull request?
In a new Spark session, when a partitioned Hive table is converted to use Spark's `HadoopFsRelation` in `HiveMetastoreCatalog`, metadata for every partition of that table are retrieved from the metastore and loaded into driver memory. In addition, every partition's metadata files are read from the filesystem to perform schema inference.
If a user queries such a table with predicates which prune that table's partitions, we would like to be able to answer that query without consulting partition metadata which are not involved in the query. When querying a table with a large number of partitions for some data from a small number of partitions (maybe even a single partition), the current conversion strategy is highly inefficient. I suspect this scenario is not uncommon in the wild.
In addition to being inefficient in running time, the current strategy is inefficient in its use of driver memory. When the sum of the number of partitions of all tables loaded in a driver reaches a certain level (somewhere in the tens of thousands), their cached data exhaust all driver heap memory in the default configuration. I suspect this scenario is less common (in that not too many deployments work with tables with tens of thousands of partitions), however this does illustrate how large the memory footprint of this metadata can be. With tables with hundreds or thousands of partitions, I would expect the `HiveMetastoreCatalog` table cache to represent a significant portion of the driver's heap space.
This PR proposes an alternative approach. Basically, it makes four changes:
1. It adds a new method, `listPartitionsByFilter` to the Catalyst `ExternalCatalog` trait which returns the partition metadata for a given sequence of partition pruning predicates.
1. It refactors the `FileCatalog` type hierarchy to include a new `TableFileCatalog` to efficiently return files only for partitions matching a sequence of partition pruning predicates.
1. It removes partition loading and caching from `HiveMetastoreCatalog`.
1. It adds a new Catalyst optimizer rule, `PruneFileSourcePartitions`, which applies a plan's partition-pruning predicates to prune out unnecessary partition files from a `HadoopFsRelation`'s underlying file catalog.
The net effect is that when a query over a partitioned Hive table is planned, the analyzer retrieves the table metadata from `HiveMetastoreCatalog`. As part of this operation, the `HiveMetastoreCatalog` builds a `HadoopFsRelation` with a `TableFileCatalog`. It does not load any partition metadata or scan any files. The optimizer prunes-away unnecessary table partitions by sending the partition-pruning predicates to the relation's `TableFileCatalog `. The `TableFileCatalog` in turn calls the `listPartitionsByFilter` method on its external catalog. This queries the Hive metastore, passing along those filters.
As a bonus, performing partition pruning during optimization leads to a more accurate relation size estimate. This, along with c481bdf, can lead to automatic, safe application of the broadcast optimization in a join where it might previously have been omitted.
## Open Issues
1. This PR omits partition metadata caching. I can add this once the overall strategy for the cold path is established, perhaps in a future PR.
1. This PR removes and omits partitioned Hive table schema reconciliation. As a result, it fails to find Parquet schema columns with upper case letters because of the Hive metastore's case-insensitivity. This issue may be fixed by #14750, but that PR appears to have stalled. ericl has contributed to this PR a workaround for Parquet wherein schema reconciliation occurs at query execution time instead of planning. Whether ORC requires a similar patch is an open issue.
1. This PR omits an implementation of `listPartitionsByFilter` for the `InMemoryCatalog`.
1. This PR breaks parquet log output redirection during query execution. I can work around this by running `Class.forName("org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$")` first thing in a Spark shell session, but I haven't figured out how to fix this properly.
## How was this patch tested?
The current Spark unit tests were run, and some ad-hoc tests were performed to validate that only the necessary partition metadata is loaded.
Author: Michael Allman <michael@videoamp.com>
Author: Eric Liang <ekl@databricks.com>
Author: Eric Liang <ekhliang@gmail.com>
Closes#14690 from mallman/spark-16980-lazy_partition_fetching.
## What changes were proposed in this pull request?
This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#15360 from wzhfy/colStats2.
## What changes were proposed in this pull request?
`HiveExternalCatalog` should be the only interface to talk to the hive metastore. In `MetastoreRelation` we can just use `ExternalCatalog` instead of `HiveClient` to interact with hive metastore, and add missing API in `ExternalCatalog`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15460 from cloud-fan/relation.
## What changes were proposed in this pull request?
correct the expected type from Length function to be Int
## How was this patch tested?
Test runs on little endian and big endian platforms
Author: Pete Robbins <robbinspg@gmail.com>
Closes#15464 from robbinspg/SPARK-17827.
### What changes were proposed in this pull request?
Hive allows users to change the table type from `Managed` to `External` or from `External` to `Managed` by altering table's property `EXTERNAL`. See the JIRA: https://issues.apache.org/jira/browse/HIVE-1329
So far, Spark SQL does not correctly support it, although users can do it. Many assumptions are broken in the implementation. Thus, this PR is to disallow users to change it.
In addition, we also do not allow users to set the property `EXTERNAL` when creating a table.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15230 from gatorsmile/alterTableSetExternal.
## What changes were proposed in this pull request?
Currently `HiveExternalCatalog` will filter out the Spark SQL internal table properties, e.g. `spark.sql.sources.provider`, `spark.sql.sources.schema`, etc. This is reasonable for external users as they don't want to see these internal properties in `DESC TABLE`.
However, as a Spark developer, sometimes we do wanna see the raw table properties. This PR adds a new internal SQL conf, `spark.sql.debug`, to enable debug mode and keep these raw table properties.
This config can also be used in similar places where we wanna retain debug information in the future.
## How was this patch tested?
new test in MetastoreDataSourcesSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15458 from cloud-fan/debug.
## What changes were proposed in this pull request?
SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.
Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.
## How was this patch tested?
new tests in SQLConfSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15295 from cloud-fan/global-conf.
## What changes were proposed in this pull request?
Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.
changes for `SessionCatalog`:
1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.
changes for SQL commands:
1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.
changes for other public API
1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`
## How was this patch tested?
new tests in `SQLViewSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14897 from cloud-fan/global-temp-view.
## What changes were proposed in this pull request?
Currently we use the same rule to parse top level and nested data fields. For example:
```
create table tbl_x(
id bigint,
nested struct<col1:string,col2:string>
)
```
Shows both syntaxes. In this PR we split this rule in a top-level and nested rule.
Before this PR,
```
sql("CREATE TABLE my_tab(column1: INT)")
```
works fine.
After this PR, it will throw a `ParseException`:
```
scala> sql("CREATE TABLE my_tab(column1: INT)")
org.apache.spark.sql.catalyst.parser.ParseException:
no viable alternative at input 'CREATE TABLE my_tab(column1:'(line 1, pos 27)
```
## How was this patch tested?
Add new testcases in `SparkSqlParserSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15346 from jiangxb1987/cdt.
## What changes were proposed in this pull request?
This PR modified the test case `test("script")` to use resource path for `test_script.sh`. Make the test case portable (even in IntelliJ).
## How was this patch tested?
Passed the test case.
Before:
Run `test("script")` in IntelliJ:
```
Caused by: org.apache.spark.SparkException: Subprocess exited with status 127. Error: bash: src/test/resources/test_script.sh: No such file or directory
```
After:
Test passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#15246 from weiqingy/hivetest.
## What changes were proposed in this pull request?
In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`.
The code below illustrates the immutability issue with InternalRow:
```scala
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
val struct = new GenericMutableRow(1)
val row = InternalRow(struct, 1)
println(row)
scala> [[null], 1]
struct.setInt(0, 42)
println(row)
scala> [[42], 1]
```
This might be somewhat controversial, so feedback is appreciated.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#15333 from hvanhovell/SPARK-17761.
## What changes were proposed in this pull request?
Currently, Spark raises `RuntimeException` when creating a view with timestamp with INTERVAL arithmetic like the following. The root cause is the arithmetic expression, `TimeAdd`, was transformed into `timeadd` function as a VIEW definition. This PR fixes the SQL definition of `TimeAdd` and `TimeSub` expressions.
```scala
scala> sql("CREATE TABLE dates (ts TIMESTAMP)")
scala> sql("CREATE VIEW view1 AS SELECT ts + INTERVAL 1 DAY FROM dates")
java.lang.RuntimeException: Failed to analyze the canonicalized SQL: ...
```
## How was this patch tested?
Pass Jenkins with a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#15318 from dongjoon-hyun/SPARK-17750.
## What changes were proposed in this pull request?
Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals.
This implications in tests are limited to a single Hive compatibility test.
## How was this patch tested?
Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14828 from hvanhovell/SPARK-17258.
## What changes were proposed in this pull request?
Added VoidObjectInspector to the list of PrimitiveObjectInspectors
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Executing following query was failing.
select SOME_UDAF*(a.arr)
from (
select Array(null) as arr from dim_one_row
) a
After the fix, I am getting the correct output:
res0: Array[org.apache.spark.sql.Row] = Array([null])
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#15337 from seyfe/add_void_object_inspector.
## What changes were proposed in this pull request?
Generate basic column statistics for all the atomic types:
- numeric types: max, min, num of nulls, ndv (number of distinct values)
- date/timestamp types: they are also represented as numbers internally, so they have the same stats as above.
- string: avg length, max length, num of nulls, ndv
- binary: avg length, max length, num of nulls
- boolean: num of nulls, num of trues, num of falsies
Also support storing and loading these statistics.
One thing to notice:
We support analyzing columns independently, e.g.:
sql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;`
sql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;`
when running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`:
`ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;`
## How was this patch tested?
add unit tests
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#15090 from wzhfy/colStats.
## What changes were proposed in this pull request?
When wrapping catalyst datatypes to Hive data type, wrap function was doing an expensive pattern matching which was consuming around 11% of cpu time. Avoid the pattern matching by returning the wrapper only once and reuse it.
## How was this patch tested?
Tested by running the job on cluster and saw around 8% cpu improvements.
Author: Sital Kedia <skedia@fb.com>
Closes#15064 from sitalkedia/skedia/hive_wrapper.
## What changes were proposed in this pull request?
This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed.
This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed.
## How was this patch tested?
Tested manually for now.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#15245 from JoshRosen/SPARK-17666-close-recordreader.
## What changes were proposed in this pull request?
After #15054 , there is no place in Spark SQL that need `SessionCatalog.tableExists` to check temp views, so this PR makes `SessionCatalog.tableExists` only check permanent table/view and removes some hacks.
This PR also improves the `getTempViewOrPermanentTableMetadata` that is introduced in #15054 , to make the code simpler.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15160 from cloud-fan/exists.
## What changes were proposed in this pull request?
Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf`
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14634 from cloud-fan/bug.
### What changes were proposed in this pull request?
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example,
```
Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`';
```
- When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example,
```
Attempted to unset non-existent property 'p' in table '`testView`';
```
- When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error:
```
ANALYZE TABLE is not supported for Project
```
- When inserting into a temporary view that is generated from `Range`, we will get the following error message:
```
assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false
+- Project [1 AS 1#20]
+- OneRowRelation$
```
This PR is to fix the above four issues.
### How was this patch tested?
Added multiple test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15054 from gatorsmile/tempViewDDL.
## What changes were proposed in this pull request?
In `SessionCatalog`, we have several operations(`tableExists`, `dropTable`, `loopupRelation`, etc) that handle both temp views and metastore tables/views. This brings some bugs to DDL commands that want to handle temp view only or metastore table/view only. These bugs are:
1. `CREATE TABLE USING` will fail if a same-name temp view exists
2. `Catalog.dropTempView`will un-cache and drop metastore table if a same-name table exists
3. `saveAsTable` will fail or have unexpected behaviour if a same-name temp view exists.
These bug fixes are pulled out from https://github.com/apache/spark/pull/14962 and targets both master and 2.0 branch
## How was this patch tested?
new regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#15099 from cloud-fan/fix-view.
### What changes were proposed in this pull request?
In Spark 2.1, we introduced a new internal provider `hive` for telling Hive serde tables from data source tables. This PR is to block users to specify this in `DataFrameWriter` and SQL APIs.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15073 from gatorsmile/formatHive.
## What changes were proposed in this pull request?
This PR fixes all the instances which was fixed in the previous PR.
To make sure, I manually debugged and also checked the Scala source. `length` in [LinearSeqOptimized.scala#L49-L57](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/LinearSeqOptimized.scala#L49-L57) is O(n). Also, `size` calls `length` via [SeqLike.scala#L106](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/SeqLike.scala#L106).
For debugging, I have created these as below:
```scala
ArrayBuffer(1, 2, 3)
Array(1, 2, 3)
List(1, 2, 3)
Seq(1, 2, 3)
```
and then called `size` and `length` for each to debug.
## How was this patch tested?
I ran the bash as below on Mac
```bash
find . -name *.scala -type f -exec grep -il "while (.*\\.length)" {} \; | grep "src/main"
find . -name *.scala -type f -exec grep -il "while (.*\\.size)" {} \; | grep "src/main"
```
and then checked each.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15093 from HyukjinKwon/SPARK-17480-followup.
### What changes were proposed in this pull request?
For the following `ALTER TABLE` DDL, we should issue an exception when the target table is a `VIEW`:
```SQL
ALTER TABLE viewName SET LOCATION '/path/to/your/lovely/heart'
ALTER TABLE viewName SET SERDE 'whatever'
ALTER TABLE viewName SET SERDEPROPERTIES ('x' = 'y')
ALTER TABLE viewName PARTITION (a=1, b=2) SET SERDEPROPERTIES ('x' = 'y')
ALTER TABLE viewName ADD IF NOT EXISTS PARTITION (a='4', b='8')
ALTER TABLE viewName DROP IF EXISTS PARTITION (a='2')
ALTER TABLE viewName RECOVER PARTITIONS
ALTER TABLE viewName PARTITION (a='1', b='q') RENAME TO PARTITION (a='100', b='p')
```
In addition, `ALTER TABLE RENAME PARTITION` is unable to handle data source tables, just like the other `ALTER PARTITION` commands. We should issue an exception instead.
### How was this patch tested?
Added a few test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15004 from gatorsmile/altertable.
## What changes were proposed in this pull request?
Currently, ORDER BY clause returns nulls value according to sorting order (ASC|DESC), considering null value is always smaller than non-null values.
However, SQL2003 standard support NULLS FIRST or NULLS LAST to allow users to specify whether null values should be returned first or last, regardless of sorting order (ASC|DESC).
This PR is to support this new feature.
## How was this patch tested?
New test cases are added to test NULLS FIRST|LAST for regular select queries and windowing queries.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Xin Wu <xinwu@us.ibm.com>
Closes#14842 from xwu0226/SPARK-10747.
### What changes were proposed in this pull request?
As explained in https://github.com/apache/spark/pull/14797:
>Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.
We should not optimize the query in CTAS more than once. For example,
```Scala
spark.range(99, 101).createOrReplaceTempView("tab1")
val sqlStmt = "SELECT id, cast(id as long) * cast('1.0' as decimal(38, 18)) as num FROM tab1"
sql(s"CREATE TABLE tab2 USING PARQUET AS $sqlStmt")
checkAnswer(spark.table("tab2"), sql(sqlStmt))
```
Before this PR, the results do not match
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
![100,100.000000000000000000] [100,null]
[99,99.000000000000000000] [99,99.000000000000000000]
```
After this PR, the results match.
```
+---+----------------------+
|id |num |
+---+----------------------+
|99 |99.000000000000000000 |
|100|100.000000000000000000|
+---+----------------------+
```
In this PR, we do not treat the `query` in CTAS as a child. Thus, the `query` will not be optimized when optimizing CTAS statement. However, we still need to analyze it for normalizing and verifying the CTAS in the Analyzer. Thus, we do it in the analyzer rule `PreprocessDDL`, because so far only this rule needs the analyzed plan of the `query`.
### How was this patch tested?
Added a test
Author: gatorsmile <gatorsmile@gmail.com>
Closes#15048 from gatorsmile/ctasOptimized.
## What changes were proposed in this pull request?
Scala's List.length method is O(N) and it makes the gatherCompressibilityStats function O(N^2). Eliminate the List.length calls by writing it in Scala way.
https://github.com/scala/scala/blob/2.10.x/src/library/scala/collection/LinearSeqOptimized.scala#L36
As suggested. Extended the fix to HiveInspectors and AggregationIterator classes as well.
## How was this patch tested?
Profiled a Spark job and found that CompressibleColumnBuilder is using 39% of the CPU. Out of this 39% CompressibleColumnBuilder->gatherCompressibilityStats is using 23% of it. 6.24% of the CPU is spend on List.length which is called inside gatherCompressibilityStats.
After this change we started to save 6.24% of the CPU.
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#15032 from seyfe/gatherCompressibilityStats.
## What changes were proposed in this pull request?
If a user provides listeners inside the Hive Conf, the configuration for these listeners are passed to the Hive Execution Client as well. This may cause issues for two reasons:
1. The Execution Client will actually generate garbage
2. The listener class needs to be both in the Spark Classpath and Hive Classpath
This PR empties the listener configurations in `HiveUtils.newTemporaryConfiguration` so that the execution client will not contain the listener confs, but the metadata client will.
## How was this patch tested?
Unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#15086 from brkyvz/null-listeners.
## What changes were proposed in this pull request?
Check the database warehouse used in Spark UT, and remove the existing database file before run the UT (SPARK-8368).
## How was this patch tested?
Run Spark UT with the command for several times:
./build/sbt -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver "test-only *HiveSparkSubmitSuit*"
Without the patch, the test case can be passed only at the first time, and always failed from the second time.
With the patch the test case always can be passed correctly.
Author: tone-zhang <tone.zhang@linaro.org>
Closes#14894 from tone-zhang/issue1.
## What changes were proposed in this pull request?
This PR fixes `ColumnVectorUtils.populate` so that Parquet vectorized reader can read partitioned table with dates/timestamps. This works fine with Parquet normal reader.
This is being only called within [VectorizedParquetRecordReader.java#L185](https://github.com/apache/spark/blob/master/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedParquetRecordReader.java#L185).
When partition column types are explicitly given to `DateType` or `TimestampType` (rather than inferring the type of partition column), this fails with the exception below:
```
16/09/01 10:30:07 ERROR Executor: Exception in task 0.0 in stage 5.0 (TID 6)
java.lang.ClassCastException: java.lang.Integer cannot be cast to java.sql.Date
at org.apache.spark.sql.execution.vectorized.ColumnVectorUtils.populate(ColumnVectorUtils.java:89)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:185)
at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.initBatch(VectorizedParquetRecordReader.java:204)
at org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$$anonfun$buildReader$1.apply(ParquetFileFormat.scala:362)
...
```
## How was this patch tested?
Unit tests in `SQLQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14919 from HyukjinKwon/SPARK-17354.
### What changes were proposed in this pull request?
The original [JIRA Hive-1642](https://issues.apache.org/jira/browse/HIVE-1642) delivered the test cases `auto_joinXYZ` for verifying the results when the joins are automatically converted to map-join. Basically, most of them are just copied from the corresponding `joinXYZ`.
After comparison between `auto_joinXYZ` and `joinXYZ`, below is a list of duplicate cases:
```
"auto_join0",
"auto_join1",
"auto_join10",
"auto_join11",
"auto_join12",
"auto_join13",
"auto_join14",
"auto_join14_hadoop20",
"auto_join15",
"auto_join17",
"auto_join18",
"auto_join2",
"auto_join20",
"auto_join21",
"auto_join23",
"auto_join24",
"auto_join3",
"auto_join4",
"auto_join5",
"auto_join6",
"auto_join7",
"auto_join8",
"auto_join9"
```
We can remove all of them without affecting the test coverage.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14635 from gatorsmile/removeAuto.
## What changes were proposed in this pull request?
Previously we have 2 conditions to decide whether a data source table is hive-compatible:
1. the data source is file-based and has a corresponding Hive serde
2. have a `path` entry in data source options/storage properties
However, if condition 1 is true, condition 2 must be true too, as we will put the default table path into data source options/storage properties for managed data source tables.
There is also a potential issue: we will set the `locationUri` even for managed table.
This PR removes the condition 2 and only set the `locationUri` for external data source tables.
Note: this is also a first step to unify the `path` of data source tables and `locationUri` of hive serde tables. For hive serde tables, `locationUri` is only set for external table. For data source tables, `path` is always set. We can make them consistent after this PR.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14809 from cloud-fan/minor2.
### What changes were proposed in this pull request?
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/64956/testReport/junit/org.apache.spark.sql.hive/StatisticsSuite/test_statistics_of_LogicalRelation_converted_from_MetastoreRelation/
```
org.apache.spark.sql.hive.StatisticsSuite.test statistics of LogicalRelation converted from MetastoreRelation
Failing for the past 1 build (Since Failed#64956 )
Took 1.4 sec.
Error Message
org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236
Stacktrace
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedException: 6871 did not equal 4236
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
```
This fix does not check the exact value of `sizeInBytes`. Instead, we compare whether it is larger than zero and compare the values between different values.
In addition, we also combine `checkMetastoreRelationStats` and `checkLogicalRelationStats` into the same checking function.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14978 from gatorsmile/spark17408.
## What changes were proposed in this pull request?
Using the public `Catalog` API, users can create a file-based data source table, without giving the path options. For this case, currently we can create the table successfully, but fail when we read it. Ideally we should fail during creation.
This is because when we create data source table, we resolve the data source relation without validating path: `resolveRelation(checkPathExist = false)`.
Looking back to why we add this trick(`checkPathExist`), it's because when we call `resolveRelation` for managed table, we add the path to data source options but the path is not created yet. So why we add this not-yet-created path to data source options? This PR fix the problem by adding path to options after we call `resolveRelation`. Then we can remove the `checkPathExist` parameter in `DataSource.resolveRelation` and do some related cleanups.
## How was this patch tested?
existing tests and new test in `CatalogSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14921 from cloud-fan/check-path.
## What changes were proposed in this pull request?
`TreeNode.toJSON` requires a subclass to explicitly override otherCopyArgs to include currying construction arguments, otherwise it reports AssertException telling that the construction argument values' count doesn't match the construction argument names' count.
For class `MetastoreRelation`, it has a currying construction parameter `client: HiveClient`, but Spark forgets to add it to the list of otherCopyArgs.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14928 from clockfly/metastore_relation_toJSON.
## What changes were proposed in this pull request?
1. Support generation table-level statistics for
- hive tables in HiveExternalCatalog
- data source tables in HiveExternalCatalog
- data source tables in InMemoryCatalog.
2. Add a property "catalogStats" in CatalogTable to hold statistics in Spark side.
3. Put logics of statistics transformation between Spark and Hive in HiveClientImpl.
4. Extend Statistics class by adding rowCount (will add estimatedSize when we have column stats).
## How was this patch tested?
add unit tests
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#14712 from wzhfy/tableStats.
### What changes were proposed in this pull request?
When we trying to read a table and then write to the same table using the `Overwrite` save mode, we got a very confusing error message:
For example,
```Scala
Seq((1, 2)).toDF("i", "j").write.saveAsTable("tab1")
table("tab1").write.mode(SaveMode.Overwrite).saveAsTable("tab1")
```
```
Job aborted.
org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1.apply$mcV$sp
...
Caused by: org.apache.spark.SparkException: Task failed while writing rows
at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:266)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143)
at org.apache.spark.sql.execution.datasources
```
After the PR, we will issue an `AnalysisException`:
```
Cannot overwrite table `tab1` that is also being read from
```
### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14954 from gatorsmile/ctasQueryAnalyze.
### What changes were proposed in this pull request?
This is another step to get rid of HiveClient from `HiveSessionState`. All the metastore interactions should be through `ExternalCatalog` interface. However, the existing implementation of `InsertIntoHiveTable ` still requires Hive clients. This PR is to remove HiveClient by moving the metastore interactions into `ExternalCatalog`.
### How was this patch tested?
Existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14888 from gatorsmile/removeClientFromInsertIntoHiveTable.
## What changes were proposed in this pull request?
was not dropping table `parquet_t3`
## How was this patch tested?
tested `LogicalPlanToSQLSuite` locally
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13767 from techaddict/minor-8.
## What changes were proposed in this pull request?
Require the use of CROSS join syntax in SQL (and a new crossJoin
DataFrame API) to specify explicit cartesian products between relations.
By cartesian product we mean a join between relations R and S where
there is no join condition involving columns from both R and S.
If a cartesian product is detected in the absence of an explicit CROSS
join, an error must be thrown. Turning on the
"spark.sql.crossJoin.enabled" configuration flag will disable this check
and allow cartesian products without an explicit CROSS join.
The new crossJoin DataFrame API must be used to specify explicit cross
joins. The existing join(DataFrame) method will produce a INNER join
that will require a subsequent join condition.
That is df1.join(df2) is equivalent to select * from df1, df2.
## How was this patch tested?
Added cross-join.sql to the SQLQueryTestSuite to test the check for cartesian products. Added a couple of tests to the DataFrameJoinSuite to test the crossJoin API. Modified various other test suites to explicitly specify a cross join where an INNER join or a comma-separated list was previously used.
Author: Srinath Shankar <srinath@databricks.com>
Closes#14866 from srinathshankar/crossjoin.
## What changes were proposed in this pull request?
Some analyzer rules have assumptions on logical plans, optimizer may break these assumption, we should not pass an optimized query plan into QueryExecution (will be analyzed again), otherwise we may some weird bugs.
For example, we have a rule for decimal calculation to promote the precision before binary operations, use PromotePrecision as placeholder to indicate that this rule should not apply twice. But a Optimizer rule will remove this placeholder, that break the assumption, then the rule applied twice, cause wrong result.
Ideally, we should make all the analyzer rules all idempotent, that may require lots of effort to double checking them one by one (may be not easy).
An easier approach could be never feed a optimized plan into Analyzer, this PR fix the case for RunnableComand, they will be optimized, during execution, the passed `query` will also be passed into QueryExecution again. This PR make these `query` not part of the children, so they will not be optimized and analyzed again.
Right now, we did not know a logical plan is optimized or not, we could introduce a flag for that, and make sure a optimized logical plan will not be analyzed again.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#14797 from davies/fix_writer.
### What changes were proposed in this pull request?
Function-related `HiveExternalCatalog` APIs do not have enough verification logics. After the PR, `HiveExternalCatalog` and `InMemoryCatalog` become consistent in the error handling.
For example, below is the exception we got when calling `renameFunction`.
```
15:13:40.369 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database db1, returning NoSuchObjectException
15:13:40.377 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database db2, returning NoSuchObjectException
15:13:40.739 ERROR DataNucleus.Datastore.Persist: Update of object "org.apache.hadoop.hive.metastore.model.MFunction205629e9" using statement "UPDATE FUNCS SET FUNC_NAME=? WHERE FUNC_ID=?" failed : org.apache.derby.shared.common.error.DerbySQLIntegrityConstraintViolationException: The statement was aborted because it would have caused a duplicate key value in a unique or primary key constraint or unique index identified by 'UNIQUEFUNCTION' defined on 'FUNCS'.
at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.Util.generateCsSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.TransactionResourceImpl.wrapInSQLException(Unknown Source)
at org.apache.derby.impl.jdbc.TransactionResourceImpl.handleException(Unknown Source)
```
### How was this patch tested?
Improved the existing test cases to check whether the messages are right.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14521 from gatorsmile/functionChecking.
## What changes were proposed in this pull request?
Add unit test for changes made in PR #14515. It makes sure that a newly created table has the same number of columns in table and partition metadata. This test fails before the changes introduced in #14515.
## How was this patch tested?
Run new unit test.
Author: Brian Cho <bcho@fb.com>
Closes#14930 from dafrista/partition-metadata-unit-test.
## What changes were proposed in this pull request?
This PR is the second step for the following feature:
For hash aggregation in Spark SQL, we use a fast aggregation hashmap to act as a "cache" in order to boost aggregation performance. Previously, the hashmap is backed by a `ColumnarBatch`. This has performance issues when we have wide schema for the aggregation table (large number of key fields or value fields).
In this JIRA, we support another implementation of fast hashmap, which is backed by a `RowBatch`. We then automatically pick between the two implementations based on certain knobs.
In this second-step PR, we enable `RowBasedHashMapGenerator` in `HashAggregateExec`.
## How was this patch tested?
Added tests: `RowBasedAggregateHashMapSuite` and ` VectorizedAggregateHashMapSuite`
Additional micro-benchmarks tests and TPCDS results will be added in a separate PR in the series.
Author: Qifan Pu <qifan.pu@gmail.com>
Author: ooq <qifan.pu@gmail.com>
Closes#14176 from ooq/rowbasedfastaggmap-pr2.
## What changes were proposed in this pull request?
This removes partition columns from column metadata of partitions to match tables.
A change introduced in SPARK-14388 removed partition columns from the column metadata of tables, but not for partitions. This causes TableReader to believe that the schema is different between table and partition, and create an unnecessary conversion object inspector in TableReader.
## How was this patch tested?
Existing unit tests.
Author: Brian Cho <bcho@fb.com>
Closes#14515 from dafrista/partition-columns-metadata.
## What changes were proposed in this pull request?
This PR adds the ability to parse SQL (hexadecimal) binary literals (AKA bit strings). It follows the following syntax `X'[Hexadecimal Characters]+'`, for example: `X'01AB'` would create a binary the following binary array `0x01AB`.
If an uneven number of hexadecimal characters is passed, then the upper 4 bits of the initial byte are kept empty, and the lower 4 bits are filled using the first character. For example `X'1C7'` would create the following binary array `0x01C7`.
Binary data (Array[Byte]) does not have a proper `hashCode` and `equals` functions. This meant that comparing `Literal`s containing binary data was a pain. I have updated Literal.hashCode and Literal.equals to deal properly with binary data.
## How was this patch tested?
Added tests to the `ExpressionParserSuite`, `SQLQueryTestSuite` and `ExpressionSQLBuilderSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14832 from hvanhovell/SPARK-17263.
## What changes were proposed in this pull request?
This is kind of a follow-up of https://github.com/apache/spark/pull/14482 . As we put `CatalogTable` in the logical plan directly, it makes sense to let physical plans take `CatalogTable` directly, instead of extracting some fields of `CatalogTable` in planner and then construct a new `CatalogTable` in physical plan.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14823 from cloud-fan/create-table.
### What changes were proposed in this pull request?
The existing `CREATE TABLE LIKE` command has multiple issues:
- The generated table is non-empty when the source table is a data source table. The major reason is the data source table is using the table property `path` to store the location of table contents. Currently, we keep it unchanged. Thus, we still create the same table with the same location.
- The table type of the generated table is `EXTERNAL` when the source table is an external Hive Serde table. Currently, we explicitly set it to `MANAGED`, but Hive is checking the table property `EXTERNAL` to decide whether the table is `EXTERNAL` or not. (See https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1407-L1408) Thus, the created table is still `EXTERNAL`.
- When the source table is a `VIEW`, the metadata of the generated table contains the original view text and view original text. So far, this does not break anything, but it could cause something wrong in Hive. (For example, https://github.com/apache/hive/blob/master/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1405-L1406)
- The issue regarding the table `comment`. To follow what Hive does, the table comment should be cleaned, but the column comments should be still kept.
- The `INDEX` table is not supported. Thus, we should throw an exception in this case.
- `owner` should not be retained. `ToHiveTable` set it [here](e679bc3c1c/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala (L793)) no matter which value we set in `CatalogTable`. We set it to an empty string for avoiding the confusing output in Explain.
- Add a support for temp tables
- Like Hive, we should not copy the table properties from the source table to the created table, especially for the statistics-related properties, which could be wrong in the created table.
- `unsupportedFeatures` should not be copied from the source table. The created table does not have these unsupported features.
- When the type of source table is a view, the target table is using the default format of data source tables: `spark.sql.sources.default`.
This PR is to fix the above issues.
### How was this patch tested?
Improve the test coverage by adding more test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14531 from gatorsmile/createTableLike.
## What changes were proposed in this pull request?
This PR implements aggregation function `percentile_approx`. Function `percentile_approx` returns the approximate percentile(s) of a column at the given percentage(s). A percentile is a watermark value below which a given percentage of the column values fall. For example, the percentile of column `col` at percentage 50% is the median value of column `col`.
### Syntax:
```
# Returns percentile at a given percentage value. The approximation error can be reduced by increasing parameter accuracy, at the cost of memory.
percentile_approx(col, percentage [, accuracy])
# Returns percentile value array at given percentage value array
percentile_approx(col, array(percentage1 [, percentage2]...) [, accuracy])
```
### Features:
1. This function supports partial aggregation.
2. The memory consumption is bounded. The larger `accuracy` parameter we choose, we smaller error we get. The default accuracy value is 10000, to match with Hive default setting. Choose a smaller value for smaller memory footprint.
3. This function supports window function aggregation.
### Example usages:
```
## Returns the 25th percentile value, with default accuracy
SELECT percentile_approx(col, 0.25) FROM table
## Returns an array of percentile value (25th, 50th, 75th), with default accuracy
SELECT percentile_approx(col, array(0.25, 0.5, 0.75)) FROM table
## Returns 25th percentile value, with custom accuracy value 100, larger accuracy parameter yields smaller approximation error
SELECT percentile_approx(col, 0.25, 100) FROM table
## Returns the 25th, and 50th percentile values, with custom accuracy value 100
SELECT percentile_approx(col, array(0.25, 0.5), 100) FROM table
```
### NOTE:
1. The `percentile_approx` implementation is different from Hive, so the result returned on same query maybe slightly different with Hive. This implementation uses `QuantileSummaries` as the underlying probabilistic data structure, and mainly follows paper `Space-efficient Online Computation of Quantile Summaries` by Greenwald, Michael and Khanna, Sanjeev. (http://dx.doi.org/10.1145/375663.375670)`
2. The current implementation of `QuantileSummaries` doesn't support automatic compression. This PR has a rule to do compression automatically at the caller side, but it may not be optimal.
## How was this patch tested?
Unit test, and Sql query test.
## Acknowledgement
1. This PR's work in based on lw-lin's PR https://github.com/apache/spark/pull/14298, with improvements like supporting partial aggregation, fixing out of memory issue.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#14868 from clockfly/appro_percentile_try_2.
## What changes were proposed in this pull request?
Currently we use `CreateViewCommand` to implement ALTER VIEW AS, which has 3 bugs:
1. SPARK-17180: ALTER VIEW AS should alter temp view if view name has no database part and temp view exists
2. SPARK-17309: ALTER VIEW AS should issue exception if view does not exist.
3. SPARK-17323: ALTER VIEW AS should keep the previous table properties, comment, create_time, etc.
The root cause is, ALTER VIEW AS is quite different from CREATE VIEW, we need different code path to handle them. However, in `CreateViewCommand`, there is no way to distinguish ALTER VIEW AS and CREATE VIEW, we have to introduce extra flag. But instead of doing this, I think a more natural way is to separate the ALTER VIEW AS logic into a new command.
## How was this patch tested?
new tests in SQLViewSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14874 from cloud-fan/minor4.
### What changes were proposed in this pull request?
Hive Index tables are not supported by Spark SQL. Thus, we issue an exception when users try to access Hive Index tables. When the internal function `tableExists` tries to access Hive Index tables, it always gets the same error message: ```Hive index table is not supported```. This message could be confusing to users, since their SQL operations could be completely unrelated to Hive Index tables. For example, when users try to alter a table to a new name and there exists an index table with the same name, the expected exception should be a `TableAlreadyExistsException`.
This PR made the following changes:
- Introduced a new `AnalysisException` type: `SQLFeatureNotSupportedException`. When users try to access an `Index Table`, we will issue a `SQLFeatureNotSupportedException`.
- `tableExists` returns `true` when hitting a `SQLFeatureNotSupportedException` and the feature is `Hive index table`.
- Add a checking `requireTableNotExists` for `SessionCatalog`'s `createTable` API; otherwise, the current implementation relies on the Hive's internal checking.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14801 from gatorsmile/tableExists.
## What changes were proposed in this pull request?
This PR split the the single `createPartitions()` call into smaller batches, which could prevent Hive metastore from OOM (caused by millions of partitions).
It will also try to gather all the fast stats (number of files and total size of all files) in parallel to avoid the bottle neck of listing the files in metastore sequential, which is controlled by spark.sql.gatherFastStats (enabled by default).
## How was this patch tested?
Tested locally with 10000 partitions and 100 files with embedded metastore, without gathering fast stats in parallel, adding partitions took 153 seconds, after enable that, gathering the fast stats took about 34 seconds, adding these partitions took 25 seconds (most of the time spent in object store), 59 seconds in total, 2.5X faster (with larger cluster, gathering will much faster).
Author: Davies Liu <davies@databricks.com>
Closes#14607 from davies/repair_batch.
## What changes were proposed in this pull request?
This PR adds parser support for `BigDecimal` literals. If you append the suffix `BD` to a valid number then this will be interpreted as a `BigDecimal`, for example `12.0E10BD` will interpreted into a BigDecimal with scale -9 and precision 3. This is useful in situations where you need exact values.
## How was this patch tested?
Added tests to `ExpressionParserSuite`, `ExpressionSQLBuilderSuite` and `SQLQueryTestSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14819 from hvanhovell/SPARK-17246.
### What changes were proposed in this pull request?
This is the first step to remove `HiveClient` from `HiveSessionState`. In the metastore interaction, we always use the fully qualified table name when accessing/operating a table. That means, we always specify the database. Thus, it is not necessary to use `HiveClient` to change the active database in Hive metastore.
In `HiveSessionCatalog `, `setCurrentDatabase` is the only function that uses `HiveClient`. Thus, we can remove it after removing `setCurrentDatabase`
### How was this patch tested?
The existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14821 from gatorsmile/setCurrentDB.
### What changes were proposed in this pull request?
Address the comments by yhuai in the original PR: https://github.com/apache/spark/pull/14207
First, issue an exception instead of logging a warning when users specify the partitioning columns without a given schema.
Second, refactor the codes a little.
### How was this patch tested?
Fixed the test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14572 from gatorsmile/followup16552.
## What changes were proposed in this pull request?
`CreateTables` rule turns a general `CreateTable` plan to `CreateHiveTableAsSelectCommand` for hive serde table. However, this rule is logically a planner strategy, we should move it to `HiveStrategies`, to be consistent with other DDL commands.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14825 from cloud-fan/ctas.
## What changes were proposed in this pull request?
This PR enables the tests for `TimestampType` for JSON and unifies the logics for verifying schema when writing in CSV.
In more details, this PR,
- Enables the tests for `TimestampType` for JSON and
This was disabled due to an issue in `DatatypeConverter.parseDateTime` which parses dates incorrectly, for example as below:
```scala
val d = javax.xml.bind.DatatypeConverter.parseDateTime("0900-01-01T00:00:00.000").getTime
println(d.toString)
```
```
Fri Dec 28 00:00:00 KST 899
```
However, since we use `FastDateFormat`, it seems we are safe now.
```scala
val d = FastDateFormat.getInstance("yyyy-MM-dd'T'HH:mm:ss.SSS").parse("0900-01-01T00:00:00.000")
println(d)
```
```
Tue Jan 01 00:00:00 PST 900
```
- Verifies all unsupported types in CSV
There is a separate logics to verify the schemas in `CSVFileFormat`. This is actually not quite correct enough because we don't support `NullType` and `CalanderIntervalType` as well `StructType`, `ArrayType`, `MapType`. So, this PR adds both types.
## How was this patch tested?
Tests in `JsonHadoopFsRelation` and `CSVSuite`
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14829 from HyukjinKwon/SPARK-16216-followup.
This patch updates `Literal.sql` to properly generate SQL for `NaN` and `Infinity` float and double literals: these special values need to be handled differently from regular values, since simply appending a suffix to the value's `toString()` representation will not work for these values.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14777 from JoshRosen/SPARK-17205.
### What changes were proposed in this pull request?
Since `HiveClient` is used to interact with the Hive metastore, it should be hidden in `HiveExternalCatalog`. After moving `HiveClient` into `HiveExternalCatalog`, `HiveSharedState` becomes a wrapper of `HiveExternalCatalog`. Thus, removal of `HiveSharedState` becomes straightforward. After removal of `HiveSharedState`, the reflection logic is directly applied on the choice of `ExternalCatalog` types, based on the configuration of `CATALOG_IMPLEMENTATION`.
~~`HiveClient` is also used/invoked by the other entities besides HiveExternalCatalog, we defines the following two APIs: getClient and getNewClient~~
### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14757 from gatorsmile/removeHiveClient.
## What changes were proposed in this pull request?
### Default - ISO 8601
Currently, CSV datasource is writing `Timestamp` and `Date` as numeric form and JSON datasource is writing both as below:
- CSV
```
// TimestampType
1414459800000000
// DateType
16673
```
- Json
```
// TimestampType
1970-01-01 11:46:40.0
// DateType
1970-01-01
```
So, for CSV we can't read back what we write and for JSON it becomes ambiguous because the timezone is being missed.
So, this PR make both **write** `Timestamp` and `Date` in ISO 8601 formatted string (please refer the [ISO 8601 specification](https://www.w3.org/TR/NOTE-datetime)).
- For `Timestamp` it becomes as below: (`yyyy-MM-dd'T'HH:mm:ss.SSSZZ`)
```
1970-01-01T02:00:01.000-01:00
```
- For `Date` it becomes as below (`yyyy-MM-dd`)
```
1970-01-01
```
### Custom date format option - `dateFormat`
This PR also adds the support to write and read dates and timestamps in a formatted string as below:
- **DateType**
- With `dateFormat` option (e.g. `yyyy/MM/dd`)
```
+----------+
| date|
+----------+
|2015/08/26|
|2014/10/27|
|2016/01/28|
+----------+
```
### Custom date format option - `timestampFormat`
- **TimestampType**
- With `dateFormat` option (e.g. `dd/MM/yyyy HH:mm`)
```
+----------------+
| date|
+----------------+
|2015/08/26 18:00|
|2014/10/27 18:30|
|2016/01/28 20:00|
+----------------+
```
## How was this patch tested?
Unit tests were added in `CSVSuite` and `JsonSuite`. For JSON, existing tests cover the default cases.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14279 from HyukjinKwon/SPARK-16216-json-csv.
## What changes were proposed in this pull request?
Actually Spark SQL doesn't support index, the catalog table type `INDEX` is from Hive. However, most operations in Spark SQL can't handle index table, e.g. create table, alter table, etc.
Logically index table should be invisible to end users, and Hive also generates special table name for index table to avoid users accessing it directly. Hive has special SQL syntax to create/show/drop index tables.
At Spark SQL side, although we can describe index table directly, but the result is unreadable, we should use the dedicated SQL syntax to do it(e.g. `SHOW INDEX ON tbl`). Spark SQL can also read index table directly, but the result is always empty.(Can hive read index table directly?)
This PR remove the table type `INDEX`, to make it clear that Spark SQL doesn't support index currently.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14752 from cloud-fan/minor2.
## What changes were proposed in this pull request?
This PR removes implemented functions from comments of `HiveSessionCatalog.scala`: `java_method`, `posexplode`, `str_to_map`.
## How was this patch tested?
Manual.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes#14769 from Sherry302/cleanComment.
When Spark emits SQL for a string literal, it should wrap the string in single quotes, not double quotes. Databases which adhere more strictly to the ANSI SQL standards, such as Postgres, allow only single-quotes to be used for denoting string literals (see http://stackoverflow.com/a/1992331/590203).
Author: Josh Rosen <joshrosen@databricks.com>
Closes#14763 from JoshRosen/SPARK-17194.
## What changes were proposed in this pull request?
The range operator previously didn't support SQL generation, which made it not possible to use in views.
## How was this patch tested?
Unit tests.
cc hvanhovell
Author: Eric Liang <ekl@databricks.com>
Closes#14724 from ericl/spark-17162.
## What changes were proposed in this pull request?
Spark SQL doesn't have its own meta store yet, and use hive's currently. However, hive's meta store has some limitations(e.g. columns can't be too many, not case-preserving, bad decimal type support, etc.), so we have some hacks to successfully store data source table metadata into hive meta store, i.e. put all the information in table properties.
This PR moves these hacks to `HiveExternalCatalog`, tries to isolate hive specific logic in one place.
changes overview:
1. **before this PR**: we need to put metadata(schema, partition columns, etc.) of data source tables to table properties before saving it to external catalog, even the external catalog doesn't use hive metastore(e.g. `InMemoryCatalog`)
**after this PR**: the table properties tricks are only in `HiveExternalCatalog`, the caller side doesn't need to take care of it anymore.
2. **before this PR**: because the table properties tricks are done outside of external catalog, so we also need to revert these tricks when we read the table metadata from external catalog and use it. e.g. in `DescribeTableCommand` we will read schema and partition columns from table properties.
**after this PR**: The table metadata read from external catalog is exactly the same with what we saved to it.
bonus: now we can create data source table using `SessionCatalog`, if schema is specified.
breaks: `schemaStringLengthThreshold` is not configurable anymore. `hive.default.rcfile.serde` is not configurable anymore.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14155 from cloud-fan/catalog-table.
## What changes were proposed in this pull request?
Currently `LogicalRelation.newInstance()` simply creates another `LogicalRelation` object with the same parameters. However, the `newInstance()` method inherited from `MultiInstanceRelation` should return a copy of object with unique expression ids. Current `LogicalRelation.newInstance()` can cause failure when doing self-join.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#14682 from viirya/fix-localrelation.
## What changes were proposed in this pull request?
This patch adds support for SQL generation for inline tables. With this, it would be possible to create a view that depends on inline tables.
## How was this patch tested?
Added a test case in LogicalPlanToSQLSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14709 from petermaxlee/SPARK-17150.
## What changes were proposed in this pull request?
This patch creates array.sql in SQLQueryTestSuite for testing array related functions, including:
- indexing
- array creation
- size
- array_contains
- sort_array
## How was this patch tested?
The patch itself is about adding tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14708 from petermaxlee/SPARK-17149.
A review of the code, working back from Hadoop's `FileSystem.exists()` and `FileSystem.isDirectory()` code, then removing uses of the calls when superfluous.
1. delete is harmless if called on a nonexistent path, so don't do any checks before deletes
1. any `FileSystem.exists()` check before `getFileStatus()` or `open()` is superfluous as the operation itself does the check. Instead the `FileNotFoundException` is caught and triggers the downgraded path. When a `FileNotFoundException` was thrown before, the code still creates a new FNFE with the error messages. Though now the inner exceptions are nested, for easier diagnostics.
Initially, relying on Jenkins test runs.
One troublespot here is that some of the codepaths are clearly error situations; it's not clear that they have coverage anyway. Trying to create the failure conditions in tests would be ideal, but it will also be hard.
Author: Steve Loughran <stevel@apache.org>
Closes#14371 from steveloughran/cloud/SPARK-16736-superfluous-fs-calls.
## What changes were proposed in this pull request?
This PR adds a field to subquery alias in order to make the usage of views in a resolved `LogicalPlan` more visible (and more understandable).
For example, the following view and query:
```sql
create view constants as select 1 as id union all select 1 union all select 42
select * from constants;
```
...now yields the following analyzed plan:
```
Project [id#39]
+- SubqueryAlias c, `default`.`constants`
+- Project [gen_attr_0#36 AS id#39]
+- SubqueryAlias gen_subquery_0
+- Union
:- Union
: :- Project [1 AS gen_attr_0#36]
: : +- OneRowRelation$
: +- Project [1 AS gen_attr_1#37]
: +- OneRowRelation$
+- Project [42 AS gen_attr_2#38]
+- OneRowRelation$
```
## How was this patch tested?
Added tests for the two code paths in `SessionCatalogSuite` (sql/core) and `HiveMetastoreCatalogSuite` (sql/hive)
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14657 from hvanhovell/SPARK-17068.
## What changes were proposed in this pull request?
This PR is a small follow-up to https://github.com/apache/spark/pull/14554. This also widens the visibility of a few (similar) Hive classes.
## How was this patch tested?
No test. Only a visibility change.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#14654 from hvanhovell/SPARK-16964-hive.
## What changes were proposed in this pull request?
This patch updates the SQL parser to parse negative numeric literals as numeric literals, instead of unary minus of positive literals.
This allows the parser to parse the minimal value for each data type, e.g. "-32768S".
## How was this patch tested?
Updated test cases.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14608 from petermaxlee/SPARK-17013.
## What changes were proposed in this pull request?
This patch introduces SQLQueryTestSuite, a basic framework for end-to-end SQL test cases defined in spark/sql/core/src/test/resources/sql-tests. This is a more standard way to test SQL queries end-to-end in different open source database systems, because it is more manageable to work with files.
This is inspired by HiveCompatibilitySuite, but simplified for general Spark SQL tests. Once this is merged, I can work towards porting SQLQuerySuite over, and eventually also move the existing HiveCompatibilitySuite to use this framework.
Unlike HiveCompatibilitySuite, SQLQueryTestSuite compares both the output schema and the output data (in string form).
When there is a mismatch, the error message looks like the following:
```
[info] - blacklist.sql !!! IGNORED !!!
[info] - number-format.sql *** FAILED *** (2 seconds, 405 milliseconds)
[info] Expected "...147483648 -214748364[8]", but got "...147483648 -214748364[9]" Result should match for query #1 (SQLQueryTestSuite.scala:171)
[info] org.scalatest.exceptions.TestFailedException:
[info] at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:495)
[info] at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
[info] at org.scalatest.Assertions$class.assertResult(Assertions.scala:1171)
```
## How was this patch tested?
This is a test infrastructure change.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14472 from petermaxlee/SPARK-16866.
### What changes were proposed in this pull request?
The `comment` in `CatalogTable` returned from Hive is always empty. We store it in the table property when creating a table. However, when we try to retrieve the table metadata from Hive metastore, we do not rebuild it. The `comment` is always empty.
This PR is to fix the issue.
### How was this patch tested?
Fixed the test case to verify the change.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14550 from gatorsmile/tableComment.
## What changes were proposed in this pull request?
MSCK REPAIR TABLE could be used to recover the partitions in external catalog based on partitions in file system.
Another syntax is: ALTER TABLE table RECOVER PARTITIONS
The implementation in this PR will only list partitions (not the files with a partition) in driver (in parallel if needed).
## How was this patch tested?
Added unit tests for it and Hive compatibility test suite.
Author: Davies Liu <davies@databricks.com>
Closes#14500 from davies/repair_table.
## What changes were proposed in this pull request?
This package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime.
This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.execution.
## How was this patch tested?
N/A - just visibility changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#14554 from rxin/remote-private.
## What changes were proposed in this pull request?
For ORC source, Spark SQL has a writer option `compression`, which is used to set the codec and its value will be also set to `orc.compress` (the orc conf used for codec). However, if a user only set `orc.compress` in the writer option, we should not use the default value of `compression` (snappy) as the codec. Instead, we should respect the value of `orc.compress`.
This PR makes ORC data source not ignoring `orc.compress` when `comperssion` is unset.
So, here is the behaviour,
1. Check `compression` and use this if it is set.
2. If `compression` is not set, check `orc.compress` and use it.
3. If `compression` and `orc.compress` are not set, then use the default snappy.
## How was this patch tested?
Unit test in `OrcQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#14518 from HyukjinKwon/SPARK-16610.
## What changes were proposed in this pull request?
Avoid using postfix operation for command execution in SQLQuerySuite where it wasn't whitelisted and audit existing whitelistings removing postfix operators from most places. Some notable places where postfix operation remains is in the XML parsing & time units (seconds, millis, etc.) where it arguably can improve readability.
## How was this patch tested?
Existing tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#14407 from holdenk/SPARK-16779.
#### What changes were proposed in this pull request?
When doing a CTAS with a Partition By clause, we got a wrong error message.
For example,
```SQL
CREATE TABLE gen__tmp
PARTITIONED BY (key string)
AS SELECT key, value FROM mytable1
```
The error message we get now is like
```
Operation not allowed: Schema may not be specified in a Create Table As Select (CTAS) statement(line 2, pos 0)
```
However, based on the code, the message we should get is like
```
Operation not allowed: A Create Table As Select (CTAS) statement is not allowed to create a partitioned table using Hive's file formats. Please use the syntax of "CREATE TABLE tableName USING dataSource OPTIONS (...) PARTITIONED BY ...\" to create a partitioned table through a CTAS statement.(line 2, pos 0)
```
Currently, partitioning columns is part of the schema. This PR fixes the bug by changing the detection orders.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14113 from gatorsmile/ctas.
## What changes were proposed in this pull request?
When we create the HiveConf for metastore client, we use a Hadoop Conf as the base, which may contain Hive settings in hive-site.xml (https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/internal/SharedState.scala#L49). However, HiveConf's initialize function basically ignores the base Hadoop Conf and always its default values (i.e. settings with non-null default values) as the base (https://github.com/apache/hive/blob/release-1.2.1/common/src/java/org/apache/hadoop/hive/conf/HiveConf.java#L2687). So, even a user put javax.jdo.option.ConnectionURL in hive-site.xml, it is not used and Hive will use its default, which is jdbc:derby:;databaseName=metastore_db;create=true.
This issue only shows up when `spark.sql.hive.metastore.jars` is not set to builtin.
## How was this patch tested?
New test in HiveSparkSubmitSuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#14497 from yhuai/SPARK-16901.
## What changes were proposed in this pull request?
we have various logical plans for CREATE TABLE and CTAS: `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateHiveTableAsSelectLogicalPlan`. This PR unifies them to reduce the complexity and centralize the error handling.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14482 from cloud-fan/table.
## What changes were proposed in this pull request?
These 2 methods take `CatalogTable` as parameter, which already have the database information.
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14476 from cloud-fan/minor5.
## What changes were proposed in this pull request?
Partition discovery is rather expensive, so we should do it at execution time instead of during physical planning. Right now there is not much benefit since ListingFileCatalog will read scan for all partitions at planning time anyways, but this can be optimized in the future. Also, there might be more information for partition pruning not available at planning time.
This PR moves a lot of the file scan logic from planning to execution time. All file scan operations are handled by `FileSourceScanExec`, which handles both batched and non-batched file scans. This requires some duplication with `RowDataSourceScanExec`, but is probably worth it so that `FileSourceScanExec` does not need to depend on an input RDD.
TODO: In another pr, move DataSourceScanExec to it's own file.
## How was this patch tested?
Existing tests (it might be worth adding a test that catalog.listFiles() is delayed until execution, but this can be delayed until there is an actual benefit to doing so).
Author: Eric Liang <ekl@databricks.com>
Closes#14241 from ericl/refactor.
### What changes were proposed in this pull request?
This PR is to remove `TestHiveSharedState`.
Also, this is also associated with the Hive refractoring for removing `HiveSharedState`.
### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14463 from gatorsmile/removeTestHiveSharedState.
## What changes were proposed in this pull request?
With SPARK-15034, we could use the value of spark.sql.warehouse.dir to set the warehouse location. In TestHive, we can now simply set the temporary warehouse path in sc's conf, and thus, param "warehousePath" could be removed.
## How was this patch tested?
exsiting testsuites.
Author: jiangxingbo <jiangxingbo@meituan.com>
Closes#14401 from jiangxb1987/warehousePath.
## What changes were proposed in this pull request?
`StructField` has very similar semantic with `CatalogColumn`, except that `CatalogColumn` use string to express data type. I think it's reasonable to use `StructType` as the `CatalogTable.schema` and remove `CatalogColumn`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14363 from cloud-fan/column.
#### What changes were proposed in this pull request?
Currently, in Spark SQL, the initial creation of schema can be classified into two groups. It is applicable to both Hive tables and Data Source tables:
**Group A. Users specify the schema.**
_Case 1 CREATE TABLE AS SELECT_: the schema is determined by the result schema of the SELECT clause. For example,
```SQL
CREATE TABLE tab STORED AS TEXTFILE
AS SELECT * from input
```
_Case 2 CREATE TABLE_: users explicitly specify the schema. For example,
```SQL
CREATE TABLE jsonTable (_1 string, _2 string)
USING org.apache.spark.sql.json
```
**Group B. Spark SQL infers the schema at runtime.**
_Case 3 CREATE TABLE_. Users do not specify the schema but the path to the file location. For example,
```SQL
CREATE TABLE jsonTable
USING org.apache.spark.sql.json
OPTIONS (path '${tempDir.getCanonicalPath}')
```
Before this PR, Spark SQL does not store the inferred schema in the external catalog for the cases in Group B. When users refreshing the metadata cache, accessing the table at the first time after (re-)starting Spark, Spark SQL will infer the schema and store the info in the metadata cache for improving the performance of subsequent metadata requests. However, the runtime schema inference could cause undesirable schema changes after each reboot of Spark.
This PR is to store the inferred schema in the external catalog when creating the table. When users intend to refresh the schema after possible changes on external files (table location), they issue `REFRESH TABLE`. Spark SQL will infer the schema again based on the previously specified table location and update/refresh the schema in the external catalog and metadata cache.
In this PR, we do not use the inferred schema to replace the user specified schema for avoiding external behavior changes . Based on the design, user-specified schemas (as described in Group A) can be changed by ALTER TABLE commands, although we do not support them now.
#### How was this patch tested?
TODO: add more cases to cover the changes.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14207 from gatorsmile/userSpecifiedSchema.
## What changes were proposed in this pull request?
We currently test subquery SQL building using the `HiveCompatibilitySuite`. The is not desired since SQL building is actually a part of `sql/core` and because we are slowly reducing our dependency on Hive. This PR adds the same tests from the whitelist of `HiveCompatibilitySuite` into `LogicalPlanToSQLSuite`.
## How was this patch tested?
This adds more testcases. Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14383 from dongjoon-hyun/SPARK-15232.
## What changes were proposed in this pull request?
Currently, the generated SQLs have not-stable IDs for generated attributes.
The stable generated SQL will give more benefit for understanding or testing the queries.
This PR provides stable SQL generation by the followings.
- Provide unique ids for generated subqueries, `gen_subquery_xxx`.
- Provide unique and stable ids for generated attributes, `gen_attr_xxx`.
**Before**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res0: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_4` AS `1` FROM (SELECT 1 AS `gen_attr_4`) AS gen_subquery_0
```
**After**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res2: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14257 from dongjoon-hyun/SPARK-16621.
## What changes were proposed in this pull request?
Currently there are 2 inconsistence:
1. for data source table, we only print partition names, for hive table, we also print partition schema. After this PR, we will always print schema
2. if column doesn't have comment, data source table will print empty string, hive table will print null. After this PR, we will always print null
## How was this patch tested?
new test in `HiveDDLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14302 from cloud-fan/minor3.
## What changes were proposed in this pull request?
This PR contains three changes.
First, this PR changes the behavior of lead/lag back to Spark 1.6's behavior, which is described as below:
1. lead/lag respect null input values, which means that if the offset row exists and the input value is null, the result will be null instead of the default value.
2. If the offset row does not exist, the default value will be used.
3. OffsetWindowFunction's nullable setting also considers the nullability of its input (because of the first change).
Second, this PR fixes the evaluation of lead/lag when the input expression is a literal. This fix is a result of the first change. In current master, if a literal is used as the input expression of a lead or lag function, the result will be this literal even if the offset row does not exist.
Third, this PR makes ResolveWindowFrame not fire if a window function is not resolved.
## How was this patch tested?
New tests in SQLWindowFunctionSuite
Author: Yin Huai <yhuai@databricks.com>
Closes#14284 from yhuai/lead-lag.
## What changes were proposed in this pull request?
Currently, `SQLBuilder` raises `empty.reduceLeft` exceptions on *unoptimized* `EXISTS` queries. We had better prevent this.
```scala
scala> sql("CREATE TABLE t1(a int)")
scala> val df = sql("select * from t1 b where exists (select * from t1 a)")
scala> new org.apache.spark.sql.catalyst.SQLBuilder(df).toSQL
java.lang.UnsupportedOperationException: empty.reduceLeft
```
## How was this patch tested?
Pass the Jenkins tests with a new test suite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14307 from dongjoon-hyun/SPARK-16672.
## What changes were proposed in this pull request?
**Issue 1: Disallow Creating/Altering a View when the same-name Table Exists (without IF NOT EXISTS)**
When we create OR alter a view, we check whether the view already exists. In the current implementation, if a table with the same name exists, we treat it as a view. However, this is not the right behavior. We should follow what Hive does. For example,
```
hive> CREATE TABLE tab1 (id int);
OK
Time taken: 0.196 seconds
hive> CREATE OR REPLACE VIEW tab1 AS SELECT * FROM t1;
FAILED: SemanticException [Error 10218]: Existing table is not a view
The following is an existing table, not a view: default.tab1
hive> ALTER VIEW tab1 AS SELECT * FROM t1;
FAILED: SemanticException [Error 10218]: Existing table is not a view
The following is an existing table, not a view: default.tab1
hive> CREATE VIEW IF NOT EXISTS tab1 AS SELECT * FROM t1;
OK
Time taken: 0.678 seconds
```
**Issue 2: Strange Error when Issuing Load Table Against A View**
Users should not be allowed to issue LOAD DATA against a view. Currently, when users doing it, we got a very strange runtime error. For example,
```SQL
LOAD DATA LOCAL INPATH "$testData" INTO TABLE $viewName
```
```
java.lang.reflect.InvocationTargetException was thrown.
java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.sql.hive.client.Shim_v0_14.loadTable(HiveShim.scala:680)
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14314 from gatorsmile/tableDDLAgainstView.
## What changes were proposed in this pull request?
This PR fixes a minor formatting issue of `WindowSpecDefinition.sql` when no partitioning expressions are present.
Before:
```sql
( ORDER BY `a` ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
```
After:
```sql
(ORDER BY `a` ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
```
## How was this patch tested?
New test case added in `ExpressionSQLBuilderSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#14334 from liancheng/window-spec-sql-format.
## What changes were proposed in this pull request?
It's weird that we have `BucketSpec` to abstract bucket info, but don't use it in `CatalogTable`. This PR moves `BucketSpec` into catalyst module.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14331 from cloud-fan/check.
## What changes were proposed in this pull request?
`CreateViewCommand` only needs some information of a `CatalogTable`, but not all of them. We have some tricks(e.g. we need to check the table type is `VIEW`, we need to make `CatalogColumn.dataType` nullable) to allow it to take a `CatalogTable`.
This PR cleans it up and only pass in necessary information to `CreateViewCommand`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14297 from cloud-fan/minor2.
## What changes were proposed in this pull request?
Default `TreeNode.withNewChildren` implementation doesn't work for `Last` and when both constructor arguments are the same, e.g.:
```sql
LAST_VALUE(FALSE) -- The 2nd argument defaults to FALSE
LAST_VALUE(FALSE, FALSE)
LAST_VALUE(TRUE, TRUE)
```
This is because although `Last` is a unary expression, both of its constructor arguments, `child` and `ignoreNullsExpr`, are `Expression`s. When they have the same value, `TreeNode.withNewChildren` treats both of them as child nodes by mistake. `First` is also affected by this issue in exactly the same way.
This PR fixes this issue by making `ignoreNullsExpr` a child expression of `First` and `Last`.
## How was this patch tested?
New test case added in `WindowQuerySuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#14295 from liancheng/spark-16648-last-value.
## What changes were proposed in this pull request?
we also store data source table options in this field, it's unreasonable to call it `serdeProperties`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14283 from cloud-fan/minor1.
## Problem
The current `sed` in `test_script.sh` is missing a `$`, leading to the failure of `script` test on OS X:
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
![x1_y1] [x1]
![x2_y2] [x2]
```
In addition, this `script` test would also fail on systems like Windows where we couldn't be able to invoke `bash` or `echo | sed`.
## What changes were proposed in this pull request?
This patch
- fixes `sed` in `test_script.sh`
- adds command guards so that the `script` test would pass on systems like Windows
## How was this patch tested?
- Jenkins
- Manually verified tests pass on OS X
Author: Liwei Lin <lwlin7@gmail.com>
Closes#14280 from lw-lin/osx-sed.
## What changes were proposed in this pull request?
after https://github.com/apache/spark/pull/12945, we renamed the `registerTempTable` to `createTempView`, as we do create a view actually. This PR renames `SQLTestUtils.withTempTable` to reflect this change.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#14318 from cloud-fan/minor4.
## What changes were proposed in this pull request?
This PR adds `str_to_map` SQL function in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests with newly added.
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13990 from techaddict/SPARK-16287.
## What changes were proposed in this pull request?
Due to backward-compatibility reasons, the following Parquet schema is ambiguous:
```
optional group f (LIST) {
repeated group list {
optional group element {
optional int32 element;
}
}
}
```
According to the parquet-format spec, when interpreted as a standard 3-level layout, this type is equivalent to the following SQL type:
```
ARRAY<STRUCT<element: INT>>
```
However, when interpreted as a legacy 2-level layout, it's equivalent to
```
ARRAY<STRUCT<element: STRUCT<element: INT>>>
```
Historically, to disambiguate these cases, we employed two methods:
- `ParquetSchemaConverter.isElementType()`
Used to disambiguate the above cases while converting Parquet types to Spark types.
- `ParquetRowConverter.isElementType()`
Used to disambiguate the above cases while instantiating row converters that convert Parquet records to Spark rows.
Unfortunately, these two methods make different decision about the above problematic Parquet type, and caused SPARK-16344.
`ParquetRowConverter.isElementType()` is necessary for Spark 1.4 and earlier versions because Parquet requested schemata are directly converted from Spark schemata in these versions. The converted Parquet schemata may be incompatible with actual schemata of the underlying physical files when the files are written by a system/library that uses a schema conversion scheme that is different from Spark when writing Parquet LIST and MAP fields.
In Spark 1.5, Parquet requested schemata are always properly tailored from schemata of physical files to be read. Thus `ParquetRowConverter.isElementType()` is no longer necessary. This PR replaces this method with a simply yet accurate scheme: whenever an ambiguous Parquet type is hit, convert the type in question back to a Spark type using `ParquetSchemaConverter` and check whether it matches the corresponding Spark type.
## How was this patch tested?
New test cases added in `ParquetHiveCompatibilitySuite` and `ParquetQuerySuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#14014 from liancheng/spark-16344-for-master-and-2.0.
## What changes were proposed in this pull request?
In 2.0, we add a new logic to convert HiveTableScan on ORC tables to Spark's native code path. However, during this conversion, we drop the original metastore schema (https://issues.apache.org/jira/browse/SPARK-15705). Because of this regression, I am changing the default value of `spark.sql.hive.convertMetastoreOrc` to false.
Author: Yin Huai <yhuai@databricks.com>
Closes#14267 from yhuai/SPARK-15705-changeDefaultValue.
https://issues.apache.org/jira/browse/SPARK-16535
## What changes were proposed in this pull request?
When I scan through the pom.xml of sub projects, I found this warning as below and attached screenshot
```
Definition of groupId is redundant, because it's inherited from the parent
```
I've tried to remove some of the lines with groupId definition, and the build on my local machine is still ok.
```
<groupId>org.apache.spark</groupId>
```
As I just find now `<maven.version>3.3.9</maven.version>` is being used in Spark 2.x, and Maven-3 supports versionless parent elements: Maven 3 will remove the need to specify the parent version in sub modules. THIS is great (in Maven 3.1).
ref: http://stackoverflow.com/questions/3157240/maven-3-worth-it/3166762#3166762
## How was this patch tested?
I've tested by re-building the project, and build succeeded.
Author: Xin Ren <iamshrek@126.com>
Closes#14189 from keypointt/SPARK-16535.
## What changes were proposed in this pull request?
This PR improves `LogicalPlanToSQLSuite` to check the generated SQL directly by **structure**. So far, `LogicalPlanToSQLSuite` relies on `checkHiveQl` to ensure the **successful SQL generation** and **answer equality**. However, it does not guarantee the generated SQL is the same or will not be changed unnoticeably.
## How was this patch tested?
Pass the Jenkins. This is only a testsuite change.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14235 from dongjoon-hyun/SPARK-16590.
## What changes were proposed in this pull request?
In ScriptInputOutputSchema, we read default RecordReader and RecordWriter from conf. Since Spark 2.0 has deleted those config keys from hive conf, we have to set default reader/writer class name by ourselves. Otherwise we will get None for LazySimpleSerde, the data written would not be able to read by script. The test case added worked fine with previous version of Spark, but would fail now.
## How was this patch tested?
added a test case in SQLQuerySuite.
Closes#14169
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#14249 from yhuai/scriptTransformation.
## What changes were proposed in this pull request?
There are some calls to methods or fields (getParameters, properties) which are then passed to Java/Scala collection converters. Unfortunately those fields can be null in some cases and then the conversions throws NPE. We fix it by wrapping calls to those fields and methods with option and then do the conversion.
## How was this patch tested?
Manually tested with a custom Hive metastore.
Author: Jacek Lewandowski <lewandowski.jacek@gmail.com>
Closes#14200 from jacek-lewandowski/SPARK-16528.
#### What changes were proposed in this pull request?
If we create a table pointing to a parquet/json datasets without specifying the schema, describe table command does not show the schema at all. It only shows `# Schema of this table is inferred at runtime`. In 1.6, describe table does show the schema of such a table.
~~For data source tables, to infer the schema, we need to load the data source tables at runtime. Thus, this PR calls the function `lookupRelation`.~~
For data source tables, we infer the schema before table creation. Thus, this PR set the inferred schema as the table schema when table creation.
#### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14148 from gatorsmile/describeSchema.
## What changes were proposed in this pull request?
This patch implements reflect SQL function, which can be used to invoke a Java method in SQL. Slightly different from Hive, this implementation requires the class name and the method name to be literals. This implementation also supports only a smaller number of data types, and requires the function to be static, as suggested by rxin in #13969.
java_method is an alias for reflect, so this should also resolve SPARK-16277.
## How was this patch tested?
Added expression unit tests and an end-to-end test.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14138 from petermaxlee/reflect-static.
This option is used by Hive to directly delete the files instead of
moving them to the trash. This is needed in certain configurations
where moving the files does not work. For non-Hive tables and partitions,
Spark already behaves as if the PURGE option was set, so there's no
need to do anything.
Hive support for PURGE was added in 0.14 (for tables) and 1.2 (for
partitions), so the code reflects that: trying to use the option with
older versions of Hive will cause an exception to be thrown.
The change is a little noisier than I would like, because of the code
to propagate the new flag through all the interfaces and implementations;
the main changes are in the parser and in HiveShim, aside from the tests
(DDLCommandSuite, VersionsSuite).
Tested by running sql and catalyst unit tests, plus VersionsSuite which
has been updated to test the version-specific behavior. I also ran an
internal test suite that uses PURGE and would not pass previously.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#13831 from vanzin/SPARK-16119.
## What changes were proposed in this pull request?
when query only use metadata (example: partition key), it can return results based on metadata without scanning files. Hive did it in HIVE-1003.
## How was this patch tested?
add unit tests
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Lianhui Wang <lianhuiwang@users.noreply.github.com>
Closes#13494 from lianhuiwang/metadata-only.
## What changes were proposed in this pull request?
In order to make it clear which filters are fully handled by the
underlying datasource we will mark them with an *. This will give a
clear visual queue to users that the filter is being treated differently
by catalyst than filters which are just presented to the underlying
DataSource.
Examples from the FilteredScanSuite, in this example `c IN (...)` is handled by the source, `b < ...` is not
### Before
```
//SELECT a FROM oneToTenFiltered WHERE a + b > 9 AND b < 16 AND c IN ('bbbbbBBBBB', 'cccccCCCCC', 'dddddDDDDD', 'foo')
== Physical Plan ==
Project [a#0]
+- Filter (((a#0 + b#1) > 9) && (b#1 < 16))
+- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]]
```
### After
```
== Physical Plan ==
Project [a#0]
+- Filter (((a#0 + b#1) > 9) && (b#1 < 16))
+- Scan SimpleFilteredScan(1,10)[a#0,b#1] PushedFilters: [LessThan(b,16), *In(c, [bbbbbBBBBB,cccccCCCCC,dddddDDDDD,foo]]
```
## How was the this patch tested?
Manually tested with the Spark Cassandra Connector, a source which fully handles underlying filters. Now fully handled filters appear with an * next to their names. I can add an automated test as well if requested
Post 1.6.1
Tested by modifying the FilteredScanSuite to run explains.
Author: Russell Spitzer <Russell.Spitzer@gmail.com>
Closes#11317 from RussellSpitzer/SPARK-12639-Star.
Some Hadoop classes needed by the Hive metastore client jars are not present
in Spark's packaging (for example, "org/apache/hadoop/mapred/MRVersion"). So
if the parent class loader fails to find a class, try to load it from the
isolated class loader, in case it's available there.
Tested by setting spark.sql.hive.metastore.jars to local paths with Hive/Hadoop
libraries and verifying that Spark can talk to the metastore.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#14020 from vanzin/SPARK-16349.
## What changes were proposed in this pull request?
After SPARK-16476 (committed earlier today as #14128), we can finally bump the version number.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#14130 from rxin/SPARK-16477.
## What changes were proposed in this pull request?
This PR prevents dropping the current database to avoid errors like the followings.
```scala
scala> sql("create database delete_db")
scala> sql("use delete_db")
scala> sql("drop database delete_db")
scala> sql("create table t as select 1")
org.apache.spark.sql.catalyst.analysis.NoSuchDatabaseException: Database `delete_db` not found;
```
## How was this patch tested?
Pass the Jenkins tests including an updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14115 from dongjoon-hyun/SPARK-16459.
## What changes were proposed in this pull request?
This patch implements all remaining xpath functions that Hive supports and not natively supported in Spark: xpath_int, xpath_short, xpath_long, xpath_float, xpath_double, xpath_string, and xpath.
## How was this patch tested?
Added unit tests and end-to-end tests.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13991 from petermaxlee/SPARK-16318.
## What changes were proposed in this pull request?
This PR adds parse_url SQL functions in order to remove Hive fallback.
A new implementation of #13999
## How was this patch tested?
Pass the exist tests including new testcases.
Author: wujian <jan.chou.wu@gmail.com>
Closes#14008 from janplus/SPARK-16281.
## What changes were proposed in this pull request?
This PR implements `sentences` SQL function.
## How was this patch tested?
Pass the Jenkins tests with a new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14004 from dongjoon-hyun/SPARK_16285.
## What changes were proposed in this pull request?
In #13537 we truncate `simpleString` if it is a long `StructType`. But sometimes we need `catalogString` to reconstruct `TypeInfo`, for example in description of [SPARK-16415 ](https://issues.apache.org/jira/browse/SPARK-16415). So we need to keep the implementation of `catalogString` not affected by our truncate.
## How was this patch tested?
added a test case.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#14089 from adrian-wang/catalogstring.
#### What changes were proposed in this pull request?
When creating a view, a common user error is the number of columns produced by the `SELECT` clause does not match the number of column names specified by `CREATE VIEW`.
For example, given Table `t1` only has 3 columns
```SQL
create view v1(col2, col4, col3, col5) as select * from t1
```
Currently, Spark SQL reports the following error:
```
requirement failed
java.lang.IllegalArgumentException: requirement failed
at scala.Predef$.require(Predef.scala:212)
at org.apache.spark.sql.execution.command.CreateViewCommand.run(views.scala:90)
```
This error message is very confusing. This PR is to detect the error and issue a meaningful error message.
#### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14047 from gatorsmile/viewMismatchedColumns.
#### What changes were proposed in this pull request?
Different from the other leaf nodes, `MetastoreRelation` and `SimpleCatalogRelation` have a pre-defined `alias`, which is used to change the qualifier of the node. However, based on the existing alias handling, alias should be put in `SubqueryAlias`.
This PR is to separate alias handling from `MetastoreRelation` and `SimpleCatalogRelation` to make it consistent with the other nodes. It simplifies the signature and conversion to a `BaseRelation`.
For example, below is an example query for `MetastoreRelation`, which is converted to a `LogicalRelation`:
```SQL
SELECT tmp.a + 1 FROM test_parquet_ctas tmp WHERE tmp.a > 2
```
Before changes, the analyzed plan is
```
== Analyzed Logical Plan ==
(a + 1): int
Project [(a#951 + 1) AS (a + 1)#952]
+- Filter (a#951 > 2)
+- SubqueryAlias tmp
+- Relation[a#951] parquet
```
After changes, the analyzed plan becomes
```
== Analyzed Logical Plan ==
(a + 1): int
Project [(a#951 + 1) AS (a + 1)#952]
+- Filter (a#951 > 2)
+- SubqueryAlias tmp
+- SubqueryAlias test_parquet_ctas
+- Relation[a#951] parquet
```
**Note: the optimized plans are the same.**
For `SimpleCatalogRelation`, the existing code always generates two Subqueries. Thus, no change is needed.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14053 from gatorsmile/removeAliasFromMetastoreRelation.
#### What changes were proposed in this pull request?
In `CREATE TABLE AS SELECT`, if the `SELECT` query failed, the table should not exist. For example,
```SQL
CREATE TABLE tab
STORED AS TEXTFILE
SELECT 1 AS a, (SELECT a FROM (SELECT 1 AS a UNION ALL SELECT 2 AS a) t) AS b
```
The above query failed as expected but an empty table `t` is created.
This PR is to drop the created table when hitting any non-fatal exception.
#### How was this patch tested?
Added a test case to verify the behavior
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13926 from gatorsmile/dropTableAfterException.
## What changes were proposed in this pull request?
These two configs should always be true after Spark 2.0. This patch removes them from the config list. Note that ideally this should've gone into branch-2.0, but due to the timing of the release we should only merge this in master for Spark 2.1.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#14061 from rxin/SPARK-16388.
## What changes were proposed in this pull request?
Currently, if due to some failure, the outstream gets destroyed or closed and later `outstream.close()` leads to IOException in such case. Due to this, the `stderrBuffer` does not get logged and there is no way for users to see why the job failed.
The change is to first display the stderr buffer and then try closing the outstream.
## How was this patch tested?
The correct way to test this fix would be to grep the log to see if the `stderrBuffer` gets logged but I dont think having test cases which do that is a good idea.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
…
Author: Tejas Patil <tejasp@fb.com>
Closes#13834 from tejasapatil/script_transform.
#### What changes were proposed in this pull request?
- Remove useless `MetastoreRelation` from the signature of `SparkHiveWriterContainer` and `SparkHiveDynamicPartitionWriterContainer`.
- Avoid unnecessary metadata retrieval using Hive client in `InsertIntoHiveTable`.
#### How was this patch tested?
Existing test cases already cover it.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14062 from gatorsmile/removeMetastoreRelation.
## What changes were proposed in this pull request?
This PR implements `stack` table generating function.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14033 from dongjoon-hyun/SPARK-16286.
## What changes were proposed in this pull request?
This PR removes `SessionState.executeSql` in favor of `SparkSession.sql`. We can remove this safely since the visibility `SessionState` is `private[sql]` and `executeSql` is only used in one **ignored** test, `test("Multiple Hive Instances")`.
## How was this patch tested?
Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14055 from dongjoon-hyun/SPARK-16383.
## What changes were proposed in this pull request?
This patch fixes the bug that the refresh command does not work on temporary views. This patch is based on https://github.com/apache/spark/pull/13989, but removes the public Dataset.refresh() API as well as improved test coverage.
Note that I actually think the public refresh() API is very useful. We can in the future implement it by also invalidating the lazy vals in QueryExecution (or alternatively just create a new QueryExecution).
## How was this patch tested?
Re-enabled a previously ignored test, and added a new test suite for Hive testing behavior of temporary views against MetastoreRelation.
Author: Reynold Xin <rxin@databricks.com>
Author: petermaxlee <petermaxlee@gmail.com>
Closes#14009 from rxin/SPARK-16311.
## What changes were proposed in this pull request?
It seems ORC supports all the types in ([`PredicateLeaf.Type`](e085b7e9bd/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java (L50-L56))) which includes boolean types. So, this was tested first.
This PR adds the support for pushing filters down for `BooleanType` in ORC data source.
This PR also removes `OrcTableScan` class and the companion object, which is not used anymore.
## How was this patch tested?
Unittest in `OrcFilterSuite` and `OrcQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12972 from HyukjinKwon/SPARK-15198.
(Please note this is a revision of PR #13686, which has been closed in favor of this PR.)
This PR addresses [SPARK-15968](https://issues.apache.org/jira/browse/SPARK-15968).
## What changes were proposed in this pull request?
The `getCached` method of [HiveMetastoreCatalog](https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala) computes `pathsInMetastore` from the metastore relation's catalog table. This only returns the table base path, which is incomplete/inaccurate for a nonempty partitioned table. As a result, cached lookups on nonempty partitioned tables always miss.
Rather than get `pathsInMetastore` from
metastoreRelation.catalogTable.storage.locationUri.toSeq
I modified the `getCached` method to take a `pathsInMetastore` argument. Calls to this method pass in the paths computed from calls to the Hive metastore. This is how `getCached` was implemented in Spark 1.5:
e0c3212a9b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala (L444).
I also added a call in `InsertIntoHiveTable.scala` to invalidate the table from the SQL session catalog.
## How was this patch tested?
I've added a new unit test to `parquetSuites.scala`:
SPARK-15968: nonempty partitioned metastore Parquet table lookup should use cached relation
Note that the only difference between this new test and the one above it in the file is that the new test populates its partitioned table with a single value, while the existing test leaves the table empty. This reveals a subtle, unexpected hole in test coverage present before this patch.
Note I also modified a different but related unit test in `parquetSuites.scala`:
SPARK-15248: explicitly added partitions should be readable
This unit test asserts that Spark SQL should return data from a table partition which has been placed there outside a metastore query immediately after it is added. I changed the test so that, instead of adding the data as a parquet file saved in the partition's location, the data is added through a SQL `INSERT` query. I made this change because I could find no way to efficiently support partitioned table caching without failing that test.
In addition to my primary motivation, I can offer a few reasons I believe this is an acceptable weakening of that test. First, it still validates a fix for [SPARK-15248](https://issues.apache.org/jira/browse/SPARK-15248), the issue for which it was written. Second, the assertion made is stronger than that required for non-partitioned tables. If you write data to the storage location of a non-partitioned metastore table without using a proper SQL DML query, a subsequent call to show that data will not return it. I believe this is an intentional limitation put in place to make table caching feasible, but I'm only speculating.
Building a large `HadoopFsRelation` requires `stat`-ing all of its data files. In our environment, where we have tables with 10's of thousands of partitions, the difference between using a cached relation versus a new one is a matter of seconds versus minutes. Caching partitioned table metadata vastly improves the usability of Spark SQL for these cases.
Thanks.
Author: Michael Allman <michael@videoamp.com>
Closes#13818 from mallman/spark-15968.
#### What changes were proposed in this pull request?
LogicalPlan `InsertIntoHiveTable` is useless. Thus, we can remove it from the code base.
#### How was this patch tested?
The existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#14037 from gatorsmile/InsertIntoHiveTable.
## What changes were proposed in this pull request?
This PR implements `inline` table generating function.
## How was this patch tested?
Pass the Jenkins tests with new testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13976 from dongjoon-hyun/SPARK-16288.
## What changes were proposed in this pull request?
This PR adds `map_keys` and `map_values` SQL functions in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests including new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13967 from dongjoon-hyun/SPARK-16278.
## What changes were proposed in this pull request?
This patch introduces a flag to disable loading test tables in TestHiveSparkSession and disables that in Python. This fixes an issue in which python/run-tests would fail due to failure to load test tables.
Note that these test tables are not used outside of HiveCompatibilitySuite. In the long run we should probably decouple the loading of test tables from the test Hive setup.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#14005 from rxin/SPARK-15954.
## What changes were proposed in this pull request?
This patch implements the elt function, as it is implemented in Hive.
## How was this patch tested?
Added expression unit test in StringExpressionsSuite and end-to-end test in StringFunctionsSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13966 from petermaxlee/SPARK-16276.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
Force the sorter to Spill when number of elements in the pointer array reach a certain size. This is to workaround the issue of timSort failing on large buffer size.
## How was this patch tested?
Tested by running a job which was failing without this change due to TimSort bug.
Author: Sital Kedia <skedia@fb.com>
Closes#13107 from sitalkedia/fix_TimSort.
## What changes were proposed in this pull request?
This patch implements xpath_boolean expression for Spark SQL, a xpath function that returns true or false. The implementation is modelled after Hive's xpath_boolean, except that how the expression handles null inputs. Hive throws a NullPointerException at runtime if either of the input is null. This implementation returns null if either of the input is null.
## How was this patch tested?
Created two new test suites. One for unit tests covering the expression, and the other for end-to-end test in SQL.
Author: petermaxlee <petermaxlee@gmail.com>
Closes#13964 from petermaxlee/SPARK-16274.
## What changes were proposed in this pull request?
After SPARK-15674, `DDLStrategy` prints out the following deprecation messages in the testsuites.
```
12:10:53.284 WARN org.apache.spark.sql.execution.SparkStrategies$DDLStrategy:
CREATE TEMPORARY TABLE normal_orc_source USING... is deprecated,
please use CREATE TEMPORARY VIEW viewName USING... instead
```
Total : 40
- JDBCWriteSuite: 14
- DDLSuite: 6
- TableScanSuite: 6
- ParquetSourceSuite: 5
- OrcSourceSuite: 2
- SQLQuerySuite: 2
- HiveCommandSuite: 2
- JsonSuite: 1
- PrunedScanSuite: 1
- FilteredScanSuite 1
This PR replaces `CREATE TEMPORARY TABLE` with `CREATE TEMPORARY VIEW` in order to remove the deprecation messages in the above testsuites except `DDLSuite`, `SQLQuerySuite`, `HiveCommandSuite`.
The Jenkins results shows only remaining 10 messages.
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/61422/consoleFull
## How was this patch tested?
This is a testsuite-only change.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13956 from dongjoon-hyun/SPARK-16267.
## What changes were proposed in this pull request?
This PR supports a fallback lookup by casting `DecimalType` into `DoubleType` for the external functions with `double`-type parameter.
**Reported Error Scenarios**
```scala
scala> sql("select percentile(value, 0.5) from values 1,2,3 T(value)")
org.apache.spark.sql.AnalysisException: ... No matching method for class org.apache.hadoop.hive.ql.udf.UDAFPercentile with (int, decimal(38,18)). Possible choices: _FUNC_(bigint, array<double>) _FUNC_(bigint, double) ; line 1 pos 7
scala> sql("select percentile_approx(value, 0.5) from values 1.0,2.0,3.0 T(value)")
org.apache.spark.sql.AnalysisException: ... Only a float/double or float/double array argument is accepted as parameter 2, but decimal(38,18) was passed instead.; line 1 pos 7
```
## How was this patch tested?
Pass the Jenkins tests (including a new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13930 from dongjoon-hyun/SPARK-16228.
#### What changes were proposed in this pull request?
Based on the previous discussion with cloud-fan hvanhovell in another related PR https://github.com/apache/spark/pull/13764#discussion_r67994276, it looks reasonable to add convenience methods for users to add `comment` when defining `StructField`.
Currently, the column-related `comment` attribute is stored in `Metadata` of `StructField`. For example, users can add the `comment` attribute using the following way:
```Scala
StructType(
StructField(
"cl1",
IntegerType,
nullable = false,
new MetadataBuilder().putString("comment", "test").build()) :: Nil)
```
This PR is to add more user friendly methods for the `comment` attribute when defining a `StructField`. After the changes, users are provided three different ways to do it:
```Scala
val struct = (new StructType)
.add("a", "int", true, "test1")
val struct = (new StructType)
.add("c", StringType, true, "test3")
val struct = (new StructType)
.add(StructField("d", StringType).withComment("test4"))
```
#### How was this patch tested?
Added test cases:
- `DataTypeSuite` is for testing three types of API changes,
- `DataFrameReaderWriterSuite` is for parquet, json and csv formats - using in-memory catalog
- `OrcQuerySuite.scala` is for orc format using Hive-metastore
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13860 from gatorsmile/newMethodForComment.
## What changes were proposed in this pull request?
This patch removes the blind fallback into Hive for functions. Instead, it creates a whitelist and adds only a small number of functions to the whitelist, i.e. the ones we intend to support in the long run in Spark.
## How was this patch tested?
Updated tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#13939 from rxin/hive-whitelist.
## What changes were proposed in this pull request?
- Fix tests regarding show functions functionality
- Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality.
Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files
## How was this patch tested?
Unit tests.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13916 from anabranch/master.
## What changes were proposed in this pull request?
When reading partitions of a partitioned Hive SerDe table, we only initializes the deserializer using partition properties. However, for SerDes like `AvroSerDe`, essential properties (e.g. Avro schema information) may be defined in table properties. We should merge both table properties and partition properties before initializing the deserializer.
Note that an individual partition may have different properties than the one defined in the table properties (e.g. partitions within a table can have different SerDes). Thus, for any property key defined in both partition and table properties, the value set in partition properties wins.
## How was this patch tested?
New test case added in `QueryPartitionSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13865 from liancheng/spark-13709-partitioned-avro-table.
## What changes were proposed in this pull request?
SPARK-14535 removed all calls to class OrcTableScan. This removes the dead code.
## How was this patch tested?
Existing unit tests.
Author: Brian Cho <bcho@fb.com>
Closes#13869 from dafrista/clean-up-orctablescan.
#### What changes were proposed in this pull request?
This PR is to improve test coverage. It verifies whether `Comment` of `Column` can be appropriate handled.
The test cases verify the related parts in Parser, both SQL and DataFrameWriter interface, and both Hive Metastore catalog and In-memory catalog.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13764 from gatorsmile/dataSourceComment.
## What changes were proposed in this pull request?
Extend the returning of unwrapper functions from primitive types to all types.
This PR is based on https://github.com/apache/spark/pull/13676. It only fixes a bug with scala-2.10 compilation. All credit should go to dafrista.
## How was this patch tested?
The patch should pass all unit tests. Reading ORC files with non-primitive types with this change reduced the read time by ~15%.
Author: Brian Cho <bcho@fb.com>
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13854 from hvanhovell/SPARK-15956-scala210.
This reverts commit 0a9c027595. It breaks the 2.10 build, I'll fix this in a different PR.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13853 from hvanhovell/SPARK-15956-revert.
## What changes were proposed in this pull request?
This PR migrates some test cases introduced in #12313 as a follow-up of #13754 and #13766. These test cases cover `DataFrameWriter.insertInto()`, while the former two only cover SQL `INSERT` statements.
Note that the `testPartitionedTable` utility method tests both Hive SerDe tables and data source tables.
## How was this patch tested?
N/A
Author: Cheng Lian <lian@databricks.com>
Closes#13810 from liancheng/spark-16037-follow-up-tests.
## What changes were proposed in this pull request?
This PR adds the static partition support to INSERT statement when the target table is a data source table.
## How was this patch tested?
New tests in InsertIntoHiveTableSuite and DataSourceAnalysisSuite.
**Note: This PR is based on https://github.com/apache/spark/pull/13766. The last commit is the actual change.**
Author: Yin Huai <yhuai@databricks.com>
Closes#13769 from yhuai/SPARK-16030-1.
## What changes were proposed in this pull request?
The current table insertion has some weird behaviours:
1. inserting into a partitioned table with mismatch columns has confusing error message for hive table, and wrong result for datasource table
2. inserting into a partitioned table without partition list has wrong result for hive table.
This PR fixes these 2 problems.
## How was this patch tested?
new test in hive `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13754 from cloud-fan/insert2.
## What changes were proposed in this pull request?
When inserting into an existing partitioned table, partitioning columns should always be determined by catalog metadata of the existing table to be inserted. Extra `partitionBy()` calls don't make sense, and mess up existing data because newly inserted data may have wrong partitioning directory layout.
## How was this patch tested?
New test case added in `InsertIntoHiveTableSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes#13747 from liancheng/spark-16033-insert-into-without-partition-by.
#### What changes were proposed in this pull request?
`IF NOT EXISTS` in `INSERT OVERWRITE` should not support dynamic partitions. If we specify `IF NOT EXISTS`, the inserted statement is not shown in the table.
This PR is to issue an exception in this case, just like what Hive does. Also issue an exception if users specify `IF NOT EXISTS` if users do not specify any `PARTITION` specification.
#### How was this patch tested?
Added test cases into `PlanParserSuite` and `InsertIntoHiveTableSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13447 from gatorsmile/insertIfNotExist.
## What changes were proposed in this pull request?
Before this patch, after a SparkSession has been created, hadoop conf set directly to SparkContext.hadoopConfiguration will not affect the hadoop conf created by SessionState. This patch makes the change to always use SparkContext.hadoopConfiguration as the base.
This patch also changes the behavior of hive-site.xml support added in https://github.com/apache/spark/pull/12689/. With this patch, we will load hive-site.xml to SparkContext.hadoopConfiguration.
## How was this patch tested?
New test in SparkSessionBuilderSuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#13711 from yhuai/SPARK-15991.
#### What changes were proposed in this pull request?
`HIVE_METASTORE_PARTITION_PRUNING` is a public `SQLConf`. When `true`, some predicates will be pushed down into the Hive metastore so that unmatching partitions can be eliminated earlier. The current default value is `false`. For performance improvement, users might turn this parameter on.
So far, the code base does not have such a test case to verify whether this `SQLConf` properly works. This PR is to improve the test case coverage for avoiding future regression.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13716 from gatorsmile/addTestMetastorePartitionPruning.
#### What changes were proposed in this pull request?
~~If the temp table already exists, we should not silently replace it when doing `CACHE TABLE AS SELECT`. This is inconsistent with the behavior of `CREAT VIEW` or `CREATE TABLE`. This PR is to fix this silent drop.~~
~~Maybe, we also can introduce new syntax for replacing the existing one. For example, in Hive, to replace a view, the syntax should be like `ALTER VIEW AS SELECT` or `CREATE OR REPLACE VIEW AS SELECT`~~
The table name in `CACHE TABLE AS SELECT` should NOT contain database prefix like "database.table". Thus, this PR captures this in Parser and outputs a better error message, instead of reporting the view already exists.
In addition, refactoring the `Parser` to generate table identifiers instead of returning the table name string.
#### How was this patch tested?
- Added a test case for caching and uncaching qualified table names
- Fixed a few test cases that do not drop temp table at the end
- Added the related test case for the issue resolved in this PR
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13572 from gatorsmile/cacheTableAsSelect.
## What changes were proposed in this pull request?
We currently immediately execute `INSERT` commands when they are issued. This is not the case as soon as we use a `WITH` to define common table expressions, for example:
```sql
WITH
tbl AS (SELECT * FROM x WHERE id = 10)
INSERT INTO y
SELECT *
FROM tbl
```
This PR fixes this problem. This PR closes https://github.com/apache/spark/pull/13561 (which fixes the a instance of this problem in the ThriftSever).
## How was this patch tested?
Added a test to `InsertSuite`
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#13678 from hvanhovell/SPARK-15824.
#### What changes were proposed in this pull request?
So far, we do not have test cases for verifying whether the external parameters `HiveUtils .CONVERT_METASTORE_ORC` and `HiveUtils.CONVERT_METASTORE_PARQUET` properly works when users use non-default values. This PR is to add such test cases for avoiding potential regression.
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13622 from gatorsmile/addTestCase4parquetOrcConversion.
## What changes were proposed in this pull request?
This PR adds the support of conf `hive.metastore.warehouse.dir` back. With this patch, the way of setting the warehouse dir is described as follows:
* If `spark.sql.warehouse.dir` is set, `hive.metastore.warehouse.dir` will be automatically set to the value of `spark.sql.warehouse.dir`. The warehouse dir is effectively set to the value of `spark.sql.warehouse.dir`.
* If `spark.sql.warehouse.dir` is not set but `hive.metastore.warehouse.dir` is set, `spark.sql.warehouse.dir` will be automatically set to the value of `hive.metastore.warehouse.dir`. The warehouse dir is effectively set to the value of `hive.metastore.warehouse.dir`.
* If neither `spark.sql.warehouse.dir` nor `hive.metastore.warehouse.dir` is set, `hive.metastore.warehouse.dir` will be automatically set to the default value of `spark.sql.warehouse.dir`. The warehouse dir is effectively set to the default value of `spark.sql.warehouse.dir`.
## How was this patch tested?
`set hive.metastore.warehouse.dir` in `HiveSparkSubmitSuite`.
JIRA: https://issues.apache.org/jira/browse/SPARK-15959
Author: Yin Huai <yhuai@databricks.com>
Closes#13679 from yhuai/hiveWarehouseDir.
## What changes were proposed in this pull request?
This test re-enables the `analyze MetastoreRelations` in `org.apache.spark.sql.hive.StatisticsSuite`.
The flakiness of this test was traced back to a shared configuration option, `hive.exec.compress.output`, in `TestHive`. This property was set to `true` by the `HiveCompatibilitySuite`. I have added configuration resetting logic to `HiveComparisonTest`, in order to prevent such a thing from happening again.
## How was this patch tested?
Is a test.
Author: Herman van Hovell <hvanhovell@databricks.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#13498 from hvanhovell/SPARK-15011.
#### What changes were proposed in this pull request?
To uncache a table, we have three different ways:
- _SQL interface_: `UNCACHE TABLE`
- _DataSet API_: `sparkSession.catalog.uncacheTable`
- _DataSet API_: `sparkSession.table(tableName).unpersist()`
When the table is not cached,
- _SQL interface_: `UNCACHE TABLE non-cachedTable` -> **no error message**
- _Dataset API_: `sparkSession.catalog.uncacheTable("non-cachedTable")` -> **report a strange error message:**
```requirement failed: Table [a: int] is not cached```
- _Dataset API_: `sparkSession.table("non-cachedTable").unpersist()` -> **no error message**
This PR will make them consistent. No operation if the table has already been uncached.
In addition, this PR also removes `uncacheQuery` and renames `tryUncacheQuery` to `uncacheQuery`, and documents it that it's noop if the table has already been uncached
#### How was this patch tested?
Improved the existing test case for verifying the cases when the table has not been cached.
Also added test cases for verifying the cases when the table does not exist
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13593 from gatorsmile/uncacheNonCachedTable.
## What changes were proposed in this pull request?
`DataFrame` with plan overriding `sameResult` but not using canonicalized plan to compare can't cacheTable.
The example is like:
```
val localRelation = Seq(1, 2, 3).toDF()
localRelation.createOrReplaceTempView("localRelation")
spark.catalog.cacheTable("localRelation")
assert(
localRelation.queryExecution.withCachedData.collect {
case i: InMemoryRelation => i
}.size == 1)
```
and this will fail as:
```
ArrayBuffer() had size 0 instead of expected size 1
```
The reason is that when do `spark.catalog.cacheTable("localRelation")`, `CacheManager` tries to cache for the plan wrapped by `SubqueryAlias` but when planning for the DataFrame `localRelation`, `CacheManager` tries to find cached table for the not-wrapped plan because the plan for DataFrame `localRelation` is not wrapped.
Some plans like `LocalRelation`, `LogicalRDD`, etc. override `sameResult` method, but not use canonicalized plan to compare so the `CacheManager` can't detect the plans are the same.
This pr modifies them to use canonicalized plan when override `sameResult` method.
## How was this patch tested?
Added a test to check if DataFrame with plan overriding sameResult but not using canonicalized plan to compare can cacheTable.
Author: Takuya UESHIN <ueshin@happy-camper.st>
Closes#13638 from ueshin/issues/SPARK-15915.
#### What changes were proposed in this pull request?
When fetching the partitioned table, the output contains wrong results. The order of partition key values do not match the order of partition key columns in output schema. For example,
```SQL
CREATE TABLE table_with_partition(c1 string) PARTITIONED BY (p1 string,p2 string,p3 string,p4 string,p5 string)
INSERT OVERWRITE TABLE table_with_partition PARTITION (p1='a',p2='b',p3='c',p4='d',p5='e') SELECT 'blarr'
SELECT p1, p2, p3, p4, p5, c1 FROM table_with_partition
```
```
+---+---+---+---+---+-----+
| p1| p2| p3| p4| p5| c1|
+---+---+---+---+---+-----+
| d| e| c| b| a|blarr|
+---+---+---+---+---+-----+
```
The expected result should be
```
+---+---+---+---+---+-----+
| p1| p2| p3| p4| p5| c1|
+---+---+---+---+---+-----+
| a| b| c| d| e|blarr|
+---+---+---+---+---+-----+
```
This PR is to fix this by enforcing the order matches the table partition definition.
#### How was this patch tested?
Added a test case into `SQLQuerySuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13400 from gatorsmile/partitionedTableFetch.
## What changes were proposed in this pull request?
SparkSession.catalog.listFunctions currently returns all functions, including the list of built-in functions. This makes the method not as useful because anytime it is run the result set contains over 100 built-in functions.
## How was this patch tested?
CatalogSuite
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13413 from techaddict/SPARK-15663.
#### What changes were proposed in this pull request?
**Issue:** Got wrong results or strange errors when append data to a table with mismatched file format.
_Example 1: PARQUET -> CSV_
```Scala
createDF(0, 9).write.format("parquet").saveAsTable("appendParquetToOrc")
createDF(10, 19).write.mode(SaveMode.Append).format("orc").saveAsTable("appendParquetToOrc")
```
Error we got:
```
Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2, localhost): java.lang.RuntimeException: file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/warehouse-bc8fedf2-aa6a-4002-a18b-524c6ac859d4/appendorctoparquet/part-r-00000-c0e3f365-1d46-4df5-a82c-b47d7af9feb9.snappy.orc is not a Parquet file. expected magic number at tail [80, 65, 82, 49] but found [79, 82, 67, 23]
```
_Example 2: Json -> CSV_
```Scala
createDF(0, 9).write.format("json").saveAsTable("appendJsonToCSV")
createDF(10, 19).write.mode(SaveMode.Append).format("parquet").saveAsTable("appendJsonToCSV")
```
No exception, but wrong results:
```
+----+----+
| c1| c2|
+----+----+
|null|null|
|null|null|
|null|null|
|null|null|
| 0|str0|
| 1|str1|
| 2|str2|
| 3|str3|
| 4|str4|
| 5|str5|
| 6|str6|
| 7|str7|
| 8|str8|
| 9|str9|
+----+----+
```
_Example 3: Json -> Text_
```Scala
createDF(0, 9).write.format("json").saveAsTable("appendJsonToText")
createDF(10, 19).write.mode(SaveMode.Append).format("text").saveAsTable("appendJsonToText")
```
Error we got:
```
Text data source supports only a single column, and you have 2 columns.
```
This PR is to issue an exception with appropriate error messages.
#### How was this patch tested?
Added test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13546 from gatorsmile/fileFormatCheck.
## What changes were proposed in this pull request?
Right now, Spark 2.0 does not load hive-site.xml. Based on users' feedback, it seems make sense to still load this conf file.
This PR adds a `hadoopConf` API in `SharedState`, which is `sparkContext.hadoopConfiguration` by default. When users are under hive context, `SharedState.hadoopConf` will load hive-site.xml and append its configs to `sparkContext.hadoopConfiguration`.
When we need to read hadoop config in spark sql, we should call `SessionState.newHadoopConf`, which contains `sparkContext.hadoopConfiguration`, hive-site.xml and sql configs.
## How was this patch tested?
new test in `HiveDataFrameSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13611 from cloud-fan/hive-site.
## What changes were proposed in this pull request?
Spark currently incorrectly continues to use cached data even if the underlying data is overwritten.
Current behavior:
```scala
val dir = "/tmp/test"
sqlContext.range(1000).write.mode("overwrite").parquet(dir)
val df = sqlContext.read.parquet(dir).cache()
df.count() // outputs 1000
sqlContext.range(10).write.mode("overwrite").parquet(dir)
sqlContext.read.parquet(dir).count() // outputs 1000 <---- We are still using the cached dataset
```
This patch fixes this bug by adding support for `REFRESH path` that invalidates and refreshes all the cached data (and the associated metadata) for any dataframe that contains the given data source path.
Expected behavior:
```scala
val dir = "/tmp/test"
sqlContext.range(1000).write.mode("overwrite").parquet(dir)
val df = sqlContext.read.parquet(dir).cache()
df.count() // outputs 1000
sqlContext.range(10).write.mode("overwrite").parquet(dir)
spark.catalog.refreshResource(dir)
sqlContext.read.parquet(dir).count() // outputs 10 <---- We are not using the cached dataset
```
## How was this patch tested?
Unit tests for overwrites and appends in `ParquetQuerySuite` and `CachedTableSuite`.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13566 from sameeragarwal/refresh-path-2.
## What changes were proposed in this pull request?
Currently, we always split the files when it's bigger than maxSplitBytes, but Hadoop LineRecordReader does not respect the splits for compressed files correctly, we should have a API for FileFormat to check whether the file could be splitted or not.
This PR is based on #13442, closes#13442
## How was this patch tested?
add regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13531 from davies/fix_split.
## What changes were proposed in this pull request?
Replace all occurrences of `None: Option[X]` with `Option.empty[X]`
## How was this patch tested?
Exisiting Tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13591 from techaddict/minor-7.
## What changes were proposed in this pull request?
This patch moves some codes in `DataFrameWriter.insertInto` that belongs to `Analyzer`.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13496 from viirya/move-analyzer-stuff.
## What changes were proposed in this pull request?
* Add DataFrameWriter.foreach to allow the user consuming data in ContinuousQuery
* ForeachWriter is the interface for the user to consume partitions of data
* Add a type parameter T to DataFrameWriter
Usage
```Scala
val ds = spark.read....stream().as[String]
ds.....write
.queryName(...)
.option("checkpointLocation", ...)
.foreach(new ForeachWriter[Int] {
def open(partitionId: Long, version: Long): Boolean = {
// prepare some resources for a partition
// check `version` if possible and return `false` if this is a duplicated data to skip the data processing.
}
override def process(value: Int): Unit = {
// process data
}
def close(errorOrNull: Throwable): Unit = {
// release resources for a partition
// check `errorOrNull` and handle the error if necessary.
}
})
```
## How was this patch tested?
New unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13342 from zsxwing/foreach.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
## What changes were proposed in this pull request?
As of this patch, the following throws an exception because the schemas may not match:
```
CREATE TABLE students (age INT, name STRING) AS SELECT * FROM boxes
```
but this is OK:
```
CREATE TABLE students AS SELECT * FROM boxes
```
## How was this patch tested?
SQLQuerySuite, HiveDDLCommandSuite
Author: Andrew Or <andrew@databricks.com>
Closes#13490 from andrewor14/ctas-no-column.
## What changes were proposed in this pull request?
For consistency, this PR updates some remaining `TungstenAggregation/SortBasedAggregate` after SPARK-15728.
- Update a comment in codegen in `VectorizedHashMapGenerator.scala`.
- `TungstenAggregationQuerySuite` --> `HashAggregationQuerySuite`
- `TungstenAggregationQueryWithControlledFallbackSuite` --> `HashAggregationQueryWithControlledFallbackSuite`
- Update two error messages in `SQLQuerySuite.scala` and `AggregationQuerySuite.scala`.
- Update several comments.
## How was this patch tested?
Manual (Only comment changes and test suite renamings).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13487 from dongjoon-hyun/SPARK-15744.
## What changes were proposed in this pull request?
This is a cleaner approach in general but my motivation behind this change in particular is to be able to run these tests from anywhere without relying on system properties.
## How was this patch tested?
Test only change
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13489 from sameeragarwal/resourcepath.
## What changes were proposed in this pull request?
This command didn't work for Hive tables. Now it does:
```
ALTER TABLE boxes PARTITION (width=3)
SET SERDE 'com.sparkbricks.serde.ColumnarSerDe'
WITH SERDEPROPERTIES ('compress'='true')
```
## How was this patch tested?
`HiveExternalCatalogSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13453 from andrewor14/alter-partition-storage.
## What changes were proposed in this pull request?
Currently we don't support bucketing for `save` and `insertInto`.
For `save`, we just write the data out into a directory users specified, and it's not a table, we don't keep its metadata. When we read it back, we have no idea if the data is bucketed or not, so it doesn't make sense to use `save` to write bucketed data, as we can't use the bucket information anyway.
We can support it in the future, once we have features like bucket discovery, or we save bucket information in the data directory too, so that we don't need to rely on a metastore.
For `insertInto`, it inserts data into an existing table, so it doesn't make sense to specify bucket information, as we should get the bucket information from the existing table.
This PR improves the error message for the above 2 cases.
## How was this patch tested?
new test in `BukctedWriteSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#13452 from cloud-fan/error-msg.
## What changes were proposed in this pull request?
This PR disables writing Parquet summary files by default (i.e., when Hadoop configuration "parquet.enable.summary-metadata" is not set).
Please refer to [SPARK-15719][1] for more details.
## How was this patch tested?
New test case added in `ParquetQuerySuite` to check no summary files are written by default.
[1]: https://issues.apache.org/jira/browse/SPARK-15719
Author: Cheng Lian <lian@databricks.com>
Closes#13455 from liancheng/spark-15719-disable-parquet-summary-files.
## What changes were proposed in this pull request?
This PR bans syntax like `CREATE TEMPORARY TABLE USING AS SELECT`
`CREATE TEMPORARY TABLE ... USING ... AS ...` is not properly implemented, the temporary data is not cleaned up when the session exits. Before a full fix, we probably should ban this syntax.
This PR only impact syntax like `CREATE TEMPORARY TABLE ... USING ... AS ...`.
Other syntax like `CREATE TEMPORARY TABLE .. USING ...` and `CREATE TABLE ... USING ...` are not impacted.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13451 from clockfly/ban_create_temp_table_using_as.
#### What changes were proposed in this pull request?
This PR is to address the following issues:
- **ISSUE 1:** For ORC source format, we are reporting the strange error message when we did not enable Hive support:
```SQL
SQL Example:
select id from `org.apache.spark.sql.hive.orc`.`file_path`
Error Message:
Table or view not found: `org.apache.spark.sql.hive.orc`.`file_path`
```
Instead, we should issue the error message like:
```
Expected Error Message:
The ORC data source must be used with Hive support enabled
```
- **ISSUE 2:** For the Avro format, we report the strange error message like:
The example query is like
```SQL
SQL Example:
select id from `avro`.`file_path`
select id from `com.databricks.spark.avro`.`file_path`
Error Message:
Table or view not found: `com.databricks.spark.avro`.`file_path`
```
The desired message should be like:
```
Expected Error Message:
Failed to find data source: avro. Please use Spark package http://spark-packages.org/package/databricks/spark-avro"
```
- ~~**ISSUE 3:** Unable to detect incompatibility libraries for Spark 2.0 in Data Source Resolution. We report a strange error message:~~
**Update**: The latest code changes contains
- For JDBC format, we added an extra checking in the rule `ResolveRelations` of `Analyzer`. Without the PR, Spark will return the error message like: `Option 'url' not specified`. Now, we are reporting `Unsupported data source type for direct query on files: jdbc`
- Make data source format name case incensitive so that error handling behaves consistent with the normal cases.
- Added the test cases for all the supported formats.
#### How was this patch tested?
Added test cases to cover all the above issues
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13283 from gatorsmile/runSQLAgainstFile.
## What changes were proposed in this pull request?
When `spark.sql.hive.convertCTAS` is true, for a CTAS statement, we will create a data source table using the default source (i.e. parquet) if the CTAS does not specify any Hive storage format. However, there are two issues with this conversion logic.
1. First, we determine if a CTAS statement defines storage format by checking the serde. However, TEXTFILE/SEQUENCEFILE does not have a default serde. When we do the check, we have not set the default serde. So, a query like `CREATE TABLE abc STORED AS TEXTFILE AS SELECT ...` actually creates a data source parquet table.
2. In the conversion logic, we are ignoring the user-specified location.
This PR fixes the above two issues.
Also, this PR makes the parser throws an exception when a CTAS statement has a PARTITIONED BY clause. This change is made because Hive's syntax does not allow it and our current implementation actually does not work for this case (the insert operation always throws an exception because the insertion does not pick up the partitioning info).
## How was this patch tested?
I am adding new tests in SQLQuerySuite and HiveDDLCommandSuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#13386 from yhuai/SPARK-14507.
This PR is an alternative to #13120 authored by xwu0226.
## What changes were proposed in this pull request?
When creating an external Spark SQL data source table and persisting its metadata to Hive metastore, we don't use the standard Hive `Table.dataLocation` field because Hive only allows directory paths as data locations while Spark SQL also allows file paths. However, if we don't set `Table.dataLocation`, Hive always creates an unexpected empty table directory under database location, but doesn't remove it while dropping the table (because the table is external).
This PR works around this issue by explicitly setting `Table.dataLocation` and then manullay removing the created directory after creating the external table.
Please refer to [this JIRA comment][1] for more details about why we chose this approach as a workaround.
[1]: https://issues.apache.org/jira/browse/SPARK-15269?focusedCommentId=15297408&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15297408
## How was this patch tested?
1. A new test case is added in `HiveQuerySuite` for this case
2. Updated `ShowCreateTableSuite` to use the same table name in all test cases. (This is how I hit this issue at the first place.)
Author: Cheng Lian <lian@databricks.com>
Closes#13270 from liancheng/spark-15269-unpleasant-fix.
## What changes were proposed in this pull request?
This PR changes function `SparkSession.builder.sparkContext(..)` from **private[sql]** into **private[spark]**, and uses it if applicable like the followings.
```
- val spark = SparkSession.builder().config(sc.getConf).getOrCreate()
+ val spark = SparkSession.builder().sparkContext(sc).getOrCreate()
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13365 from dongjoon-hyun/SPARK-15618.
In this case, the result type of the expression becomes DECIMAL(38, 36) as we promote the individual string literals to DECIMAL(38, 18) when we handle string promotions for `BinaryArthmaticExpression`.
I think we need to cast the string literals to Double type instead. I looked at the history and found that this was changed to use decimal instead of double to avoid potential loss of precision when we cast decimal to double.
To double check i ran the query against hive, mysql. This query returns non NULL result for both the databases and both promote the expression to use double.
Here is the output.
- Hive
```SQL
hive> create table l2 as select (cast(99 as decimal(19,6)) + '2') from l1;
OK
hive> describe l2;
OK
_c0 double
```
- MySQL
```SQL
mysql> create table foo2 as select (cast(99 as decimal(19,6)) + '2') from test;
Query OK, 1 row affected (0.01 sec)
Records: 1 Duplicates: 0 Warnings: 0
mysql> describe foo2;
+-----------------------------------+--------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-----------------------------------+--------+------+-----+---------+-------+
| (cast(99 as decimal(19,6)) + '2') | double | NO | | 0 | |
+-----------------------------------+--------+------+-----+---------+-------+
```
## How was this patch tested?
Added a new test in SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#13368 from dilipbiswal/spark-15557.
## What changes were proposed in this pull request?
in HiveTableScanExec, schema is lazy and is related with relation.attributeMap. So it needs to serialize MetastoreRelation when serializing task binary bytes.It can avoid to serialize MetastoreRelation.
## How was this patch tested?
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#13397 from lianhuiwang/avoid-serialize.
## What changes were proposed in this pull request?
This patch contains a list of changes as a result of my auditing Dataset, SparkSession, and SQLContext. The patch audits the categorization of experimental APIs, function groups, and deprecations. For the detailed list of changes, please see the diff.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13370 from rxin/SPARK-15638.
## What changes were proposed in this pull request?
I create a bucketed table bucketed_table with bucket column i,
```scala
case class Data(i: Int, j: Int, k: Int)
sc.makeRDD(Array((1, 2, 3))).map(x => Data(x._1, x._2, x._3)).toDF.write.bucketBy(2, "i").saveAsTable("bucketed_table")
```
and I run the following SQLs:
```sql
SELECT j FROM bucketed_table;
Error in query: bucket column i not found in existing columns (j);
SELECT j, MAX(k) FROM bucketed_table GROUP BY j;
Error in query: bucket column i not found in existing columns (j, k);
```
I think we should add a check that, we only enable bucketing when it satisfies all conditions below:
1. the conf is enabled
2. the relation is bucketed
3. the output contains all bucketing columns
## How was this patch tested?
Updated test cases to reflect the changes.
Author: Yadong Qi <qiyadong2010@gmail.com>
Closes#13321 from watermen/SPARK-15549.
## What changes were proposed in this pull request?
- Refer to the Jira for the problem: jira : https://issues.apache.org/jira/browse/SPARK-14400
- The fix is to check if the process has exited with a non-zero exit code in `hasNext()`. I have moved this and checking of writer thread exception to a separate method.
## How was this patch tested?
- Ran a job which had incorrect transform script command and saw that the job fails
- Existing unit tests for `ScriptTransformationSuite`. Added a new unit test
Author: Tejas Patil <tejasp@fb.com>
Closes#12194 from tejasapatil/script_transform.
## What changes were proposed in this pull request?
This PR replaces `spark.sql.sources.` strings with `CreateDataSourceTableUtils.*` constant variables.
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13349 from dongjoon-hyun/SPARK-15584.
#### What changes were proposed in this pull request?
This PR is to use the new entrance `Sparksession` to replace the existing `SQLContext` and `HiveContext` in SQL test suites.
No change is made in the following suites:
- `ListTablesSuite` is to test the APIs of `SQLContext`.
- `SQLContextSuite` is to test `SQLContext`
- `HiveContextCompatibilitySuite` is to test `HiveContext`
**Update**: Move tests in `ListTableSuite` to `SQLContextSuite`
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13337 from gatorsmile/sparkSessionTest.
## What changes were proposed in this pull request?
`a` -> `an`
I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.
## How was this patch tested?
local build
`lint-java` checking
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13317 from zhengruifeng/a_an.
## What changes were proposed in this pull request?
Certain table properties (and SerDe properties) are in the protected namespace `spark.sql.sources.`, which we use internally for datasource tables. The user should not be allowed to
(1) Create a Hive table setting these properties
(2) Alter these properties in an existing table
Previously, we threw an exception if the user tried to alter the properties of an existing datasource table. However, this is overly restrictive for datasource tables and does not do anything for Hive tables.
## How was this patch tested?
DDLSuite
Author: Andrew Or <andrew@databricks.com>
Closes#13341 from andrewor14/alter-table-props.
## What changes were proposed in this pull request?
This PR changes SQLContext/HiveContext's public constructor to use SparkSession.build.getOrCreate and removes isRootContext from SQLContext.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#13310 from yhuai/SPARK-15532.
## What changes were proposed in this pull request?
SparkSession has a list of unnecessary private[sql] methods. These methods cause some trouble because private[sql] doesn't apply in Java. In the cases that they are easy to remove, we can simply remove them. This patch does that.
As part of this pull request, I also replaced a bunch of protected[sql] with private[sql], to tighten up visibility.
## How was this patch tested?
Updated test cases to reflect the changes.
Author: Reynold Xin <rxin@databricks.com>
Closes#13319 from rxin/SPARK-15552.
## What changes were proposed in this pull request?
Same as #13302, but for DROP TABLE.
## How was this patch tested?
`DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13307 from andrewor14/drop-table.
## What changes were proposed in this pull request?
For some of the test cases, e.g. `OrcSourceSuite`, it will create temp folders and temp files inside them. But after tests finish, the folders are not removed. This will cause lots of temp files created and space occupied, if we keep running the test cases.
The reason is dir.delete() won't work if dir is not empty. We need to recursively delete the content before deleting the folder.
## How was this patch tested?
Manually checked the temp folder to make sure the temp files were deleted.
Author: Bo Meng <mengbo@hotmail.com>
Closes#13304 from bomeng/SPARK-15537.
## What changes were proposed in this pull request?
This patch renames various DefaultSources to make their names more self-describing. The choice of "DefaultSource" was from the days when we did not have a good way to specify short names.
They are now named:
- LibSVMFileFormat
- CSVFileFormat
- JdbcRelationProvider
- JsonFileFormat
- ParquetFileFormat
- TextFileFormat
Backward compatibility is maintained through aliasing.
## How was this patch tested?
Updated relevant test cases too.
Author: Reynold Xin <rxin@databricks.com>
Closes#13311 from rxin/SPARK-15543.
## What changes were proposed in this pull request?
Two changes:
- When things fail, `TRUNCATE TABLE` just returns nothing. Instead, we should throw exceptions.
- Remove `TRUNCATE TABLE ... COLUMN`, which was never supported by either Spark or Hive.
## How was this patch tested?
Jenkins.
Author: Andrew Or <andrew@databricks.com>
Closes#13302 from andrewor14/truncate-table.
fixed typos for source code for components [mllib] [streaming] and [SQL]
None and obvious.
Author: lfzCarlosC <lfz.carlos@gmail.com>
Closes#13298 from lfzCarlosC/master.
## What changes were proposed in this pull request?
This patch removes the last two commands defined in the catalyst module: DescribeFunction and ShowFunctions. They were unnecessary since the parser could just generate DescribeFunctionCommand and ShowFunctionsCommand directly.
## How was this patch tested?
Created a new SparkSqlParserSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#13292 from rxin/SPARK-15436.
## What changes were proposed in this pull request?
Currently if a table is used in join operation we rely on Metastore returned size to calculate if we can convert the operation to Broadcast join. This optimization only kicks in for table's that have the statistics available in metastore. Hive generally rolls over to HDFS if the statistics are not available directly from metastore and this seems like a reasonable choice to adopt given the optimization benefit of using broadcast joins.
## How was this patch tested?
I have executed queries locally to test.
Author: Parth Brahmbhatt <pbrahmbhatt@netflix.com>
Closes#13150 from Parth-Brahmbhatt/SPARK-15365.
## What changes were proposed in this pull request?
spark.sql("CREATE FUNCTION myfunc AS 'com.haizhi.bdp.udf.UDFGetGeoCode'") throws "org.apache.hadoop.hive.ql.metadata.HiveException:MetaException(message:NoSuchObjectException(message:Function default.myfunc does not exist))" with hive 1.2.1.
I think it is introduced by pr #12853. Fixing it by catching Exception (not NoSuchObjectException) and string matching.
## How was this patch tested?
added a unit test and also tested it manually
Author: wangyang <wangyang@haizhi.com>
Closes#13177 from wangyang1992/fixCreateFunc2.
## What changes were proposed in this pull request?
Currently command `ADD FILE|JAR <filepath | jarpath>` is supported natively in SparkSQL. However, when this command is run, the file/jar is added to the resources that can not be looked up by `LIST FILE(s)|JAR(s)` command because the `LIST` command is passed to Hive command processor in Spark-SQL or simply not supported in Spark-shell. There is no way users can find out what files/jars are added to the spark context.
Refer to [Hive commands](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli)
This PR is to support following commands:
`LIST (FILE[s] [filepath ...] | JAR[s] [jarfile ...])`
### For example:
##### LIST FILE(s)
```
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt")
res1: org.apache.spark.sql.DataFrame = []
scala> spark.sql("add file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt")
res2: org.apache.spark.sql.DataFrame = []
scala> spark.sql("list file hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt").show(false)
+----------------------------------------------+
|result |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
+----------------------------------------------+
scala> spark.sql("list files").show(false)
+----------------------------------------------+
|result |
+----------------------------------------------+
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test1.txt|
|hdfs://bdavm009.svl.ibm.com:8020/tmp/test.txt |
+----------------------------------------------+
```
##### LIST JAR(s)
```
scala> spark.sql("add jar /Users/xinwu/spark/core/src/test/resources/TestUDTF.jar")
res9: org.apache.spark.sql.DataFrame = [result: int]
scala> spark.sql("list jar TestUDTF.jar").show(false)
+---------------------------------------------+
|result |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+
scala> spark.sql("list jars").show(false)
+---------------------------------------------+
|result |
+---------------------------------------------+
|spark://192.168.1.234:50131/jars/TestUDTF.jar|
+---------------------------------------------+
```
## How was this patch tested?
New test cases are added for Spark-SQL, Spark-Shell and SparkContext API code path.
Author: Xin Wu <xinwu@us.ibm.com>
Author: xin Wu <xinwu@us.ibm.com>
Closes#13212 from xwu0226/list_command.
## What changes were proposed in this pull request?
The user may do something like:
```
CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS PARQUET
CREATE TABLE my_tab ROW FORMAT SERDE 'anything' STORED AS ... SERDE 'myserde'
CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ORC
CREATE TABLE my_tab ROW FORMAT DELIMITED ... STORED AS ... SERDE 'myserde'
```
None of these should be allowed because the SerDe's conflict. As of this patch:
- `ROW FORMAT DELIMITED` is only compatible with `TEXTFILE`
- `ROW FORMAT SERDE` is only compatible with `TEXTFILE`, `RCFILE` and `SEQUENCEFILE`
## How was this patch tested?
New tests in `DDLCommandSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#13068 from andrewor14/row-format-conflict.
## What changes were proposed in this pull request?
In order to prevent users from inadvertently writing queries with cartesian joins, this patch introduces a new conf `spark.sql.crossJoin.enabled` (set to `false` by default) that if not set, results in a `SparkException` if the query contains one or more cartesian products.
## How was this patch tested?
Added a test to verify the new behavior in `JoinSuite`. Additionally, `SQLQuerySuite` and `SQLMetricsSuite` were modified to explicitly enable cartesian products.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#13209 from sameeragarwal/disallow-cartesian.
## What changes were proposed in this pull request?
Add new test cases for including distinct aggregate in having clause in 2.0 branch.
This is a followup PR for [#12974](https://github.com/apache/spark/pull/12974), which is for 1.6 branch.
Author: xin Wu <xinwu@us.ibm.com>
Closes#12984 from xwu0226/SPARK-15206.
## What changes were proposed in this pull request?
Refactoring: Separated ORC serialization logic from OrcOutputWriter and moved to a new class called OrcSerializer.
## How was this patch tested?
Manual tests & existing tests.
Author: Ergin Seyfe <eseyfe@fb.com>
Closes#13066 from seyfe/orc_serializer.
## What changes were proposed in this pull request?
I initially asked to create a hivecontext-compatibility module to put the HiveContext there. But we are so close to Spark 2.0 release and there is only a single class in it. It seems overkill to have an entire package, which makes it more inconvenient, for a single class.
## How was this patch tested?
Tests were moved.
Author: Reynold Xin <rxin@databricks.com>
Closes#13207 from rxin/SPARK-15424.
This reverts commit 8d05a7a from #12855, which seems to have caused regressions when working with empty DataFrames.
Author: Michael Armbrust <michael@databricks.com>
Closes#13181 from marmbrus/revert12855.
## What changes were proposed in this pull request?
We started this convention to append Command suffix to all SQL commands. However, not all commands follow that convention. This patch adds Command suffix to all RunnableCommands.
## How was this patch tested?
Updated test cases to reflect the renames.
Author: Reynold Xin <rxin@databricks.com>
Closes#13215 from rxin/SPARK-15435.
#### What changes were proposed in this pull request?
`refreshTable` was a method in `HiveContext`. It was deleted accidentally while we were migrating the APIs. This PR is to add it back to `HiveContext`.
In addition, in `SparkSession`, we put it under the catalog namespace (`SparkSession.catalog.refreshTable`).
#### How was this patch tested?
Changed the existing test cases to use the function `refreshTable`. Also added a test case for refreshTable in `hivecontext-compatibility`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13156 from gatorsmile/refreshTable.
## What changes were proposed in this pull request?
Like TRUNCATE TABLE Command in Hive, TRUNCATE TABLE is also supported by Hive. See the link: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
Below is the related Hive JIRA: https://issues.apache.org/jira/browse/HIVE-446
This PR is to implement such a command for truncate table excluded column truncation(HIVE-4005).
## How was this patch tested?
Added a test case.
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#13170 from lianhuiwang/truncate.
## What changes were proposed in this pull request?
Currently SparkSession.Builder use SQLContext.getOrCreate. It should probably the the other way around, i.e. all the core logic goes in SparkSession, and SQLContext just calls that. This patch does that.
This patch also makes sure config options specified in the builder are propagated to the existing (and of course the new) SparkSession.
## How was this patch tested?
Updated tests to reflect the change, and also introduced a new SparkSessionBuilderSuite that should cover all the branches.
Author: Reynold Xin <rxin@databricks.com>
Closes#13200 from rxin/SPARK-15075.
## What changes were proposed in this pull request?
This PR is a follow-up of #13079. It replaces `hasUnsupportedFeatures: Boolean` in `CatalogTable` with `unsupportedFeatures: Seq[String]`, which contains unsupported Hive features of the underlying Hive table. In this way, we can accurately report all unsupported Hive features in the exception message.
## How was this patch tested?
Updated existing test case to check exception message.
Author: Cheng Lian <lian@databricks.com>
Closes#13173 from liancheng/spark-14346-follow-up.
## What changes were proposed in this pull request?
This PR fixes a Scala 2.10 compilation failure introduced in PR #13127.
## How was this patch tested?
Jenkins build.
Author: Cheng Lian <lian@databricks.com>
Closes#13166 from liancheng/hotfix-for-scala-2.10.
## What changes were proposed in this pull request?
HiveClient facade is not compatible with Hive 0.12.
This PR Fixes the following compatibility issues:
1. `org.apache.spark.sql.hive.client.HiveClientImpl` use `AddPartitionDesc(db, table, ignoreIfExists)` to create partitions, however, Hive 0.12 doesn't have this constructor for `AddPartitionDesc`.
2. `HiveClientImpl` uses `PartitionDropOptions` when dropping partition, however, class `PartitionDropOptions` doesn't exist in Hive 0.12.
3. Hive 0.12 doesn't support adding permanent functions. It is not valid to call `org.apache.hadoop.hive.ql.metadata.Hive.createFunction`, `org.apache.hadoop.hive.ql.metadata.Hive.alterFunction`, and `org.apache.hadoop.hive.ql.metadata.Hive.alterFunction`
4. `org.apache.spark.sql.hive.client.VersionsSuite` doesn't have enough test coverage for different hive versions 0.12, 0.13, 0.14, 1.0.0, 1.1.0, 1.2.0.
## How was this patch tested?
Unit test.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13127 from clockfly/versionSuite.
## What changes were proposed in this pull request?
Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.
## How was this patch tested?
This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13098 from clockfly/spark-15171-remove-deprecation.
## What changes were proposed in this pull request?
This is a follow-up of #12781. It adds native `SHOW CREATE TABLE` support for Hive tables and views. A new field `hasUnsupportedFeatures` is added to `CatalogTable` to indicate whether all table metadata retrieved from the concrete underlying external catalog (i.e. Hive metastore in this case) can be mapped to fields in `CatalogTable`. This flag is useful when the target Hive table contains structures that can't be handled by Spark SQL, e.g., skewed columns and storage handler, etc..
## How was this patch tested?
New test cases are added in `ShowCreateTableSuite` to do round-trip tests.
Author: Cheng Lian <lian@databricks.com>
Closes#13079 from liancheng/spark-14346-show-create-table-for-hive-tables.
## What changes were proposed in this pull request?
Currently, `INSERT INTO` with `GROUP BY` query tries to make at least 200 files (default value of `spark.sql.shuffle.partition`), which results in lots of empty files.
This PR makes it avoid creating empty files during overwriting into Hive table and in internal data sources with group by query.
This checks whether the given partition has data in it or not and creates/writes file only when it actually has data.
## How was this patch tested?
Unittests in `InsertIntoHiveTableSuite` and `HadoopFsRelationTest`.
Closes#8411
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Keuntae Park <sirpkt@apache.org>
Closes#12855 from HyukjinKwon/pr/8411.
## What changes were proposed in this pull request?
(See https://github.com/apache/spark/pull/12416 where most of this was already reviewed and committed; this is just the module structure and move part. This change does not move the annotations into test scope, which was the apparently problem last time.)
Rename `spark-test-tags` -> `spark-tags`; move common annotations like `Since` to `spark-tags`
## How was this patch tested?
Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#13074 from srowen/SPARK-15290.
## What changes were proposed in this pull request?
"DESCRIBE table" is broken when table schema is stored at key "spark.sql.sources.schema".
Originally, we used spark.sql.sources.schema to store the schema of a data source table.
After SPARK-6024, we removed this flag. Although we are not using spark.sql.sources.schema any more, we need to still support it.
## How was this patch tested?
Unit test.
When using spark2.0 to load a table generated by spark 1.2.
Before change:
`DESCRIBE table` => Schema of this table is inferred at runtime,,
After change:
`DESCRIBE table` => correct output.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13073 from clockfly/spark-15253.
## What changes were proposed in this pull request?
Currently, Parquet, JSON and CSV data sources have a class for thier options, (`ParquetOptions`, `JSONOptions` and `CSVOptions`).
It is convenient to manage options for sources to gather options into a class. Currently, `JDBC`, `Text`, `libsvm` and `ORC` datasources do not have this class. This might be nicer if these options are in a unified format so that options can be added and
This PR refactors the options in Spark internal data sources adding new classes, `OrcOptions`, `TextOptions`, `JDBCOptions` and `LibSVMOptions`.
Also, this PR change the default compression codec for ORC from `NONE` to `SNAPPY`.
## How was this patch tested?
Existing tests should cover this for refactoring and unittests in `OrcHadoopFsRelationSuite` for changing the default compression codec for ORC.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13048 from HyukjinKwon/SPARK-15267.
## What changes were proposed in this pull request?
This patch adds support for a few SQL functions to improve compatibility with other databases: IFNULL, NULLIF, NVL and NVL2. In order to do this, this patch introduced a RuntimeReplaceable expression trait that allows replacing an unevaluable expression in the optimizer before evaluation.
Note that the semantics are not completely identical to other databases in esoteric cases.
## How was this patch tested?
Added a new test suite SQLCompatibilityFunctionSuite.
Closes#12373.
Author: Reynold Xin <rxin@databricks.com>
Closes#13084 from rxin/SPARK-14541.
## What changes were proposed in this pull request?
We currently use the Hive implementations for the collect_list/collect_set aggregate functions. This has a few major drawbacks: the use of HiveUDAF (which has quite a bit of overhead) and the lack of support for struct datatypes. This PR adds native implementation of these functions to Spark.
The size of the collected list/set may vary, this means we cannot use the fast, Tungsten, aggregation path to perform the aggregation, and that we fallback to the slower sort based path. Another big issue with these operators is that when the size of the collected list/set grows too large, we can start experiencing large GC pauzes and OOMEs.
This `collect*` aggregates implemented in this PR rely on the sort based aggregate path for correctness. They maintain their own internal buffer which holds the rows for one group at a time. The sortbased aggregation path is triggered by disabling `partialAggregation` for these aggregates (which is kinda funny); this technique is also employed in `org.apache.spark.sql.hiveHiveUDAFFunction`.
I have done some performance testing:
```scala
import org.apache.spark.sql.{Dataset, Row}
sql("create function collect_list2 as 'org.apache.hadoop.hive.ql.udf.generic.GenericUDAFCollectList'")
val df = range(0, 10000000).select($"id", (rand(213123L) * 100000).cast("int").as("grp"))
df.select(countDistinct($"grp")).show
def benchmark(name: String, plan: Dataset[Row], maxItr: Int = 5): Unit = {
// Do not measure planning.
plan1.queryExecution.executedPlan
// Execute the plan a number of times and average the result.
val start = System.nanoTime
var i = 0
while (i < maxItr) {
plan.rdd.foreach(row => Unit)
i += 1
}
val time = (System.nanoTime - start) / (maxItr * 1000000L)
println(s"[$name] $maxItr iterations completed in an average time of $time ms.")
}
val plan1 = df.groupBy($"grp").agg(collect_list($"id"))
val plan2 = df.groupBy($"grp").agg(callUDF("collect_list2", $"id"))
benchmark("Spark collect_list", plan1)
...
> [Spark collect_list] 5 iterations completed in an average time of 3371 ms.
benchmark("Hive collect_list", plan2)
...
> [Hive collect_list] 5 iterations completed in an average time of 9109 ms.
```
Performance is improved by a factor 2-3.
## How was this patch tested?
Added tests to `DataFrameAggregateSuite`.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12874 from hvanhovell/implode.
#### What changes were proposed in this pull request?
~~Currently, multiple partitions are allowed to drop by using a single DDL command: Alter Table Drop Partition. However, the internal implementation could break atomicity. That means, we could just drop a subset of qualified partitions, if hitting an exception when dropping one of qualified partitions~~
~~This PR contains the following behavior changes:~~
~~- disallow dropping multiple partitions by a single command ~~
~~- allow users to input predicates in partition specification and issue a nicer error message if the predicate's comparison operator is not `=`.~~
~~- verify the partition spec in SessionCatalog. This can ensure each partition spec in `Drop Partition` does not correspond to multiple partitions.~~
This PR has two major parts:
- Verify the partition spec in SessionCatalog for fixing the following issue:
```scala
sql(s"ALTER TABLE $externalTab DROP PARTITION (ds='2008-04-09', unknownCol='12')")
```
Above example uses an invalid partition spec. Without this PR, we will drop all the partitions. The reason is Hive megastores getPartitions API returns all the partitions if we provide an invalid spec.
- Re-implemented the `dropPartitions` in `HiveClientImpl`. Now, we always check if all the user-specified partition specs exist before attempting to drop the partitions. Previously, we start drop the partition before completing checking the existence of all the partition specs. If any failure happened after we start to drop the partitions, we will log an error message to indicate which partitions have been dropped and which partitions have not been dropped.
#### How was this patch tested?
Modified the existing test cases and added new test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12801 from gatorsmile/banDropMultiPart.
## What changes were proposed in this pull request?
Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#12945 from clockfly/spark-15171.
## What changes were proposed in this pull request?
This PR adds a new rule to convert `SimpleCatalogRelation` to data source table if its table property contains data source information.
## How was this patch tested?
new test in SQLQuerySuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12935 from cloud-fan/ds-table.
## What changes were proposed in this pull request?
This PR adds native `SHOW CREATE TABLE` DDL command for data source tables. Support for Hive tables will be added in follow-up PR(s).
To show table creation DDL for data source tables created by CTAS statements, this PR also added partitioning and bucketing support for normal `CREATE TABLE ... USING ...` syntax.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
A new test suite `ShowCreateTableSuite` is added in sql/hive package to test the new feature.
Author: Cheng Lian <lian@databricks.com>
Closes#12781 from liancheng/spark-14346-show-create-table.
## What changes were proposed in this pull request?
Before:
```sql
-- uses that location but issues a warning
CREATE TABLE my_tab LOCATION /some/path
-- deletes any existing data in the specified location
DROP TABLE my_tab
```
After:
```sql
-- uses that location but creates an EXTERNAL table instead
CREATE TABLE my_tab LOCATION /some/path
-- does not delete the data at /some/path
DROP TABLE my_tab
```
This patch essentially makes the `EXTERNAL` field optional. This is related to #13032.
## How was this patch tested?
New test in `DDLCommandSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#13060 from andrewor14/location-implies-external.
## What changes were proposed in this pull request?
Before:
```sql
-- uses warehouse dir anyway
CREATE EXTERNAL TABLE my_tab
-- doesn't actually delete the data
DROP TABLE my_tab
```
After:
```sql
-- no location is provided, throws exception
CREATE EXTERNAL TABLE my_tab
-- creates an external table using that location
CREATE EXTERNAL TABLE my_tab LOCATION '/path/to/something'
-- doesn't delete the data, which is expected
DROP TABLE my_tab
```
## How was this patch tested?
New test in `DDLCommandSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#13032 from andrewor14/create-external-table-location.
Table partitions can be added with locations different from default warehouse location of a hive table.
`CREATE TABLE parquetTable (a int) PARTITIONED BY (b int) STORED AS parquet `
`ALTER TABLE parquetTable ADD PARTITION (b=1) LOCATION '/partition'`
Querying such a table throws error as the MetastoreFileCatalog does not list the added partition directory, it only lists the default base location.
```
[info] - SPARK-15248: explicitly added partitions should be readable *** FAILED *** (1 second, 8 milliseconds)
[info] java.util.NoSuchElementException: key not found: file:/Users/tdas/Projects/Spark/spark2/target/tmp/spark-b39ad224-c5d1-4966-8981-fb45a2066d61/partition
[info] at scala.collection.MapLike$class.default(MapLike.scala:228)
[info] at scala.collection.AbstractMap.default(Map.scala:59)
[info] at scala.collection.MapLike$class.apply(MapLike.scala:141)
[info] at scala.collection.AbstractMap.apply(Map.scala:59)
[info] at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog$$anonfun$listFiles$1.apply(PartitioningAwareFileCatalog.scala:59)
[info] at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog$$anonfun$listFiles$1.apply(PartitioningAwareFileCatalog.scala:55)
[info] at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
[info] at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
[info] at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
[info] at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
[info] at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
[info] at scala.collection.AbstractTraversable.map(Traversable.scala:104)
[info] at org.apache.spark.sql.execution.datasources.PartitioningAwareFileCatalog.listFiles(PartitioningAwareFileCatalog.scala:55)
[info] at org.apache.spark.sql.execution.datasources.FileSourceStrategy$.apply(FileSourceStrategy.scala:93)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info] at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
[info] at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:60)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:55)
[info] at org.apache.spark.sql.execution.SparkStrategies$SpecialLimits$.apply(SparkStrategies.scala:55)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:59)
[info] at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
[info] at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
[info] at org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:60)
[info] at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:77)
[info] at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:75)
[info] at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:82)
[info] at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:82)
[info] at org.apache.spark.sql.QueryTest.assertEmptyMissingInput(QueryTest.scala:330)
[info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:146)
[info] at org.apache.spark.sql.QueryTest.checkAnswer(QueryTest.scala:159)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7$$anonfun$apply$mcV$sp$25.apply(parquetSuites.scala:554)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7$$anonfun$apply$mcV$sp$25.apply(parquetSuites.scala:535)
[info] at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:125)
[info] at org.apache.spark.sql.hive.ParquetPartitioningTest.withTempDir(parquetSuites.scala:726)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12$$anonfun$apply$mcV$sp$7.apply$mcV$sp(parquetSuites.scala:535)
[info] at org.apache.spark.sql.test.SQLTestUtils$class.withTable(SQLTestUtils.scala:166)
[info] at org.apache.spark.sql.hive.ParquetPartitioningTest.withTable(parquetSuites.scala:726)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply$mcV$sp(parquetSuites.scala:534)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply(parquetSuites.scala:534)
[info] at org.apache.spark.sql.hive.ParquetMetastoreSuite$$anonfun$12.apply(parquetSuites.scala:534)
```
The solution in this PR to get the paths to list from the partition spec and not rely on the default table path alone.
unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13022 from tdas/SPARK-15248.
## What changes were proposed in this pull request?
This fixes compile errors.
## How was this patch tested?
Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13053 from dongjoon-hyun/hotfix_sqlquerysuite.
## What changes were proposed in this pull request?
#### Symptom
If a table is created as parquet or ORC table with hive syntaxt DDL, such as
```SQL
create table t1 (c1 int, c2 string) stored as parquet
```
The following command will fail
```SQL
create view v1 as select * from t1
```
#### Root Cause
Currently, `HiveMetaStoreCatalog` converts Paruqet/Orc tables to `LogicalRelation` without giving any `tableIdentifier`. `SQLBuilder` expects the `LogicalRelation` to have an associated `tableIdentifier`. However, the `LogicalRelation` created earlier does not have such a `tableIdentifier`. Thus, `SQLBuilder.toSQL` can not recognize this logical plan and issue an exception.
This PR is to assign a `TableIdentifier` to the `LogicalRelation` when resolving parquet or orc tables in `HiveMetaStoreCatalog`.
## How was this patch tested?
testcases created and dev/run-tests is run.
Author: xin Wu <xinwu@us.ibm.com>
Closes#12716 from xwu0226/SPARK_14933.
## What changes were proposed in this pull request?
This PR adds documents about the different behaviors between `insertInto` and `saveAsTable`, and throws an exception when the user try to add too man columns using `saveAsTable with append`.
## How was this patch tested?
Unit tests added in this PR.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13013 from zsxwing/SPARK-15231.
This replaces `sparkSession` with `spark` in CatalogSuite.scala.
Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13030 from dongjoon-hyun/hotfix_sparkSession.
Since we cannot really trust if the underlying external catalog can throw exceptions when there is an invalid metadata operation, let's do it in SessionCatalog.
- [X] The first step is to unify the error messages issued in Hive-specific Session Catalog and general Session Catalog.
- [X] The second step is to verify the inputs of metadata operations for partitioning-related operations. This is moved to a separate PR: https://github.com/apache/spark/pull/12801
- [X] The third step is to add database existence verification in `SessionCatalog`
- [X] The fourth step is to add table existence verification in `SessionCatalog`
- [X] The fifth step is to add function existence verification in `SessionCatalog`
Add test cases and verify the error messages we issued
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12385 from gatorsmile/verifySessionAPIs.
## What changes were proposed in this pull request?
Use SparkSession instead of SQLContext in Scala/Java TestSuites
as this PR already very big working Python TestSuites in a diff PR.
## How was this patch tested?
Existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#12907 from techaddict/SPARK-15037.
## What changes were proposed in this pull request?
This PR fixes SQL building for predicate subqueries and correlated scalar subqueries. It also enables most Hive subquery tests.
## How was this patch tested?
Enabled new tests in HiveComparisionSuite.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12988 from hvanhovell/SPARK-14773.
#### What changes were proposed in this pull request?
This PR is to address a few existing issues in `EXPLAIN`:
- The `EXPLAIN` options `LOGICAL | FORMATTED | EXTENDED | CODEGEN` should not be 0 or more match. It should 0 or one match. Parser does not allow users to use more than one option in a single command.
- The option `LOGICAL` is not supported. Issue an exception when users specify this option in the command.
- The output of `EXPLAIN ` contains a weird empty line when the output of analyzed plan is empty. We should remove it. For example:
```
== Parsed Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
== Analyzed Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
== Optimized Logical Plan ==
CreateTable CatalogTable(`t`,CatalogTableType(MANAGED),CatalogStorageFormat(None,Some(org.apache.hadoop.mapred.TextInputFormat),Some(org.apache.hadoop.hive.ql.io. HiveIgnoreKeyTextOutputFormat),None,false,Map()),List(CatalogColumn(col,int,true,None)),List(),List(),List(),-1,,1462725171656,-1,Map(),None,None,None), false
...
```
#### How was this patch tested?
Added and modified a few test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12991 from gatorsmile/explainCreateTable.
#### What changes were proposed in this pull request?
In Hive Metastore, dropping default database is not allowed. However, in `InMemoryCatalog`, this is allowed.
This PR is to disallow users to drop default database.
#### How was this patch tested?
Previously, we already have a test case in HiveDDLSuite. Now, we also add the same one in DDLSuite
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12962 from gatorsmile/dropDefaultDB.
## What changes were proposed in this pull request?
The issue is that when the user provides the path option with uppercase "PATH" key, `options` contains `PATH` key and will get into the non-external case in the following code in `createDataSourceTables.scala`, where a new key "path" is created with a default path.
```
val optionsWithPath =
if (!options.contains("path")) {
isExternal = false
options + ("path" -> sessionState.catalog.defaultTablePath(tableIdent))
} else {
options
}
```
So before creating hive table, serdeInfo.parameters will contain both "PATH" and "path" keys and different directories. and Hive table's dataLocation contains the value of "path".
The fix in this PR is to convert `options` in the code above to `CaseInsensitiveMap` before checking for containing "path" key.
## How was this patch tested?
A testcase is added
Author: xin Wu <xinwu@us.ibm.com>
Closes#12804 from xwu0226/SPARK-15025.
When we parse `CREATE TABLE USING`, we should build a `CreateTableUsing` plan with the `managedIfNoPath` set to true. Then we will add default table path to options when write it to hive.
new test in `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12949 from cloud-fan/bug.
## What changes were proposed in this pull request?
This also simplifies the code being moved.
## How was this patch tested?
Existing tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12941 from andrewor14/move-code.
## What changes were proposed in this pull request?
This is a follow-up of PR #12844. It makes the newly updated `DescribeTableCommand` to support data sources tables.
## How was this patch tested?
A test case is added to check `DESC [EXTENDED | FORMATTED] <table>` output.
Author: Cheng Lian <lian@databricks.com>
Closes#12934 from liancheng/spark-14127-desc-table-follow-up.
## What changes were proposed in this pull request?
This detects a relation's partitioning and adds checks to the analyzer.
If an InsertIntoTable node has no partitioning, it is replaced by the
relation's partition scheme and input columns are correctly adjusted,
placing the partition columns at the end in partition order. If an
InsertIntoTable node has partitioning, it is checked against the table's
reported partitions.
These changes required adding a PartitionedRelation trait to the catalog
interface because Hive's MetastoreRelation doesn't extend
CatalogRelation.
This commit also includes a fix to InsertIntoTable's resolved logic,
which now detects that all expected columns are present, including
dynamic partition columns. Previously, the number of expected columns
was not checked and resolved was true if there were missing columns.
## How was this patch tested?
This adds new tests to the InsertIntoTableSuite that are fixed by this PR.
Author: Ryan Blue <blue@apache.org>
Closes#12239 from rdblue/SPARK-14459-detect-hive-partitioning.
## What changes were proposed in this pull request?
Lets says there are json files in the following directories structure
```
xyz/file0.json
xyz/subdir1/file1.json
xyz/subdir2/file2.json
xyz/subdir1/subsubdir1/file3.json
```
`sqlContext.read.json("xyz")` should read only file0.json according to behavior in Spark 1.6.1. However in current master, all the 4 files are read.
The fix is to make FileCatalog return only the children files of the given path if there is not partitioning detected (instead of all the recursive list of files).
Closes#12774
## How was this patch tested?
unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12856 from tdas/SPARK-14997.
#### What changes were proposed in this pull request?
When Describe a UDTF, the command returns a wrong result. The command is unable to find the function, which has been created and cataloged in the catalog but not in the functionRegistry.
This PR is to correct it. If the function is not in the functionRegistry, we will check the catalog for collecting the information of the UDTF function.
#### How was this patch tested?
Added test cases to verify the results
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12885 from gatorsmile/showFunction.
## What changes were proposed in this pull request?
Enable the test that was disabled when HiveContext was removed.
## How was this patch tested?
Made sure the enabled test passes with the new jar.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#12924 from dilipbiswal/spark-14893.
## What changes were proposed in this pull request?
Went through SparkSession and its members and fixed non-thread-safe classes used by SparkSession
## How was this patch tested?
Existing unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#12915 from zsxwing/spark-session-thread-safe.
## What changes were proposed in this pull request?
Removing the `withHiveSupport` method of `SparkSession`, instead use `enableHiveSupport`
## How was this patch tested?
ran tests locally
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#12851 from techaddict/SPARK-15072.
#### What changes were proposed in this pull request?
First, a few test cases failed in mac OS X because the property value of `java.io.tmpdir` does not include a trailing slash on some platform. Hive always removes the last trailing slash. For example, what I got in the web:
```
Win NT --> C:\TEMP\
Win XP --> C:\TEMP
Solaris --> /var/tmp/
Linux --> /var/tmp
```
Second, a couple of test cases are added to verify if the commands work properly.
#### How was this patch tested?
Added a test case for it and correct the previous test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12081 from gatorsmile/mkdir.
## What changes were proposed in this pull request?
This PR support new SQL syntax CREATE TEMPORARY VIEW.
Like:
```
CREATE TEMPORARY VIEW viewName AS SELECT * from xx
CREATE OR REPLACE TEMPORARY VIEW viewName AS SELECT * from xx
CREATE TEMPORARY VIEW viewName (c1 COMMENT 'blabla', c2 COMMENT 'blabla') AS SELECT * FROM xx
```
## How was this patch tested?
Unit tests.
Author: Sean Zhong <clockfly@gmail.com>
Closes#12872 from clockfly/spark-6399.
## What changes were proposed in this pull request?
File Stream Sink writes the list of written files in a metadata log. StreamFileCatalog reads the list of the files for processing. However StreamFileCatalog does not infer partitioning like HDFSFileCatalog.
This PR enables that by refactoring HDFSFileCatalog to create an abstract class PartitioningAwareFileCatalog, that has all the functionality to infer partitions from a list of leaf files.
- HDFSFileCatalog has been renamed to ListingFileCatalog and it extends PartitioningAwareFileCatalog by providing a list of leaf files from recursive directory scanning.
- StreamFileCatalog has been renamed to MetadataLogFileCatalog and it extends PartitioningAwareFileCatalog by providing a list of leaf files from the metadata log.
- The above two classes has been moved into their own files as they are not interfaces that should be in fileSourceInterfaces.scala.
## How was this patch tested?
- FileStreamSinkSuite was update to see if partitioning gets inferred, and on reading whether the partitions get pruned correctly based on the query.
- Other unit tests are unchanged and pass as expected.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12879 from tdas/SPARK-15103.
## What changes were proposed in this pull request?
This PR improve the error message for `Generate` in 3 cases:
1. generator is nested in expressions, e.g. `SELECT explode(list) + 1 FROM tbl`
2. generator appears more than one time in SELECT, e.g. `SELECT explode(list), explode(list) FROM tbl`
3. generator appears in other operator which is not project, e.g. `SELECT * FROM tbl SORT BY explode(list)`
## How was this patch tested?
new tests in `AnalysisErrorSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12810 from cloud-fan/bug.
## What changes were proposed in this pull request?
Currently, various `FileFormat` data sources share approximately the same code for partition value appending. This PR tries to eliminate this duplication.
A new method `buildReaderWithPartitionValues()` is added to `FileFormat` with a default implementation that appends partition values to `InternalRow`s produced by the reader function returned by `buildReader()`.
Special data sources like Parquet, which implements partition value appending inside `buildReader()` because of the vectorized reader, and the Text data source, which doesn't support partitioning, override `buildReaderWithPartitionValues()` and simply delegate to `buildReader()`.
This PR brings two benefits:
1. Apparently, it de-duplicates partition value appending logic
2. Now the reader function returned by `buildReader()` is only required to produce `InternalRow`s rather than `UnsafeRow`s if the data source doesn't override `buildReaderWithPartitionValues()`.
Because the safe-to-unsafe conversion is also performed while appending partition values. This makes 3rd-party data sources (e.g. spark-avro) easier to implement since they no longer need to access private APIs involving `UnsafeRow`.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#12866 from liancheng/spark-14237-simplify-partition-values-appending.
## What changes were proposed in this pull request?
Just a bunch of small tweaks on DDL exception messages.
## How was this patch tested?
`DDLCommandSuite` et al.
Author: Andrew Or <andrew@databricks.com>
Closes#12853 from andrewor14/make-exceptions-consistent.
## What changes were proposed in this pull request?
This PR addresses a few minor issues in SQL parser:
- Removes some unused rules and keywords in the grammar.
- Removes code path for fallback SQL parsing (was needed for Hive native parsing).
- Use `UnresolvedGenerator` instead of hard-coding `Explode` & `JsonTuple`.
- Adds a more generic way of creating error messages for unsupported Hive features.
- Use `visitFunctionName` as much as possible.
- Interpret a `CatalogColumn`'s `DataType` directly instead of parsing it again.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12826 from hvanhovell/SPARK-15047.
This PR contains three changes:
1. We will use spark.sql.warehouse.dir set warehouse location. We will not use hive.metastore.warehouse.dir.
2. SessionCatalog needs to set the location to default db. Otherwise, when creating a table in SparkSession without hive support, the default db's path will be an empty string.
3. When we create a database, we need to make the path qualified.
Existing tests and new tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12812 from yhuai/warehouse.
## What changes were proposed in this pull request?
This patch removes some code that are no longer relevant -- mainly HiveSessionState.setDefaultOverrideConfs.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#12806 from rxin/SPARK-15028.
## What changes were proposed in this pull request?
CatalystSqlParser can parse data types. So, we do not need to have an individual DataTypeParser.
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12796 from yhuai/removeDataTypeParser.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14917
As it is described in the JIRA, it seems Hive 1.2.1 which Spark uses now supports snappy and none.
So, this PR enables some tests for writing ORC files with compression codes, `SNAPPY` and `NONE`.
## How was this patch tested?
Unittests in `OrcQuerySuite` and `sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12699 from HyukjinKwon/SPARK-14917.
## What changes were proposed in this pull request?
1. Remove all the `spark.setConf` etc. Just expose `spark.conf`
2. Make `spark.conf` take in things set in the core `SparkConf` as well, otherwise users may get confused
This was done for both the Python and Scala APIs.
## How was this patch tested?
`SQLConfSuite`, python tests.
This one fixes the failed tests in #12787Closes#12787
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12798 from yhuai/conf-api.
## What changes were proposed in this pull request?
This PR makes two changes:
1. We will propagate Spark Confs to HiveConf created in HiveClientImpl. So, users can also use spark conf to set warehouse location and metastore url.
2. In sql/hive, HiveClientImpl will be the only place where we create a new HiveConf.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#12791 from yhuai/onlyUseHiveConfInHiveClientImpl.
## What changes were proposed in this pull request?
The hiveConf in HiveSessionState is not actually used anymore. Let's remove it.
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12786 from yhuai/removeHiveConf.
## What changes were proposed in this pull request?
Currently Spark SQL doesn't support sorting columns in descending order. However, the parser accepts the syntax and silently drops sorting directions. This PR fixes this by throwing an exception if `DESC` is specified as sorting direction of a sorting column.
## How was this patch tested?
A test case is added to test the invalid sorting order by checking exception message.
Author: Cheng Lian <lian@databricks.com>
Closes#12759 from liancheng/spark-14981.
This test always fail with sbt's hadoop 2.3 and 2.4 tests. Let'e disable it for now and investigate the problem.
Author: Yin Huai <yhuai@databricks.com>
Closes#12783 from yhuai/SPARK-15011-ignore.
## What changes were proposed in this pull request?
This patch removes executionHive from HiveSessionState and HiveSharedState.
## How was this patch tested?
Updated test cases.
Author: Reynold Xin <rxin@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12770 from rxin/SPARK-14994.
## What changes were proposed in this pull request?
This patch removes HiveNativeCommand, so we can continue to remove the dependency on Hive. This pull request also removes the ability to generate golden result file using Hive.
## How was this patch tested?
Updated tests to reflect this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12769 from rxin/SPARK-14991.
## What changes were proposed in this pull request?
This PR adds `since` tag into the matrix and vector classes in spark-mllib-local.
## How was this patch tested?
Scala-style checks passed.
Author: Pravin Gadakh <prgadakh@in.ibm.com>
Closes#12416 from pravingadakh/SPARK-14613.
## What changes were proposed in this pull request?
This PR introduces a new accumulator API which is much simpler than before:
1. the type hierarchy is simplified, now we only have an `Accumulator` class
2. Combine `initialValue` and `zeroValue` concepts into just one concept: `zeroValue`
3. there in only one `register` method, the accumulator registration and cleanup registration are combined.
4. the `id`,`name` and `countFailedValues` are combined into an `AccumulatorMetadata`, and is provided during registration.
`SQLMetric` is a good example to show the simplicity of this new API.
What we break:
1. no `setValue` anymore. In the new API, the intermedia type can be different from the result type, it's very hard to implement a general `setValue`
2. accumulator can't be serialized before registered.
Problems need to be addressed in follow-ups:
1. with this new API, `AccumulatorInfo` doesn't make a lot of sense, the partial output is not partial updates, we need to expose the intermediate value.
2. `ExceptionFailure` should not carry the accumulator updates. Why do users care about accumulator updates for failed cases? It looks like we only use this feature to update the internal metrics, how about we sending a heartbeat to update internal metrics after the failure event?
3. the public event `SparkListenerTaskEnd` carries a `TaskMetrics`. Ideally this `TaskMetrics` don't need to carry external accumulators, as the only method of `TaskMetrics` that can access external accumulators is `private[spark]`. However, `SQLListener` use it to retrieve sql metrics.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12612 from cloud-fan/acc.
## What changes were proposed in this pull request?
`interfaces.scala` was getting big. This just moves the biggest class in there to a new file for cleanliness.
## How was this patch tested?
Just moving things around.
Author: Andrew Or <andrew@databricks.com>
Closes#12721 from andrewor14/move-external-catalog.
Currently, we can only create persisted partitioned and/or bucketed data source tables using the Dataset API but not using SQL DDL. This PR implements the following syntax to add partitioning and bucketing support to the SQL DDL:
```
CREATE TABLE <table-name>
USING <provider> [OPTIONS (<key1> <value1>, <key2> <value2>, ...)]
[PARTITIONED BY (col1, col2, ...)]
[CLUSTERED BY (col1, col2, ...) [SORTED BY (col1, col2, ...)] INTO <n> BUCKETS]
AS SELECT ...
```
Test cases are added in `MetastoreDataSourcesSuite` to check the newly added syntax.
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12734 from liancheng/spark-14954.
## What changes were proposed in this pull request?
This patch removes the use of HiveConf from InsertIntoHiveTable. I think this is the last major use of HiveConf and after this we can try to remove the execution HiveConf.
## How was this patch tested?
Internal refactoring and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12728 from rxin/SPARK-14949.
## What changes were proposed in this pull request?
This PR will make Spark SQL not allow ALTER TABLE ADD/REPLACE/CHANGE COLUMN, ALTER TABLE SET FILEFORMAT, DFS, and transaction related commands.
## How was this patch tested?
Existing tests. For those tests that I put in the blacklist, I am adding the useful parts back to SQLQuerySuite.
Author: Yin Huai <yhuai@databricks.com>
Closes#12714 from yhuai/banNativeCommand.
## What changes were proposed in this pull request?
This patch removes HiveConf from HiveTableScanExec and HiveTableReader and instead just uses our own configuration system. I'm splitting the large change of removing HiveConf into multiple independent pull requests because it is very difficult to debug test failures when they are all combined in one giant one.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12727 from rxin/SPARK-14944.
## What changes were proposed in this pull request?
We currently expose both Hadoop configuration and Spark SQL configuration in RuntimeConfig. I think we can remove the Hadoop configuration part, and simply generate Hadoop Configuration on the fly by passing all the SQL configurations into it. This way, there is a single interface (in Java/Scala/Python/SQL) for end-users.
As part of this patch, I also removed some config options deprecated in Spark 1.x.
## How was this patch tested?
Updated relevant tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12689 from rxin/SPARK-14913.
## What changes were proposed in this pull request?
#12625 exposed a new user-facing conf interface in `SparkSession`. This patch adds a catalog interface.
## How was this patch tested?
See `CatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12713 from andrewor14/user-facing-catalog.
## What changes were proposed in this pull request?
This PR adds Native execution of SHOW COLUMNS and SHOW PARTITION commands.
Command Syntax:
``` SQL
SHOW COLUMNS (FROM | IN) table_identifier [(FROM | IN) database]
```
``` SQL
SHOW PARTITIONS [db_name.]table_name [PARTITION(partition_spec)]
```
## How was this patch tested?
Added test cases in HiveCommandSuite to verify execution and DDLCommandSuite
to verify plans.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#12222 from dilipbiswal/dkb_show_columns.
## What changes were proposed in this pull request?
While the vectorized hash map in `TungstenAggregate` is currently supported for all primitive data types during partial aggregation, this patch only enables the hash map for a subset of cases that've been verified to show performance improvements on our benchmarks subject to an internal conf that sets an upper limit on the maximum length of the aggregate key/value schema. This list of supported use-cases should be expanded over time.
## How was this patch tested?
This is no new change in functionality so existing tests should suffice. Performance tests were done on TPCDS benchmarks.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12710 from sameeragarwal/vectorized-enable.
## What changes were proposed in this pull request?
This PR update SortMergeJoinExec to support LeftSemi/LeftAnti, so it could support all the join types, same as other three join implementations: BroadcastHashJoinExec, ShuffledHashJoinExec,and BroadcastNestedLoopJoinExec.
This PR also simplify the join selection in SparkStrategy.
## How was this patch tested?
Added new tests.
Author: Davies Liu <davies@databricks.com>
Closes#12668 from davies/smj_semi.
## What changes were proposed in this pull request?
We currently have no way for users to propagate options to the underlying library that rely in Hadoop configurations to work. For example, there are various options in parquet-mr that users might want to set, but the data source API does not expose a per-job way to set it. This patch propagates the user-specified options also into Hadoop Configuration.
## How was this patch tested?
Used a mock data source implementation to test both the read path and the write path.
Author: Reynold Xin <rxin@databricks.com>
Closes#12688 from rxin/SPARK-14912.
## What changes were proposed in this pull request?
In Spark 2.0, `SparkSession` is the new thing. Internally we should stop using `SQLContext` everywhere since that's supposed to be not the main user-facing API anymore.
In this patch I took care to not break any public APIs. The one place that's suspect is `o.a.s.ml.source.libsvm.DefaultSource`, but according to mengxr it's not supposed to be public so it's OK to change the underlying `FileFormat` trait.
**Reviewers**: This is a big patch that may be difficult to review but the changes are actually really straightforward. If you prefer I can break it up into a few smaller patches, but it will delay the progress of this issue a little.
## How was this patch tested?
No change in functionality intended.
Author: Andrew Or <andrew@databricks.com>
Closes#12625 from andrewor14/spark-session-refactor.
## What changes were proposed in this pull request?
`RuntimeConfig` is the new user-facing API in 2.0 added in #11378. Until now, however, it's been dead code. This patch uses `RuntimeConfig` in `SessionState` and exposes that through the `SparkSession`.
## How was this patch tested?
New test in `SQLContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#12669 from andrewor14/use-runtime-conf.
## What changes were proposed in this pull request?
This patch changes UnresolvedFunction and UnresolvedGenerator to use a FunctionIdentifier rather than just a String for function name. Also changed SessionCatalog to accept FunctionIdentifier in lookupFunction.
## How was this patch tested?
Updated related unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12659 from rxin/SPARK-14888.
## What changes were proposed in this pull request?
This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class.
Note: A couple of things will break after this patch. These will be fixed separately.
- the python HiveContext
- all the documentation / comments referencing HiveContext
- there will be no more HiveContext in the REPL (fixed by #12589)
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12585 from andrewor14/delete-hive-context.
#### What changes were proposed in this pull request?
Disable the test case involving INPUTDRIVER and OUTPUTDRIVER, which are not supported
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12662 from gatorsmile/disableInOutDriver.
## What changes were proposed in this pull request?
This method was accidentally made `private[sql]` in Spark 2.0. This PR makes it public again, since 3rd party data sources like spark-avro depend on it.
## How was this patch tested?
N/A
Author: Cheng Lian <lian@databricks.com>
Closes#12652 from liancheng/spark-14875.
## What changes were proposed in this pull request?
This patch changes SparkSession to be case insensitive by default, in order to match other database systems.
## How was this patch tested?
N/A - I'm sure some tests will fail and I will need to fix those.
Author: Reynold Xin <rxin@databricks.com>
Closes#12643 from rxin/SPARK-14876.
#### What changes were proposed in this pull request?
So far, we are capturing each unsupported Alter Table in separate visit functions. They should be unified and issue the same ParseException instead.
This PR is to refactor the existing implementation and make error message consistent for Alter Table DDL.
#### How was this patch tested?
Updated the existing test cases and also added new test cases to ensure all the unsupported statements are covered.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12459 from gatorsmile/cleanAlterTable.
## What changes were proposed in this pull request?
CreateMetastoreDataSource and CreateMetastoreDataSourceAsSelect are not Hive-specific. So, this PR moves them from sql/hive to sql/core. Also, I am adding `Command` suffix to these two classes.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#12645 from yhuai/moveCreateDataSource.
## What changes were proposed in this pull request?
It is unnecessary as DataType.catalogString largely replaces the need for this class.
## How was this patch tested?
Mostly removing dead code and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12644 from rxin/SPARK-14877.
## What changes were proposed in this pull request?
This patch improves error handling in view creation. CreateViewCommand itself will analyze the view SQL query first, and if it cannot successfully analyze it, throw an AnalysisException.
In addition, I also added the following two conservative guards for easier identification of Spark bugs:
1. If there is a bug and the generated view SQL cannot be analyzed, throw an exception at runtime. Note that this is not an AnalysisException because it is not caused by the user and more likely indicate a bug in Spark.
2. SQLBuilder when it gets an unresolved plan, it will also show the plan in the error message.
I also took the chance to simplify the internal implementation of CreateViewCommand, and *removed* a fallback path that would've masked an exception from before.
## How was this patch tested?
1. Added a unit test for the user facing error handling.
2. Manually introduced some bugs in Spark to test the internal defensive error handling.
3. Also added a test case to test nested views (not super relevant).
Author: Reynold Xin <rxin@databricks.com>
Closes#12633 from rxin/SPARK-14865.
## What changes were proposed in this pull request?
In order to support running SQL directly on files, we added some code in ResolveRelations to catch the exception thrown by catalog.lookupRelation and ignore it. This unfortunately masks all the exceptions. This patch changes the logic to simply test the table's existence.
## How was this patch tested?
I manually hacked some bugs into Spark and made sure the exceptions were being propagated up.
Author: Reynold Xin <rxin@databricks.com>
Closes#12634 from rxin/SPARK-14869.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Reynold Xin <rxin@databricks.com>
Closes#12565 from rxin/test-flaky.
## What changes were proposed in this pull request?
When FileSourceStrategy is used, record reader is created which incurs a NN call internally. Later in OrcRelation.unwrapOrcStructs, it ends ups reading the file information to get the ObjectInspector. This incurs additional NN call. It would be good to avoid this additional NN call (specifically for partitioned datasets).
Added OrcRecordReader which is very similar to OrcNewInputFormat.OrcRecordReader with an option of exposing the ObjectInspector. This eliminates the need to look up the file later for generating the object inspector. This would be specifically be useful for partitioned tables/datasets.
## How was this patch tested?
Ran tpc-ds queries manually and also verified by running org.apache.spark.sql.hive.orc.OrcSuite,org.apache.spark.sql.hive.orc.OrcQuerySuite,org.apache.spark.sql.hive.orc.OrcPartitionDiscoverySuite,OrcPartitionDiscoverySuite.OrcHadoopFsRelationSuite,org.apache.spark.sql.hive.execution.HiveCompatibilitySuite
…SourceStrategy mode
Author: Rajesh Balamohan <rbalamohan@apache.org>
Closes#12319 from rajeshbalamohan/SPARK-14551.
## What changes were proposed in this pull request?
This patch breaks SQLQuerySuite out into smaller test suites. It was a little bit too large for debugging.
## How was this patch tested?
This is a test only change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12630 from rxin/SPARK-14866.
## What changes were proposed in this pull request?
This patch re-implements view creation command in sql/core, based on the pre-existing view creation command in the Hive module. This consolidates the view creation logical command and physical command into a single one, called CreateViewCommand.
## How was this patch tested?
All the code should've been tested by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12615 from rxin/SPARK-14842-2.
## What changes were proposed in this pull request?
This patch adds "Exec" suffix to all physical operators. Before this patch, Spark's physical operators and logical operators are named the same (e.g. Project could be logical.Project or execution.Project), which caused small issues in code review and bigger issues in code refactoring.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#12617 from rxin/exec-node.
## What changes were proposed in this pull request?
This patch moves SQLBuilder into sql/core so we can in the future move view generation also into sql/core.
## How was this patch tested?
Also moved unit tests.
Author: Reynold Xin <rxin@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12602 from rxin/SPARK-14841.
## What changes were proposed in this pull request?
Add the native support for LOAD DATA DDL command that loads data into Hive table/partition.
## How was this patch tested?
`HiveDDLCommandSuite` and `HiveQuerySuite`. Besides, few Hive tests (`WindowQuerySuite`, `HiveTableScanSuite` and `HiveSerDeSuite`) also use `LOAD DATA` command.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12412 from viirya/ddl-load-data.
## What changes were proposed in this pull request?
This patch removes HiveQueryExecution. As part of this, I consolidated all the describe commands into DescribeTableCommand.
## How was this patch tested?
Should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12588 from rxin/SPARK-14826.
## What changes were proposed in this pull request?
This patch removes SQLBuilder's dependency on MetastoreRelation. We should be able to move SQLBuilder into the sql/core package after this change.
## How was this patch tested?
N/A - covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12594 from rxin/SPARK-14835.
(This PR is a rebased version of PR #12153.)
## What changes were proposed in this pull request?
This PR adds preliminary locality support for `FileFormat` data sources by overriding `FileScanRDD.preferredLocations()`. The strategy can be divided into two parts:
1. Block location lookup
Unlike `HadoopRDD` or `NewHadoopRDD`, `FileScanRDD` doesn't have access to the underlying `InputFormat` or `InputSplit`, and thus can't rely on `InputSplit.getLocations()` to gather locality information. Instead, this PR queries block locations using `FileSystem.getBlockLocations()` after listing all `FileStatus`es in `HDFSFileCatalog` and convert all `FileStatus`es into `LocatedFileStatus`es.
Note that although S3/S3A/S3N file systems don't provide valid locality information, their `getLocatedStatus()` implementations don't actually issue remote calls either. So there's no need to special case these file systems.
2. Selecting preferred locations
For each `FilePartition`, we pick up top 3 locations that containing the most data to be retrieved. This isn't necessarily the best algorithm out there. Further improvements may be brought up in follow-up PRs.
## How was this patch tested?
Tested by overriding default `FileSystem` implementation for `file:///` with a mocked one, which returns mocked block locations.
Author: Cheng Lian <lian@databricks.com>
Closes#12527 from liancheng/spark-14369-locality-rebased.
## What changes were proposed in this pull request?
Just a rename so we can get rid of `HiveContext.scala`. Note that this will conflict with #12585.
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12586 from andrewor14/rename-hc-object.
## What changes were proposed in this pull request?
This patch moves analyze table parsing into SparkSqlAstBuilder and removes HiveSqlAstBuilder.
In order to avoid extensive refactoring, I created a common trait for CatalogRelation and MetastoreRelation, and match on that. In the future we should probably just consolidate the two into a single thing so we don't need this common trait.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12584 from rxin/SPARK-14821.
## What changes were proposed in this pull request?
This patch moves native command and script transformation into SparkSqlAstBuilder. This builds on #12561. See the last commit for diff.
## How was this patch tested?
Updated test cases to reflect this.
Author: Reynold Xin <rxin@databricks.com>
Closes#12564 from rxin/SPARK-14798.
## What changes were proposed in this pull request?
This class is currently in HiveMetastoreCatalog.scala, which is a large file that makes refactoring and searching of usage difficult. Moving it out so I can then do SPARK-14799 and make the review of that simpler.
## How was this patch tested?
N/A - this is a straightforward move and should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12567 from rxin/SPARK-14801.
## What changes were proposed in this pull request?
This patch builds on #12556 and completely removes the use of Hive's variable substitution.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12561 from rxin/SPARK-14795.
## What changes were proposed in this pull request?
This patch isolates AnalyzeTable's dependency on MetastoreRelation into a single line. After this we can work on converging MetastoreRelation and CatalogTable.
## How was this patch tested?
Covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12566 from rxin/SPARK-14799.
In IsolatedClientLoader, we have a`catch` block which throws an exception without wrapping the original exception, causing the full exception stacktrace and any nested exceptions to be lost. This patch fixes this, improving the usefulness of classloading error messages.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12548 from JoshRosen/improve-logging-for-hive-classloader-issues.
## What changes were proposed in this pull request?
We shouldn't pass analyze command to Hive because some of those would require running MapReduce jobs. For now, let's just always run the no scan analyze.
## How was this patch tested?
Updated test case to reflect this change.
Author: Reynold Xin <rxin@databricks.com>
Closes#12558 from rxin/parser-analyze.
## What changes were proposed in this pull request?
This patch moves as many parsing rules as possible into SQL parser. There are only three more left after this patch: (1) run native command, (2) analyze, and (3) script IO. These 3 will be dealt with in a follow-up PR.
## How was this patch tested?
No test change. This simply moves code around.
Author: Reynold Xin <rxin@databricks.com>
Closes#12556 from rxin/SPARK-14792.
The `hive` subproject currently depends on `hive-cli` in order to perform a check to see whether a `SessionState` is an instance of `org.apache.hadoop.hive.cli.CliSessionState` (see #9589). The introduction of this `hive-cli` dependency has caused problems for users whose Hive metastore JAR classpaths don't include the `hive-cli` classes (such as in #11495).
This patch removes this dependency on `hive-cli` and replaces the `isInstanceOf` check by reflection. I added a Maven Enforcer rule to ban `hive-cli` from the `hive` subproject in order to make sure that this dependency is not accidentally reintroduced.
/cc rxin yhuai adrian-wang preecet
Author: Josh Rosen <joshrosen@databricks.com>
Closes#12551 from JoshRosen/remove-hive-cli-dep-from-hive-subproject.
## What changes were proposed in this pull request?
The patch removes HiveConf dependency from HiveSqlAstBuilder. This is required in order to merge HiveSqlParser and SparkSqlAstBuilder, which would require getting rid of the Hive specific dependencies in HiveSqlParser.
This patch also accomplishes [SPARK-14778] Remove HiveSessionState.substitutor.
## How was this patch tested?
This should be covered by existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12550 from rxin/SPARK-14782.
## What changes were proposed in this pull request?
The path rewrite in TestHiveSparkSession is pretty hacky. I think we can remove those complexity and just do a string replacement when we read the query files in. This would remove the overloading of runNativeSql in TestHive, which will simplify the removal of Hive specific variable substitution.
## How was this patch tested?
This is a small test refactoring to simplify test infrastructure.
Author: Reynold Xin <rxin@databricks.com>
Closes#12543 from rxin/SPARK-14775.
## What changes were proposed in this pull request?
We currently have five folders in queries: clientcompare, clientnegative, clientpositive, negative, and positive. Only clientpositive is used. We can remove the rest.
## How was this patch tested?
N/A - removing unused test resources.
Author: Reynold Xin <rxin@databricks.com>
Closes#12540 from rxin/SPARK-14770.
## What changes were proposed in this pull request?
This PR has two main changes.
1. Move Hive-specific methods from HiveContext to HiveSessionState, which help the work of removing HiveContext.
2. Create a SparkSession Class, which will later be the entry point of Spark SQL users.
## How was this patch tested?
Existing tests
This PR is trying to fix test failures of https://github.com/apache/spark/pull/12485.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12522 from yhuai/spark-session.
## What changes were proposed in this pull request?
Since [SPARK-12719: SQL Generation supports for generators](https://issues.apache.org/jira/browse/SPARK-12719) was resolved, this PR enables the related testcases: `explode()` and `json_tuple()`.
## How was this patch tested?
Pass the Jenkins tests (with re-enabled test cases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12329 from dongjoon-hyun/minor_enable_testcases.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14600
This PR makes `Expand.output` have different attributes from the grouping attributes produced by the underlying `Project`, as they have different meaning, so that we can safely push down filter through `Expand`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12496 from cloud-fan/expand.
## What changes were proposed in this pull request?
Enable ScalaReflection and User Defined Types for plain Scala classes.
This involves the move of `schemaFor` from `ScalaReflection` trait (which is Runtime and Compile time (macros) reflection) to the `ScalaReflection` object (runtime reflection only) as I believe this code wouldn't work at compile time anyway as it manipulates `Class`'s that are not compiled yet.
## How was this patch tested?
Unit test
Author: Joan <joan@goyeau.com>
Closes#12149 from joan38/SPARK-13929-Scala-reflection.
## What changes were proposed in this pull request?
This PR moves `HadoopFsRelation` related data source API into `execution/datasources` package.
Note that to avoid conflicts, this PR is based on #12153. Effective changes for this PR only consist of the last three commits. Will rebase after merging #12153.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#12361 from liancheng/spark-14407-hide-hadoop-fs-relation.
## What changes were proposed in this pull request?
These test suites were removed while refactoring `HadoopFsRelation` related API. This PR brings them back.
This PR also fixes two regressions:
- SPARK-14458, which causes runtime error when saving partitioned tables using `FileFormat` data sources that are not able to infer their own schemata. This bug wasn't detected by any built-in data sources because all of them happen to have schema inference feature.
- SPARK-14566, which happens to be covered by SPARK-14458 and causes wrong query result or runtime error when
- appending a Dataset `ds` to a persisted partitioned data source relation `t`, and
- partition columns in `ds` don't all appear after data columns
## How was this patch tested?
`CommitFailureTestRelationSuite` uses a testing relation that always fails when committing write tasks to test write job cleanup.
`SimpleTextHadoopFsRelationSuite` uses a testing relation to test general `HadoopFsRelation` and `FileFormat` interfaces.
The two regressions are both covered by existing test cases.
Author: Cheng Lian <lian@databricks.com>
Closes#12179 from liancheng/spark-13681-commit-failure-test.
## What changes were proposed in this pull request?
This is just cleanup. This allows us to remove HiveContext later without inflating the diff too much. This PR fixes the conflicts of https://github.com/apache/spark/pull/12431. It also removes the `def hiveConf` from `HiveSqlParser`. So, we will pass the HiveConf associated with a session explicitly instead of relying on Hive's `SessionState` to pass `HiveConf`.
## How was this patch tested?
Existing tests.
Closes#12431
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12449 from yhuai/hiveconf.
## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#12463 from yhuai/sharedState.
## What changes were proposed in this pull request?
Move the implementation of `hiveContext.analyze` to the command of `AnalyzeTable`.
## How was this patch tested?
Existing tests.
Closes#12429
Author: Yin Huai <yhuai@databricks.com>
Author: Andrew Or <andrew@databricks.com>
Closes#12448 from yhuai/analyzeTable.
## What changes were proposed in this pull request?
This patch adds a SharedState that groups state shared across multiple SQLContexts. This is analogous to the SessionState added in SPARK-13526 that groups session-specific state. This cleanup makes the constructors of the contexts simpler and ultimately allows us to remove HiveContext in the near future.
## How was this patch tested?
Existing tests.
Closes#12405
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12447 from yhuai/sharedState.
## What changes were proposed in this pull request?
This PR removes
- Inappropriate type notations
For example, from
```scala
words.foreachRDD { (rdd: RDD[String], time: Time) =>
...
```
to
```scala
words.foreachRDD { (rdd, time) =>
...
```
- Extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
and corrects some obvious style nits.
## How was this patch tested?
This was tested after adding rules in `scalastyle-config.xml`, which ended up with not finding all perfectly.
The rules applied were below:
- For the first correction,
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">(?m)\.[a-zA-Z_][a-zA-Z0-9]*\(\s*[^,]+s*=>\s*\{[^\}]+\}\s*\)</parameter></parameters>
</check>
```
```xml
<check customId="NoExtraClosure" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]([^\n>,]+=>)?\s*\{([^()]|(?R))*\}^[,]</parameter></parameters>
</check>
```
- For the second correction
```xml
<check customId="TypeNotation" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">\.[a-zA-Z_][a-zA-Z0-9]*\s*[\{|\(]\s*\([^):]*:R))*\}^[,]</parameter></parameters>
</check>
```
**Those rules were not added**
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12413 from HyukjinKwon/SPARK-style.
## What changes were proposed in this pull request?
This PR moves `CurrentDatabase` from sql/hive package to sql/catalyst. It also adds the function description, which looks like the following.
```
scala> sqlContext.sql("describe function extended current_database").collect.foreach(println)
[Function: current_database]
[Class: org.apache.spark.sql.execution.command.CurrentDatabase]
[Usage: current_database() - Returns the current database.]
[Extended Usage:
> SELECT current_database()]
```
## How was this patch tested?
Existing tests
Author: Yin Huai <yhuai@databricks.com>
Closes#12424 from yhuai/SPARK-14668.
## What changes were proposed in this pull request?
This patch speeds up group-by aggregates by around 3-5x by leveraging an in-memory `AggregateHashMap` (please see https://github.com/apache/spark/pull/12161), an append-only aggregate hash map that can act as a 'cache' for extremely fast key-value lookups while evaluating aggregates (and fall back to the `BytesToBytesMap` if a given key isn't found).
Architecturally, it is backed by a power-of-2-sized array for index lookups and a columnar batch that stores the key-value pairs. The index lookups in the array rely on linear probing (with a small number of maximum tries) and use an inexpensive hash function which makes it really efficient for a majority of lookups. However, using linear probing and an inexpensive hash function also makes it less robust as compared to the `BytesToBytesMap` (especially for a large number of keys or even for certain distribution of keys) and requires us to fall back on the latter for correctness.
## How was this patch tested?
Java HotSpot(TM) 64-Bit Server VM 1.8.0_73-b02 on Mac OS X 10.11.4
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
Aggregate w keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
codegen = F 2124 / 2204 9.9 101.3 1.0X
codegen = T hashmap = F 1198 / 1364 17.5 57.1 1.8X
codegen = T hashmap = T 369 / 600 56.8 17.6 5.8X
Author: Sameer Agarwal <sameer@databricks.com>
Closes#12345 from sameeragarwal/tungsten-aggregate-integration.
## What changes were proposed in this pull request?
Removing references to assembly jar in documentation.
Adding an additional (previously undocumented) usage of spark-submit to run examples.
## How was this patch tested?
Ran spark-submit usage to ensure formatting was fine. Ran examples using SparkSubmit.
Author: Mark Grover <mark@apache.org>
Closes#12365 from markgrover/spark-14601.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14592
This patch adds native support for DDL command `CREATE TABLE LIKE`.
The SQL syntax is like:
CREATE TABLE table_name LIKE existing_table
CREATE TABLE IF NOT EXISTS table_name LIKE existing_table
## How was this patch tested?
`HiveDDLCommandSuite`. `HiveQuerySuite` already tests `CREATE TABLE LIKE`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes#12362 from viirya/create-table-like.
#### What changes were proposed in this pull request?
This PR is to add a test to ensure drop partitions of an external table will not delete data.
cc yhuai andrewor14
#### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Andrew Or <andrew@databricks.com>
Closes#12350 from gatorsmile/testDropPartition.
#### What changes were proposed in this pull request?
This PR is to provide a native DDL support for the following three Alter View commands:
Based on the Hive DDL document:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
##### 1. ALTER VIEW RENAME
**Syntax:**
```SQL
ALTER VIEW view_name RENAME TO new_view_name
```
- to change the name of a view to a different name
- not allowed to rename a view's name by ALTER TABLE
##### 2. ALTER VIEW SET TBLPROPERTIES
**Syntax:**
```SQL
ALTER VIEW view_name SET TBLPROPERTIES ('comment' = new_comment);
```
- to add metadata to a view
- not allowed to set views' properties by ALTER TABLE
- ignore it if trying to set a view's existing property key when the value is the same
- overwrite the value if trying to set a view's existing key to a different value
##### 3. ALTER VIEW UNSET TBLPROPERTIES
**Syntax:**
```SQL
ALTER VIEW view_name UNSET TBLPROPERTIES [IF EXISTS] ('comment', 'key')
```
- to remove metadata from a view
- not allowed to unset views' properties by ALTER TABLE
- issue an exception if trying to unset a view's non-existent key
#### How was this patch tested?
Added test cases to verify if it works properly.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12324 from gatorsmile/alterView.
#### What changes were proposed in this pull request?
**HQL Syntax**: [Create View](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/AlterView
)
```SQL
CREATE VIEW [IF NOT EXISTS] [db_name.]view_name [(column_name [COMMENT column_comment], ...) ]
[COMMENT view_comment]
[TBLPROPERTIES (property_name = property_value, ...)]
AS SELECT ...;
```
Add a support for the `[COMMENT view_comment]` clause
#### How was this patch tested?
Modified the existing test cases to verify the correctness.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12288 from gatorsmile/addCommentInCreateView.
## What changes were proposed in this pull request?
This PR removes extra anonymous closure within functional transformations.
For example,
```scala
.map(item => {
...
})
```
which can be just simply as below:
```scala
.map { item =>
...
}
```
## How was this patch tested?
Related unit tests and `sbt scalastyle`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12382 from HyukjinKwon/minor-extra-closers.
## What changes were proposed in this pull request?
This patch implements the `CREATE TABLE` command using the `SessionCatalog`. Previously we handled only `CTAS` and `CREATE TABLE ... USING`. This requires us to refactor `CatalogTable` to accept various fields (e.g. bucket and skew columns) and pass them to Hive.
WIP: Note that I haven't verified whether this actually works yet! But I believe it does.
## How was this patch tested?
Tests will come in a future commit.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12271 from andrewor14/create-table-ddl.
## What changes were proposed in this pull request?
It looks several recent commits for datasources (maybe while removing old `HadoopFsRelation` interface) missed removing some unused imports.
This PR removes some unused imports in datasources.
## How was this patch tested?
`sbt scalastyle` and some unit tests for them.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12326 from HyukjinKwon/minor-imports.
## What changes were proposed in this pull request?
Before we are using `AnalysisException`, `ParseException`, `NoSuchFunctionException` etc when a parsing error encounters. I am trying to make it consistent and also **minimum** code impact to the current implementation by changing the class hierarchy.
1. `NoSuchItemException` is removed, since it is an abstract class and it just simply takes a message string.
2. `NoSuchDatabaseException`, `NoSuchTableException`, `NoSuchPartitionException` and `NoSuchFunctionException` now extends `AnalysisException`, as well as `ParseException`, they are all under `AnalysisException` umbrella, but you can also determine how to use them in a granular way.
## How was this patch tested?
The existing test cases should cover this patch.
Author: bomeng <bmeng@us.ibm.com>
Closes#12314 from bomeng/SPARK-14414.
## What changes were proposed in this pull request?
When planning logical plan node `CreateTableUsingAsSelect`, we neglected its `temporary` field and always generates a `CreateMetastoreDataSourceAsSelect`. This PR fixes this issue generating `CreateTempTableUsingAsSelect` when `temporary` is true.
This PR also fixes SPARK-14493 since the root cause of SPARK-14493 is that we were `CreateMetastoreDataSourceAsSelect` uses default Hive warehouse location when `PATH` data source option is absent.
## How was this patch tested?
Added a test case to create a temporary table using the target syntax and check whether it's indeed a temporary table.
Author: Cheng Lian <lian@databricks.com>
Closes#12303 from liancheng/spark-14488-fix-ctas-using.
## What changes were proposed in this pull request?
Now `HadoopFsRelation` with all kinds of file formats can be handled in `FileSourceStrategy`, we can remove the branches for `HadoopFsRelation` in `FileSourceStrategy` and the `buildInternalScan` API from `FileFormat`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#12300 from cloud-fan/remove.
## What changes were proposed in this pull request?
This implements a few alter table partition commands using the `SessionCatalog`. In particular:
```
ALTER TABLE ... ADD PARTITION ...
ALTER TABLE ... DROP PARTITION ...
ALTER TABLE ... RENAME PARTITION ... TO ...
```
The following operations are not supported, and an `AnalysisException` with a helpful error message will be thrown if the user tries to use them:
```
ALTER TABLE ... EXCHANGE PARTITION ...
ALTER TABLE ... ARCHIVE PARTITION ...
ALTER TABLE ... UNARCHIVE PARTITION ...
ALTER TABLE ... TOUCH ...
ALTER TABLE ... COMPACT ...
ALTER TABLE ... CONCATENATE
MSCK REPAIR TABLE ...
```
## How was this patch tested?
`DDLSuite`, `DDLCommandSuite` and `HiveDDLCommandSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#12220 from andrewor14/alter-partition-ddl.
#### What changes were proposed in this pull request?
This PR is to address the comment: https://github.com/apache/spark/pull/12146#discussion-diff-59092238. It removes the function `isViewSupported` from `SessionCatalog`. After the removal, we still can capture the user errors if users try to drop a table using `DROP VIEW`.
#### How was this patch tested?
Modified the existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12284 from gatorsmile/followupDropTable.
## What changes were proposed in this pull request?
Currently, many functions do now show usages like the followings.
```
scala> sql("desc function extended `sin`").collect().foreach(println)
[Function: sin]
[Class: org.apache.spark.sql.catalyst.expressions.Sin]
[Usage: To be added.]
[Extended Usage:
To be added.]
```
This PR adds descriptions for functions and adds a testcase prevent adding function without usage.
```
scala> sql("desc function extended `sin`").collect().foreach(println);
[Function: sin]
[Class: org.apache.spark.sql.catalyst.expressions.Sin]
[Usage: sin(x) - Returns the sine of x.]
[Extended Usage:
> SELECT sin(0);
0.0]
```
The only exceptions are `cube`, `grouping`, `grouping_id`, `rollup`, `window`.
## How was this patch tested?
Pass the Jenkins tests (including new testcases.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12185 from dongjoon-hyun/SPARK-14415.
## What changes were proposed in this pull request?
For an external table's metadata (in Hive's representation), its table type needs to be EXTERNAL_TABLE. Also, there needs to be a field called EXTERNAL set in the table property with a value of TRUE (for a MANAGED_TABLE it will be FALSE) based on https://github.com/apache/hive/blob/release-1.2.1/metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java#L1095-L1105. HiveClientImpl's toHiveTable misses to set this table property.
## How was this patch tested?
Added a new test.
Author: Yin Huai <yhuai@databricks.com>
Closes#12275 from yhuai/SPARK-14506.
#### What changes were proposed in this pull request?
This PR is to provide a native support for DDL `DROP VIEW` and `DROP TABLE`. The PR includes native parsing and native analysis.
Based on the HIVE DDL document for [DROP_VIEW_WEB_LINK](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-
DropView
), `DROP VIEW` is defined as,
**Syntax:**
```SQL
DROP VIEW [IF EXISTS] [db_name.]view_name;
```
- to remove metadata for the specified view.
- illegal to use DROP TABLE on a view.
- illegal to use DROP VIEW on a table.
- this command only works in `HiveContext`. In `SQLContext`, we will get an exception.
This PR also handles `DROP TABLE`.
**Syntax:**
```SQL
DROP TABLE [IF EXISTS] table_name [PURGE];
```
- Previously, the `DROP TABLE` command only can drop Hive tables in `HiveContext`. Now, after this PR, this command also can drop temporary table, external table, external data source table in `SQLContext`.
- In `HiveContext`, we will not issue an exception if the to-be-dropped table does not exist and users did not specify `IF EXISTS`. Instead, we just log an error message. If `IF EXISTS` is specified, we will not issue any error message/exception.
- In `SQLContext`, we will issue an exception if the to-be-dropped table does not exist, unless `IF EXISTS` is specified.
- Data will not be deleted if the tables are `external`, unless table type is `managed_table`.
#### How was this patch tested?
For verifying command parsing, added test cases in `spark/sql/hive/HiveDDLCommandSuite.scala`
For verifying command analysis, added test cases in `spark/sql/hive/execution/HiveDDLSuite.scala`
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12146 from gatorsmile/dropView.
#### What changes were proposed in this pull request?
"Not good to slightly ignore all the un-supported options/clauses. We should either support it or throw an exception." A comment from yhuai in another PR https://github.com/apache/spark/pull/12146
- Can `Explain` be an exception? The `Formatted` clause is used in `HiveCompatibilitySuite`.
- Two unsupported clauses in `Drop Table` are handled in a separate PR: https://github.com/apache/spark/pull/12146
#### How was this patch tested?
Test cases are added to verify all the cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12255 from gatorsmile/warningToException.
## What changes were proposed in this pull request?
…because some of built-in functions are not in function registry.
This fix tries to fix issues in `describe function` command where some of the outputs
still shows Hive's function because some built-in functions are not in FunctionRegistry.
The following built-in functions have been added to FunctionRegistry:
```
-
!
*
/
&
%
^
+
<
<=
<=>
=
==
>
>=
|
~
and
in
like
not
or
rlike
when
```
The following listed functions are not added, but hard coded in `commands.scala` (hvanhovell):
```
!=
<>
between
case
```
Below are the existing result of the above functions that have not been added:
```
spark-sql> describe function `!=`;
Function: <>
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPNotEqual
Usage: a <> b - Returns TRUE if a is not equal to b
```
```
spark-sql> describe function `<>`;
Function: <>
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPNotEqual
Usage: a <> b - Returns TRUE if a is not equal to b
```
```
spark-sql> describe function `between`;
Function: between
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFBetween
Usage: between a [NOT] BETWEEN b AND c - evaluate if a is [not] in between b and c
```
```
spark-sql> describe function `case`;
Function: case
Class: org.apache.hadoop.hive.ql.udf.generic.GenericUDFCase
Usage: CASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f
```
## How was this patch tested?
Existing tests passed. Additional test cases added.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#12128 from yongtang/SPARK-14335.
## What changes were proposed in this pull request?
This is a followup to #12117 and addresses some of the TODOs introduced there. In particular, the resolution of database is now pushed into session catalog, which knows about the current database. Further, the logic for checking whether a function exists is pushed into the external catalog.
No change in functionality is expected.
## How was this patch tested?
`SessionCatalogSuite`, `DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#12198 from andrewor14/function-exists.
## What changes were proposed in this pull request?
This patch removes DirectParquetOutputCommitter. This was initially created by Databricks as a faster way to write Parquet data to S3. However, given how the underlying S3 Hadoop implementation works, this committer only works when there are no failures. If there are multiple attempts of the same task (e.g. speculation or task failures or node failures), the output data can be corrupted. I don't think this performance optimization outweighs the correctness issue.
## How was this patch tested?
Removed the related tests also.
Author: Reynold Xin <rxin@databricks.com>
Closes#12229 from rxin/SPARK-10063.
### What changes were proposed in this pull request?
This PR adds support for `LEFT ANTI JOIN` to Spark SQL. A `LEFT ANTI JOIN` is the exact opposite of a `LEFT SEMI JOIN` and can be used to identify rows in one dataset that are not in another dataset. Note that `nulls` on the left side of the join cannot match a row on the right hand side of the join; the result is that left anti join will always select a row with a `null` in one or more of its keys.
We currently add support for the following SQL join syntax:
SELECT *
FROM tbl1 A
LEFT ANTI JOIN tbl2 B
ON A.Id = B.Id
Or using a dataframe:
tbl1.as("a").join(tbl2.as("b"), $"a.id" === $"b.id", "left_anti)
This PR provides serves as the basis for implementing `NOT EXISTS` and `NOT IN (...)` correlated sub-queries. It would also serve as good basis for implementing an more efficient `EXCEPT` operator.
The PR has been (losely) based on PR's by both davies (https://github.com/apache/spark/pull/10706) and chenghao-intel (https://github.com/apache/spark/pull/10563); credit should be given where credit is due.
This PR adds supports for `LEFT ANTI JOIN` to `BroadcastHashJoin` (including codegeneration), `ShuffledHashJoin` and `BroadcastNestedLoopJoin`.
### How was this patch tested?
Added tests to `JoinSuite` and ported `ExistenceJoinSuite` from https://github.com/apache/spark/pull/10563.
cc davies chenghao-intel rxin
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12214 from hvanhovell/SPARK-12610.
#### What changes were proposed in this pull request?
This PR is to fix the compilation errors in Scala 2.10 build, as shown in the link:
https://amplab.cs.berkeley.edu/jenkins/job/spark-master-compile-maven-scala-2.10/735/console
```
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:266: value contains is not a member of Option[String]
[error] assert(desc.viewText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:267: value contains is not a member of Option[String]
[error] assert(desc.viewOriginalText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:293: value contains is not a member of Option[String]
[error] assert(desc.viewText.contains("SELECT * FROM tab1"))
[error] ^
[error] /home/jenkins/workspace/spark-master-compile-maven-scala-2.10/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDDLCommandSuite.scala:294: value contains is not a member of Option[String]
[error] assert(desc.viewOriginalText.contains("SELECT * FROM tab1"))
[error] ^
[error] four errors found
[error] Compile failed at Apr 5, 2016 10:59:09 PM [10.502s]
```
#### How was this patch tested?
Not sure how to trigger Scala 2.10 compilation in the test environment.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#12201 from gatorsmile/buildBreak2.10.
#### What changes were proposed in this pull request?
Because the concept of partitioning is associated with physical tables, we disable all the supports of partitioned views, which are defined in the following three commands in [Hive DDL Manual](https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/AlterView):
```
ALTER VIEW view DROP [IF EXISTS] PARTITION spec1[, PARTITION spec2, ...];
ALTER VIEW view ADD [IF NOT EXISTS] PARTITION spec;
CREATE VIEW [IF NOT EXISTS] [db_name.]view_name [(column_name [COMMENT column_comment], ...) ]
[COMMENT view_comment]
[TBLPROPERTIES (property_name = property_value, ...)]
AS SELECT ...;
```
An exception is thrown when users issue any of these three DDL commands.
#### How was this patch tested?
Added test cases for parsing create view and changed the existing test cases to verify if the exceptions are thrown.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12169 from gatorsmile/viewPartition.
Because SQL keeps track of all known configs, some customization was
needed in SQLConf to allow that, since the core API does not have that
feature.
Tested via existing (and slightly updated) unit tests.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11570 from vanzin/SPARK-529-sql.
## What changes were proposed in this pull request?
In Spark 2.0, we want to handle the most common `ALTER TABLE` commands ourselves instead of passing the entire query text to Hive. This is done using the new `SessionCatalog` API introduced recently.
The commands supported in this patch include:
```
ALTER TABLE ... RENAME TO ...
ALTER TABLE ... SET TBLPROPERTIES ...
ALTER TABLE ... UNSET TBLPROPERTIES ...
ALTER TABLE ... SET LOCATION ...
ALTER TABLE ... SET SERDE ...
```
The commands we explicitly do not support are:
```
ALTER TABLE ... CLUSTERED BY ...
ALTER TABLE ... SKEWED BY ...
ALTER TABLE ... NOT CLUSTERED
ALTER TABLE ... NOT SORTED
ALTER TABLE ... NOT SKEWED
ALTER TABLE ... NOT STORED AS DIRECTORIES
```
For these we throw exceptions complaining that they are not supported.
## How was this patch tested?
`DDLSuite`
Author: Andrew Or <andrew@databricks.com>
Closes#12121 from andrewor14/alter-table-ddl.
## What changes were proposed in this pull request?
This PR implements CreateFunction and DropFunction commands. Besides implementing these two commands, we also change how to manage functions. Here are the main changes.
* `FunctionRegistry` will be a container to store all functions builders and it will not actively load any functions. Because of this change, we do not need to maintain a separate registry for HiveContext. So, `HiveFunctionRegistry` is deleted.
* SessionCatalog takes care the job of loading a function if this function is not in the `FunctionRegistry` but its metadata is stored in the external catalog. For this case, SessionCatalog will (1) load the metadata from the external catalog, (2) load all needed resources (i.e. jars and files), (3) create a function builder based on the function definition, (4) register the function builder in the `FunctionRegistry`.
* A `UnresolvedGenerator` is created. So, the parser will not need to call `FunctionRegistry` directly during parsing, which is not a good time to create a Hive UDTF. In the analysis phase, we will resolve `UnresolvedGenerator`.
This PR is based on viirya's https://github.com/apache/spark/pull/12036/
## How was this patch tested?
Existing tests and new tests.
## TODOs
[x] Self-review
[x] Cleanup
[x] More tests for create/drop functions (we need to more tests for permanent functions).
[ ] File JIRAs for all TODOs
[x] Standardize the error message when a function does not exist.
Author: Yin Huai <yhuai@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#12117 from yhuai/function.
#### What changes were proposed in this pull request?
Currently, the weird error messages are issued if we use Hive Context-only operations in SQL Context.
For example,
- When calling `Drop Table` in SQL Context, we got the following message:
```
Expected exception org.apache.spark.sql.catalyst.parser.ParseException to be thrown, but java.lang.ClassCastException was thrown.
```
- When calling `Script Transform` in SQL Context, we got the message:
```
assertion failed: No plan for ScriptTransformation [key#9,value#10], cat, [tKey#155,tValue#156], null
+- LogicalRDD [key#9,value#10], MapPartitionsRDD[3] at beforeAll at BeforeAndAfterAll.scala:187
```
Updates:
Based on the investigation from hvanhovell , the root cause is `visitChildren`, which is the default implementation. It always returns the result of the last defined context child. After merging the code changes from hvanhovell , it works! Thank you hvanhovell !
#### How was this patch tested?
A few test cases are added.
Not sure if the same issue exist for the other operators/DDL/DML. hvanhovell
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Herman van Hovell <hvanhovell@questtec.nl>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12134 from gatorsmile/hiveParserCommand.
## What changes were proposed in this pull request?
This PR adds Native execution of SHOW TBLPROPERTIES command.
Command Syntax:
``` SQL
SHOW TBLPROPERTIES table_name[(property_key_literal)]
```
## How was this patch tested?
Tests added in HiveComandSuiie and DDLCommandSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#12133 from dilipbiswal/dkb_show_tblproperties.
This change modifies the "assembly/" module to just copy needed
dependencies to its build directory, and modifies the packaging
script to pick those up (and remove duplicate jars packages in the
examples module).
I also made some minor adjustments to dependencies to remove some
test jars from the final packaging, and remove jars that conflict with each
other when packaged separately (e.g. servlet api).
Also note that this change restores guava in applications' classpaths, even
though it's still shaded inside Spark. This is now needed for the Hadoop
libraries that are packaged with Spark, which now are not processed by
the shade plugin.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11796 from vanzin/SPARK-13579.
## What changes were proposed in this pull request?
Currently we extract Python UDFs into a special logical plan EvaluatePython in analyzer, But EvaluatePython is not part of catalyst, many rules have no knowledge of it , which will break many things (for example, filter push down or column pruning).
We should treat Python UDFs as normal expressions, until we want to evaluate in physical plan, we could extract them in end of optimizer, or physical plan.
This PR extract Python UDFs in physical plan.
Closes#10935
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#12127 from davies/py_udf.
## What changes were proposed in this pull request?
This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines).
- Fix typos(exception/log strings, testcase name, comments) in 44 lines.
- Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011)
- Use diamond operators in 40 lines. (New codes after SPARK-13702)
- Fix redundant semicolon in 5 lines.
- Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala.
## How was this patch tested?
Manual and pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12139 from dongjoon-hyun/SPARK-14355.
## What changes were proposed in this pull request?
We throw an AnalysisException that looks like this:
```
scala> sqlContext.sql("CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x))")
org.apache.spark.sql.catalyst.parser.ParseException:
Unsupported SQL statement
== SQL ==
CREATE TEMPORARY MACRO SIGMOID (x DOUBLE) 1.0 / (1.0 + EXP(-x))
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.nativeCommand(ParseDriver.scala:66)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:56)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:53)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:86)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:53)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:749)
... 48 elided
```
## How was this patch tested?
Add test cases in HiveQuerySuite.scala
Author: bomeng <bmeng@us.ibm.com>
Closes#12125 from bomeng/SPARK-14341.
## What changes were proposed in this pull request?
EXPLAIN output should be in a single cell.
**Before**
```
scala> sql("explain select 1").collect()
res0: Array[org.apache.spark.sql.Row] = Array([== Physical Plan ==], [WholeStageCodegen], [: +- Project [1 AS 1#1]], [: +- INPUT], [+- Scan OneRowRelation[]])
```
**After**
```
scala> sql("explain select 1").collect()
res1: Array[org.apache.spark.sql.Row] =
Array([== Physical Plan ==
WholeStageCodegen
: +- Project [1 AS 1#4]
: +- INPUT
+- Scan OneRowRelation[]])
```
Or,
```
scala> sql("explain select 1").head
res1: org.apache.spark.sql.Row =
[== Physical Plan ==
WholeStageCodegen
: +- Project [1 AS 1#5]
: +- INPUT
+- Scan OneRowRelation[]]
```
Please note that `Spark-shell(Scala-shell)` trims long string output. So, you may need to use `println` to get full strings.
```
scala> println(sql("explain codegen select 'a' as a group by 1").head)
[Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
WholeStageCodegen
...
/* 059 */ }
/* 060 */ }
]
```
## How was this patch tested?
Pass the Jenkins tests. (Testcases are updated.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12137 from dongjoon-hyun/SPARK-14350.
## What changes were proposed in this pull request?
Appends s3 specific configurations and spark.hadoop configurations to hive configuration.
## How was this patch tested?
Tested by running a job on cluster.
…figurations to hive configuration.
Author: Sital Kedia <skedia@fb.com>
Closes#11876 from sitalkedia/hiveConf.
## What changes were proposed in this pull request?
This PR implements `EXPLAIN CODEGEN` SQL command which returns generated codes like `debugCodegen`. In `spark-shell`, we don't need to `import debug` module. In `spark-sql`, we can use this SQL command now.
**Before**
```
scala> import org.apache.spark.sql.execution.debug._
scala> sql("select 'a' as a group by 1").debugCodegen()
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
...
Generated code:
...
== Subtree 2 / 2 ==
...
Generated code:
...
```
**After**
```
scala> sql("explain extended codegen select 'a' as a group by 1").collect().foreach(println)
[Found 2 WholeStageCodegen subtrees.]
[== Subtree 1 / 2 ==]
...
[]
[Generated code:]
...
[]
[== Subtree 2 / 2 ==]
...
[]
[Generated code:]
...
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12099 from dongjoon-hyun/SPARK-14251.
## What changes were proposed in this pull request?
`SizeBasedWindowFunction.n` is a global singleton attribute created for evaluating size based aggregate window functions like `CUME_DIST`. However, this attribute gets different expression IDs when created on both driver side and executor side. This PR adds `withPartitionSize` method to `SizeBasedWindowFunction` so that we can easily rewrite `SizeBasedWindowFunction.n` on executor side.
## How was this patch tested?
A test case is added in `HiveSparkSubmitSuite`, which supports launching multi-process clusters.
Author: Cheng Lian <lian@databricks.com>
Closes#12040 from liancheng/spark-14244-fix-sized-window-function.
This PR adds the ability to perform aggregations inside of a `ContinuousQuery`. In order to implement this feature, the planning of aggregation has augmented with a new `StatefulAggregationStrategy`. Unlike batch aggregation, stateful-aggregation uses the `StateStore` (introduced in #11645) to persist the results of partial aggregation across different invocations. The resulting physical plan performs the aggregation using the following progression:
- Partial Aggregation
- Shuffle
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreRestore (now there is 1 tuple from this batch + optionally one from the previous)
- Partial Merge (now there is at most 1 tuple per group)
- StateStoreSave (saves the tuple for the next batch)
- Complete (output the current result of the aggregation)
The following refactoring was also performed to allow us to plug into existing code:
- The get/put implementation is taken from #12013
- The logic for breaking down and de-duping the physical execution of aggregation has been move into a new pattern `PhysicalAggregation`
- The `AttributeReference` used to identify the result of an `AggregateFunction` as been moved into the `AggregateExpression` container. This change moves the reference into the same object as the other intermediate references used in aggregation and eliminates the need to pass around a `Map[(AggregateFunction, Boolean), Attribute]`. Further clean up (using a different aggregation container for logical/physical plans) is deferred to a followup.
- Some planning logic is moved from the `SessionState` into the `QueryExecution` to make it easier to override in the streaming case.
- The ability to write a `StreamTest` that checks only the output of the last batch has been added to simulate the future addition of output modes.
Author: Michael Armbrust <michael@databricks.com>
Closes#12048 from marmbrus/statefulAgg.
## What changes were proposed in this pull request?
This patch enables use of OrcRelation for SQL queries which read data from Hive tables. Changes in this patch:
- Added a new rule `OrcConversions` which would alter the plan to use `OrcRelation`. In this diff, the conversion is done only for reads.
- Added a new config `spark.sql.hive.convertMetastoreOrc` to control the conversion
BEFORE
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#171,value#172]
+- MetastoreRelation default, orc_table, None
== Optimized Logical Plan ==
MetastoreRelation default, orc_table, None
== Physical Plan ==
HiveTableScan [key#171,value#172], MetastoreRelation default, orc_table, None
```
AFTER
```
scala> hqlContext.sql("SELECT * FROM orc_table").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias(*, None)]
+- 'UnresolvedRelation `orc_table`, None
== Analyzed Logical Plan ==
key: string, value: string
Project [key#76,value#77]
+- SubqueryAlias orc_table
+- Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Optimized Logical Plan ==
Relation[key#76,value#77] ORC part: struct<>, data: struct<key:string,value:string>
== Physical Plan ==
WholeStageCodegen
: +- Scan ORC part: struct<>, data: struct<key:string,value:string>[key#76,value#77] InputPaths: file:/user/hive/warehouse/orc_table
```
## How was this patch tested?
- Added a new unit test. Ran existing unit tests
- Ran with production like data
## Performance gains
Ran on a production table in Facebook (note that the data was in DWRF file format which is similar to ORC)
Best case : when there was no matching rows for the predicate in the query (everything is filtered out)
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 541_515 sec 25.0 mins 165.8 hours
With change 407 sec 1.5 mins 15 mins
```
Average case: A subset of rows in the data match the query predicate
```
CPU time Wall time Total wall time across all tasks
================================================================
Without the change 624_630 sec 31.0 mins 199.0 h
With change 14_769 sec 5.3 mins 7.7 h
```
Author: Tejas Patil <tejasp@fb.com>
Closes#11891 from tejasapatil/orc_ppd.
## What changes were proposed in this pull request?
This PR throws Unsupported Operation exception for create index, drop index, alter index , lock table , lock database, unlock table, and unlock database operations that are not supported in Spark SQL. Currently these operations are executed executed by Hive.
Error:
spark-sql> drop index my_index on my_table;
Error in query:
Unsupported operation: drop index(line 1, pos 0)
## How was this patch tested?
Added test cases to HiveQuerySuite
yhuai hvanhovell andrewor14
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#12069 from sureshthalamati/unsupported_ddl_spark-14133.
## What changes were proposed in this pull request?
This PR addresses the following
1. Supports native execution of SHOW DATABASES command
2. Fixes SHOW TABLES to apply the identifier_with_wildcards pattern if supplied.
SHOW TABLE syntax
```
SHOW TABLES [IN database_name] ['identifier_with_wildcards'];
```
SHOW DATABASES syntax
```
SHOW (DATABASES|SCHEMAS) [LIKE 'identifier_with_wildcards'];
```
## How was this patch tested?
Tests added in SQLQuerySuite (both hive and sql contexts) and DDLCommandSuite
Note: Since the table name pattern was not working , tests are added in both SQLQuerySuite to
verify the application of the table pattern.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#11991 from dilipbiswal/dkb_show_database.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
## What changes were proposed in this pull request?
Major changes:
1. Implement `FileFormat.buildReader()` for the CSV data source.
1. Add an extra argument to `FileFormat.buildReader()`, `physicalSchema`, which is basically the result of `FileFormat.inferSchema` or user specified schema.
This argument is necessary because the CSV data source needs to know all the columns of the underlying files to read the file.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes#12002 from liancheng/spark-14206-csv-build-reader.
#### What changes were proposed in this pull request?
This PR is to implement the following four Database-related DDL commands:
- `CREATE DATABASE|SCHEMA [IF NOT EXISTS] database_name`
- `DROP DATABASE [IF EXISTS] database_name [RESTRICT|CASCADE]`
- `DESCRIBE DATABASE [EXTENDED] db_name`
- `ALTER (DATABASE|SCHEMA) database_name SET DBPROPERTIES (property_name=property_value, ...)`
Another PR will be submitted to handle the unsupported commands. In the Database-related DDL commands, we will issue an error exception for `ALTER (DATABASE|SCHEMA) database_name SET OWNER [USER|ROLE] user_or_role`.
cc yhuai andrewor14 rxin Could you review the changes? Is it in the right direction? Thanks!
#### How was this patch tested?
Added a few test cases in `command/DDLSuite.scala` for testing DDL command execution in `SQLContext`. Since `HiveContext` also shares the same implementation, the existing test cases in `\hive` also verifies the correctness of these commands.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12009 from gatorsmile/dbDDL.
## What changes were proposed in this pull request?
This PR fixes two trivial typos: 'does not **much**' --> 'does not **match**'.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12042 from dongjoon-hyun/fix_typo_by_replacing_much_with_match.
### What changes were proposed in this pull request?
This PR migrates all HiveQl parsing to the new ANTLR4 parser. This PR is build on top of https://github.com/apache/spark/pull/12011, and we should wait with merging until that one is in (hence the WIP tag).
As soon as this PR is merged we can start removing much of the old parser infrastructure.
### How was this patch tested?
Exisiting Hive unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12015 from hvanhovell/SPARK-14213.
## What changes were proposed in this pull request?
Before: We just pass all role commands to Hive even though it doesn't work.
After: We throw an `AnalysisException` that looks like this:
```
scala> sql("CREATE ROLE x")
org.apache.spark.sql.AnalysisException: Unsupported Hive operation: CREATE ROLE;
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:213)
at org.apache.spark.sql.hive.HiveQl$$anonfun$parsePlan$1.apply(HiveQl.scala:208)
at org.apache.spark.sql.catalyst.parser.CatalystQl.safeParse(CatalystQl.scala:49)
at org.apache.spark.sql.hive.HiveQl.parsePlan(HiveQl.scala:208)
at org.apache.spark.sql.SQLContext.parseSql(SQLContext.scala:198)
```
## How was this patch tested?
`HiveQuerySuite`
Author: Andrew Or <andrew@databricks.com>
Closes#11948 from andrewor14/ddl-role-management.
## What changes were proposed in this pull request?
Session catalog was added in #11750. However, it doesn't really support temporary functions properly; right now we only store the metadata in the form of `CatalogFunction`, but this doesn't make sense for temporary functions because there is no class name.
This patch moves the `FunctionRegistry` into the `SessionCatalog`. With this, the user can call `catalog.createTempFunction` and `catalog.lookupFunction` to use the function they registered previously. This is currently still dead code, however.
## How was this patch tested?
`SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11972 from andrewor14/temp-functions.
## What changes were proposed in this pull request?
This patch addresses the remaining comments left in #11750 and #11918 after they are merged. For a full list of changes in this patch, just trace the commits.
## How was this patch tested?
`SessionCatalogSuite` and `CatalogTestCases`
Author: Andrew Or <andrew@databricks.com>
Closes#12006 from andrewor14/session-catalog-followup.
## What changes were proposed in this pull request?
JIRA: https://issues.apache.org/jira/browse/SPARK-14156
In HiveComparisonTest, when catalyst results are different to hive results, we will collect the messages for computed tables during the test. During creating the message, we use sparkPlan. But we actually run the query with executedPlan. So the error message is sometimes confusing.
For example, as wholestage codegen is enabled by default now. The shown spark plan for computed tables is the plan before wholestage codegen.
A concrete is the following error message shown before this patch. It is the error shown when running `HiveCompatibilityTest` `auto_join26`.
auto_join26 has one SQL to create table:
INSERT OVERWRITE TABLE dest_j1
SELECT x.key, count(1) FROM src1 x JOIN src y ON (x.key = y.key) group by x.key; (1)
Then a SQL to retrieve the result:
select * from dest_j1 x order by x.key; (2)
When the above SQL (2) to retrieve the result fails, In `HiveComparisonTest` we will try to collect and show the generated data from table `dest_j1` using the SQL (1)'s spark plan. The you will see this error:
TungstenAggregate(key=[key#8804], functions=[(count(1),mode=Partial,isDistinct=false)], output=[key#8804,count#8834L])
+- Project [key#8804]
+- BroadcastHashJoin [key#8804], [key#8806], Inner, BuildRight, None
:- Filter isnotnull(key#8804)
: +- InMemoryColumnarTableScan [key#8804], [isnotnull(key#8804)], InMemoryRelation [key#8804,value#8805], true, 5, StorageLevel(true, true, false, true, 1), HiveTableScan [key#8717,value#8718], MetastoreRelation default, src1, None, Some(src1)
+- Filter isnotnull(key#8806)
+- InMemoryColumnarTableScan [key#8806], [isnotnull(key#8806)], InMemoryRelation [key#8806,value#8807], true, 5, StorageLevel(true, true, false, true, 1), HiveTableScan [key#8760,value#8761], MetastoreRelation default, src, None, Some(src)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:47)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate.doExecute(TungstenAggregate.scala:82)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:121)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:121)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:140)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:137)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:120)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:87)
at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.apply(TungstenAggregate.scala:82)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:46)
... 70 more
Caused by: java.lang.UnsupportedOperationException: Filter does not implement doExecuteBroadcast
at org.apache.spark.sql.execution.SparkPlan.doExecuteBroadcast(SparkPlan.scala:221)
The message is confusing because it is not the plan actually run by SparkSQL engine to create the generated table. The plan actually run is no problem. But as before this patch, we run `e.sparkPlan.collect` to retrieve and show the generated data, spark plan is not the plan we can run. So the above error will be shown.
After this patch, we won't see the error because the executed plan is no problem and works.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#11957 from viirya/use-executedplan.
## What changes were proposed in this pull request?
This PR fixes the following two testcases in order to test the correct usages.
```
checkSqlGeneration("SELECT substr('This is a test', 'is')")
checkSqlGeneration("SELECT substring('This is a test', 'is')")
```
Actually, the testcases works but tests on exceptional cases.
```
scala> sql("SELECT substr('This is a test', 'is')")
res0: org.apache.spark.sql.DataFrame = [substring(This is a test, CAST(is AS INT), 2147483647): string]
scala> sql("SELECT substr('This is a test', 'is')").collect()
res1: Array[org.apache.spark.sql.Row] = Array([null])
```
## How was this patch tested?
Pass the modified unit tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11963 from dongjoon-hyun/fix_substr_testcase.
#### What changes were proposed in this pull request?
This PR is to provide native parsing support for two DDL commands: ```Describe Database``` and ```Alter Database Set Properties```
Based on the Hive DDL document:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
##### 1. ALTER DATABASE
**Syntax:**
```SQL
ALTER (DATABASE|SCHEMA) database_name SET DBPROPERTIES (property_name=property_value, ...)
```
- `ALTER DATABASE` is to add new (key, value) pairs into `DBPROPERTIES`
##### 2. DESCRIBE DATABASE
**Syntax:**
```SQL
DESCRIBE DATABASE [EXTENDED] db_name
```
- `DESCRIBE DATABASE` shows the name of the database, its comment (if one has been set), and its root location on the filesystem. When `extended` is true, it also shows the database's properties
#### How was this patch tested?
Added the related test cases to `DDLCommandSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
This patch had conflicts when merged, resolved by
Committer: Yin Huai <yhuai@databricks.com>
Closes#11977 from gatorsmile/parseAlterDatabase.
## What changes were proposed in this pull request?
This PR implements `FileFormat.buildReader()` for our ORC data source. It also fixed several minor styling issues related to `HadoopFsRelation` planning code path.
Note that `OrcNewInputFormat` doesn't rely on `OrcNewSplit` for creating `OrcRecordReader`s, plain `FileSplit` is just fine. That's why we can simply create the record reader with the help of `OrcNewInputFormat` and `FileSplit`.
## How was this patch tested?
Existing test cases should do the work
Author: Cheng Lian <lian@databricks.com>
Closes#11936 from liancheng/spark-14116-build-reader-for-orc.
### What changes were proposed in this pull request?
Based on the Hive DDL document https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
The syntax of DDL command for Drop Database is
```SQL
DROP (DATABASE|SCHEMA) [IF EXISTS] database_name [RESTRICT|CASCADE];
```
- If `IF EXISTS` is not specified, the default behavior is to issue a warning message if `database_name` does't exist
- `RESTRICT` is the default behavior.
This PR is to provide a native parsing support for `DROP DATABASE`.
#### How was this patch tested?
Added a test case `DDLCommandSuite`
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11962 from gatorsmile/parseDropDatabase.
## What changes were proposed in this pull request?
This PR adds support for automatically inferring `IsNotNull` constraints from any non-nullable attributes that are part of an operator's output. This also fixes the issue that causes the optimizer to hit the maximum number of iterations for certain queries in https://github.com/apache/spark/pull/11828.
## How was this patch tested?
Unit test in `ConstraintPropagationSuite`
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11953 from sameeragarwal/infer-isnotnull.
## What changes were proposed in this pull request?
As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API.
## How was this patch tested?
various new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11879 from cloud-fan/create_map.
## What changes were proposed in this pull request?
This PR fix the conflict between ColumnPruning and PushPredicatesThroughProject, because ColumnPruning will try to insert a Project before Filter, but PushPredicatesThroughProject will move the Filter before Project.This is fixed by remove the Project before Filter, if the Project only do column pruning.
The RuleExecutor will fail the test if reached max iterations.
Closes#11745
## How was this patch tested?
Existing tests.
This is a test case still failing, disabled for now, will be fixed by https://issues.apache.org/jira/browse/SPARK-14137
Author: Davies Liu <davies@databricks.com>
Closes#11828 from davies/fail_rule.
## What changes were proposed in this pull request?
This reopens#11836, which was merged but promptly reverted because it introduced flaky Hive tests.
## How was this patch tested?
See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11938 from andrewor14/session-catalog-again.
## What changes were proposed in this pull request?
unionAll has been deprecated in SPARK-14088.
## How was this patch tested?
Should be covered by all existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11946 from rxin/SPARK-14142.
## What changes were proposed in this pull request?
`SessionCatalog`, introduced in #11750, is a catalog that keeps track of temporary functions and tables, and delegates metastore operations to `ExternalCatalog`. This functionality overlaps a lot with the existing `analysis.Catalog`.
As of this commit, `SessionCatalog` and `ExternalCatalog` will no longer be dead code. There are still things that need to be done after this patch, namely:
- SPARK-14013: Properly implement temporary functions in `SessionCatalog`
- SPARK-13879: Decide which DDL/DML commands to support natively in Spark
- SPARK-?????: Implement the ones we do want to support through `SessionCatalog`.
- SPARK-?????: Merge SQL/HiveContext
## How was this patch tested?
This is largely a refactoring task so there are no new tests introduced. The particularly relevant tests are `SessionCatalogSuite` and `ExternalCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#11836 from andrewor14/use-session-catalog.
## What changes were proposed in this pull request?
This PR adds support for TimestampType in the vectorized parquet reader
## How was this patch tested?
1. `VectorizedColumnReader` initially had a gating condition on `primitiveType.getPrimitiveTypeName() == PrimitiveType.PrimitiveTypeName.INT96)` that made us fall back on parquet-mr for handling timestamps. This condition is now removed.
2. The `ParquetHadoopFsRelationSuite` (that tests for all supported hive types -- including `TimestampType`) fails when the gating condition is removed (https://github.com/apache/spark/pull/11808) and should now pass with this change. Similarly, the `ParquetHiveCompatibilitySuite.SPARK-10177 timestamp` test that fails when the gating condition is removed, should now pass as well.
3. Added tests in `HadoopFsRelationTest` that test both the dictionary encoded and non-encoded versions across all supported datatypes.
Author: Sameer Agarwal <sameer@databricks.com>
Closes#11882 from sameeragarwal/timestamp-parquet.
This patch refactors the `MemoryStore` so that it can be tested without needing to construct / mock an entire `BlockManager`.
- The block manager's serialization- and compression-related methods have been moved from `BlockManager` to `SerializerManager`.
- `BlockInfoManager `is now passed directly to classes that need it, rather than being passed via the `BlockManager`.
- The `MemoryStore` now calls `dropFromMemory` via a new `BlockEvictionHandler` interface rather than directly calling the `BlockManager`. This change helps to enforce a narrow interface between the `MemoryStore` and `BlockManager` functionality and makes this interface easier to mock in tests.
- Several of the block unrolling tests have been moved from `BlockManagerSuite` into a new `MemoryStoreSuite`.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11899 from JoshRosen/reduce-memorystore-blockmanager-coupling.
## What changes were proposed in this pull request?
This PR does the renaming as suggested by marmbrus in [this comment][1].
## How was this patch tested?
Existing tests.
[1]: 6d37e1eb90 (commitcomment-16654694)
Author: Cheng Lian <lian@databricks.com>
Closes#11889 from liancheng/spark-13817-follow-up.
SPARK-13774: IllegalArgumentException: Can not create a Path from an empty string for incorrect file path
**Overview:**
- If a non-existent path is given in this call
``
scala> sqlContext.read.format("csv").load("file-path-is-incorrect.csv")
``
it throws the following error:
`java.lang.IllegalArgumentException: Can not create a Path from an empty string` …..
`It gets called from inferSchema call in org.apache.spark.sql.execution.datasources.DataSource.resolveRelation`
- The purpose of this JIRA is to throw a better error message.
- With the fix, you will now get a _Path does not exist_ error message.
```
scala> sqlContext.read.format("csv").load("file-path-is-incorrect.csv")
org.apache.spark.sql.AnalysisException: Path does not exist: file:/Users/ksunitha/trunk/spark/file-path-is-incorrect.csv;
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:215)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$12.apply(DataSource.scala:204)
...
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:204)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:131)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:141)
... 49 elided
```
**Details**
_Changes include:_
- Check if path exists or not in resolveRelation in DataSource, and throw an AnalysisException with message like “Path does not exist: $path”
- AnalysisException is thrown similar to the exceptions thrown in resolveRelation.
- The glob path and the non glob path is checked with minimal calls to path exists. If the globPath is empty, then it is a nonexistent glob pattern and an error will be thrown. In the scenario that it is not globPath, it is necessary to only check if the first element in the Seq is valid or not.
_Test modifications:_
- Changes went in for 3 tests to account for this error checking.
- SQLQuerySuite:test("run sql directly on files") – Error message needed to be updated.
- 2 tests failed in MetastoreDataSourcesSuite because they had a dummy path and so test is modified to give a tempdir and allow it to move past so it can continue to test the codepath it meant to test
_New Tests:_
2 new tests are added to DataFrameSuite to validate that glob and non-glob path will throw the new error message.
_Testing:_
Unit tests were run with the fix.
**Notes/Questions to reviewers:**
- There is some code duplication in DataSource.scala in resolveRelation method and also createSource with respect to getting the paths. I have not made any changes to the createSource codepath. Should we make the change there as well ?
- From other JIRAs, I know there is restructuring and changes going on in this area, not sure how that will affect these changes, but since this seemed like a starter issue, I looked into it. If we prefer not to add the overhead of the checks, or if there is a better place to do so, let me know.
I would appreciate your review. Thanks for your time and comments.
Author: Sunitha Kambhampati <skambha@us.ibm.com>
Closes#11775 from skambha/improve_errmsg.
## What changes were proposed in this pull request?
As we have completed the `SQLBuilder`, we can safely turn on native view by default.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11872 from cloud-fan/native-view.
This PR add implements the new `buildReader` interface for the Parquet `FileFormat`. An simple implementation of `FileScanRDD` is also included.
This code should be tested by the many existing tests for parquet.
Author: Michael Armbrust <michael@databricks.com>
Author: Sameer Agarwal <sameer@databricks.com>
Author: Nong Li <nong@databricks.com>
Closes#11709 from marmbrus/parquetReader.
This PR resolves two issues:
First, expanding * inside aggregate functions of structs when using Dataframe/Dataset APIs. For example,
```scala
structDf.groupBy($"a").agg(min(struct($"record.*")))
```
Second, it improves the error messages when having invalid star usage when using Dataframe/Dataset APIs. For example,
```scala
pagecounts4PartitionsDS
.map(line => (line._1, line._3))
.toDF()
.groupBy($"_1")
.agg(sum("*") as "sumOccurances")
```
Before the fix, the invalid usage will issue a confusing error message, like:
```
org.apache.spark.sql.AnalysisException: cannot resolve '_1' given input columns _1, _2;
```
After the fix, the message is like:
```
org.apache.spark.sql.AnalysisException: Invalid usage of '*' in function 'sum'
```
cc: rxin nongli cloud-fan
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11208 from gatorsmile/sumDataSetResolution.
## What changes were proposed in this pull request?
This patch merges DatasetHolder and DataFrameHolder. This makes more sense because DataFrame/Dataset are now one class.
In addition, fixed some minor issues with pull request #11732.
## How was this patch tested?
Updated existing unit tests that test these implicits.
Author: Reynold Xin <rxin@databricks.com>
Closes#11737 from rxin/SPARK-13898.
## What changes were proposed in this pull request?
This is a more aggressive version of PR #11820, which not only fixes the original problem, but also does the following updates to enforce the at-most-one-qualifier constraint:
- Renames `NamedExpression.qualifiers` to `NamedExpression.qualifier`
- Uses `Option[String]` rather than `Seq[String]` for `NamedExpression.qualifier`
Quoted PR description of #11820 here:
> Current implementations of `AttributeReference.sql` and `Alias.sql` joins all available qualifiers, which is logically wrong. But this implementation mistake doesn't cause any real SQL generation bugs though, since there is always at most one qualifier for any given `AttributeReference` or `Alias`.
## How was this patch tested?
Existing tests should be enough.
Author: Cheng Lian <lian@databricks.com>
Closes#11822 from liancheng/spark-14004-aggressive.
## What changes were proposed in this pull request?
`SubqueryHolder` is only used when generate SQL string in `SQLBuilder`, it's more clear to make it an inner class in `SQLBuilder`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11861 from cloud-fan/gensql.
## What changes were proposed in this pull request?
[Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`.
```xml
- <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places -->
- <!--
<module name="LineLength">
<property name="max" value="100"/>
<property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
</module>
- -->
<module name="NoLineWrap"/>
<module name="EmptyBlock">
<property name="option" value="TEXT"/>
-167,5 +164,7
</module>
<module name="CommentsIndentation"/>
<module name="UnusedImports"/>
+ <module name="RedundantImport"/>
+ <module name="RedundantModifier"/>
```
## How was this patch tested?
Currently, `lint-java` is disabled in Jenkins. It needs a manual test.
After passing the Jenkins tests, `dev/lint-java` should passes locally.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11831 from dongjoon-hyun/SPARK-14011.
## What changes were proposed in this pull request?
As part of testing generating SQL query from a analyzed SQL plan, we run the generated SQL for tests in HiveComparisonTest. This PR makes the generated SQL get eagerly analyzed. So, when a generated SQL has any analysis error, we can see the error message created by
```
case NonFatal(e) => fail(
s"""Failed to analyze the converted SQL string:
|
|# Original HiveQL query string:
|$queryString
|
|# Resolved query plan:
|${originalQuery.analyzed.treeString}
|
|# Converted SQL query string:
|$convertedSQL
""".stripMargin, e)
```
Right now, if we can parse a generated SQL but fail to analyze it, we will see error message generated by the following code (it only mentions that we cannot execute the original query, i.e. `queryString`).
```
case e: Throwable =>
val errorMessage =
s"""
|Failed to execute query using catalyst:
|Error: ${e.getMessage}
|${stackTraceToString(e)}
|$queryString
|$query
|== HIVE - ${hive.size} row(s) ==
|${hive.mkString("\n")}
""".stripMargin
```
## How was this patch tested?
Existing tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#11825 from yhuai/SPARK-13972-follow-up.
## What changes were proposed in this pull request?
ShuffledHashJoin (also outer join) is removed in 1.6, in favor of SortMergeJoin, which is more robust and also fast.
ShuffledHashJoin is still useful in this case: 1) one table is much smaller than the other one, then cost to build a hash table on smaller table is smaller than sorting the larger table 2) any partition of the small table could fit in memory.
This PR brings back ShuffledHashJoin, basically revert #9645, and fix the conflict. Also merging outer join and left-semi join into the same class. This PR does not implement full outer join, because it's not implemented efficiently (requiring build hash table on both side).
A simple benchmark (one table is 5x smaller than other one) show that ShuffledHashJoin could be 2X faster than SortMergeJoin.
## How was this patch tested?
Added new unit tests for ShuffledHashJoin.
Author: Davies Liu <davies@databricks.com>
Closes#11788 from davies/shuffle_join.
## What changes were proposed in this pull request?
Now we should be able to convert all logical plans to SQL string, if they are parsed from hive query. This PR changes the error handling to throw exceptions instead of just log.
We will send new PRs for spotted bugs, and merge this one after all bugs are fixed.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11782 from cloud-fan/test.
## What changes were proposed in this pull request?
The fix is simple, use the existing `CombineUnions` rule to combine adjacent Unions before build SQL string.
## How was this patch tested?
The re-enabled test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11818 from cloud-fan/bug-fix.
PR #11696 introduced a complex pattern match that broke Scala 2.10 match unreachability check and caused build failure. This PR fixes this issue by expanding this pattern match into several simpler ones.
Note that tuning or turning off `-Dscalac.patmat.analysisBudget` doesn't work for this case.
Compilation against Scala 2.10
Author: tedyu <yuzhihong@gmail.com>
Closes#11798 from yy2016/master.
## What changes were proposed in this pull request?
We haven't figured out the corrected logical to add sub-queries yet, so we should not clear all sub-queries before generate SQL. This PR changed the logic to only remove sub-queries above table relation.
an example for this bug, original SQL: `SELECT a FROM (SELECT a FROM tbl) t WHERE a = 1`
before this PR, we will generate:
```
SELECT attr_1 AS a FROM
SELECT attr_1 FROM (
SELECT a AS attr_1 FROM tbl
) AS sub_q0
WHERE attr_1 = 1
```
We missed a sub-query and this SQL string is illegal.
After this PR, we will generate:
```
SELECT attr_1 AS a FROM (
SELECT attr_1 FROM (
SELECT a AS attr_1 FROM tbl
) AS sub_q0
WHERE attr_1 = 1
) AS t
```
TODO: for long term, we should find a way to add sub-queries correctly, so that arbitrary logical plans can be converted to SQL string.
## How was this patch tested?
`LogicalPlanToSQLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11786 from cloud-fan/bug-fix.
## What changes were proposed in this pull request?
We only need to make sub-query names unique every time we generate a SQL string, but not all the time. This PR moves the `newSubqueryName` method to `class SQLBuilder` and remove `object SQLBuilder`.
also addressed 2 minor comments in https://github.com/apache/spark/pull/11696
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11783 from cloud-fan/tmp.
## What changes were proposed in this pull request?
Compilation against Scala 2.10 fails with:
```
[error] [warn] /home/jenkins/workspace/spark-master-compile-sbt-scala-2.10/sql/hive/src/main/scala/org/apache/spark/sql/hive/SQLBuilder.scala:483: Cannot check match for unreachability.
[error] (The analysis required more space than allowed. Please try with scalac -Dscalac.patmat.analysisBudget=512 or -Dscalac.patmat.analysisBudget=off.)
[error] [warn] private def addSubqueryIfNeeded(plan: LogicalPlan): LogicalPlan = plan match {
```
## How was this patch tested?
Compilation against Scala 2.10
Author: tedyu <yuzhihong@gmail.com>
Closes#11787 from yy2016/master.
## What changes were proposed in this pull request?
This PR adds SQL generation support for `Generate` operator. It always converts `Generate` operator into `LATERAL VIEW` format as there are many limitations to put UDTF in project list.
This PR is based on https://github.com/apache/spark/pull/11658, please see the last commit to review the real changes.
Thanks dilipbiswal for his initial work! Takes over https://github.com/apache/spark/pull/11596
## How was this patch tested?
new tests in `LogicalPlanToSQLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11696 from cloud-fan/generate.
## What changes were proposed in this pull request?
Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11764 from cloud-fan/logger.
This commit updates the HiveContext so that sc.hadoopConfiguration is used to instantiate its internal instances of HiveConf.
I tested this by overriding the S3 FileSystem implementation from spark-defaults.conf as "spark.hadoop.fs.s3.impl" (to avoid [HADOOP-12810](https://issues.apache.org/jira/browse/HADOOP-12810)).
Author: Ryan Blue <blue@apache.org>
Closes#11273 from rdblue/SPARK-13403-new-hive-conf-from-hadoop-conf.
## What changes were proposed in this pull request?
Since developer API of plug-able parser has been removed in #10801 , docs should be updated accordingly.
## How was this patch tested?
This patch will not affect the real code path.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#11758 from adrian-wang/spark12855.
## What changes were proposed in this pull request?
As part of the effort to merge `SQLContext` and `HiveContext`, this patch implements an internal catalog called `SessionCatalog` that handles temporary functions and tables and delegates metastore operations to `ExternalCatalog`. Currently, this is still dead code, but in the future it will be part of `SessionState` and will replace `o.a.s.sql.catalyst.analysis.Catalog`.
A recent patch #11573 parses Hive commands ourselves in Spark, but still passes the entire query text to Hive. In a future patch, we will use `SessionCatalog` to implement the parsed commands.
## How was this patch tested?
800+ lines of tests in `SessionCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11750 from andrewor14/temp-catalog.
#### What changes were proposed in this pull request?
This PR is to convert to SQL from analyzed logical plans containing operator `ScriptTransformation`.
For example, below is the SQL containing `Transform`
```
SELECT TRANSFORM (a, b, c, d) USING 'cat' FROM parquet_t2
```
Its logical plan is like
```
ScriptTransformation [a#210L,b#211L,c#212L,d#213L], cat, [key#208,value#209], HiveScriptIOSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),true)
+- SubqueryAlias parquet_t2
+- Relation[a#210L,b#211L,c#212L,d#213L] ParquetRelation
```
The generated SQL will be like
```
SELECT TRANSFORM (`parquet_t2`.`a`, `parquet_t2`.`b`, `parquet_t2`.`c`, `parquet_t2`.`d`) USING 'cat' AS (`key` string, `value` string) FROM `default`.`parquet_t2`
```
#### How was this patch tested?
Seven test cases are added to `LogicalPlanToSQLSuite`.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11503 from gatorsmile/transformToSQL.
## What changes were proposed in this pull request?
This PR tries to solve a fundamental issue in the `SQLBuilder`. When we want to turn a logical plan into SQL string and put it after FROM clause, we need to wrap it with a sub-query. However, a logical plan is allowed to have same-name outputs with different qualifiers(e.g. the `Join` operator), and this kind of plan can't be put under a subquery as we will erase and assign a new qualifier to all outputs and make it impossible to distinguish same-name outputs.
To solve this problem, this PR renames all attributes with globally unique names(using exprId), so that we don't need qualifiers to resolve ambiguity anymore.
For example, `SELECT x.key, MAX(y.key) OVER () FROM t x JOIN t y`, we will parse this SQL to a Window operator and a Project operator, and add a sub-query between them. The generated SQL looks like:
```
SELECT sq_1.key, sq_1.max
FROM (
SELECT sq_0.key, sq_0.key, MAX(sq_0.key) OVER () AS max
FROM (
SELECT x.key, y.key FROM t1 AS x JOIN t2 AS y
) AS sq_0
) AS sq_1
```
You can see, the `key` columns become ambiguous after `sq_0`.
After this PR, it will generate something like:
```
SELECT attr_30 AS key, attr_37 AS max
FROM (
SELECT attr_30, attr_37
FROM (
SELECT attr_30, attr_35, MAX(attr_35) AS attr_37
FROM (
SELECT attr_30, attr_35 FROM
(SELECT key AS attr_30 FROM t1) AS sq_0
INNER JOIN
(SELECT key AS attr_35 FROM t1) AS sq_1
) AS sq_2
) AS sq_3
) AS sq_4
```
The outermost SELECT is used to turn the generated named to real names back, and the innermost SELECT is used to alias real columns to our generated names. Between them, there is no name ambiguity anymore.
## How was this patch tested?
existing tests and new tests in LogicalPlanToSQLSuite.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11658 from cloud-fan/gensql.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13894
Change the return type of the `SQLContext.range` API from `DataFrame` to `Dataset`.
## How was this patch tested?
No additional unit test required.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#11730 from chenghao-intel/range.
## What changes were proposed in this pull request?
There is a feature of hive SQL called multi-insert. For example:
```
FROM src
INSERT OVERWRITE TABLE dest1
SELECT key + 1
INSERT OVERWRITE TABLE dest2
SELECT key WHERE key > 2
INSERT OVERWRITE TABLE dest3
SELECT col EXPLODE(arr) exp AS col
...
```
We partially support it currently, with some limitations: 1) WHERE can't reference columns produced by LATERAL VIEW. 2) It's not executed eagerly, i.e. `sql("...multi-insert clause...")` won't take place right away like other commands, e.g. CREATE TABLE.
This PR removes these limitations and make us fully support multi-insert.
## How was this patch tested?
new tests in `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11754 from cloud-fan/lateral-view.
