## 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.
