## What changes were proposed in this pull request?
Support Filter in ConvertToLocalRelation, similar to how Project works.
Additionally, in Optimizer, run ConvertToLocalRelation earlier to simplify the plan. This is good for very short queries which often are queries on local relations.
## How was this patch tested?
New test. Manual benchmark.
Author: Bogdan Raducanu <bogdan@databricks.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: Yinan Li <ynli@google.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: s71955 <sujithchacko.2010@gmail.com>
Author: DB Tsai <d_tsai@apple.com>
Author: jaroslav chládek <mastermism@gmail.com>
Author: Huangweizhe <huangweizhe@bbdservice.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: hyukjinkwon <gurwls223@apache.org>
Author: Kent Yao <yaooqinn@hotmail.com>
Author: caoxuewen <cao.xuewen@zte.com.cn>
Author: liuxian <liu.xian3@zte.com.cn>
Author: Adam Bradbury <abradbury@users.noreply.github.com>
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Author: Yuming Wang <yumwang@ebay.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#22205 from bogdanrdc/local-relation-filter.
## What changes were proposed in this pull request?
This PR implements the possibility of the user to override the maximum number of buckets when saving to a table.
Currently the limit is a hard-coded 100k, which might be insufficient for large workloads.
A new configuration entry is proposed: `spark.sql.bucketing.maxBuckets`, which defaults to the previous 100k.
## How was this patch tested?
Added unit tests in the following spark.sql test suites:
- CreateTableAsSelectSuite
- BucketedWriteSuite
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#21087 from ferdonline/enh/configurable_bucket_limit.
## What changes were proposed in this pull request?
this pr add a configuration parameter to configure the capacity of fast aggregation.
Performance comparison:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Windows 7 6.1
Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
Aggregate w multiple keys: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
fasthash = default 5612 / 5882 3.7 267.6 1.0X
fasthash = config 3586 / 3595 5.8 171.0 1.6X
```
## How was this patch tested?
the existed test cases.
Closes#21931 from heary-cao/FastHashCapacity.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Update to janino 3.0.9 to address Java 8 + Scala 2.12 incompatibility. The error manifests as test failures like this in `ExpressionEncoderSuite`:
```
- encode/decode for seq of string: List(abc, xyz) *** FAILED ***
java.lang.RuntimeException: Error while encoding: org.codehaus.janino.InternalCompilerException: failed to compile: org.codehaus.janino.InternalCompilerException: Compiling "GeneratedClass": Two non-abstract methods "public int scala.collection.TraversableOnce.size()" have the same parameter types, declaring type and return type
```
It comes up pretty immediately in any generated code that references Scala collections, and virtually always concerning the `size()` method.
## How was this patch tested?
Existing tests
Closes#22203 from srowen/SPARK-25029.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-4502)
_N.B. This is a restart of PR #16578 which includes a subset of that code. Relevant review comments from that PR should be considered incorporated by reference. Please avoid duplication in review by reviewing that PR first. The summary below is an edited copy of the summary of the previous PR._
## What changes were proposed in this pull request?
One of the hallmarks of a column-oriented data storage format is the ability to read data from a subset of columns, efficiently skipping reads from other columns. Spark has long had support for pruning unneeded top-level schema fields from the scan of a parquet file. For example, consider a table, `contacts`, backed by parquet with the following Spark SQL schema:
```
root
|-- name: struct
| |-- first: string
| |-- last: string
|-- address: string
```
Parquet stores this table's data in three physical columns: `name.first`, `name.last` and `address`. To answer the query
```SQL
select address from contacts
```
Spark will read only from the `address` column of parquet data. However, to answer the query
```SQL
select name.first from contacts
```
Spark will read `name.first` and `name.last` from parquet.
This PR modifies Spark SQL to support a finer-grain of schema pruning. With this patch, Spark reads only the `name.first` column to answer the previous query.
### Implementation
There are two main components of this patch. First, there is a `ParquetSchemaPruning` optimizer rule for gathering the required schema fields of a `PhysicalOperation` over a parquet file, constructing a new schema based on those required fields and rewriting the plan in terms of that pruned schema. The pruned schema fields are pushed down to the parquet requested read schema. `ParquetSchemaPruning` uses a new `ProjectionOverSchema` extractor for rewriting a catalyst expression in terms of a pruned schema.
Second, the `ParquetRowConverter` has been patched to ensure the ordinals of the parquet columns read are correct for the pruned schema. `ParquetReadSupport` has been patched to address a compatibility mismatch between Spark's built in vectorized reader and the parquet-mr library's reader.
### Limitation
Among the complex Spark SQL data types, this patch supports parquet column pruning of nested sequences of struct fields only.
## How was this patch tested?
Care has been taken to ensure correctness and prevent regressions. A more advanced version of this patch incorporating optimizations for rewriting queries involving aggregations and joins has been running on a production Spark cluster at VideoAmp for several years. In that time, one bug was found and fixed early on, and we added a regression test for that bug.
We forward-ported this patch to Spark master in June 2016 and have been running this patch against Spark 2.x branches on ad-hoc clusters since then.
Closes#21320 from mallman/spark-4502-parquet_column_pruning-foundation.
Lead-authored-by: Michael Allman <msa@allman.ms>
Co-authored-by: Adam Jacques <adam@technowizardry.net>
Co-authored-by: Michael Allman <michael@videoamp.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Casting to `DecimalType` is not always needed to force nullable.
If the decimal type to cast is wider than original type, or only truncating or precision loss, the casted value won't be `null`.
## How was this patch tested?
Added and modified tests.
Closes#22200 from ueshin/issues/SPARK-25208/cast_nullable_decimal.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #22031 which added `zip_with` function to fix an example.
## How was this patch tested?
Existing tests.
Closes#22194 from ueshin/issues/SPARK-23932/fix_examples.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR moves the compilation of the regexp for code formatting outside the method which is called for each code block when splitting expressions, in order to avoid recompiling the regexp every time.
Credit should be given to Izek Greenfield.
## How was this patch tested?
existing UTs
Closes#22135 from mgaido91/SPARK-25093.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This fixes a perf regression caused by https://github.com/apache/spark/pull/21376 .
We should not use `RDD#toLocalIterator`, which triggers one Spark job per RDD partition. This is very bad for RDDs with a lot of small partitions.
To fix it, this PR introduces a way to access SQLConf in the scheduler event loop thread, so that we don't need to use `RDD#toLocalIterator` anymore in `JsonInferSchema`.
## How was this patch tested?
a new test
Closes#22152 from cloud-fan/conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This pr is to fix bugs when expr codegen fails; we need to catch `java.util.concurrent.ExecutionException` instead of `InternalCompilerException` and `CompileException` . This handling is the same with the `WholeStageCodegenExec ` one: 60af2501e1/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala (L585)
## How was this patch tested?
Added tests in `CodeGeneratorWithInterpretedFallbackSuite`
Closes#22154 from maropu/SPARK-25140.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In https://issues.apache.org/jira/browse/SPARK-24924, the data source provider com.databricks.spark.avro is mapped to the new package org.apache.spark.sql.avro .
As per the discussion in the [Jira](https://issues.apache.org/jira/browse/SPARK-24924) and PR #22119, we should make the mapping configurable.
This PR also improve the error message when data source of Avro/Kafka is not found.
## How was this patch tested?
Unit test
Closes#22133 from gengliangwang/configurable_avro_mapping.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This patch proposes a new flag option for stateful aggregation: remove redundant key data from value.
Enabling new option runs similar with current, and uses less memory for state according to key/value fields of state operator.
Please refer below link to see detailed perf. test result:
https://issues.apache.org/jira/browse/SPARK-24763?focusedCommentId=16536539&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16536539
Since the state between enabling the option and disabling the option is not compatible, the option is set to 'disable' by default (to ensure backward compatibility), and OffsetSeqMetadata would prevent modifying the option after executing query.
## How was this patch tested?
Modify unit tests to cover both disabling option and enabling option.
Also did manual tests to see whether propose patch improves state memory usage.
Closes#21733 from HeartSaVioR/SPARK-24763.
Authored-by: Jungtaek Lim <kabhwan@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/22079#discussion_r209705612 It is possible for two objects to be unequal and yet we consider them as equal with this code, if the long values are separated by Int.MaxValue.
This PR fixes the issue.
## How was this patch tested?
Add new test cases in `RecordBinaryComparatorSuite`.
Closes#22101 from jiangxb1987/fix-rbc.
Authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
In the PR for supporting logical timestamp types https://github.com/apache/spark/pull/21935, a SQL configuration spark.sql.avro.outputTimestampType is added, so that user can specify the output timestamp precision they want.
With PR https://github.com/apache/spark/pull/21847, the output file can be written with user specified types.
So there is no need to have such trivial configuration. Otherwise to make it consistent we need to add configuration for all the Catalyst types that can be converted into different Avro types.
This PR also add a test case for user specified output schema with different timestamp types.
## How was this patch tested?
Unit test
Closes#22151 from gengliangwang/removeOutputTimestampType.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We should also check `HigherOrderFunction.bind` method passes expected parameters.
This pr modifies tests for higher-order functions to check `bind` method.
## How was this patch tested?
Modified tests.
Closes#22131 from ueshin/issues/SPARK-25141/bind_test.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to skip invoking of the CSV/JSON parser per each line in the case if the required schema is empty. Added benchmarks for `count()` shows performance improvement up to **3.5 times**.
Before:
```
Count a dataset with 10 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------------
JSON count() 7676 / 7715 1.3 767.6
CSV count() 3309 / 3363 3.0 330.9
```
After:
```
Count a dataset with 10 columns: Best/Avg Time(ms) Rate(M/s) Per Row(ns)
--------------------------------------------------------------------------------------
JSON count() 2104 / 2156 4.8 210.4
CSV count() 2332 / 2386 4.3 233.2
```
## How was this patch tested?
It was tested by `CSVSuite` and `JSONSuite` as well as on added benchmarks.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Author: Maxim Gekk <max.gekk@gmail.com>
Closes#21909 from MaxGekk/empty-schema-optimization.
## What changes were proposed in this pull request?
Put annotation args in one line, or API doc generation will fail.
~~~
[error] /Users/meng/src/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala:1559: annotation argument needs to be a constant; found: "_FUNC_(expr) - Returns the character length of string data or number of bytes of ".+("binary data. The length of string data includes the trailing spaces. The length of binary ").+("data includes binary zeros.")
[error] "binary data. The length of string data includes the trailing spaces. The length of binary " +
[error] ^
[info] No documentation generated with unsuccessful compiler run
[error] one error found
[error] (catalyst/compile:doc) Scaladoc generation failed
[error] Total time: 27 s, completed Aug 17, 2018 3:20:08 PM
~~~
## How was this patch tested?
sbt catalyst/compile:doc passed
Closes#22137 from mengxr/minor-doc-fix.
Authored-by: Xiangrui Meng <meng@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a follow-up pr of #22017 which added `map_zip_with` function.
In the test, when creating a lambda function, we use the `valueContainsNull` values for the nullabilities of the value arguments, but we should've used `true` as the same as `bind` method because the values might be `null` if the keys don't match.
## How was this patch tested?
Added small tests and existing tests.
Closes#22126 from ueshin/issues/SPARK-23938/fix_tests.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
The method ```*supportEquals``` determining whether elements of a data type could be used as items in a hash set or as keys in a hash map is duplicated across multiple collection and higher-order functions.
This PR suggests to deduplicate the method.
## How was this patch tested?
Run tests in:
- DataFrameFunctionsSuite
- CollectionExpressionsSuite
- HigherOrderExpressionsSuite
Closes#22110 from mn-mikke/SPARK-25122.
Authored-by: Marek Novotny <mn.mikke@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr adds `transform_values` function which applies the function to each entry of the map and transforms the values.
```javascript
> SELECT transform_values(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> v + 1);
map(1->2, 2->3, 3->4)
> SELECT transform_values(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + v);
map(1->2, 2->4, 3->6)
```
## How was this patch tested?
New Tests added to
`DataFrameFunctionsSuite`
`HigherOrderFunctionsSuite`
`SQLQueryTestSuite`
Closes#22045 from codeatri/SPARK-23940.
Authored-by: codeatri <nehapatil6@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
Add RewriteCorrelatedScalarSubquery in the list of nonExcludableRules since its used to transform correlated scalar subqueries to joins.
## How was this patch tested?
Added test in OptimizerRuleExclusionSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#22108 from dilipbiswal/scalar_exclusion.
## What changes were proposed in this pull request?
Merges the two given arrays, element-wise, into a single array using function. If one array is shorter, nulls are appended at the end to match the length of the longer array, before applying function:
```
SELECT zip_with(ARRAY[1, 3, 5], ARRAY['a', 'b', 'c'], (x, y) -> (y, x)); -- [ROW('a', 1), ROW('b', 3), ROW('c', 5)]
SELECT zip_with(ARRAY[1, 2], ARRAY[3, 4], (x, y) -> x + y); -- [4, 6]
SELECT zip_with(ARRAY['a', 'b', 'c'], ARRAY['d', 'e', 'f'], (x, y) -> concat(x, y)); -- ['ad', 'be', 'cf']
SELECT zip_with(ARRAY['a'], ARRAY['d', null, 'f'], (x, y) -> coalesce(x, y)); -- ['a', null, 'f']
```
## How was this patch tested?
Added tests
Closes#22031 from techaddict/SPARK-23932.
Authored-by: Sandeep Singh <sandeep@techaddict.me>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This pr adds transform_keys function which applies the function to each entry of the map and transforms the keys.
```javascript
> SELECT transform_keys(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + 1);
map(2->1, 3->2, 4->3)
> SELECT transform_keys(map(array(1, 2, 3), array(1, 2, 3)), (k,v) -> k + v);
map(2->1, 4->2, 6->3)
```
## How was this patch tested?
Added tests.
Closes#22013 from codeatri/SPARK-23939.
Authored-by: codeatri <nehapatil6@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is split from #21520. This includes changes of `BoundAttribute` and `Cast`.
This patch also adds few convenient APIs:
```scala
CodeGenerator.freshVariable(name: String, dt: DataType): VariableValue
CodeGenerator.freshVariable(name: String, javaClass: Class[_]): VariableValue
JavaCode.javaType(javaClass: Class[_]): Inline
JavaCode.javaType(dataType: DataType): Inline
JavaCode.boxedType(dataType: DataType): Inline
```
## How was this patch tested?
Existing tests.
Closes#21537 from viirya/SPARK-24505-1.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add logging for all generated methods from the `CodeGenerator` whose bytecode size goes above 8000 bytes.
This is to help with gathering stats on how often Spark is generating methods too big to be JIT'd. It covers all codegen scenarios, include whole-stage codegen and also individual expression codegen, e.g. unsafe projection, mutable projection, etc.
## How was this patch tested?
Manually tested that logging did happen when generated method was above 8000 bytes.
Also added a new unit test case to `CodeGenerationSuite` to verify that the logging did happen.
Author: Kris Mok <kris.mok@databricks.com>
Closes#22103 from rednaxelafx/codegen-8k-logging.
## What changes were proposed in this pull request?
This PR adds a new SQL function called ```map_zip_with```. It merges the two given maps into a single map by applying function to the pair of values with the same key.
## How was this patch tested?
Added new tests into:
- DataFrameFunctionsSuite.scala
- HigherOrderFunctionsSuite.scala
Closes#22017 from mn-mikke/SPARK-23938.
Authored-by: Marek Novotny <mn.mikke@gmail.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This PR fixes the an example for `to_json` in doc and function description.
- http://spark.apache.org/docs/2.3.0/api/sql/#to_json
- `describe function extended`
## How was this patch tested?
Pass the Jenkins with the updated test.
Closes#22096 from dongjoon-hyun/minor_json.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a follow-up pr of #21954 to address comments.
- Rename ambiguous name `inputs` to `arguments`.
- Add argument type check and remove hacky workaround.
- Address other small comments.
## How was this patch tested?
Existing tests and some additional tests.
Closes#22075 from ueshin/issues/SPARK-23908/fup1.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The PR removes a restriction for element types of array type which exists in `from_json` for the root type. Currently, the function can handle only arrays of structs. Even array of primitive types is disallowed. The PR allows arrays of any types currently supported by JSON datasource. Here is an example of an array of a primitive type:
```
scala> import org.apache.spark.sql.functions._
scala> val df = Seq("[1, 2, 3]").toDF("a")
scala> val schema = new ArrayType(IntegerType, false)
scala> val arr = df.select(from_json($"a", schema))
scala> arr.printSchema
root
|-- jsontostructs(a): array (nullable = true)
| |-- element: integer (containsNull = true)
```
and result of converting of the json string to the `ArrayType`:
```
scala> arr.show
+----------------+
|jsontostructs(a)|
+----------------+
| [1, 2, 3]|
+----------------+
```
## How was this patch tested?
I added a few positive and negative tests:
- array of primitive types
- array of arrays
- array of structs
- array of maps
Closes#21439 from MaxGekk/from_json-array.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In type coercion for complex types, if the found type is force-nullable to cast, we should loosen the nullability to be able to cast. Also for map key type, we can't use the type.
## How was this patch tested?
Added some test.
Closes#22086 from ueshin/issues/SPARK-25096/fix_type_coercion.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Support Avro logical date type:
https://avro.apache.org/docs/1.8.2/spec.html#Decimal
## How was this patch tested?
Unit test
Closes#22037 from gengliangwang/avro_decimal.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add RewriteExceptAll and RewriteIntersectAll in the list of nonExcludableRules as the rewrites are essential for the functioning of EXCEPT ALL and INTERSECT ALL feature.
## How was this patch tested?
Added test in OptimizerRuleExclusionSuite.
Closes#22080 from dilipbiswal/exceptall_rewrite_exclusion.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
"distribute by" on multiple columns (wrap in brackets) may lead to codegen issue.
Simple way to reproduce:
```scala
val df = spark.range(1000)
val columns = (0 until 400).map{ i => s"id as id$i" }
val distributeExprs = (0 until 100).map(c => s"id$c").mkString(",")
df.selectExpr(columns : _*).createTempView("test")
spark.sql(s"select * from test distribute by ($distributeExprs)").count()
```
## How was this patch tested?
Add UT.
Closes#22066 from yucai/SPARK-25084.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Parquet file provides six codecs: "snappy", "gzip", "lzo", "lz4", "brotli", "zstd".
This pr add missing compression codec :"lz4", "brotli", "zstd" .
## How was this patch tested?
N/A
Closes#22068 from 10110346/nosupportlz4.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
A logical `Limit` is performed physically by two operations `LocalLimit` and `GlobalLimit`.
Most of time, we gather all data into a single partition in order to run `GlobalLimit`. If we use a very big limit number, shuffling data causes performance issue also reduces parallelism.
We can avoid shuffling into single partition if we don't care data ordering. This patch implements this idea by doing a map stage during global limit. It collects the info of row numbers at each partition. For each partition, we locally retrieves limited data without any shuffling to finish this global limit.
For example, we have three partitions with rows (100, 100, 50) respectively. In global limit of 100 rows, we may take (34, 33, 33) rows for each partition locally. After global limit we still have three partitions.
If the data partition has certain ordering, we can't distribute required rows evenly to each partitions because it could change data ordering. But we still can avoid shuffling.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#16677 from viirya/improve-global-limit-parallelism.
## What changes were proposed in this pull request?
This PR simplified code generation for `ArrayDistinct`. #21966 enabled code generation only if the type can be specialized by the hash set. This PR follows this strategy.
Optimization of null handling will be implemented in #21912.
## How was this patch tested?
Existing UTs
Closes#22044 from kiszk/SPARK-23912-follow.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up to #21305 that adds a test suite for AppendData analysis.
This also fixes the following problems uncovered by these tests:
* Incorrect order of data types passed to `canWrite` is fixed
* The field check calls `canWrite` first to ensure all errors are found
* `AppendData#resolved` must check resolution of the query's attributes
* Column names are quoted to show empty names
## How was this patch tested?
This PR adds a test suite for AppendData analysis.
Closes#22043 from rdblue/SPARK-24251-add-append-data-analysis-tests.
Authored-by: Ryan Blue <blue@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This pr adds `exists` function which tests whether a predicate holds for one or more elements in the array.
```sql
> SELECT exists(array(1, 2, 3), x -> x % 2 == 0);
true
```
## How was this patch tested?
Added tests.
Closes#22052 from ueshin/issues/SPARK-25068/exists.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently, Analyze table calculates table size sequentially for each partition. We can parallelize size calculations over partitions.
Results : Tested on a table with 100 partitions and data stored in S3.
With changes :
- 10.429s
- 10.557s
- 10.439s
- 9.893s
Without changes :
- 110.034s
- 99.510s
- 100.743s
- 99.106s
## How was this patch tested?
Simple unit test.
Closes#21608 from Achuth17/improveAnalyze.
Lead-authored-by: Achuth17 <Achuth.narayan@gmail.com>
Co-authored-by: arajagopal17 <arajagopal@qubole.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Correct the class name typo checked in through SPARK-24891
## How was this patch tested?
Passed all existing tests.
Closes#22049 from maryannxue/known-not-null.
Authored-by: maryannxue <maryannxue@apache.org>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR addresses two issues in `BufferHolderSparkSubmitSuite`.
1. While `BufferHolderSparkSubmitSuite` tried to allocate a large object several times, it actually allocated an object once and reused the object.
2. `BufferHolderSparkSubmitSuite` may fail due to timeout
To assign a small object before allocating a large object each time solved issue 1 by avoiding reuse.
To increasing heap size from 4g to 7g solved issue 2. It can also avoid OOM after fixing issue 1.
## How was this patch tested?
Updated existing `BufferHolderSparkSubmitSuite`
Closes#20636 from kiszk/SPARK-23415.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.
## How was this patch tested?
N/A
Closes#22040 from kiszk/spellcheck1.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
We should use `Block.isEmpty/nonEmpty` instead of comparing with empty string to check whether the code is empty or not.
```
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala:278: org.apache.spark.sql.catalyst.expressions.codegen.Block and String are unrelated: they will most likely always compare unequal
[error] [warn] if (ev.code != "" && required.contains(attributes(i))) {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala:323: org.apache.spark.sql.catalyst.expressions.codegen.Block and String are unrelated: they will most likely never compare equal
[error] [warn] | ${buildVars.filter(_.code == "").map(v => s"${v.isNull} = true;").mkString("\n")}
[error] [warn]
```
## How was this patch tested?
Existing tests.
Closes#22041 from ueshin/issues/SPARK-25058/fix_comparison.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
We have completed a significant subset of the object related Expressions to provide an interpreted fallback. This PR is going to modify the tests to also test the interpreted code paths.
One concern right now is that by testing the interpreted code paths too, we will double current test time or more. Otherwise, we can only choose to test the interpreted code paths for just few test suites such as encoder related.
## How was this patch tested?
Existing tests.
Closes#21535 from viirya/SPARK-23596.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This pr is a follow-up pr of #21982 and fixes the examples.
## How was this patch tested?
Existing tests.
Closes#22035 from ueshin/issues/SPARK-23911/fup1.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
The PR remove the following compilation error using scala-2.12 with sbt by adding a default case to `match`.
```
/home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/ValueInterval.scala:63: match may not be exhaustive.
[error] It would fail on the following inputs: (NumericValueInterval(_, _), _), (_, NumericValueInterval(_, _)), (_, _)
[error] [warn] def isIntersected(r1: ValueInterval, r2: ValueInterval): Boolean = (r1, r2) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/ValueInterval.scala:79: match may not be exhaustive.
[error] It would fail on the following inputs: (NumericValueInterval(_, _), _), (_, NumericValueInterval(_, _)), (_, _)
[error] [warn] (r1, r2) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/ApproxCountDistinctForIntervals.scala:67: match may not be exhaustive.
[error] It would fail on the following inputs: (ArrayType(_, _), _), (_, ArrayData()), (_, _)
[error] [warn] (endpointsExpression.dataType, endpointsExpression.eval()) match {
[error] [warn]
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala:470: match may not be exhaustive.
[error] It would fail on the following inputs: NewFunctionSpec(_, None, Some(_)), NewFunctionSpec(_, Some(_), None)
[error] [warn] newFunction match {
[error] [warn]
[error] [warn] [error] [warn] /home/ishizaki/Spark/PR/scala212/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala:709: match may not be exhaustive.
[error] It would fail on the following input: Schema((x: org.apache.spark.sql.types.DataType forSome x not in org.apache.spark.sql.types.StructType), _)
[error] [warn] def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match {
[error] [warn]
```
## How was this patch tested?
Existing UTs with Scala-2.11.
Manually build with Scala-2.12
Closes#22014 from kiszk/SPARK-25036b.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR fixes an exception during the compilation of generated code of `mapEntry`. This error occurs since the current code uses `key` type to store a `value` when `key` and `value` types are primitive type.
```
val mid0 = Literal.create(Map(1 -> 1.1, 2 -> 2.2), MapType(IntegerType, DoubleType))
checkEvaluation(MapEntries(mid0), Seq(r(1, 1.1), r(2, 2.2)))
```
```
[info] Code generation of map_entries(keys: [1,2], values: [1.1,2.2]) failed:
[info] java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: No applicable constructor/method found for actual parameters "int, double"; candidates are: "public void org.apache.spark.sql.catalyst.expressions.UnsafeRow.setInt(int, int)", "public void org.apache.spark.sql.catalyst.InternalRow.setInt(int, int)"
[info] java.util.concurrent.ExecutionException: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 80, Column 20: No applicable constructor/method found for actual parameters "int, double"; candidates are: "public void org.apache.spark.sql.catalyst.expressions.UnsafeRow.setInt(int, int)", "public void org.apache.spark.sql.catalyst.InternalRow.setInt(int, int)"
[info] at com.google.common.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
[info] at com.google.common.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
[info] at com.google.common.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
[info] at com.google.common.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
[info] at com.google.common.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
[info] at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
[info] at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
[info] at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2257)
[info] at com.google.common.cache.LocalCache.get(LocalCache.java:4000)
[info] at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:4004)
[info] at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
[info] at org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:1290)
...
```
## How was this patch tested?
Added a new test to `CollectionExpressionsSuite`
Closes#22033 from kiszk/SPARK-23935-followup.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #21980.
`Shuffle` can also be `ExpressionWithRandomSeed` to produce different values for each execution in streaming query.
## How was this patch tested?
Added a test.
Closes#22027 from ueshin/issues/SPARK-25010/random_seed.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This adds a new logical plan, AppendData, that was proposed in SPARK-23521: Standardize SQL logical plans.
* DataFrameWriter uses the new AppendData plan for DataSourceV2 appends
* AppendData is resolved if its output columns match the incoming data frame
* A new analyzer rule, ResolveOutputColumns, validates data before it is appended. This rule will add safe casts, rename columns, and checks nullability
## How was this patch tested?
Existing tests for v2 appends. Will add AppendData tests to validate logical plan analysis.
Closes#21305 from rdblue/SPARK-24251-add-append-data.
Lead-authored-by: Ryan Blue <blue@apache.org>
Co-authored-by: Ryan Blue <rdblue@users.noreply.github.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
