## What changes were proposed in this pull request?
When `SHOW CREATE TABLE` for Datasource tables, we are missing `TBLPROPERTIES` and `COMMENT`, and we should use `LOCATION` instead of path in `OPTION`.
## How was this patch tested?
Splitted `ShowCreateTableSuite` to confirm to work with both `InMemoryCatalog` and `HiveExternalCatalog`, and added some tests.
Closes#22892 from ueshin/issues/SPARK-25884/show_create_table.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
When `spark.sql.hive.metastore.version` is misconfigured, we had better give a directional error message.
**BEFORE**
```scala
scala> sql("show databases").show
scala.MatchError: 2.4 (of class java.lang.String)
```
**AFTER**
```scala
scala> sql("show databases").show
java.lang.UnsupportedOperationException: Unsupported Hive Metastore version (2.4).
Please set spark.sql.hive.metastore.version with a valid version.
```
## How was this patch tested?
Manual.
Closes#22902 from dongjoon-hyun/SPARK-25893.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Set main args correctly in BenchmarkBase, to make it accessible for its subclass.
It will benefit:
- BuiltInDataSourceWriteBenchmark
- AvroWriteBenchmark
## How was this patch tested?
manual tests
Closes#22872 from yucai/main_args.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The instance of `FileSplit` is redundant for `ParquetFileFormat` and `hive\orc\OrcFileFormat` class.
## How was this patch tested?
Existing unit tests in `ParquetQuerySuite.scala` and `HiveOrcQuerySuite.scala`
Closes#22802 from 10110346/FileSplitnotneed.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Change the version number in comment of `HiveUtils.newClientForExecution` from `13` to `1.2.1` .
## How was this patch tested?
N/A
Closes#22850 from laskfla/HiveUtils-Comment.
Authored-by: laskfla <wwlsax11@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Refactor ObjectHashAggregateExecBenchmark to use main method
## How was this patch tested?
Manually tested:
```
bin/spark-submit --class org.apache.spark.sql.execution.benchmark.ObjectHashAggregateExecBenchmark --jars sql/catalyst/target/spark-catalyst_2.11-3.0.0-SNAPSHOT-tests.jar,core/target/spark-core_2.11-3.0.0-SNAPSHOT-tests.jar,sql/hive/target/spark-hive_2.11-3.0.0-SNAPSHOT.jar --packages org.spark-project.hive:hive-exec:1.2.1.spark2 sql/hive/target/spark-hive_2.11-3.0.0-SNAPSHOT-tests.jar
```
Generated results with:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "hive/test:runMain org.apache.spark.sql.execution.benchmark.ObjectHashAggregateExecBenchmark"
```
Closes#22804 from peter-toth/SPARK-25665.
Lead-authored-by: Peter Toth <peter.toth@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
SQL interface support specify `StorageLevel` when cache table. The semantic is:
```sql
CACHE TABLE tableName OPTIONS('storageLevel' 'DISK_ONLY');
```
All supported `StorageLevel` are:
eefdf9f9dd/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala (L172-L183)
## How was this patch tested?
unit tests and manual tests.
manual tests configuration:
```
--executor-memory 15G --executor-cores 5 --num-executors 50
```
Data:
Input Size / Records: 1037.7 GB / 11732805788
Result:
Closes#22263 from wangyum/SPARK-25269.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Without this PR some UDAFs like `GenericUDAFPercentileApprox` can throw an exception because expecting a constant parameter (object inspector) as a particular argument.
The exception is thrown because `toPrettySQL` call in `ResolveAliases` analyzer rule transforms a `Literal` parameter to a `PrettyAttribute` which is then transformed to an `ObjectInspector` instead of a `ConstantObjectInspector`.
The exception comes from `getEvaluator` method of `GenericUDAFPercentileApprox` that actually shouldn't be called during `toPrettySQL` transformation. The reason why it is called are the non lazy fields in `HiveUDAFFunction`.
This PR makes all fields of `HiveUDAFFunction` lazy.
## How was this patch tested?
added new UT
Closes#22766 from peter-toth/SPARK-25768.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
this PR correct some comment error:
1. change from "as low a possible" to "as low as possible" in RewriteDistinctAggregates.scala
2. delete redundant word “with” in HiveTableScanExec’s doExecute() method
## How was this patch tested?
Existing unit tests.
Closes#22694 from CarolinePeng/update_comment.
Authored-by: 彭灿00244106 <00244106@zte.intra>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
LOAD DATA INPATH didn't work if the defaultFS included a port for hdfs.
Handling this just requires a small change to use the correct URI
constructor.
## How was this patch tested?
Added a unit test, ran all tests via jenkins
Closes#22733 from squito/SPARK-25738.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
According to the SQL standard, when a query contains `HAVING`, it indicates an aggregate operator. For more details please refer to https://blog.jooq.org/2014/12/04/do-you-really-understand-sqls-group-by-and-having-clauses/
However, in Spark SQL parser, we treat HAVING as a normal filter when there is no GROUP BY, which breaks SQL semantic and lead to wrong result. This PR fixes the parser.
## How was this patch tested?
new test
Closes#22696 from cloud-fan/having.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR can correctly cause assertion failure when incorrect nullable of DataType in the result is generated by a target function to be tested.
Let us think the following example. In the future, a developer would write incorrect code that returns unexpected result. We have to correctly cause fail in this test since `valueContainsNull=false` while `expr` includes `null`. However, without this PR, this test passes. This PR can correctly cause fail.
```
test("test TARGETFUNCTON") {
val expr = TARGETMAPFUNCTON()
// expr = UnsafeMap(3 -> 6, 7 -> null)
// expr.dataType = (IntegerType, IntegerType, false)
expected = Map(3 -> 6, 7 -> null)
checkEvaluation(expr, expected)
```
In [`checkEvaluationWithUnsafeProjection`](https://github.com/apache/spark/blob/master/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala#L208-L235), the results are compared using `UnsafeRow`. When the given `expected` is [converted](https://github.com/apache/spark/blob/master/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala#L226-L227)) to `UnsafeRow` using the `DataType` of `expr`.
```
val expectedRow = UnsafeProjection.create(Array(expression.dataType, expression.dataType)).apply(lit)
```
In summary, `expr` is `[0,1800000038,5000000038,18,2,0,700000003,2,0,6,18,2,0,700000003,2,0,6]` with and w/o this PR. `expected` is converted to
* w/o this PR, `[0,1800000038,5000000038,18,2,0,700000003,2,0,6,18,2,0,700000003,2,0,6]`
* with this PR, `[0,1800000038,5000000038,18,2,0,700000003,2,2,6,18,2,0,700000003,2,2,6]`
As a result, w/o this PR, the test unexpectedly passes.
This is because, w/o this PR, based on given `dataType`, generated code of projection for `expected` avoids to set nullbit.
```
// tmpInput_2 is expected
/* 155 */ for (int index_1 = 0; index_1 < numElements_1; index_1++) {
/* 156 */ mutableStateArray_1[1].write(index_1, tmpInput_2.getInt(index_1));
/* 157 */ }
```
With this PR, generated code of projection for `expected` always checks whether nullbit should be set by `isNullAt`
```
// tmpInput_2 is expected
/* 161 */ for (int index_1 = 0; index_1 < numElements_1; index_1++) {
/* 162 */
/* 163 */ if (tmpInput_2.isNullAt(index_1)) {
/* 164 */ mutableStateArray_1[1].setNull4Bytes(index_1);
/* 165 */ } else {
/* 166 */ mutableStateArray_1[1].write(index_1, tmpInput_2.getInt(index_1));
/* 167 */ }
/* 168 */
/* 169 */ }
```
## How was this patch tested?
Existing UTs
Closes#22375 from kiszk/SPARK-25388.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove Hadoop 2.6 references and make 2.7 the default.
Obviously, this is for master/3.0.0 only.
After this we can also get rid of the separate test jobs for Hadoop 2.6.
## How was this patch tested?
Existing tests
Closes#22615 from srowen/SPARK-25016.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Inspired by https://github.com/apache/spark/pull/22574 .
We can partially push down top level conjunctive predicates to Orc.
This PR improves Orc predicate push down in both SQL and Hive module.
## How was this patch tested?
New unit test.
Closes#22684 from gengliangwang/pushOrcFilters.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Reduced the combination of codecs from 9 to 3 to improve the test runtime.
## How was this patch tested?
This is a test fix.
Closes#22641 from dilipbiswal/SPARK-25611.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There was 5 suites extends `HadoopFsRelationTest`, for testing "orc"/"parquet"/"text"/"json" data sources.
This PR refactor the base trait `HadoopFsRelationTest`:
1. Rename unnecessary loop for setting parquet conf
2. The test case `SPARK-8406: Avoids name collision while writing files` takes about 14 to 20 seconds. As now all the file format data source are using common code, for creating result files, we can test one data source(Parquet) only to reduce test time.
To run related 5 suites:
```
./build/sbt "hive/testOnly *HadoopFsRelationSuite"
```
The total test run time is reduced from 5 minutes 40 seconds to 3 minutes 50 seconds.
## How was this patch tested?
Unit test
Closes#22643 from gengliangwang/refactorHadoopFsRelationTest.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The total run time of `HiveSparkSubmitSuite` is about 10 minutes.
While the related code is stable, add tag `ExtendedHiveTest` for it.
## How was this patch tested?
Unit test.
Closes#22642 from gengliangwang/addTagForHiveSparkSubmitSuite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Before ORC 1.5.3, `orc.dictionary.key.threshold` and `hive.exec.orc.dictionary.key.size.threshold` are applied for all columns. This has been a big huddle to enable dictionary encoding. From ORC 1.5.3, `orc.column.encoding.direct` is added to enforce direct encoding selectively in a column-wise manner. This PR aims to add that feature by upgrading ORC from 1.5.2 to 1.5.3.
The followings are the patches in ORC 1.5.3 and this feature is the only one related to Spark directly.
```
ORC-406: ORC: Char(n) and Varchar(n) writers truncate to n bytes & corrupts multi-byte data (gopalv)
ORC-403: [C++] Add checks to avoid invalid offsets in InputStream
ORC-405: Remove calcite as a dependency from the benchmarks.
ORC-375: Fix libhdfs on gcc7 by adding #include <functional> two places.
ORC-383: Parallel builds fails with ConcurrentModificationException
ORC-382: Apache rat exclusions + add rat check to travis
ORC-401: Fix incorrect quoting in specification.
ORC-385: Change RecordReader to extend Closeable.
ORC-384: [C++] fix memory leak when loading non-ORC files
ORC-391: [c++] parseType does not accept underscore in the field name
ORC-397: Allow selective disabling of dictionary encoding. Original patch was by Mithun Radhakrishnan.
ORC-389: Add ability to not decode Acid metadata columns
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22622 from dongjoon-hyun/SPARK-25635.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Improve the runtime by reducing the number of partitions created in the test. The number of partitions are reduced from 280 to 60.
Here are the test times for the `getPartitionsByFilter returns all partitions` test on my laptop.
```
[info] - 0.13: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (4 seconds, 230 milliseconds)
[info] - 0.14: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (3 seconds, 576 milliseconds)
[info] - 1.0: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (3 seconds, 495 milliseconds)
[info] - 1.1: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (6 seconds, 728 milliseconds)
[info] - 1.2: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (7 seconds, 260 milliseconds)
[info] - 2.0: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (8 seconds, 270 milliseconds)
[info] - 2.1: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (6 seconds, 856 milliseconds)
[info] - 2.2: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (7 seconds, 587 milliseconds)
[info] - 2.3: getPartitionsByFilter returns all partitions when hive.metastore.try.direct.sql=false (7 seconds, 230 milliseconds)
## How was this patch tested?
Test only.
Closes#22644 from dilipbiswal/SPARK-25626.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Rename method `benchmark` in `BenchmarkBase` as `runBenchmarkSuite `. Also add comments.
Currently the method name `benchmark` is a bit confusing. Also the name is the same as instances of `Benchmark`:
f246813afb/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala (L330-L339)
## How was this patch tested?
Unit test.
Closes#22599 from gengliangwang/renameBenchmarkSuite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds a rule to force `.toLowerCase(Locale.ROOT)` or `toUpperCase(Locale.ROOT)`.
It produces an error as below:
```
[error] Are you sure that you want to use toUpperCase or toLowerCase without the root locale? In most cases, you
[error] should use toUpperCase(Locale.ROOT) or toLowerCase(Locale.ROOT) instead.
[error] If you must use toUpperCase or toLowerCase without the root locale, wrap the code block with
[error] // scalastyle:off caselocale
[error] .toUpperCase
[error] .toLowerCase
[error] // scalastyle:on caselocale
```
This PR excludes the cases above for SQL code path for external calls like table name, column name and etc.
For test suites, or when it's clear there's no locale problem like Turkish locale problem, it uses `Locale.ROOT`.
One minor problem is, `UTF8String` has both methods, `toLowerCase` and `toUpperCase`, and the new rule detects them as well. They are ignored.
## How was this patch tested?
Manually tested, and Jenkins tests.
Closes#22581 from HyukjinKwon/SPARK-25565.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor OrcReadBenchmark to use main method.
Generate benchmark result:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "hive/test:runMain org.apache.spark.sql.hive.orc.OrcReadBenchmark"
```
## How was this patch tested?
manual tests
Closes#22580 from yucai/SPARK-25508.
Lead-authored-by: yucai <yyu1@ebay.com>
Co-authored-by: Yucai Yu <yucai.yu@foxmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR aims to prevent test slowdowns at `HiveExternalCatalogVersionsSuite` by using the latest Apache Spark 2.3.2 link because the Apache mirrors will remove the old Spark 2.3.1 binaries eventually. `HiveExternalCatalogVersionsSuite` will not fail because [SPARK-24813](https://issues.apache.org/jira/browse/SPARK-24813) implements a fallback logic. However, it will cause many trials and fallbacks in all builds over `branch-2.3/branch-2.4/master`. We had better fix this issue.
## How was this patch tested?
Pass the Jenkins with the updated version.
Closes#22587 from dongjoon-hyun/SPARK-25570.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
**Description from the JIRA :**
Currently, to collect the statistics of all the columns, users need to specify the names of all the columns when calling the command "ANALYZE TABLE ... FOR COLUMNS...". This is not user friendly. Instead, we can introduce the following SQL command to achieve it without specifying the column names.
```
ANALYZE TABLE [db_name.]tablename COMPUTE STATISTICS FOR ALL COLUMNS;
```
## How was this patch tested?
Added new tests in SparkSqlParserSuite and StatisticsSuite
Closes#22566 from dilipbiswal/SPARK-25458.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
The `show create table` will show a lot of generated attributes for views that created by older Spark version. This PR will basically revert https://issues.apache.org/jira/browse/SPARK-19272 back, so when you `DESC [FORMATTED|EXTENDED] view` will show the original view DDL text.
## How was this patch tested?
Unit test.
Closes#22458 from zheyuan28/testbranch.
Lead-authored-by: Chris Zhao <chris.zhao@databricks.com>
Co-authored-by: Christopher Zhao <chris.zhao@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently, Spark has 7 `withTempPath` and 6 `withSQLConf` functions. This PR aims to remove duplicated and inconsistent code and reduce them to the following meaningful implementations.
**withTempPath**
- `SQLHelper.withTempPath`: The one which was used in `SQLTestUtils`.
**withSQLConf**
- `SQLHelper.withSQLConf`: The one which was used in `PlanTest`.
- `ExecutorSideSQLConfSuite.withSQLConf`: The one which doesn't throw `AnalysisException` on StaticConf changes.
- `SQLTestUtils.withSQLConf`: The one which overrides intentionally to change the active session.
```scala
protected override def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
SparkSession.setActiveSession(spark)
super.withSQLConf(pairs: _*)(f)
}
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#22548 from dongjoon-hyun/SPARK-25534.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The function actually exists in current selected database, and it's failed to init during `lookupFunciton`, but the exception message is:
```
This function is neither a registered temporary function nor a permanent function registered in the database 'default'.
```
This is not conducive to positioning problems. This PR fix the problem.
## How was this patch tested?
new test case + manual tests
Closes#18544 from stanzhai/fix-udf-error-message.
Authored-by: Stan Zhai <mail@stanzhai.site>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Current the file [parquetSuites.scala](f29c2b5287/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala) is not recognizable.
When I tried to find test suites for built-in Parquet conversions for Hive serde, I can only find [HiveParquetSuite](f29c2b5287/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala) in the first few minutes.
This PR is to:
1. Rename `ParquetMetastoreSuite` to `HiveParquetMetastoreSuite`, and create a single file for it.
2. Rename `ParquetSourceSuite` to `HiveParquetSourceSuite`, and create a single file for it.
3. Create a single file for `ParquetPartitioningTest`.
4. Delete `parquetSuites.scala` .
## How was this patch tested?
Unit test
Closes#22467 from gengliangwang/refactor_parquet_suites.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Currently there are two classes with the same naming BenchmarkBase:
1. `org.apache.spark.util.BenchmarkBase`
2. `org.apache.spark.sql.execution.benchmark.BenchmarkBase`
This is very confusing. And the benchmark object `org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark` is using the one in `org.apache.spark.util.BenchmarkBase`, while there is another class `BenchmarkBase` in the same package of it...
Here I propose:
1. the package `org.apache.spark.util.BenchmarkBase` should be in test package of core module. Move it to package `org.apache.spark.benchmark` .
2. Move `org.apache.spark.util.Benchmark` to test package of core module. Move it to package `org.apache.spark.benchmark` .
3. Rename the class `org.apache.spark.sql.execution.benchmark.BenchmarkBase` as `BenchmarkWithCodegen`
## How was this patch tested?
Unit test
Closes#22513 from gengliangwang/refactorBenchmarkBase.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This goes to revert sequential PRs based on some discussion and comments at https://github.com/apache/spark/pull/16677#issuecomment-422650759.
#22344#22330#22239#16677
## How was this patch tested?
Existing tests.
Closes#22481 from viirya/revert-SPARK-19355-1.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Spark supports BloomFilter creation for ORC files. This PR aims to add test coverages to prevent accidental regressions like [SPARK-12417](https://issues.apache.org/jira/browse/SPARK-12417).
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22418 from dongjoon-hyun/SPARK-25427.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
What changes were proposed in this pull request
Updated the Migration guide for the behavior changes done in the JIRA issue SPARK-23425.
How was this patch tested?
Manually verified.
Closes#22396 from sujith71955/master_newtest.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
When Hive support enabled, Hive catalog puts extra storage properties into table metadata even for DataSource tables, but we should not have them.
## How was this patch tested?
Modified a test.
Closes#22410 from ueshin/issues/SPARK-25418/hive_metadata.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR ensures to call `super.afterAll()` in `override afterAll()` method for test suites.
* Some suites did not call `super.afterAll()`
* Some suites may call `super.afterAll()` only under certain condition
* Others never call `super.afterAll()`.
This PR also ensures to call `super.beforeAll()` in `override beforeAll()` for test suites.
## How was this patch tested?
Existing UTs
Closes#22337 from kiszk/SPARK-25338.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Like `INSERT OVERWRITE DIRECTORY USING` syntax, `INSERT OVERWRITE DIRECTORY STORED AS` should not generate files with duplicate fields because Spark cannot read those files back.
**INSERT OVERWRITE DIRECTORY USING**
```scala
scala> sql("INSERT OVERWRITE DIRECTORY 'file:///tmp/parquet' USING parquet SELECT 'id', 'id2' id")
... ERROR InsertIntoDataSourceDirCommand: Failed to write to directory ...
org.apache.spark.sql.AnalysisException: Found duplicate column(s) when inserting into file:/tmp/parquet: `id`;
```
**INSERT OVERWRITE DIRECTORY STORED AS**
```scala
scala> sql("INSERT OVERWRITE DIRECTORY 'file:///tmp/parquet' STORED AS parquet SELECT 'id', 'id2' id")
// It generates corrupted files
scala> spark.read.parquet("/tmp/parquet").show
18/09/09 22:09:57 WARN DataSource: Found duplicate column(s) in the data schema and the partition schema: `id`;
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22378 from dongjoon-hyun/SPARK-25389.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
How to reproduce:
```scala
spark.sql("CREATE TABLE tbl(id long)")
spark.sql("INSERT OVERWRITE TABLE tbl VALUES 4")
spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
spark.sql(s"INSERT OVERWRITE LOCAL DIRECTORY '/tmp/spark/parquet' " +
"STORED AS PARQUET SELECT ID FROM view1")
spark.read.parquet("/tmp/spark/parquet").schema
scala> spark.read.parquet("/tmp/spark/parquet").schema
res10: org.apache.spark.sql.types.StructType = StructType(StructField(id,LongType,true))
```
The schema should be `StructType(StructField(ID,LongType,true))` as we `SELECT ID FROM view1`.
This pr fix this issue.
## How was this patch tested?
unit tests
Closes#22359 from wangyum/SPARK-25313-FOLLOW-UP.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
At Spark 2.0.0, SPARK-14335 adds some [commented-out test coverages](https://github.com/apache/spark/pull/12117/files#diff-dd4b39a56fac28b1ced6184453a47358R177
). This PR enables them because it's supported since 2.0.0.
## How was this patch tested?
Pass the Jenkins with re-enabled test coverage.
Closes#22363 from dongjoon-hyun/SPARK-25375.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Before Apache Spark 2.3, table properties were ignored when writing data to a hive table(created with STORED AS PARQUET/ORC syntax), because the compression configurations were not passed to the FileFormatWriter in hadoopConf. Then it was fixed in #20087. But actually for CTAS with USING PARQUET/ORC syntax, table properties were ignored too when convertMastore, so the test case for CTAS not supported.
Now it has been fixed in #20522 , the test case should be enabled too.
## How was this patch tested?
This only re-enables the test cases of previous PR.
Closes#22302 from fjh100456/compressionCodec.
Authored-by: fjh100456 <fu.jinhua6@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In SharedSparkSession and TestHive, we need to disable the rule ConvertToLocalRelation for better test case coverage.
## How was this patch tested?
Identify the failures after excluding "ConvertToLocalRelation" rule.
Closes#22270 from dilipbiswal/SPARK-25267-final.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
`HiveExternalCatalogVersionsSuite` Scala-2.12 test has been failing due to class path issue. It is marked as `ABORTED` because it fails at `beforeAll` during data population stage.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/
```
org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite *** ABORTED ***
Exception encountered when invoking run on a nested suite - spark-submit returned with exit code 1.
```
The root cause of the failure is that `runSparkSubmit` mixes 2.4.0-SNAPSHOT classes and old Spark (2.1.3/2.2.2/2.3.1) together during `spark-submit`. This PR aims to provide `non-test` mode execution mode to `runSparkSubmit` by removing the followings.
- SPARK_TESTING
- SPARK_SQL_TESTING
- SPARK_PREPEND_CLASSES
- SPARK_DIST_CLASSPATH
Previously, in the class path, new Spark classes are behind the old Spark classes. So, new ones are unseen. However, Spark 2.4.0 reveals this bug due to the recent data source class changes.
## How was this patch tested?
Manual test. After merging, it will be tested via Jenkins.
```scala
$ dev/change-scala-version.sh 2.12
$ build/mvn -DskipTests -Phive -Pscala-2.12 clean package
$ build/mvn -Phive -Pscala-2.12 -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite test
...
HiveExternalCatalogVersionsSuite:
- backward compatibility
...
Tests: succeeded 1, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#22340 from dongjoon-hyun/SPARK-25337.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Let's see the follow example:
```
val location = "/tmp/t"
val df = spark.range(10).toDF("id")
df.write.format("parquet").saveAsTable("tbl")
spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
spark.sql(s"CREATE TABLE tbl2(ID long) USING parquet location $location")
spark.sql("INSERT OVERWRITE TABLE tbl2 SELECT ID FROM view1")
println(spark.read.parquet(location).schema)
spark.table("tbl2").show()
```
The output column name in schema will be `id` instead of `ID`, thus the last query shows nothing from `tbl2`.
By enabling the debug message we can see that the output naming is changed from `ID` to `id`, and then the `outputColumns` in `InsertIntoHadoopFsRelationCommand` is changed in `RemoveRedundantAliases`.
**To guarantee correctness**, we should change the output columns from `Seq[Attribute]` to `Seq[String]` to avoid its names being replaced by optimizer.
I will fix project elimination related rules in https://github.com/apache/spark/pull/22311 after this one.
## How was this patch tested?
Unit test.
Closes#22320 from gengliangwang/fixOutputSchema.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In both ORC data sources, `createFilter` function has exponential time complexity due to its skewed filter tree generation. This PR aims to improve it by using new `buildTree` function.
**REPRODUCE**
```scala
// Create and read 1 row table with 1000 columns
sql("set spark.sql.orc.filterPushdown=true")
val selectExpr = (1 to 1000).map(i => s"id c$i")
spark.range(1).selectExpr(selectExpr: _*).write.mode("overwrite").orc("/tmp/orc")
print(s"With 0 filters, ")
spark.time(spark.read.orc("/tmp/orc").count)
// Increase the number of filters
(20 to 30).foreach { width =>
val whereExpr = (1 to width).map(i => s"c$i is not null").mkString(" and ")
print(s"With $width filters, ")
spark.time(spark.read.orc("/tmp/orc").where(whereExpr).count)
}
```
**RESULT**
```scala
With 0 filters, Time taken: 653 ms
With 20 filters, Time taken: 962 ms
With 21 filters, Time taken: 1282 ms
With 22 filters, Time taken: 1982 ms
With 23 filters, Time taken: 3855 ms
With 24 filters, Time taken: 6719 ms
With 25 filters, Time taken: 12669 ms
With 26 filters, Time taken: 25032 ms
With 27 filters, Time taken: 49585 ms
With 28 filters, Time taken: 98980 ms // over 1 min 38 seconds
With 29 filters, Time taken: 198368 ms // over 3 mins
With 30 filters, Time taken: 393744 ms // over 6 mins
```
**AFTER THIS PR**
```scala
With 0 filters, Time taken: 774 ms
With 20 filters, Time taken: 601 ms
With 21 filters, Time taken: 399 ms
With 22 filters, Time taken: 679 ms
With 23 filters, Time taken: 363 ms
With 24 filters, Time taken: 342 ms
With 25 filters, Time taken: 336 ms
With 26 filters, Time taken: 352 ms
With 27 filters, Time taken: 322 ms
With 28 filters, Time taken: 302 ms
With 29 filters, Time taken: 307 ms
With 30 filters, Time taken: 301 ms
```
## How was this patch tested?
Pass the Jenkins with newly added test cases.
Closes#22313 from dongjoon-hyun/SPARK-25306.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
remove test-2.10.jar and add test-2.12.jar.
## How was this patch tested?
```
$ sbt -Dscala-2.12
> ++ 2.12.6
> project hive
> testOnly *HiveSparkSubmitSuite -- -z "8489"
```
Closes#22308 from sadhen/SPARK-8489-FOLLOWUP.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
### For `SPARK-5775 read array from partitioned_parquet_with_key_and_complextypes`:
scala2.12
```
scala> (1 to 10).toString
res4: String = Range 1 to 10
```
scala2.11
```
scala> (1 to 10).toString
res2: String = Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
```
And
```
def prepareAnswer(answer: Seq[Row], isSorted: Boolean): Seq[Row] = {
val converted: Seq[Row] = answer.map(prepareRow)
if (!isSorted) converted.sortBy(_.toString()) else converted
}
```
sortBy `_.toString` is not a good idea.
### Other failures are caused by
```
Array(Int.box(1)).toSeq == Array(Double.box(1.0)).toSeq
```
It is false in 2.12.2 + and is true in 2.11.x , 2.12.0, 2.12.1
## How was this patch tested?
This is a patch on a specific unit test.
Closes#22264 from sadhen/SPARK25256.
Authored-by: 忍冬 <rendong@wacai.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Since https://github.com/apache/spark/pull/21696. Spark uses Parquet schema instead of Hive metastore schema to do pushdown.
That change can avoid wrong records returned when Hive metastore schema and parquet schema are in different letter cases. This pr add a test case for it.
More details:
https://issues.apache.org/jira/browse/SPARK-25206
## How was this patch tested?
unit tests
Closes#22267 from wangyum/SPARK-24716-TESTS.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Introduced by #21320 and #11744
```
$ sbt
> ++2.12.6
> project sql
> compile
...
[error] [warn] spark/sql/core/src/main/scala/org/apache/spark/sql/execution/ProjectionOverSchema.scala:41: match may not be exhaustive.
[error] It would fail on the following inputs: (_, ArrayType(_, _)), (_, _)
[error] [warn] getProjection(a.child).map(p => (p, p.dataType)).map {
[error] [warn]
[error] [warn] spark/sql/core/src/main/scala/org/apache/spark/sql/execution/ProjectionOverSchema.scala:52: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] getProjection(child).map(p => (p, p.dataType)).map {
[error] [warn]
...
```
And
```
$ sbt
> ++2.12.6
> project hive
> testOnly *ParquetMetastoreSuite
...
[error] /Users/rendong/wdi/spark/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala:22: object tools is not a member of package scala
[error] import scala.tools.nsc.Properties
[error] ^
[error] /Users/rendong/wdi/spark/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala:146: not found: value Properties
[error] val version = Properties.versionNumberString match {
[error] ^
[error] two errors found
...
```
## How was this patch tested?
Existing tests.
Closes#22260 from sadhen/fix_exhaustive_match.
Authored-by: 忍冬 <rendong@wacai.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
In the PR, I propose to not perform recursive parallel listening of files in the `scanPartitions` method because it can cause a deadlock. Instead of that I propose to do `scanPartitions` in parallel for top level partitions only.
## How was this patch tested?
I extended an existing test to trigger the deadlock.
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#22233 from MaxGekk/fix-recover-partitions.
## What changes were proposed in this pull request?
**Problem statement**
load data command with hdfs file paths consists of wild card strings like * are not working
eg:
"load data inpath 'hdfs://hacluster/user/ext* into table t1"
throws Analysis exception while executing this query
**Analysis -**
Currently fs.exists() API which is used for path validation in load command API cannot resolve the path with wild card pattern, To mitigate this problem i am using globStatus() API another api which can resolve the paths with hdfs supported wildcards like *,? etc(inline with hive wildcard support).
**Improvement identified as part of this issue -**
Currently system wont support wildcard character to be used for folder level path in a local file system. This PR has handled this scenario, the same globStatus API will unify the validation logic of local and non local file systems, this will ensure the behavior consistency between the hdfs and local file path in load command.
with this improvement user will be able to use a wildcard character in folder level path of a local file system in load command inline with hive behaviour, in older versions user can use wildcards only in file path of the local file system if they use in folder path system use to give an error by mentioning that not supported.
eg: load data local inpath '/localfilesystem/folder* into table t1
## How was this patch tested?
a) Manually tested by executing test-cases in HDFS yarn cluster. Reports is been attached in below section.
b) Existing test-case can verify the impact and functionality for local file path scenarios
c) A test-case is been added for verifying the functionality when wild card is been used in folder level path of a local file system
## Test Results
Note: all ip's were updated to localhost for security reasons.
HDFS path details
```
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith1
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith1/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith1/typeddata61.txt
vm1:/opt/ficlient # hadoop fs -ls /user/data/sujith2
Found 2 items
-rw-r--r-- 3 shahid hadoop 4802 2018-03-26 15:45 /user/data/sujith2/typeddata60.txt
-rw-r--r-- 3 shahid hadoop 4883 2018-03-26 15:45 /user/data/sujith2/typeddata61.txt
```
positive scenario by specifying complete file path to know about record size
```
0: jdbc:hive2://localhost:22550/default> create table wild_spark (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.217 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata60.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (4.236 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/typeddata61.txt' into table wild_spark;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.602 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from wild_spark;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (18.529 seconds)
0: jdbc:hive2://localhost:22550/default>
```
With wild card character in file path
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.409 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/type*' into table spark_withWildChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.502 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
```
with ? wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_DiffChar (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata60.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujith1/?ypeddata61.txt' into table spark_withWildChar_DiffChar;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.644 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_DiffChar;
+-----------+--+
| count(1) |
+-----------+--+
| 121 |
+-----------+--+
1 row selected (16.078 seconds)
```
with folder level wild card scenario
```
0: jdbc:hive2://localhost:22550/default> create table spark_withWildChar_folderlevel (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) row format delimited fields terminated by ',';
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (0.489 seconds)
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/suji*/*' into table spark_withWildChar_folderlevel;
+---------+--+
| Result |
+---------+--+
+---------+--+
No rows selected (1.152 seconds)
0: jdbc:hive2://localhost:22550/default> select count(*) from spark_withWildChar_folderlevel;
+-----------+--+
| count(1) |
+-----------+--+
| 242 |
+-----------+--+
1 row selected (16.078 seconds)
```
Negative scenario invalid path
```
0: jdbc:hive2://localhost:22550/default> load data inpath '/user/data/sujiinvalid*/*' into table spark_withWildChar_folder;
Error: org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /user/data/sujiinvalid*/*; (state=,code=0)
0: jdbc:hive2://localhost:22550/default>
```
Hive Test results- file level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_files (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.723 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/sujith1/type*' into table hive_withWildChar_files;
INFO : Loading data to table default.hive_withwildchar_files from hdfs://hacluster/user/sujith1/type*
No rows affected (0.682 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_files;
+------+--+
| _c0 |
+------+--+
| 121 |
+------+--+
1 row selected (50.832 seconds)
```
Hive Test results- folder level
```
0: jdbc:hive2://localhost:21066/> create table hive_withWildChar_folder (time timestamp, name string, isright boolean, datetoday date, num binary, height double, score float, decimaler decimal(10,0), id tinyint, age int, license bigint, length smallint) stored as TEXTFILE;
No rows affected (0.459 seconds)
0: jdbc:hive2://localhost:21066/> load data inpath '/user/data/suji*/*' into table hive_withWildChar_folder;
INFO : Loading data to table default.hive_withwildchar_folder from hdfs://hacluster/user/data/suji*/*
No rows affected (0.76 seconds)
0: jdbc:hive2://localhost:21066/> select count(*) from hive_withWildChar_folder;
+------+--+
| _c0 |
+------+--+
| 242 |
+------+--+
1 row selected (46.483 seconds)
```
Closes#20611 from sujith71955/master_wldcardsupport.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
[SPARK-25126] (https://issues.apache.org/jira/browse/SPARK-25126)
reports loading a large number of orc files consumes a lot of memory
in both 2.0 and 2.3. The issue is caused by creating a Reader for every
orc file in order to infer the schema.
In OrFileOperator.ReadSchema, a Reader is created for every file
although only the first valid one is used. This uses significant
amount of memory when there `paths` have a lot of files. In 2.3
a different code path (OrcUtils.readSchema) is used for inferring
schema for orc files. This commit changes both functions to create
Reader lazily.
## How was this patch tested?
Pass the Jenkins with a newly added test case by dongjoon-hyun
Closes#22157 from raofu/SPARK-25126.
Lead-authored-by: Rao Fu <rao@coupang.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Co-authored-by: Rao Fu <raofu04@gmail.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This pr proposed to show RDD/relation names in RDD/Hive table scan nodes.
This change made these names show up in the webUI and explain results.
For example;
```
scala> sql("CREATE TABLE t(c1 int) USING hive")
scala> sql("INSERT INTO t VALUES(1)")
scala> spark.table("t").explain()
== Physical Plan ==
Scan hive default.t [c1#8], HiveTableRelation `default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#8]
^^^^^^^^^^^
```
<img width="212" alt="spark-pr-hive" src="https://user-images.githubusercontent.com/692303/44501013-51264c80-a6c6-11e8-94f8-0704aee83bb6.png">
Closes#20226
## How was this patch tested?
Added tests in `DataFrameSuite`, `DatasetSuite`, and `HiveExplainSuite`
Closes#22153 from maropu/pr20226.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Tejas Patil <tejasp@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix issues arising from the fact that builtins __file__, __long__, __raw_input()__, __unicode__, __xrange()__, etc. were all removed from Python 3. __Undefined names__ have the potential to raise [NameError](https://docs.python.org/3/library/exceptions.html#NameError) at runtime.
## How was this patch tested?
* $ __python2 -m flake8 . --count --select=E9,F82 --show-source --statistics__
* $ __python3 -m flake8 . --count --select=E9,F82 --show-source --statistics__
holdenk
flake8 testing of https://github.com/apache/spark on Python 3.6.3
$ __python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics__
```
./dev/merge_spark_pr.py:98:14: F821 undefined name 'raw_input'
result = raw_input("\n%s (y/n): " % prompt)
^
./dev/merge_spark_pr.py:136:22: F821 undefined name 'raw_input'
primary_author = raw_input(
^
./dev/merge_spark_pr.py:186:16: F821 undefined name 'raw_input'
pick_ref = raw_input("Enter a branch name [%s]: " % default_branch)
^
./dev/merge_spark_pr.py:233:15: F821 undefined name 'raw_input'
jira_id = raw_input("Enter a JIRA id [%s]: " % default_jira_id)
^
./dev/merge_spark_pr.py:278:20: F821 undefined name 'raw_input'
fix_versions = raw_input("Enter comma-separated fix version(s) [%s]: " % default_fix_versions)
^
./dev/merge_spark_pr.py:317:28: F821 undefined name 'raw_input'
raw_assignee = raw_input(
^
./dev/merge_spark_pr.py:430:14: F821 undefined name 'raw_input'
pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ")
^
./dev/merge_spark_pr.py:442:18: F821 undefined name 'raw_input'
result = raw_input("Would you like to use the modified title? (y/n): ")
^
./dev/merge_spark_pr.py:493:11: F821 undefined name 'raw_input'
while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y":
^
./dev/create-release/releaseutils.py:58:16: F821 undefined name 'raw_input'
response = raw_input("%s [y/n]: " % msg)
^
./dev/create-release/releaseutils.py:152:38: F821 undefined name 'unicode'
author = unidecode.unidecode(unicode(author, "UTF-8")).strip()
^
./python/setup.py:37:11: F821 undefined name '__version__'
VERSION = __version__
^
./python/pyspark/cloudpickle.py:275:18: F821 undefined name 'buffer'
dispatch[buffer] = save_buffer
^
./python/pyspark/cloudpickle.py:807:18: F821 undefined name 'file'
dispatch[file] = save_file
^
./python/pyspark/sql/conf.py:61:61: F821 undefined name 'unicode'
if not isinstance(obj, str) and not isinstance(obj, unicode):
^
./python/pyspark/sql/streaming.py:25:21: F821 undefined name 'long'
intlike = (int, long)
^
./python/pyspark/streaming/dstream.py:405:35: F821 undefined name 'long'
return self._sc._jvm.Time(long(timestamp * 1000))
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:21:10: F821 undefined name 'xrange'
for i in xrange(50):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:22:14: F821 undefined name 'xrange'
for j in xrange(5):
^
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py:23:18: F821 undefined name 'xrange'
for k in xrange(20022):
^
20 F821 undefined name 'raw_input'
20
```
Closes#20838 from cclauss/fix-undefined-names.
Authored-by: cclauss <cclauss@bluewin.ch>
Signed-off-by: Bryan Cutler <cutlerb@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?
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?
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?
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?
This is a follow-up pr of #22014.
We still have some more compilation errors in scala-2.12 with sbt:
```
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala:493: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] val typeMatches = (targetType, f.dataType) match {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/MicroBatchExecution.scala:393: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] prevBatchOff.get.toStreamProgress(sources).foreach {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/AggUtils.scala:173: match may not be exhaustive.
[error] It would fail on the following input: AggregateExpression(_, _, false, _)
[error] [warn] val rewrittenDistinctFunctions = functionsWithDistinct.map {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/SymmetricHashJoinStateManager.scala:271: match may not be exhaustive.
[error] It would fail on the following input: (_, _)
[error] [warn] keyWithIndexToValueMetrics.customMetrics.map {
[error] [warn]
[error] [warn] /.../sql/core/src/main/scala/org/apache/spark/sql/execution/command/tables.scala:959: match may not be exhaustive.
[error] It would fail on the following input: CatalogTableType(_)
[error] [warn] val tableTypeString = metadata.tableType match {
[error] [warn]
[error] [warn] /.../sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala:923: match may not be exhaustive.
[error] It would fail on the following input: CatalogTableType(_)
[error] [warn] hiveTable.setTableType(table.tableType match {
[error] [warn]
```
## How was this patch tested?
Manually build with Scala-2.12.
Closes#22039 from ueshin/issues/SPARK-25036/fix_match.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
The design details is attached to the JIRA issue [here](https://drive.google.com/file/d/1zKm3aNZ3DpsqIuoMvRsf0kkDkXsAasxH/view)
High level overview of the changes are:
- Enhance the qualifier to be more than one string
- Add support to store the qualifier. Enhance the lookupRelation to keep the qualifier appropriately.
- Enhance the table matching column resolution algorithm to account for qualifier being more than a string.
- Enhance the table matching algorithm in UnresolvedStar.expand
- Ensure that we continue to support select t1.i1 from db1.t1
## How was this patch tested?
- New tests are added.
- Several test scenarios were added in a separate [test pr 17067](https://github.com/apache/spark/pull/17067). The tests that were not supported earlier are marked with TODO markers and those are now supported with the code changes here.
- Existing unit tests ( hive, catalyst and sql) were run successfully.
Closes#17185 from skambha/colResolution.
Authored-by: Sunitha Kambhampati <skambha@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
There are many warnings in the current build (for instance see https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
**common**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237: warning: [rawtypes] found raw type: LevelDBIterator
[warn] void closeIterator(LevelDBIterator it) throws IOException {
[warn] ^
[warn] missing type arguments for generic class LevelDBIterator<T>
[warn] where T is a type-variable:
[warn] T extends Object declared in class LevelDBIterator
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] bootstrap.group().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] bootstrap.childGroup().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112: warning: [deprecation] PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in PooledByteBufAllocator has been deprecated
[warn] return new PooledByteBufAllocator(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321: warning: [rawtypes] found raw type: Future
[warn] public void operationComplete(Future future) throws Exception {
[warn] ^
[warn] missing type arguments for generic class Future<V>
[warn] where V is a type-variable:
[warn] V extends Object declared in interface Future
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:270: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteRawChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:304: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:119: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] while (in.transfered() < in.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:120: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] in.transferTo(channel, in.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:80: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-300363099, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:84: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-1210324667, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:88: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-634919701, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
```
**launcher**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/launcher/src/main/java/org/apache/spark/launcher/AbstractLauncher.java:31: warning: [rawtypes] found raw type: AbstractLauncher
[warn] public abstract class AbstractLauncher<T extends AbstractLauncher> {
[warn] ^
[warn] missing type arguments for generic class AbstractLauncher<T>
[warn] where T is a type-variable:
[warn] T extends AbstractLauncher declared in class AbstractLauncher
```
**core**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:99: method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala💯 method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.group().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:102: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:103: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.childGroup().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:151: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] This can be achieved by adding the import clause 'import scala.language.reflectiveCalls'
[warn] or by setting the compiler option -language:reflectiveCalls.
[warn] See the Scaladoc for value scala.language.reflectiveCalls for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member value innerObject2 should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/LocalSparkContext.scala:32: constructor Slf4JLoggerFactory in class Slf4JLoggerFactory is deprecated: see corresponding Javadoc for more information.
[warn] InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory())
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:218: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] assert(wrapper.stageAttemptId === stages.head.attemptId)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:261: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:287: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:471: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.last.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:966: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskStart(SparkListenerTaskStart(dropped.stageId, dropped.attemptId, task))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:972: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskEnd(SparkListenerTaskEnd(dropped.stageId, dropped.attemptId,
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:976: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] .taskSummary(dropped.stageId, dropped.attemptId, Array(0.25d, 0.50d, 0.75d))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1146: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(1), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1150: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(0), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:197: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] while (region.transfered() < region.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:198: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] region.transferTo(byteChannel, region.transfered())
[warn] ^
```
**sql**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala:323: inferred existential type Option[Class[_$1]]( forSome { type _$1 }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val optClass = Option(collectionCls)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:226: warning: [deprecation] ParquetFileReader(Configuration,FileMetaData,Path,List<BlockMetaData>,List<ColumnDescriptor>) in ParquetFileReader has been deprecated
[warn] this.reader = new ParquetFileReader(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:178: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT32 ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:179: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT64 &&
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:181: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.FLOAT ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:182: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.DOUBLE ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:183: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.BINARY))) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:198: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:221: warning: [deprecation] getTypeLength() in ColumnDescriptor has been deprecated
[warn] readFixedLenByteArrayBatch(rowId, num, column, descriptor.getTypeLength());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:224: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new IOException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:246: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType().toString(),
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:258: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:384: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new UnsupportedOperationException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:458: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int index = rowId * accessor.OFFSET_WIDTH;
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:460: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int end = offsets.getInt(index + accessor.OFFSET_WIDTH);
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala:57: a pure expression does nothing in statement position; you may be omitting necessary parentheses
[warn] case s => s
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:182: inferred existential type org.apache.parquet.column.statistics.Statistics[?0]( forSome { type ?0 <: Comparable[?0] }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val columnStats = oneBlockColumnMeta.getStatistics
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:146: implicit conversion method conv should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] This can be achieved by adding the import clause 'import scala.language.implicitConversions'
[warn] or by setting the compiler option -language:implicitConversions.
[warn] See the Scaladoc for value scala.language.implicitConversions for a discussion
[warn] why the feature should be explicitly enabled.
[warn] implicit def conv(x: (Int, Long)): KV = KV(x._1, x._2)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/shuffle/ContinuousShuffleSuite.scala:48: implicit conversion method unsafeRow should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] private implicit def unsafeRow(value: Int) = {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:178: method getType in class ColumnDescriptor is deprecated: see corresponding Javadoc for more information.
[warn] assert(oneFooter.getFileMetaData.getSchema.getColumns.get(0).getType() ===
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala:154: method readAllFootersInParallel in object ParquetFileReader is deprecated: see corresponding Javadoc for more information.
[warn] ParquetFileReader.readAllFootersInParallel(configuration, fs.getFileStatus(path)).asScala.toSeq
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java:679: warning: [cast] redundant cast to Complex
[warn] Complex typedOther = (Complex)other;
[warn] ^
```
**mllib**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala:597: match may not be exhaustive.
[warn] It would fail on the following inputs: None, Some((x: Tuple2[?, ?] forSome x not in (?, ?)))
[warn] val df = dfs.find {
[warn] ^
```
This PR does not target fix all of them since some look pretty tricky to fix and there look too many warnings including false positive (like deprecated API but it's used in its test, etc.)
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21975 from HyukjinKwon/remove-build-warnings.
## What changes were proposed in this pull request?
This PR addresses issues 2,3 in this [document](https://docs.google.com/document/d/1fbkjEL878witxVQpOCbjlvOvadHtVjYXeB-2mgzDTvk).
* We modified the closure cleaner to identify closures that are implemented via the LambdaMetaFactory mechanism (serializedLambdas) (issue2).
* We also fix the issue due to scala/bug#11016. There are two options for solving the Unit issue, either add () at the end of the closure or use the trick described in the doc. Otherwise overloading resolution does not work (we are not going to eliminate either of the methods) here. Compiler tries to adapt to Unit and makes these two methods candidates for overloading, when there is polymorphic overloading there is no ambiguity (that is the workaround implemented). This does not look that good but it serves its purpose as we need to support two different uses for method: `addTaskCompletionListener`. One that passes a TaskCompletionListener and one that passes a closure that is wrapped with a TaskCompletionListener later on (issue3).
Note: regarding issue 1 in the doc the plan is:
> Do Nothing. Don’t try to fix this as this is only a problem for Java users who would want to use 2.11 binaries. In that case they can cast to MapFunction to be able to utilize lambdas. In Spark 3.0.0 the API should be simplified so that this issue is removed.
## How was this patch tested?
This was manually tested:
```./dev/change-scala-version.sh 2.12
./build/mvn -DskipTests -Pscala-2.12 clean package
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.serializer.ProactiveClosureSerializationSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.util.ClosureCleanerSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.streaming.DStreamClosureSuite -Dtest=None```
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21930 from skonto/scala2.12-sup.
## What changes were proposed in this pull request?
When we do an average, the result is computed dividing the sum of the values by their count. In the case the result is a DecimalType, the way we are casting/managing the precision and scale is not really optimized and it is not coherent with what we do normally.
In particular, a problem can happen when the `Divide` operand returns a result which contains a precision and scale different by the ones which are expected as output of the `Divide` operand. In the case reported in the JIRA, for instance, the result of the `Divide` operand is a `Decimal(38, 36)`, while the output data type for `Divide` is 38, 22. This is not an issue when the `Divide` is followed by a `CheckOverflow` or a `Cast` to the right data type, as these operations return a decimal with the defined precision and scale. Despite in the `Average` operator we do have a `Cast`, this may be bypassed if the result of `Divide` is the same type which it is casted to, hence the issue reported in the JIRA may arise.
The PR proposes to use the normal rules/handling of the arithmetic operators with Decimal data type, so we both reuse the existing code (having a single logic for operations between decimals) and we fix this problem as the result is always guarded by `CheckOverflow`.
## How was this patch tested?
added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21910 from mgaido91/SPARK-24957.
## What changes were proposed in this pull request?
AnalysisBarrier was introduced in SPARK-20392 to improve analysis speed (don't re-analyze nodes that have already been analyzed).
Before AnalysisBarrier, we already had some infrastructure in place, with analysis specific functions (resolveOperators and resolveExpressions). These functions do not recursively traverse down subplans that are already analyzed (with a mutable boolean flag _analyzed). The issue with the old system was that developers started using transformDown, which does a top-down traversal of the plan tree, because there was not top-down resolution function, and as a result analyzer performance became pretty bad.
In order to fix the issue in SPARK-20392, AnalysisBarrier was introduced as a special node and for this special node, transform/transformUp/transformDown don't traverse down. However, the introduction of this special node caused a lot more troubles than it solves. This implicit node breaks assumptions and code in a few places, and it's hard to know when analysis barrier would exist, and when it wouldn't. Just a simple search of AnalysisBarrier in PR discussions demonstrates it is a source of bugs and additional complexity.
Instead, this pull request removes AnalysisBarrier and reverts back to the old approach. We added infrastructure in tests that fail explicitly if transform methods are used in the analyzer.
## How was this patch tested?
Added a test suite AnalysisHelperSuite for testing the resolve* methods and transform* methods.
Author: Reynold Xin <rxin@databricks.com>
Author: Xiao Li <gatorsmile@gmail.com>
Closes#21822 from rxin/SPARK-24865.
## What changes were proposed in this pull request?
In most cases, we should use `spark.sessionState.newHadoopConf()` instead of `sparkContext.hadoopConfiguration`, so that the hadoop configurations specified in Spark session
configuration will come into effect.
Add a rule matching `spark.sparkContext.hadoopConfiguration` or `spark.sqlContext.sparkContext.hadoopConfiguration` to prevent the usage.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21873 from gengliangwang/linterRule.
## What changes were proposed in this pull request?
In the PR, I propose to extend the `StructType`/`StructField` classes by new method `toDDL` which converts a value of the `StructType`/`StructField` type to a string formatted in DDL style. The resulted string can be used in a table creation.
The `toDDL` method of `StructField` is reused in `SHOW CREATE TABLE`. In this way the PR fixes the bug of unquoted names of nested fields.
## How was this patch tested?
I add a test for checking the new method and 2 round trip tests: `fromDDL` -> `toDDL` and `toDDL` -> `fromDDL`
Author: Maxim Gekk <maxim.gekk@databricks.com>
Closes#21803 from MaxGekk/to-ddl.
## What changes were proposed in this pull request?
Last Access Time will always displayed wrong date Thu Jan 01 05:30:00 IST 1970 when user run DESC FORMATTED table command
In hive its displayed as "UNKNOWN" which makes more sense than displaying wrong date. seems to be a limitation as of now even from hive, better we can follow the hive behavior unless the limitation has been resolved from hive.
spark client output
Hive client output
## How was this patch tested?
UT has been added which makes sure that the wrong date "Thu Jan 01 05:30:00 IST 1970 "
shall not be added as value for the Last Access property
Author: s71955 <sujithchacko.2010@gmail.com>
Closes#21775 from sujith71955/master_hive.
## What changes were proposed in this pull request?
Modify the strategy in ColumnPruning to add a Project between ScriptTransformation and its child, this strategy can reduce the scan time especially in the scenario of the table has many columns.
## How was this patch tested?
Add UT in ColumnPruningSuite and ScriptTransformationSuite.
Author: Yuanjian Li <xyliyuanjian@gmail.com>
Closes#21839 from xuanyuanking/SPARK-24339.
## What changes were proposed in this pull request?
according to the context, "makeRDDForTablePartitions" in assert message should be "makeRDDForPartitionedTable", because "makeRDDForTablePartitions" does't exist in spark code.
## How was this patch tested?
unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: SongYadong <song.yadong1@zte.com.cn>
Closes#21836 from SongYadong/assert_info_modify.
## What changes were proposed in this pull request?
We get a NPE when we have a filter on a partition column of the form `col in (x, null)`. This is due to the filter converter in HiveShim not handling `null`s correctly. This patch fixes this bug while still pushing down as much of the partition pruning predicates as possible, by filtering out `null`s from any `in` predicate. Since Hive only supports very simple partition pruning filters, this change should preserve correctness.
## How was this patch tested?
Unit tests, manual tests
Author: William Sheu <william.sheu@databricks.com>
Closes#21832 from PenguinToast/partition-pruning-npe.
## What changes were proposed in this pull request?
As stated in https://github.com/apache/spark/pull/21321, in the error messages we should use `catalogString`. This is not the case, as SPARK-22893 used `simpleString` in order to have the same representation everywhere and it missed some places.
The PR unifies the messages using alway the `catalogString` representation of the dataTypes in the messages.
## How was this patch tested?
existing/modified UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21804 from mgaido91/SPARK-24268_catalog.
## What changes were proposed in this pull request?
Test HiveExternalCatalogVersionsSuite vs only current Spark releases
## How was this patch tested?
`HiveExternalCatalogVersionsSuite`
Author: Sean Owen <srowen@gmail.com>
Closes#21793 from srowen/SPARK-24813.3.
## What changes were proposed in this pull request?
Three legacy statements are removed by this patch:
- in HiveExternalCatalog: The withClient wrapper is not necessary for the private method getRawTable.
- in HiveClientImpl: There are some redundant code in both the tableExists and getTableOption method.
This PR takes over https://github.com/apache/spark/pull/20425
## How was this patch tested?
Existing tests
Closes#20425
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21780 from HyukjinKwon/SPARK-23259.
## What changes were proposed in this pull request?
Try only unique ASF mirrors to download Spark release; fall back to Apache archive if no mirrors available or release is not mirrored
## How was this patch tested?
Existing HiveExternalCatalogVersionsSuite
Author: Sean Owen <srowen@gmail.com>
Closes#21776 from srowen/SPARK-24813.
## What changes were proposed in this pull request?
Add `org.apache.derby` to `IsolatedClientLoader`, otherwise it may throw an exception:
```scala
...
[info] Cause: java.sql.SQLException: Failed to start database 'metastore_db' with class loader org.apache.spark.sql.hive.client.IsolatedClientLoader$$anon$12439ab23, see the next exception for details.
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.SQLExceptionFactory.getSQLException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.bootDatabase(Unknown Source)
[info] at org.apache.derby.impl.jdbc.EmbedConnection.<init>(Unknown Source)
[info] at org.apache.derby.jdbc.InternalDriver$1.run(Unknown Source)
...
```
## How was this patch tested?
unit tests and manual tests
Author: Yuming Wang <yumwang@ebay.com>
Closes#20944 from wangyum/SPARK-23831.
## What changes were proposed in this pull request?
This PR will cache the function name from external catalog, it is used by lookupFunctions in the analyzer, and it is cached for each query plan. The original problem is reported in the [ spark-19737](https://issues.apache.org/jira/browse/SPARK-19737)
## How was this patch tested?
create new test file LookupFunctionsSuite and add test case in SessionCatalogSuite
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20795 from kevinyu98/spark-23486.
## What changes were proposed in this pull request?
With https://github.com/apache/spark/pull/21389, data source schema is validated on driver side before launching read/write tasks.
However,
1. Putting all the validations together in `DataSourceUtils` is tricky and hard to maintain. On second thought after review, I find that the `OrcFileFormat` in hive package is not matched, so that its validation wrong.
2. `DataSourceUtils.verifyWriteSchema` and `DataSourceUtils.verifyReadSchema` is not supposed to be called in every file format. We can move them to some upper entry.
So, I propose we can add a new method `validateDataType` in FileFormat. File format implementation can override the method to specify its supported/non-supported data types.
Although we should focus on data source V2 API, `FileFormat` should remain workable for some time. Adding this new method should be helpful.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21667 from gengliangwang/refactorSchemaValidate.
## What changes were proposed in this pull request?
SPARK-22893 tried to unify error messages about dataTypes. Unfortunately, still many places were missing the `simpleString` method in other to have the same representation everywhere.
The PR unified the messages using alway the simpleString representation of the dataTypes in the messages.
## How was this patch tested?
existing/modified UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21321 from mgaido91/SPARK-24268.
## What changes were proposed in this pull request?
As mentioned in https://github.com/apache/spark/pull/21586 , `Cast.mayTruncate` is not 100% safe, string to boolean is allowed. Since changing `Cast.mayTruncate` also changes the behavior of Dataset, here I propose to add a new `Cast.canSafeCast` for partition pruning.
## How was this patch tested?
new test cases
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21712 from cloud-fan/safeCast.
## What changes were proposed in this pull request?
This pr added code to verify a schema in Json/Orc/ParquetFileFormat along with CSVFileFormat.
## How was this patch tested?
Added verification tests in `FileBasedDataSourceSuite` and `HiveOrcSourceSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21389 from maropu/SPARK-24204.
## What changes were proposed in this pull request?
Set createTime for every hive partition created in Spark SQL, which could be used to manage data lifecycle in Hive warehouse. We found that almost every partition modified by spark sql has not been set createTime.
```
mysql> select * from partitions where create_time=0 limit 1\G;
*************************** 1. row ***************************
PART_ID: 1028584
CREATE_TIME: 0
LAST_ACCESS_TIME: 1502203611
PART_NAME: date=20170130
SD_ID: 1543605
TBL_ID: 211605
LINK_TARGET_ID: NULL
1 row in set (0.27 sec)
```
## How was this patch tested?
N/A
Author: debugger87 <yangchaozhong.2009@gmail.com>
Author: Chaozhong Yang <yangchaozhong.2009@gmail.com>
Closes#18900 from debugger87/fix/set-create-time-for-hive-partition.
## What changes were proposed in this pull request?
This PR fixes possible overflow in int add or multiply. In particular, their overflows in multiply are detected by [Spotbugs](https://spotbugs.github.io/)
The following assignments may cause overflow in right hand side. As a result, the result may be negative.
```
long = int * int
long = int + int
```
To avoid this problem, this PR performs cast from int to long in right hand side.
## How was this patch tested?
Existing UTs.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21481 from kiszk/SPARK-24452.
## What changes were proposed in this pull request?
The PR updates the 2.3 version tested to the new release 2.3.1.
## How was this patch tested?
existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21543 from mgaido91/patch-1.
## What changes were proposed in this pull request?
Removing version 2.2.0 from testing versions in HiveExternalCatalogVersionsSuite as it is not present anymore in the mirrors and this is blocking all the open PRs.
## How was this patch tested?
running UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21540 from mgaido91/SPARK-24531.
## What changes were proposed in this pull request?
Sql below will get all partitions from metastore, which put much burden on metastore;
```
CREATE TABLE `partition_test`(`col` int) PARTITIONED BY (`pt` byte)
SELECT * FROM partition_test WHERE CAST(pt AS INT)=1
```
The reason is that the the analyzed attribute `dt` is wrapped in `Cast` and `HiveShim` fails to generate a proper partition filter.
This pr proposes to take `Cast` into consideration when generate partition filter.
## How was this patch tested?
Test added.
This pr proposes to use analyzed expressions in `HiveClientSuite`
Author: jinxing <jinxing6042@126.com>
Closes#19602 from jinxing64/SPARK-22384.
## What changes were proposed in this pull request?
In current parquet version,the conf ENABLE_JOB_SUMMARY is deprecated.
When writing to Parquet files, the warning message
```WARN org.apache.parquet.hadoop.ParquetOutputFormat: Setting parquet.enable.summary-metadata is deprecated, please use parquet.summary.metadata.level```
keeps showing up.
From https://github.com/apache/parquet-mr/blame/master/parquet-hadoop/src/main/java/org/apache/parquet/hadoop/ParquetOutputFormat.java#L164 we can see that we should use JOB_SUMMARY_LEVEL.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21411 from gengliangwang/summaryLevel.
## What changes were proposed in this pull request?
We reverted `spark.sql.hive.convertMetastoreOrc` at https://github.com/apache/spark/pull/20536 because we should not ignore the table-specific compression conf. Now, it's resolved via [SPARK-23355](8aa1d7b0ed).
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21186 from dongjoon-hyun/SPARK-24112.
## What changes were proposed in this pull request?
This refactors the external catalog to be an interface. It can be easier for the future work in the catalog federation. After the refactoring, `ExternalCatalog` is much cleaner without mixing the listener event generation logic.
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#21122 from gatorsmile/refactorExternalCatalog.
## What changes were proposed in this pull request?
Although [SPARK-22654](https://issues.apache.org/jira/browse/SPARK-22654) made `HiveExternalCatalogVersionsSuite` download from Apache mirrors three times, it has been flaky because it didn't verify the downloaded file. Some Apache mirrors terminate the downloading abnormally, the *corrupted* file shows the following errors.
```
gzip: stdin: not in gzip format
tar: Child returned status 1
tar: Error is not recoverable: exiting now
22:46:32.700 WARN org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite:
===== POSSIBLE THREAD LEAK IN SUITE o.a.s.sql.hive.HiveExternalCatalogVersionsSuite, thread names: Keep-Alive-Timer =====
*** RUN ABORTED ***
java.io.IOException: Cannot run program "./bin/spark-submit" (in directory "/tmp/test-spark/spark-2.2.0"): error=2, No such file or directory
```
This has been reported weirdly in two ways. For example, the above case is reported as Case 2 `no failures`.
- Case 1. [Test Result (1 failure / +1)](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/4389/)
- Case 2. [Test Result (no failures)](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.6/4811/)
This PR aims to make `HiveExternalCatalogVersionsSuite` more robust by verifying the downloaded `tgz` file by extracting and checking the existence of `bin/spark-submit`. If it turns out that the file is empty or corrupted, `HiveExternalCatalogVersionsSuite` will do retry logic like the download failure.
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#21210 from dongjoon-hyun/SPARK-23489.
## What changes were proposed in this pull request?
In this case, the partition pruning happens before the planning phase of scalar subquery expressions.
For scalar subquery expressions, the planning occurs late in the cycle (after the physical planning) in "PlanSubqueries" just before execution. Currently we try to execute the scalar subquery expression as part of partition pruning and fail as it implements Unevaluable.
The fix attempts to ignore the Subquery expressions from partition pruning computation. Another option can be to somehow plan the subqueries before the partition pruning. Since this may not be a commonly occuring expression, i am opting for a simpler fix.
Repro
``` SQL
CREATE TABLE test_prc_bug (
id_value string
)
partitioned by (id_type string)
location '/tmp/test_prc_bug'
stored as parquet;
insert into test_prc_bug values ('1','a');
insert into test_prc_bug values ('2','a');
insert into test_prc_bug values ('3','b');
insert into test_prc_bug values ('4','b');
select * from test_prc_bug
where id_type = (select 'b');
```
## How was this patch tested?
Added test in SubquerySuite and hive/SQLQuerySuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#21174 from dilipbiswal/spark-24085.
## What changes were proposed in this pull request?
Previously, SPARK-22158 fixed for `USING hive` syntax. This PR aims to fix for `STORED AS` syntax. Although the test case covers ORC part, the patch considers both `convertMetastoreOrc` and `convertMetastoreParquet`.
## How was this patch tested?
Pass newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20522 from dongjoon-hyun/SPARK-22158-2.
## What changes were proposed in this pull request?
This pr fixed code so that `cache` could prevent any jobs from being triggered.
For example, in the current master, an operation below triggers a actual job;
```
val df = spark.range(10000000000L)
.filter('id > 1000)
.orderBy('id.desc)
.cache()
```
This triggers a job while the cache should be lazy. The problem is that, when creating `InMemoryRelation`, we build the RDD, which calls `SparkPlan.execute` and may trigger jobs, like sampling job for range partitioner, or broadcast job.
This pr removed the code to build a cached `RDD` in the constructor of `InMemoryRelation` and added `CachedRDDBuilder` to lazily build the `RDD` in `InMemoryRelation`. Then, the first call of `CachedRDDBuilder.cachedColumnBuffers` triggers a job to materialize the cache in `InMemoryTableScanExec` .
## How was this patch tested?
Added tests in `CachedTableSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#21018 from maropu/SPARK-23880.
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/20988
`PhysicalOperation` can collect Project and Filters over a certain plan and substitute the alias with the original attributes in the bottom plan. We can use it in `OptimizeMetadataOnlyQuery` rule to handle the Project and Filter over partitioned relation.
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21111 from cloud-fan/refactor.
## What changes were proposed in this pull request?
This updates the OptimizeMetadataOnlyQuery rule to use filter expressions when listing partitions, if there are filter nodes in the logical plan. This avoids listing all partitions for large tables on the driver.
This also fixes a minor bug where the partitions returned from fsRelation cannot be serialized without hitting a stack level too deep error. This is caused by serializing a stream to executors, where the stream is a recursive structure. If the stream is too long, the serialization stack reaches the maximum level of depth. The fix is to create a LocalRelation using an Array instead of the incoming Seq.
## How was this patch tested?
Existing tests for metadata-only queries.
Author: Ryan Blue <blue@apache.org>
Closes#20988 from rdblue/SPARK-23877-metadata-only-push-filters.
## What changes were proposed in this pull request?
This pr address comments in https://github.com/apache/spark/pull/19868 ;
Fix the code style for `org.apache.spark.sql.hive.QueryPartitionSuite` by using:
`withTempView`, `withTempDir`, `withTable`...
Author: jinxing <jinxing6042@126.com>
Closes#21091 from jinxing64/SPARK-22676-FOLLOW-UP.
## What changes were proposed in this pull request?
In current code, it will scanning all partition paths when spark.sql.hive.verifyPartitionPath=true.
e.g. table like below:
```
CREATE TABLE `test`(
`id` int,
`age` int,
`name` string)
PARTITIONED BY (
`A` string,
`B` string)
load data local inpath '/tmp/data0' into table test partition(A='00', B='00')
load data local inpath '/tmp/data1' into table test partition(A='01', B='01')
load data local inpath '/tmp/data2' into table test partition(A='10', B='10')
load data local inpath '/tmp/data3' into table test partition(A='11', B='11')
```
If I query with SQL – "select * from test where A='00' and B='01' ", current code will scan all partition paths including '/data/A=00/B=00', '/data/A=00/B=00', '/data/A=01/B=01', '/data/A=10/B=10', '/data/A=11/B=11'. It costs much time and memory cost.
This pr proposes to avoid iterating all partition paths. Add a config `spark.files.ignoreMissingFiles` and ignore the `file not found` when `getPartitions/compute`(for hive table scan). This is much like the logic brought by
`spark.sql.files.ignoreMissingFiles`(which is for datasource scan).
## How was this patch tested?
UT
Author: jinxing <jinxing6042@126.com>
Closes#19868 from jinxing64/SPARK-22676.
## What changes were proposed in this pull request?
TableReader would get disproportionately slower as the number of columns in the query increased.
I fixed the way TableReader was looking up metadata for each column in the row. Previously, it had been looking up this data in linked lists, accessing each linked list by an index (column number). Now it looks up this data in arrays, where indexing by column number works better.
## How was this patch tested?
Manual testing
All sbt unit tests
python sql tests
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#21043 from bersprockets/tabreadfix.
## What changes were proposed in this pull request?
Currently `PartitioningAwareFileIndex` accepts an optional parameter `userPartitionSchema`. If provided, it will combine the inferred partition schema with the parameter.
However,
1. to get `userPartitionSchema`, we need to combine inferred partition schema with `userSpecifiedSchema`
2. to get the inferred partition schema, we have to create a temporary file index.
Only after that, a final version of `PartitioningAwareFileIndex` can be created.
This can be improved by passing `userSpecifiedSchema` to `PartitioningAwareFileIndex`.
With the improvement, we can reduce redundant code and avoid parsing the file partition twice.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21004 from gengliangwang/PartitioningAwareFileIndex.
## What changes were proposed in this pull request?
In the PR #20886, I mistakenly check the table location only when `ignoreIfExists` is false, which was following the original deprecated PR.
That was wrong. When `ignoreIfExists` is true and the target table doesn't exist, we should also check the table location. In other word, **`ignoreIfExists` has nothing to do with table location validation**.
This is a follow-up PR to fix the mistake.
## How was this patch tested?
Add one unit test.
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21001 from gengliangwang/SPARK-19724-followup.
## What changes were proposed in this pull request?
In TestHive, the base spark session does this in getOrCreate(), we emulate that behavior for tests.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20969 from gatorsmile/setDefault.
## What changes were proposed in this pull request?
Currently, the active spark session is set inconsistently (e.g., in createDataFrame, prior to query execution). Many places in spark also incorrectly query active session when they should be calling activeSession.getOrElse(defaultSession) and so might get None even if a Spark session exists.
The semantics here can be cleaned up if we also set the active session when the default session is set.
Related: https://github.com/apache/spark/pull/20926/files
## How was this patch tested?
Unit test, existing test. Note that if https://github.com/apache/spark/pull/20926 merges first we should also update the tests there.
Author: Eric Liang <ekl@databricks.com>
Closes#20927 from ericl/active-session-cleanup.
## What changes were proposed in this pull request?
isSharedClass returns if some classes can/should be shared or not. It checks if the classes names have some keywords or start with some names. Following the logic, it can occur unintended behaviors when a custom package has `slf4j` inside the package or class name. As I guess, the first intention seems to figure out the class containing `org.slf4j`. It would be better to change the comparison logic to `name.startsWith("org.slf4j")`
## How was this patch tested?
This patch should pass all of the current tests and keep all of the current behaviors. In my case, I'm using ProtobufDeserializer to get a table schema from hive tables. Thus some Protobuf packages and names have `slf4j` inside. Without this patch, it cannot be resolved because of ClassCastException from different classloaders.
Author: Jongyoul Lee <jongyoul@gmail.com>
Closes#20860 from jongyoul/SPARK-23743.
## What changes were proposed in this pull request?
Set default Spark session in the TestSparkSession and TestHiveSparkSession constructors.
## How was this patch tested?
new unit tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#20926 from jose-torres/test3.
## What changes were proposed in this pull request?
Currently, some tests have an assumption that `spark.sql.sources.default=parquet`. In fact, that is a correct assumption, but that assumption makes it difficult to test new data source format.
This PR aims to
- Improve test suites more robust and makes it easy to test new data sources in the future.
- Test new native ORC data source with the full existing Apache Spark test coverage.
As an example, the PR uses `spark.sql.sources.default=orc` during reviews. The value should be `parquet` when this PR is accepted.
## How was this patch tested?
Pass the Jenkins with updated tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20705 from dongjoon-hyun/SPARK-23553.
## What changes were proposed in this pull request?
This PR fixes a minor issue in `HadoopFsRelationTest`, that you should create table using `dataSourceName` instead of `parquet`. The issue won't affect the correctness, but it will generate wrong error message in case the test fails.
## How was this patch tested?
Exsiting tests.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20780 from jiangxb1987/dataSourceName.
A few different things going on:
- Remove unused methods.
- Move JSON methods to the only class that uses them.
- Move test-only methods to TestUtils.
- Make getMaxResultSize() a config constant.
- Reuse functionality from existing libraries (JRE or JavaUtils) where possible.
The change also includes changes to a few tests to call `Utils.createTempFile` correctly,
so that temp dirs are created under the designated top-level temp dir instead of
potentially polluting git index.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20706 from vanzin/SPARK-23550.
## What changes were proposed in this pull request?
Add Spark 2.3.0 in HiveExternalCatalogVersionsSuite since Spark 2.3.0 is released for ensuring backward compatibility.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20720 from gatorsmile/add2.3.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18944 added one patch, which allowed a spark session to be created when the hive metastore server is down. However, it did not allow running any commands with the spark session. This brings troubles to the user who only wants to read / write data frames without metastore setup.
## How was this patch tested?
Added some unit tests to read and write data frames based on the original HiveMetastoreLazyInitializationSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20681 from liufengdb/completely-lazy.
## What changes were proposed in this pull request?
This is based on https://github.com/apache/spark/pull/20668 for supporting Hive 2.2 and Hive 2.3 metastore.
When we merge the PR, we should give the major credit to wangyum
## How was this patch tested?
Added the test cases
Author: Yuming Wang <yumwang@ebay.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20671 from gatorsmile/pr-20668.
## What changes were proposed in this pull request?
A few places in `spark-sql` were using `sc.hadoopConfiguration` directly. They should be using `sessionState.newHadoopConf()` to blend in configs that were set through `SQLConf`.
Also, for better UX, for these configs blended in from `SQLConf`, we should consider removing the `spark.hadoop` prefix, so that the settings are recognized whether or not they were specified by the user.
## How was this patch tested?
Tested that AlterTableRecoverPartitions now correctly recognizes settings that are passed in to the FileSystem through SQLConf.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#20679 from juliuszsompolski/SPARK-23514.
## What changes were proposed in this pull request?
Refactor ColumnStat to be more flexible.
* Split `ColumnStat` and `CatalogColumnStat` just like `CatalogStatistics` is split from `Statistics`. This detaches how the statistics are stored from how they are processed in the query plan. `CatalogColumnStat` keeps `min` and `max` as `String`, making it not depend on dataType information.
* For `CatalogColumnStat`, parse column names from property names in the metastore (`KEY_VERSION` property), not from metastore schema. This means that `CatalogColumnStat`s can be created for columns even if the schema itself is not stored in the metastore.
* Make all fields optional. `min`, `max` and `histogram` for columns were optional already. Having them all optional is more consistent, and gives flexibility to e.g. drop some of the fields through transformations if they are difficult / impossible to calculate.
The added flexibility will make it possible to have alternative implementations for stats, and separates stats collection from stats and estimation processing in plans.
## How was this patch tested?
Refactored existing tests to work with refactored `ColumnStat` and `CatalogColumnStat`.
New tests added in `StatisticsSuite` checking that backwards / forwards compatibility is not broken.
Author: Juliusz Sompolski <julek@databricks.com>
Closes#20624 from juliuszsompolski/SPARK-23445.
## What changes were proposed in this pull request?
This PR updates Apache ORC dependencies to 1.4.3 released on February 9th. Apache ORC 1.4.2 release removes unnecessary dependencies and 1.4.3 has 5 more patches (https://s.apache.org/Fll8).
Especially, the following ORC-285 is fixed at 1.4.3.
```scala
scala> val df = Seq(Array.empty[Float]).toDF()
scala> df.write.format("orc").save("/tmp/floatarray")
scala> spark.read.orc("/tmp/floatarray")
res1: org.apache.spark.sql.DataFrame = [value: array<float>]
scala> spark.read.orc("/tmp/floatarray").show()
18/02/12 22:09:10 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
java.io.IOException: Error reading file: file:/tmp/floatarray/part-00000-9c0b461b-4df1-4c23-aac1-3e4f349ac7d6-c000.snappy.orc
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1191)
at org.apache.orc.mapreduce.OrcMapreduceRecordReader.ensureBatch(OrcMapreduceRecordReader.java:78)
...
Caused by: java.io.EOFException: Read past EOF for compressed stream Stream for column 2 kind DATA position: 0 length: 0 range: 0 offset: 0 limit: 0
```
## How was this patch tested?
Pass the Jenkins test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20511 from dongjoon-hyun/SPARK-23340.
## What changes were proposed in this pull request?
This replaces `Sparkcurrently` to `Spark currently` in the following error message.
```scala
scala> sql("insert into t2 select * from v1")
org.apache.spark.sql.AnalysisException: Output Hive table `default`.`t2`
is bucketed but Sparkcurrently does NOT populate bucketed ...
```
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20617 from dongjoon-hyun/SPARK-ERROR-MSG.
## What changes were proposed in this pull request?
This PR is to revert the PR https://github.com/apache/spark/pull/20302, because it causes a regression.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20614 from gatorsmile/revertJsonFix.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/19579 introduces a behavior change. We need to document it in the migration guide.
## How was this patch tested?
Also update the HiveExternalCatalogVersionsSuite to verify it.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20606 from gatorsmile/addMigrationGuide.
## What changes were proposed in this pull request?
If the target database name is as same as the current database, we should be able to skip one metastore access.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20565 from liufengdb/remove-redundant.
When hive.default.fileformat is other kinds of file types, create textfile table cause a serde error.
We should take the default type of textfile and sequencefile both as org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe.
```
set hive.default.fileformat=orc;
create table tbl( i string ) stored as textfile;
desc formatted tbl;
Serde Library org.apache.hadoop.hive.ql.io.orc.OrcSerde
InputFormat org.apache.hadoop.mapred.TextInputFormat
OutputFormat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
```
Author: sychen <sychen@ctrip.com>
Closes#20406 from cxzl25/default_serde.
## What changes were proposed in this pull request?
This removes the special case that `alterPartitions` call from `HiveExternalCatalog` can reset the current database in the hive client as a side effect.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20564 from liufengdb/move.
## What changes were proposed in this pull request?
This is a regression in Spark 2.3.
In Spark 2.2, we have a fragile UI support for SQL data writing commands. We only track the input query plan of `FileFormatWriter` and display its metrics. This is not ideal because we don't know who triggered the writing(can be table insertion, CTAS, etc.), but it's still useful to see the metrics of the input query.
In Spark 2.3, we introduced a new mechanism: `DataWritigCommand`, to fix the UI issue entirely. Now these writing commands have real children, and we don't need to hack into the `FileFormatWriter` for the UI. This also helps with `explain`, now `explain` can show the physical plan of the input query, while in 2.2 the physical writing plan is simply `ExecutedCommandExec` and it has no child.
However there is a regression in CTAS. CTAS commands don't extend `DataWritigCommand`, and we don't have the UI hack in `FileFormatWriter` anymore, so the UI for CTAS is just an empty node. See https://issues.apache.org/jira/browse/SPARK-22977 for more information about this UI issue.
To fix it, we should apply the `DataWritigCommand` mechanism to CTAS commands.
TODO: In the future, we should refactor this part and create some physical layer code pieces for data writing, and reuse them in different writing commands. We should have different logical nodes for different operators, even some of them share some same logic, e.g. CTAS, CREATE TABLE, INSERT TABLE. Internally we can share the same physical logic.
## How was this patch tested?
manually tested.
For data source table
<img width="644" alt="1" src="https://user-images.githubusercontent.com/3182036/35874155-bdffab28-0ba6-11e8-94a8-e32e106ba069.png">
For hive table
<img width="666" alt="2" src="https://user-images.githubusercontent.com/3182036/35874161-c437e2a8-0ba6-11e8-98ed-7930f01432c5.png">
Author: Wenchen Fan <wenchen@databricks.com>
Closes#20521 from cloud-fan/UI.
## What changes were proposed in this pull request?
Currently, we use SBT and MAVN to spark unit test, are affected by the parameters of `spark.testing`. However, when using the IDE test tool, `spark.testing` support is not very good, sometimes need to be manually added to the beforeEach. example: HiveSparkSubmitSuite RPackageUtilsSuite SparkSubmitSuite. The PR unified `spark.testing` parameter extraction to SparkFunSuite, support IDE test tool, and the test code is more compact.
## How was this patch tested?
the existed test cases.
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#20582 from heary-cao/sparktesting.
## What changes were proposed in this pull request?
This is a follow up of https://github.com/apache/spark/pull/20441.
The two lines actually can trigger the hive metastore bug: https://issues.apache.org/jira/browse/HIVE-16844
The two configs are not in the default `ObjectStore` properties, so any run hive commands after these two lines will set the `propsChanged` flag in the `ObjectStore.setConf` and then cause thread leaks.
I don't think the two lines are very useful. They can be removed safely.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20562 from liufengdb/fix-omm.
## What changes were proposed in this pull request?
Typo fixes (with expanding a Hive property)
## How was this patch tested?
local build. Awaiting Jenkins
Author: Jacek Laskowski <jacek@japila.pl>
Closes#20550 from jaceklaskowski/hiveutils-typos.
## What changes were proposed in this pull request?
This is to revert the changes made in https://github.com/apache/spark/pull/19499 , because this causes a regression. We should not ignore the table-specific compression conf when the Hive serde tables are converted to the data source tables.
## How was this patch tested?
The existing tests.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20536 from gatorsmile/revert22279.
## What changes were proposed in this pull request?
SpecifiedWindowFrame.defaultWindowFrame(hasOrderSpecification, acceptWindowFrame) was designed to handle the cases when some Window functions don't support setting a window frame (e.g. rank). However this param is never used.
We may inline the whole of this function to simplify the code.
## How was this patch tested?
Existing tests.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20463 from jiangxb1987/defaultWindowFrame.
Signed-off-by: Atallah Hezbor <atallahhezborgmail.com>
## What changes were proposed in this pull request?
This PR proposes modifying the match statement that gets the columns of a row in HiveThriftServer. There was previously no case for `UserDefinedType`, so querying a table that contained them would throw a match error. The changes catch that case and return the string representation.
## How was this patch tested?
While I would have liked to add a unit test, I couldn't easily incorporate UDTs into the ``HiveThriftServer2Suites`` pipeline. With some guidance I would be happy to push a commit with tests.
Instead I did a manual test by loading a `DataFrame` with Point UDT in a spark shell with a HiveThriftServer. Then in beeline, connecting to the server and querying that table.
Here is the result before the change
```
0: jdbc:hive2://localhost:10000> select * from chicago;
Error: scala.MatchError: org.apache.spark.sql.PointUDT2d980dc3 (of class org.apache.spark.sql.PointUDT) (state=,code=0)
```
And after the change:
```
0: jdbc:hive2://localhost:10000> select * from chicago;
+---------------------------------------+--------------+------------------------+---------------------+--+
| __fid__ | case_number | dtg | geom |
+---------------------------------------+--------------+------------------------+---------------------+--+
| 109602f9-54f8-414b-8c6f-42b1a337643e | 2 | 2016-01-01 19:00:00.0 | POINT (-77 38) |
| 709602f9-fcff-4429-8027-55649b6fd7ed | 1 | 2015-12-31 19:00:00.0 | POINT (-76.5 38.5) |
| 009602f9-fcb5-45b1-a867-eb8ba10cab40 | 3 | 2016-01-02 19:00:00.0 | POINT (-78 39) |
+---------------------------------------+--------------+------------------------+---------------------+--+
```
Author: Atallah Hezbor <atallahhezbor@gmail.com>
Closes#20385 from atallahhezbor/udts_over_hive.
## What changes were proposed in this pull request?
Like Parquet, ORC test suites should enable UDT tests.
## How was this patch tested?
Pass the Jenkins with newly enabled test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20440 from dongjoon-hyun/SPARK-23276.
## What changes were proposed in this pull request?
hive tests have been failing when they are run locally (Mac Os) after a recent change in the trunk. After running the tests for some time, the test fails with OOM with Error: unable to create new native thread.
I noticed the thread count goes all the way up to 2000+ after which we start getting these OOM errors. Most of the threads seem to be related to the connection pool in hive metastore (BoneCP-xxxxx-xxxx ). This behaviour change is happening after we made the following change to HiveClientImpl.reset()
``` SQL
def reset(): Unit = withHiveState {
try {
// code
} finally {
runSqlHive("USE default") ===> this is causing the issue
}
```
I am proposing to temporarily back-out part of a fix made to address SPARK-23000 to resolve this issue while we work-out the exact reason for this sudden increase in thread counts.
## How was this patch tested?
Ran hive/test multiple times in different machines.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#20441 from dilipbiswal/hive_tests.
## What changes were proposed in this pull request?
It is a common pattern to apply multiple transforms to a `Dataset` (using `Dataset.withColumn` for example. This is currently quite expensive because we run `CheckAnalysis` on the full plan and create an encoder for each intermediate `Dataset`.
This PR extends the usage of the `AnalysisBarrier` to include `CheckAnalysis`. By doing this we hide the already analyzed plan from `CheckAnalysis` because barrier is a `LeafNode`. The `AnalysisBarrier` is in the `FinishAnalysis` phase of the optimizer.
We also make binding the `Dataset` encoder lazy. The bound encoder is only needed when we materialize the dataset.
## How was this patch tested?
Existing test should cover this.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#20402 from hvanhovell/SPARK-23223.
## What changes were proposed in this pull request?
Correct some improper with view related method usage
Only change test cases
like:
```
test("list global temp views") {
try {
sql("CREATE GLOBAL TEMP VIEW v1 AS SELECT 3, 4")
sql("CREATE TEMP VIEW v2 AS SELECT 1, 2")
checkAnswer(sql(s"SHOW TABLES IN $globalTempDB"),
Row(globalTempDB, "v1", true) ::
Row("", "v2", true) :: Nil)
assert(spark.catalog.listTables(globalTempDB).collect().toSeq.map(_.name) == Seq("v1", "v2"))
} finally {
spark.catalog.dropTempView("v1")
spark.catalog.dropGlobalTempView("v2")
}
}
```
other change please review the code.
## How was this patch tested?
See test case.
Author: xubo245 <601450868@qq.com>
Closes#20250 from xubo245/DropTempViewError.
## What changes were proposed in this pull request?
**Proposal**
Add a per-query ID to the codegen stages as represented by `WholeStageCodegenExec` operators. This ID will be used in
- the explain output of the physical plan, and in
- the generated class name.
Specifically, this ID will be stable within a query, counting up from 1 in depth-first post-order for all the `WholeStageCodegenExec` inserted into a plan.
The ID value 0 is reserved for "free-floating" `WholeStageCodegenExec` objects, which may have been created for one-off purposes, e.g. for fallback handling of codegen stages that failed to codegen the whole stage and wishes to codegen a subset of the children operators (as seen in `org.apache.spark.sql.execution.FileSourceScanExec#doExecute`).
Example: for the following query:
```scala
scala> spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 1)
scala> val df1 = spark.range(10).select('id as 'x, 'id + 1 as 'y).orderBy('x).select('x + 1 as 'z, 'y)
df1: org.apache.spark.sql.DataFrame = [z: bigint, y: bigint]
scala> val df2 = spark.range(5)
df2: org.apache.spark.sql.Dataset[Long] = [id: bigint]
scala> val query = df1.join(df2, 'z === 'id)
query: org.apache.spark.sql.DataFrame = [z: bigint, y: bigint ... 1 more field]
```
The explain output before the change is:
```scala
scala> query.explain
== Physical Plan ==
*SortMergeJoin [z#9L], [id#13L], Inner
:- *Sort [z#9L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(z#9L, 200)
: +- *Project [(x#3L + 1) AS z#9L, y#4L]
: +- *Sort [x#3L ASC NULLS FIRST], true, 0
: +- Exchange rangepartitioning(x#3L ASC NULLS FIRST, 200)
: +- *Project [id#0L AS x#3L, (id#0L + 1) AS y#4L]
: +- *Range (0, 10, step=1, splits=8)
+- *Sort [id#13L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#13L, 200)
+- *Range (0, 5, step=1, splits=8)
```
Note how codegen'd operators are annotated with a prefix `"*"`. See how the `SortMergeJoin` operator and its direct children `Sort` operators are adjacent and all annotated with the `"*"`, so it's hard to tell they're actually in separate codegen stages.
and after this change it'll be:
```scala
scala> query.explain
== Physical Plan ==
*(6) SortMergeJoin [z#9L], [id#13L], Inner
:- *(3) Sort [z#9L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(z#9L, 200)
: +- *(2) Project [(x#3L + 1) AS z#9L, y#4L]
: +- *(2) Sort [x#3L ASC NULLS FIRST], true, 0
: +- Exchange rangepartitioning(x#3L ASC NULLS FIRST, 200)
: +- *(1) Project [id#0L AS x#3L, (id#0L + 1) AS y#4L]
: +- *(1) Range (0, 10, step=1, splits=8)
+- *(5) Sort [id#13L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#13L, 200)
+- *(4) Range (0, 5, step=1, splits=8)
```
Note that the annotated prefix becomes `"*(id) "`. See how the `SortMergeJoin` operator and its direct children `Sort` operators have different codegen stage IDs.
It'll also show up in the name of the generated class, as a suffix in the format of `GeneratedClass$GeneratedIterator$id`.
For example, note how `GeneratedClass$GeneratedIteratorForCodegenStage3` and `GeneratedClass$GeneratedIteratorForCodegenStage6` in the following stack trace corresponds to the IDs shown in the explain output above:
```
"Executor task launch worker for task 42412957" daemon prio=5 tid=0x58 nid=NA runnable
java.lang.Thread.State: RUNNABLE
at org.apache.spark.sql.execution.UnsafeExternalRowSorter.insertRow(UnsafeExternalRowSorter.java:109)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.sort_addToSorter$(generated.java:32)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(generated.java:41)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$9$$anon$1.hasNext(WholeStageCodegenExec.scala:494)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage6.findNextInnerJoinRows$(generated.java:42)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage6.processNext(generated.java:101)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$2.hasNext(WholeStageCodegenExec.scala:513)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:828)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:828)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
```
**Rationale**
Right now, the codegen from Spark SQL lacks the means to differentiate between a couple of things:
1. It's hard to tell which physical operators are in the same WholeStageCodegen stage. Note that this "stage" is a separate notion from Spark's RDD execution stages; this one is only to delineate codegen units.
There can be adjacent physical operators that are both codegen'd but are in separate codegen stages. Some of this is due to hacky implementation details, such as the case with `SortMergeJoin` and its `Sort` inputs -- they're hard coded to be split into separate stages although both are codegen'd.
When printing out the explain output of the physical plan, you'd only see the codegen'd physical operators annotated with a preceding star (`'*'`) but would have no way to figure out if they're in the same stage.
2. Performance/error diagnosis
The generated code has class/method names that are hard to differentiate between queries or even between codegen stages within the same query. If we use a Java-level profiler to collect profiles, or if we encounter a Java-level exception with a stack trace in it, it's really hard to tell which part of a query it's at.
By introducing a per-query codegen stage ID, we'd at least be able to know which codegen stage (and in turn, which group of physical operators) was a profile tick or an exception happened.
The reason why this proposal uses a per-query ID is because it's stable within a query, so that multiple runs of the same query will see the same resulting IDs. This both benefits understandability for users, and also it plays well with the codegen cache in Spark SQL which uses the generated source code as the key.
The downside to using per-query IDs as opposed to a per-session or globally incrementing ID is of course we can't tell apart different query runs with this ID alone. But for now I believe this is a good enough tradeoff.
## How was this patch tested?
Existing tests. This PR does not involve any runtime behavior changes other than some name changes.
The SQL query test suites that compares explain outputs have been updates to ignore the newly added `codegenStageId`.
Author: Kris Mok <kris.mok@databricks.com>
Closes#20224 from rednaxelafx/wsc-codegenstageid.
Because the call to the constructor of HiveClientImpl crosses class loader
boundaries, different versions of the same class (Configuration in this
case) were loaded, and that caused a runtime error when instantiating the
client. By using a safer type in the signature of the constructor, it's
possible to avoid the problem.
I considered removing 'sharesHadoopClasses', but it may still be desired
(even though there are 0 users of it since it was not working). When Spark
starts to support Hadoop 3, it may be necessary to use that option to
load clients for older Hive metastore versions that don't know about
Hadoop 3.
Tested with added unit test.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20169 from vanzin/SPARK-17088.
## What changes were proposed in this pull request?
Typo fixes
## How was this patch tested?
Local build / Doc-only changes
Author: Jacek Laskowski <jacek@japila.pl>
Closes#20344 from jaceklaskowski/typo-fixes.
## What changes were proposed in this pull request?
Revert the unneeded test case changes we made in SPARK-23000
Also fixes the test suites that do not call `super.afterAll()` in the local `afterAll`. The `afterAll()` of `TestHiveSingleton` actually reset the environments.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20341 from gatorsmile/testRelated.
[SPARK-21786][SQL] The 'spark.sql.parquet.compression.codec' and 'spark.sql.orc.compression.codec' configuration doesn't take effect on hive table writing
What changes were proposed in this pull request?
Pass ‘spark.sql.parquet.compression.codec’ value to ‘parquet.compression’.
Pass ‘spark.sql.orc.compression.codec’ value to ‘orc.compress’.
How was this patch tested?
Add test.
Note:
This is the same issue mentioned in #19218 . That branch was deleted mistakenly, so make a new pr instead.
gatorsmile maropu dongjoon-hyun discipleforteen
Author: fjh100456 <fu.jinhua6@zte.com.cn>
Author: Takeshi Yamamuro <yamamuro@apache.org>
Author: Wenchen Fan <wenchen@databricks.com>
Author: gatorsmile <gatorsmile@gmail.com>
Author: Yinan Li <liyinan926@gmail.com>
Author: Marcelo Vanzin <vanzin@cloudera.com>
Author: Juliusz Sompolski <julek@databricks.com>
Author: Felix Cheung <felixcheung_m@hotmail.com>
Author: jerryshao <sshao@hortonworks.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: Gera Shegalov <gera@apache.org>
Author: chetkhatri <ckhatrimanjal@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Bago Amirbekian <bago@databricks.com>
Author: Xianjin YE <advancedxy@gmail.com>
Author: Bruce Robbins <bersprockets@gmail.com>
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Author: Kent Yao <yaooqinn@hotmail.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Adrian Ionescu <adrian@databricks.com>
Closes#20087 from fjh100456/HiveTableWriting.
## What changes were proposed in this pull request?
After session cloning in `TestHive`, the conf of the singleton SparkContext for derby DB location is changed to a new directory. The new directory is created in `HiveUtils.newTemporaryConfiguration(useInMemoryDerby = false)`.
This PR is to keep the conf value of `ConfVars.METASTORECONNECTURLKEY.varname` unchanged during the session clone.
## How was this patch tested?
The issue can be reproduced by the command:
> build/sbt -Phive "hive/test-only org.apache.spark.sql.hive.HiveSessionStateSuite org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite"
Also added a test case.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20328 from gatorsmile/fixTestFailure.
## What changes were proposed in this pull request?
There were two related fixes regarding `from_json`, `get_json_object` and `json_tuple` ([Fix#1](c8803c0685),
[Fix#2](86174ea89b)), but they weren't comprehensive it seems. I wanted to extend those fixes to all the parsers, and add tests for each case.
## How was this patch tested?
Regression tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#20302 from brkyvz/json-invfix.
## What changes were proposed in this pull request?
`DataSourceV2Strategy` is missing in `HiveSessionStateBuilder`'s planner, which will throw exception as described in [SPARK-23140](https://issues.apache.org/jira/browse/SPARK-23140).
## How was this patch tested?
Manual test.
Author: jerryshao <sshao@hortonworks.com>
Closes#20305 from jerryshao/SPARK-23140.
## What changes were proposed in this pull request?
Lots of our tests don't properly shutdown everything they create, and end up leaking lots of threads. For example, `TaskSetManagerSuite` doesn't stop the extra `TaskScheduler` and `DAGScheduler` it creates. There are a couple more instances, eg. in `DAGSchedulerSuite`.
This PR adds the possibility to print out the not properly stopped thread list after a test suite executed. The format is the following:
```
===== FINISHED o.a.s.scheduler.DAGSchedulerSuite: 'task end event should have updated accumulators (SPARK-20342)' =====
...
===== Global thread whitelist loaded with name /thread_whitelist from classpath: rpc-client.*, rpc-server.*, shuffle-client.*, shuffle-server.*' =====
ScalaTest-run:
===== THREADS NOT STOPPED PROPERLY =====
ScalaTest-run: dag-scheduler-event-loop
ScalaTest-run: globalEventExecutor-2-5
ScalaTest-run:
===== END OF THREAD DUMP =====
ScalaTest-run:
===== EITHER PUT THREAD NAME INTO THE WHITELIST FILE OR SHUT IT DOWN PROPERLY =====
```
With the help of this leaking threads has been identified in TaskSetManagerSuite. My intention is to hunt down and fix such bugs in later PRs.
## How was this patch tested?
Manual: TaskSetManagerSuite test executed and found out where are the leaking threads.
Automated: Pass the Jenkins.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#19893 from gaborgsomogyi/SPARK-16139.
## What changes were proposed in this pull request?
In another attempt to fix DataSourceWithHiveMetastoreCatalogSuite, this patch uses qualified table names (`default.t`) in the individual tests.
## How was this patch tested?
N/A (Test Only Change)
Author: Sameer Agarwal <sameerag@apache.org>
Closes#20273 from sameeragarwal/flaky-test.
## What changes were proposed in this pull request?
When `spark.sql.files.ignoreCorruptFiles=true`, we should ignore corrupted ORC files.
## How was this patch tested?
Pass the Jenkins with a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20240 from dongjoon-hyun/SPARK-23049.
## What changes were proposed in this pull request?
`AnalysisBarrier` in the current master cuts off explain results for parsed logical plans;
```
scala> Seq((1, 1)).toDF("a", "b").groupBy("a").count().sample(0.1).explain(true)
== Parsed Logical Plan ==
Sample 0.0, 0.1, false, -7661439431999668039
+- AnalysisBarrier Aggregate [a#5], [a#5, count(1) AS count#14L]
```
To fix this, `AnalysisBarrier` needs to override `innerChildren` and this pr changed the output to;
```
== Parsed Logical Plan ==
Sample 0.0, 0.1, false, -5086223488015741426
+- AnalysisBarrier
+- Aggregate [a#5], [a#5, count(1) AS count#14L]
+- Project [_1#2 AS a#5, _2#3 AS b#6]
+- LocalRelation [_1#2, _2#3]
```
## How was this patch tested?
Added tests in `DataFrameSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20247 from maropu/SPARK-23021-2.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
The Spark 2.3 branch still failed due to the flaky test suite `DataSourceWithHiveMetastoreCatalogSuite `. https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-2.3-test-sbt-hadoop-2.6/
Although https://github.com/apache/spark/pull/20207 is unable to reproduce it in Spark 2.3, it sounds like the current DB of Spark's Catalog is changed based on the following stacktrace. Thus, we just need to reset it.
```
[info] DataSourceWithHiveMetastoreCatalogSuite:
02:40:39.486 ERROR org.apache.hadoop.hive.ql.parse.CalcitePlanner: org.apache.hadoop.hive.ql.parse.SemanticException: Line 1:14 Table not found 't'
at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.getMetaData(SemanticAnalyzer.java:1594)
at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.getMetaData(SemanticAnalyzer.java:1545)
at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.genResolvedParseTree(SemanticAnalyzer.java:10077)
at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.analyzeInternal(SemanticAnalyzer.java:10128)
at org.apache.hadoop.hive.ql.parse.CalcitePlanner.analyzeInternal(CalcitePlanner.java:209)
at org.apache.hadoop.hive.ql.parse.BaseSemanticAnalyzer.analyze(BaseSemanticAnalyzer.java:227)
at org.apache.hadoop.hive.ql.Driver.compile(Driver.java:424)
at org.apache.hadoop.hive.ql.Driver.compile(Driver.java:308)
at org.apache.hadoop.hive.ql.Driver.compileInternal(Driver.java:1122)
at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1170)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1059)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:1049)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:694)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:683)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:272)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:210)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:209)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:255)
at org.apache.spark.sql.hive.client.HiveClientImpl.runHive(HiveClientImpl.scala:683)
at org.apache.spark.sql.hive.client.HiveClientImpl.runSqlHive(HiveClientImpl.scala:673)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$9$$anonfun$apply$1$$anonfun$apply$mcV$sp$3.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:185)
at org.apache.spark.sql.test.SQLTestUtilsBase$class.withTable(SQLTestUtils.scala:273)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.withTable(HiveMetastoreCatalogSuite.scala:139)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$9$$anonfun$apply$1.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:163)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$9$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:163)
at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$9$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:163)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:68)
at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:183)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:196)
at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:196)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:289)
at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:196)
at org.scalatest.FunSuite.runTest(FunSuite.scala:1560)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:229)
at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:229)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:396)
at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:384)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:384)
at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:379)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:461)
at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:229)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1560)
at org.scalatest.Suite$class.run(Suite.scala:1147)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1560)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:233)
at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:233)
at org.scalatest.SuperEngine.runImpl(Engine.scala:521)
at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:233)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:31)
at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:210)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:31)
at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:314)
at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:480)
at sbt.ForkMain$Run$2.call(ForkMain.java:296)
at sbt.ForkMain$Run$2.call(ForkMain.java:286)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
```
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20218 from gatorsmile/testFixAgain.
## What changes were proposed in this pull request?
Two unit test will fail due to Windows format path:
1.test(s"$version: read avro file containing decimal")
```
org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
2.test(s"$version: SPARK-17920: Insert into/overwrite avro table")
```
Unable to infer the schema. The schema specification is required to create the table `default`.`tab2`.;
org.apache.spark.sql.AnalysisException: Unable to infer the schema. The schema specification is required to create the table `default`.`tab2`.;
```
This pr fix these two unit test by change Windows path into URI path.
## How was this patch tested?
Existed.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: wuyi5 <ngone_5451@163.com>
Closes#20199 from Ngone51/SPARK-22967.
## What changes were proposed in this pull request?
When users' DB description is NULL, users might hit `NullPointerException`. This PR is to fix the issue.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20215 from gatorsmile/SPARK-23001.
## What changes were proposed in this pull request?
This is mostly from https://github.com/apache/spark/pull/13775
The wrapper solution is pretty good for string/binary type, as the ORC column vector doesn't keep bytes in a continuous memory region, and has a significant overhead when copying the data to Spark columnar batch. For other cases, the wrapper solution is almost same with the current solution.
I think we can treat the wrapper solution as a baseline and keep improving the writing to Spark solution.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#20205 from cloud-fan/orc.
## What changes were proposed in this pull request?
This PR adds an ORC columnar-batch reader to native `OrcFileFormat`. Since both Spark `ColumnarBatch` and ORC `RowBatch` are used together, it is faster than the current Spark implementation. This replaces the prior PR, #17924.
Also, this PR adds `OrcReadBenchmark` to show the performance improvement.
## How was this patch tested?
Pass the existing test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19943 from dongjoon-hyun/SPARK-16060.
## What changes were proposed in this pull request?
https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-2.3-test-sbt-hadoop-2.6/
The test suite DataSourceWithHiveMetastoreCatalogSuite of Branch 2.3 always failed in hadoop 2.6
The table `t` exists in `default`, but `runSQLHive` reported the table does not exist. Obviously, Hive client's default database is different. The fix is to clean the environment and use `DEFAULT` as the database.
```
org.apache.spark.sql.execution.QueryExecutionException: FAILED: SemanticException [Error 10001]: Line 1:14 Table not found 't'
Stacktrace
sbt.ForkMain$ForkError: org.apache.spark.sql.execution.QueryExecutionException: FAILED: SemanticException [Error 10001]: Line 1:14 Table not found 't'
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:699)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$runHive$1.apply(HiveClientImpl.scala:683)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$withHiveState$1.apply(HiveClientImpl.scala:272)
at org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:210)
at org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:209)
at org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:255)
at org.apache.spark.sql.hive.client.HiveClientImpl.runHive(HiveClientImpl.scala:683)
at org.apache.spark.sql.hive.client.HiveClientImpl.runSqlHive(HiveClientImpl.scala:673)
```
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20196 from gatorsmile/testFix.
## What changes were proposed in this pull request?
Fix the warning: Couldn't find corresponding Hive SerDe for data source provider org.apache.spark.sql.hive.orc.
## How was this patch tested?
test("SPARK-22972: hive orc source")
assert(HiveSerDe.sourceToSerDe("org.apache.spark.sql.hive.orc")
.equals(HiveSerDe.sourceToSerDe("orc")))
Author: xubo245 <601450868@qq.com>
Closes#20165 from xubo245/HiveSerDe.
# What changes were proposed in this pull request?
1. Start HiveThriftServer2.
2. Connect to thriftserver through beeline.
3. Close the beeline.
4. repeat step2 and step 3 for many times.
we found there are many directories never be dropped under the path `hive.exec.local.scratchdir` and `hive.exec.scratchdir`, as we know the scratchdir has been added to deleteOnExit when it be created. So it means that the cache size of FileSystem `deleteOnExit` will keep increasing until JVM terminated.
In addition, we use `jmap -histo:live [PID]`
to printout the size of objects in HiveThriftServer2 Process, we can find the object `org.apache.spark.sql.hive.client.HiveClientImpl` and `org.apache.hadoop.hive.ql.session.SessionState` keep increasing even though we closed all the beeline connections, which may caused the leak of Memory.
# How was this patch tested?
manual tests
This PR follw-up the https://github.com/apache/spark/pull/19989
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#20029 from zuotingbing/SPARK-22793.
## What changes were proposed in this pull request?
Modified HiveExternalCatalogVersionsSuite.scala to use Utils.doFetchFile to download different versions of Spark binaries rather than launching wget as an external process.
On platforms that don't have wget installed, this suite fails with an error.
cloud-fan : would you like to check this change?
## How was this patch tested?
1) test-only of HiveExternalCatalogVersionsSuite on several platforms. Tested bad mirror, read timeout, and redirects.
2) ./dev/run-tests
Author: Bruce Robbins <bersprockets@gmail.com>
Closes#20147 from bersprockets/SPARK-22940-alt.
## What changes were proposed in this pull request?
ChildFirstClassLoader's parent is set to null, so we can't get jars from its parent. This will cause ClassNotFoundException during HiveClient initialization with builtin hive jars, where we may should use spark context loader instead.
## How was this patch tested?
add new ut
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#20145 from yaooqinn/SPARK-22950.
## What changes were proposed in this pull request?
Currently, our CREATE TABLE syntax require the EXACT order of clauses. It is pretty hard to remember the exact order. Thus, this PR is to make optional clauses order insensitive for `CREATE TABLE` SQL statement.
```
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] [db_name.]table_name
[(col_name1 col_type1 [COMMENT col_comment1], ...)]
USING datasource
[OPTIONS (key1=val1, key2=val2, ...)]
[PARTITIONED BY (col_name1, col_name2, ...)]
[CLUSTERED BY (col_name3, col_name4, ...) INTO num_buckets BUCKETS]
[LOCATION path]
[COMMENT table_comment]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
[AS select_statement]
```
The proposal is to make the following clauses order insensitive.
```
[OPTIONS (key1=val1, key2=val2, ...)]
[PARTITIONED BY (col_name1, col_name2, ...)]
[CLUSTERED BY (col_name3, col_name4, ...) INTO num_buckets BUCKETS]
[LOCATION path]
[COMMENT table_comment]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
```
The same idea is also applicable to Create Hive Table.
```
CREATE [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name
[(col_name1[:] col_type1 [COMMENT col_comment1], ...)]
[COMMENT table_comment]
[PARTITIONED BY (col_name2[:] col_type2 [COMMENT col_comment2], ...)]
[ROW FORMAT row_format]
[STORED AS file_format]
[LOCATION path]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
[AS select_statement]
```
The proposal is to make the following clauses order insensitive.
```
[COMMENT table_comment]
[PARTITIONED BY (col_name2[:] col_type2 [COMMENT col_comment2], ...)]
[ROW FORMAT row_format]
[STORED AS file_format]
[LOCATION path]
[TBLPROPERTIES (key1=val1, key2=val2, ...)]
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20133 from gatorsmile/createDataSourceTableDDL.
## What changes were proposed in this pull request?
With #19474, children of insertion commands are missing in UI.
To fix it:
1. Create a new physical plan `DataWritingCommandExec` to exec `DataWritingCommand` with children. So that the other commands won't be affected.
2. On creation of `DataWritingCommand`, a new field `allColumns` must be specified, which is the output of analyzed plan.
3. In `FileFormatWriter`, the output schema will use `allColumns` instead of the output of optimized plan.
Before code changes:
After code changes:
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#20020 from gengliangwang/insert.
## What changes were proposed in this pull request?
This is to walk around the hive issue: https://issues.apache.org/jira/browse/HIVE-11935
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Feng Liu <fengliu@databricks.com>
Closes#20109 from liufengdb/synchronized.
## What changes were proposed in this pull request?
I found this problem while auditing the analyzer code. It's dangerous to introduce extra `AnalysisBarrer` during analysis, as the plan inside it will bypass all analysis afterward, which may not be expected. We should only preserve `AnalysisBarrer` but not introduce new ones.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#20094 from cloud-fan/barrier.
## What changes were proposed in this pull request?
fix table owner is null when create new table through spark sql
## How was this patch tested?
manual test.
1、first create a table
2、then select the table properties from mysql which connected to hive metastore
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: xu.wenchun <xu.wenchun@immomo.com>
Closes#20034 from BruceXu1991/SPARK-22846.
## What changes were proposed in this pull request?
Currently Spark can read table stats (e.g. `totalSize, numRows`) from Hive, we can also support to read partition stats from Hive using the same logic.
## How was this patch tested?
Added a new test case and modified an existing test case.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19932 from wzhfy/read_hive_partition_stats.
## What changes were proposed in this pull request?
As a follow-up of #19948 , this PR moves the test case and adds comments.
## How was this patch tested?
Pass the Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19960 from dongjoon-hyun/SPARK-19809-2.
## What changes were proposed in this pull request?
Until 2.2.1, Spark raises `NullPointerException` on zero-size ORC files. Usually, these zero-size ORC files are generated by 3rd-party apps like Flume.
```scala
scala> sql("create table empty_orc(a int) stored as orc location '/tmp/empty_orc'")
$ touch /tmp/empty_orc/zero.orc
scala> sql("select * from empty_orc").show
java.lang.RuntimeException: serious problem at
org.apache.hadoop.hive.ql.io.orc.OrcInputFormat.generateSplitsInfo(OrcInputFormat.java:1021)
...
Caused by: java.lang.NullPointerException at
org.apache.hadoop.hive.ql.io.orc.OrcInputFormat$BISplitStrategy.getSplits(OrcInputFormat.java:560)
```
After [SPARK-22279](https://github.com/apache/spark/pull/19499), Apache Spark with the default configuration doesn't have this bug. Although Hive 1.2.1 library code path still has the problem, we had better have a test coverage on what we have now in order to prevent future regression on it.
## How was this patch tested?
Pass a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19948 from dongjoon-hyun/SPARK-19809-EMPTY-FILE.
## What changes were proposed in this pull request?
Before we deliver the Hive compatibility mode, we plan to write a set of test cases that can be easily run in both Spark and Hive sides. We can easily compare whether they are the same or not. When new typeCoercion rules are added, we also can easily track the changes. These test cases can also be backported to the previous Spark versions for determining the changes we made.
This PR is the first attempt for improving the test coverage for type coercion compatibility. We generate these test cases for our binary comparison and ImplicitTypeCasts based on the Apache Derby test cases in https://github.com/apache/derby/blob/10.14/java/testing/org/apache/derbyTesting/functionTests/tests/lang/implicitConversions.sql
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19918 from gatorsmile/typeCoercionTests.
## What changes were proposed in this pull request?
We found staging directories will not be dropped sometimes in our production environment.
The createdTempDir will not be deleted if an exception occurs, we should delete createdTempDir with try-finally.
This PR is follow-up SPARK-18703.
## How was this patch tested?
exist tests
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#19841 from zuotingbing/SPARK-stagedir.
## What changes were proposed in this pull request?
Until 2.2.1, with the default configuration, Apache Spark returns incorrect results when ORC file schema is different from metastore schema order. This is due to Hive 1.2.1 library and some issues on `convertMetastoreOrc` option.
```scala
scala> Seq(1 -> 2).toDF("c1", "c2").write.format("orc").mode("overwrite").save("/tmp/o")
scala> sql("CREATE EXTERNAL TABLE o(c2 INT, c1 INT) STORED AS orc LOCATION '/tmp/o'")
scala> spark.table("o").show // This is wrong.
+---+---+
| c2| c1|
+---+---+
| 1| 2|
+---+---+
scala> spark.read.orc("/tmp/o").show // This is correct.
+---+---+
| c1| c2|
+---+---+
| 1| 2|
+---+---+
```
After [SPARK-22279](https://github.com/apache/spark/pull/19499), the default configuration doesn't have this bug. Although Hive 1.2.1 library code path still has the problem, we had better have a test coverage on what we have now in order to prevent future regression on it.
## How was this patch tested?
Pass the Jenkins with a newly added test test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19928 from dongjoon-hyun/SPARK-22267.
## What changes were proposed in this pull request?
Like Parquet, this PR aims to turn on `spark.sql.hive.convertMetastoreOrc` by default.
## How was this patch tested?
Pass all the existing test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19499 from dongjoon-hyun/SPARK-22279.
…a-2.12 and JDK9
## What changes were proposed in this pull request?
Some compile error after upgrading to scala-2.12
```javascript
spark_source/core/src/main/scala/org/apache/spark/executor/Executor.scala:455: ambiguous reference to overloaded definition, method limit in class ByteBuffer of type (x$1: Int)java.nio.ByteBuffer
method limit in class Buffer of type ()Int
match expected type ?
val resultSize = serializedDirectResult.limit
error
```
The limit method was moved from ByteBuffer to the superclass Buffer and it can no longer be called without (). The same reason for position method.
```javascript
/home/zly/prj/oss/jdk9_HOS_SOURCE/spark_source/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformationExec.scala:427: ambiguous reference to overloaded definition, [error] both method putAll in class Properties of type (x$1: java.util.Map[_, _])Unit [error] and method putAll in class Hashtable of type (x$1: java.util.Map[_ <: Object, _ <: Object])Unit [error] match argument types (java.util.Map[String,String])
[error] props.putAll(outputSerdeProps.toMap.asJava)
[error] ^
```
This is because the key type is Object instead of String which is unsafe.
## How was this patch tested?
running tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: kellyzly <kellyzly@126.com>
Closes#19854 from kellyzly/SPARK-22660.
## What changes were proposed in this pull request?
Since SPARK-20682, we have two `OrcFileFormat`s. This PR refactors ORC tests with three principles (with a few exceptions)
1. Move test suite into `sql/core`.
2. Create `HiveXXX` test suite in `sql/hive` by reusing `sql/core` test suite.
3. `OrcTest` will provide common helper functions and `val orcImp: String`.
**Test Suites**
*Native OrcFileFormat*
- org.apache.spark.sql.hive.orc
- OrcFilterSuite
- OrcPartitionDiscoverySuite
- OrcQuerySuite
- OrcSourceSuite
- o.a.s.sql.hive.orc
- OrcHadoopFsRelationSuite
*Hive built-in OrcFileFormat*
- o.a.s.sql.hive.orc
- HiveOrcFilterSuite
- HiveOrcPartitionDiscoverySuite
- HiveOrcQuerySuite
- HiveOrcSourceSuite
- HiveOrcHadoopFsRelationSuite
**Hierarchy**
```
OrcTest
-> OrcSuite
-> OrcSourceSuite
-> OrcQueryTest
-> OrcQuerySuite
-> OrcPartitionDiscoveryTest
-> OrcPartitionDiscoverySuite
-> OrcFilterSuite
HadoopFsRelationTest
-> OrcHadoopFsRelationSuite
-> HiveOrcHadoopFsRelationSuite
```
Please note the followings.
- Unlike the other test suites, `OrcHadoopFsRelationSuite` doesn't inherit `OrcTest`. It is inside `sql/hive` like `ParquetHadoopFsRelationSuite` due to the dependencies and follows the existing convention to use `val dataSourceName: String`
- `OrcFilterSuite`s cannot reuse test cases due to the different function signatures using Hive 1.2.1 ORC classes and Apache ORC 1.4.1 classes.
## How was this patch tested?
Pass the Jenkins tests with reorganized test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19882 from dongjoon-hyun/SPARK-22672.
## What changes were proposed in this pull request?
The SQL `Analyzer` goes through a whole query plan even most part of it is analyzed. This increases the time spent on query analysis for long pipelines in ML, especially.
This patch adds a logical node called `AnalysisBarrier` that wraps an analyzed logical plan to prevent it from analysis again. The barrier is applied to the analyzed logical plan in `Dataset`. It won't change the output of wrapped logical plan and just acts as a wrapper to hide it from analyzer. New operations on the dataset will be put on the barrier, so only the new nodes created will be analyzed.
This analysis barrier will be removed at the end of analysis stage.
## How was this patch tested?
Added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19873 from viirya/SPARK-20392-reopen.
## What changes were proposed in this pull request?
This PR aims to provide a configuration to choose the default `OrcFileFormat` from legacy `sql/hive` module or new `sql/core` module.
For example, this configuration will affects the following operations.
```scala
spark.read.orc(...)
```
```sql
CREATE TABLE t
USING ORC
...
```
## How was this patch tested?
Pass the Jenkins with new test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19871 from dongjoon-hyun/spark-sql-orc-enabled.
## What changes were proposed in this pull request?
PropagateTypes are called twice in TypeCoercion. We do not need to call it twice. Instead, we should call it after each change on the types.
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19874 from gatorsmile/deduplicatePropagateTypes.
## What changes were proposed in this pull request?
This PR improves documentation for not using zero `numRows` statistics and simplifies the test case.
The reason why some Hive tables have zero `numRows` is that, in Hive, when stats gathering is disabled, `numRows` is always zero after INSERT command:
```
hive> create table src (key int, value string) stored as orc;
hive> desc formatted src;
Table Parameters:
COLUMN_STATS_ACCURATE {\"BASIC_STATS\":\"true\"}
numFiles 0
numRows 0
rawDataSize 0
totalSize 0
transient_lastDdlTime 1512399590
hive> set hive.stats.autogather=false;
hive> insert into src select 1, 'a';
hive> desc formatted src;
Table Parameters:
numFiles 1
numRows 0
rawDataSize 0
totalSize 275
transient_lastDdlTime 1512399647
hive> insert into src select 1, 'b';
hive> desc formatted src;
Table Parameters:
numFiles 2
numRows 0
rawDataSize 0
totalSize 550
transient_lastDdlTime 1512399687
```
## How was this patch tested?
Modified existing test.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19880 from wzhfy/doc_zero_rowCount.
This pr to ensure that the Hive's statistics `totalSize` (or `rawDataSize`) > 0, `rowCount` also must be > 0. Otherwise may cause OOM when CBO is enabled.
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19831 from wangyum/SPARK-22626.
## What changes were proposed in this pull request?
When user tries to load data with a non existing hdfs file path system is not validating it and the load command operation is getting successful.
This is misleading to the user. already there is a validation in the scenario of none existing local file path. This PR has added validation in the scenario of nonexisting hdfs file path
## How was this patch tested?
UT has been added for verifying the issue, also snapshots has been added after the verification in a spark yarn cluster
Author: sujith71955 <sujithchacko.2010@gmail.com>
Closes#19823 from sujith71955/master_LoadComand_Issue.
## What changes were proposed in this pull request?
SPARK-22146 fix the FileNotFoundException issue only for the `inferSchema` method, ie. only for the schema inference, but it doesn't fix the problem when actually reading the data. Thus nearly the same exception happens when someone tries to use the data. This PR covers fixing the problem also there.
## How was this patch tested?
enhanced UT
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19844 from mgaido91/SPARK-22635.
## What changes were proposed in this pull request?
Adds a simple loop to retry download of Spark tarballs from different mirrors if the download fails.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19851 from srowen/SPARK-22654.
## What changes were proposed in this pull request?
* JIRA: [SPARK-22431](https://issues.apache.org/jira/browse/SPARK-22431) : Creating Permanent view with illegal type
**Description:**
- It is possible in Spark SQL to create a permanent view that uses an nested field with an illegal name.
- For example if we create the following view:
```create view x as select struct('a' as `$q`, 1 as b) q```
- A simple select fails with the following exception:
```
select * from x;
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$q:string,b:int>
at org.apache.spark.sql.hive.client.HiveClientImpl$.fromHiveColumn(HiveClientImpl.scala:812)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11$$anonfun$7.apply(HiveClientImpl.scala:378)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$getTableOption$1$$anonfun$apply$11$$anonfun$7.apply(HiveClientImpl.scala:378)
...
```
**Issue/Analysis**: Right now, we can create a view with a schema that cannot be read back by Spark from the Hive metastore. For more details, please see the discussion about the analysis and proposed fix options in comment 1 and comment 2 in the [SPARK-22431](https://issues.apache.org/jira/browse/SPARK-22431)
**Proposed changes**:
- Fix the hive table/view codepath to check whether the schema datatype is parseable by Spark before persisting it in the metastore. This change is localized to HiveClientImpl to do the check similar to the check in FromHiveColumn. This is fail-fast and we will avoid the scenario where we write something to the metastore that we are unable to read it back.
- Added new unit tests
- Ran the sql related unit test suites ( hive/test, sql/test, catalyst/test) OK
With the fix:
```
create view x as select struct('a' as `$q`, 1 as b) q;
17/11/28 10:44:55 ERROR SparkSQLDriver: Failed in [create view x as select struct('a' as `$q`, 1 as b) q]
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$q:string,b:int>
at org.apache.spark.sql.hive.client.HiveClientImpl$.org$apache$spark$sql$hive$client$HiveClientImpl$$getSparkSQLDataType(HiveClientImpl.scala:884)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$org$apache$spark$sql$hive$client$HiveClientImpl$$verifyColumnDataType$1.apply(HiveClientImpl.scala:906)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$org$apache$spark$sql$hive$client$HiveClientImpl$$verifyColumnDataType$1.apply(HiveClientImpl.scala:906)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
...
```
## How was this patch tested?
- New unit tests have been added.
hvanhovell, Please review and share your thoughts/comments. Thank you so much.
Author: Sunitha Kambhampati <skambha@us.ibm.com>
Closes#19747 from skambha/spark22431.
## What changes were proposed in this pull request?
Currently, relation size is computed as the sum of file size, which is error-prone because storage format like parquet may have a much smaller file size compared to in-memory size. When we choose broadcast join based on file size, there's a risk of OOM. But if the number of rows is available in statistics, we can get a better estimation by `numRows * rowSize`, which helps to alleviate this problem.
## How was this patch tested?
Added a new test case for data source table and hive table.
Author: Zhenhua Wang <wzh_zju@163.com>
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19743 from wzhfy/better_leaf_size.
## What changes were proposed in this pull request?
Currently, relation stats is the same whether cbo is enabled or not. While relation (`LogicalRelation` or `HiveTableRelation`) is a `LogicalPlan`, its behavior is inconsistent with other plans. This can cause confusion when user runs EXPLAIN COST commands. Besides, when CBO is disabled, we apply the size-only estimation strategy, so there's no need to propagate other catalog statistics to relation.
## How was this patch tested?
Enhanced existing tests case and added a test case.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19757 from wzhfy/catalog_stats_conversion.
## What changes were proposed in this pull request?
We have 2 different methods to convert filters for hive, regarding a config. This introduces duplicated and inconsistent code(e.g. one use helper objects for pattern match and one doesn't).
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19801 from cloud-fan/cleanup.
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/19779 , to simplify the file creation.
## How was this patch tested?
test only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19799 from cloud-fan/minor.
## What changes were proposed in this pull request?
SPARK-19580 Support for avro.schema.url while writing to hive table
SPARK-19878 Add hive configuration when initialize hive serde in InsertIntoHiveTable.scala
SPARK-17920 HiveWriterContainer passes null configuration to serde.initialize, causing NullPointerException in AvroSerde when using avro.schema.url
Support writing to Hive table which uses Avro schema url 'avro.schema.url'
For ex:
create external table avro_in (a string) stored as avro location '/avro-in/' tblproperties ('avro.schema.url'='/avro-schema/avro.avsc');
create external table avro_out (a string) stored as avro location '/avro-out/' tblproperties ('avro.schema.url'='/avro-schema/avro.avsc');
insert overwrite table avro_out select * from avro_in; // fails with java.lang.NullPointerException
WARN AvroSerDe: Encountered exception determining schema. Returning signal schema to indicate problem
java.lang.NullPointerException
at org.apache.hadoop.fs.FileSystem.getDefaultUri(FileSystem.java:182)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:174)
## Changes proposed in this fix
Currently 'null' value is passed to serializer, which causes NPE during insert operation, instead pass Hadoop configuration object
## How was this patch tested?
Added new test case in VersionsSuite
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#19779 from vinodkc/br_Fix_SPARK-17920.
## What changes were proposed in this pull request?
ScalaTest 3.0 uses an implicit `Signaler`. This PR makes it sure all Spark tests uses `ThreadSignaler` explicitly which has the same default behavior of interrupting a thread on the JVM like ScalaTest 2.2.x. This will reduce potential flakiness.
## How was this patch tested?
This is testsuite-only update. This should passes the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19784 from dongjoon-hyun/use_thread_signaler.
## What changes were proposed in this pull request?
Equi-height histogram is effective in cardinality estimation, and more accurate than basic column stats (min, max, ndv, etc) especially in skew distribution. So we need to support it.
For equi-height histogram, all buckets (intervals) have the same height (frequency).
In this PR, we use a two-step method to generate an equi-height histogram:
1. use `ApproximatePercentile` to get percentiles `p(0), p(1/n), p(2/n) ... p((n-1)/n), p(1)`;
2. construct range values of buckets, e.g. `[p(0), p(1/n)], [p(1/n), p(2/n)] ... [p((n-1)/n), p(1)]`, and use `ApproxCountDistinctForIntervals` to count ndv in each bucket. Each bucket is of the form: `(lowerBound, higherBound, ndv)`.
## How was this patch tested?
Added new test cases and modified some existing test cases.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#19479 from wzhfy/generate_histogram.
## What changes were proposed in this pull request?
a followup of https://github.com/apache/spark/pull/19712 , adds back the `spark.sql.hive.version`, so that if users try to read this config, they can still get a default value instead of null.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19719 from cloud-fan/minor.
## What changes were proposed in this pull request?
The current internal `table()` API of `SparkSession` bypasses the Analyzer and directly calls `sessionState.catalog.lookupRelation` API. This skips the view resolution logics in our Analyzer rule `ResolveRelations`. This internal API is widely used by various DDL commands, public and internal APIs.
Users might get the strange error caused by view resolution when the default database is different.
```
Table or view not found: t1; line 1 pos 14
org.apache.spark.sql.AnalysisException: Table or view not found: t1; line 1 pos 14
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
```
This PR is to fix it by enforcing it to use `ResolveRelations` to resolve the table.
## How was this patch tested?
Added a test case and modified the existing test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19713 from gatorsmile/viewResolution.
## What changes were proposed in this pull request?
At the beginning https://github.com/apache/spark/pull/2843 added `spark.sql.hive.version` to reveal underlying hive version for jdbc connections. For some time afterwards, it was used as a version identifier for the execution hive client.
Actually there is no hive client for executions in spark now and there are no usages of HIVE_EXECUTION_VERSION found in whole spark project. HIVE_EXECUTION_VERSION is set by `spark.sql.hive.version`, which is still set internally in some places or by users, this may confuse developers and users with HIVE_METASTORE_VERSION(spark.sql.hive.metastore.version).
It might better to be removed.
## How was this patch tested?
modify some existing ut
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#19712 from yaooqinn/SPARK-22487.
## What changes were proposed in this pull request?
We're building a data lineage tool in which we need to monitor the metadata changes in ExternalCatalog, current ExternalCatalog already provides several useful events like "CreateDatabaseEvent" for custom SparkListener to use. But still there's some event missing, like alter database event and alter table event. So here propose to and new ExternalCatalogEvent.
## How was this patch tested?
Enrich the current UT and tested on local cluster.
CC hvanhovell please let me know your comments about current proposal, thanks.
Author: jerryshao <sshao@hortonworks.com>
Closes#19649 from jerryshao/SPARK-22405.
## What changes were proposed in this pull request?
`<=>` is not supported by Hive metastore partition predicate pushdown. We should not push down it to Hive metastore when they are be using in partition predicates.
## How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19682 from gatorsmile/fixLimitPushDown.
## What changes were proposed in this pull request?
`spark.sql.statistics.autoUpdate.size` should be `spark.sql.statistics.size.autoUpdate.enabled`. The previous name is confusing as users may treat it as a size config.
This config is in master branch only, no backward compatibility issue.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19667 from cloud-fan/minor.
## What changes were proposed in this pull request?
Scala test source files like TestHiveSingleton.scala should be in scala source root
## How was this patch tested?
Just move scala file from java directory to scala directory
No new test case in this PR.
```
renamed: mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala -> mllib/src/test/scala/org/apache/spark/ml/util/IdentifiableSuite.scala
renamed: streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala -> streaming/src/test/scala/org/apache/spark/streaming/JavaTestUtils.scala
renamed: streaming/src/test/java/org/apache/spark/streaming/api/java/JavaStreamingListenerWrapperSuite.scala -> streaming/src/test/scala/org/apache/spark/streaming/api/java/JavaStreamingListenerWrapperSuite.scala
renamed: sql/hive/src/test/java/org/apache/spark/sql/hive/test/TestHiveSingleton.scala sql/hive/src/test/scala/org/apache/spark/sql/hive/test/TestHiveSingleton.scala
```
Author: xubo245 <601450868@qq.com>
Closes#19639 from xubo245/scalaDirectory.
forward-port https://github.com/apache/spark/pull/19622 to master branch.
This bug doesn't exist in master because we've added hive bucketing support and the hive bucketing metadata can be recognized by Spark, but we should still port it to master: 1) there may be other unsupported hive metadata removed by Spark. 2) reduce code difference between master and 2.2 to ease the backport in the feature.
***
When we alter table schema, we set the new schema to spark `CatalogTable`, convert it to hive table, and finally call `hive.alterTable`. This causes a problem in Spark 2.2, because hive bucketing metedata is not recognized by Spark, which means a Spark `CatalogTable` representing a hive table is always non-bucketed, and when we convert it to hive table and call `hive.alterTable`, the original hive bucketing metadata will be removed.
To fix this bug, we should read out the raw hive table metadata, update its schema, and call `hive.alterTable`. By doing this we can guarantee only the schema is changed, and nothing else.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19644 from cloud-fan/infer.
## What changes were proposed in this pull request?
According to the [discussion](https://github.com/apache/spark/pull/19571#issuecomment-339472976) on SPARK-15474, we will add new OrcFileFormat in `sql/core` module and allow users to use both old and new OrcFileFormat.
To do that, `OrcOptions` should be visible in `sql/core` module, too. Previously, it was `private[orc]` in `sql/hive`. This PR removes `private[orc]` because we don't use `private[sql]` in `sql/execution` package after [SPARK-16964](https://github.com/apache/spark/pull/14554).
## How was this patch tested?
Pass the Jenkins with the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19636 from dongjoon-hyun/SPARK-22416.
## What changes were proposed in this pull request?
We made a mistake in https://github.com/apache/spark/pull/16944 . In `HiveMetastoreCatalog#inferIfNeeded` we infer the data schema, merge with full schema, and return the new full schema. At caller side we treat the full schema as data schema and set it to `HadoopFsRelation`.
This doesn't cause any problem because both parquet and orc can work with a wrong data schema that has extra columns, but it's better to fix this mistake.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19615 from cloud-fan/infer.
## What changes were proposed in this pull request?
Canonicalized plans are not supposed to be executed. I ran into a case in which there's some code that accidentally calls execute on a canonicalized plan. This patch throws a more explicit exception when that happens.
## How was this patch tested?
Added a test case in SparkPlanSuite.
Author: Reynold Xin <rxin@databricks.com>
Closes#18828 from rxin/SPARK-21619.
## What changes were proposed in this pull request?
This is a regression introduced by #14207. After Spark 2.1, we store the inferred schema when creating the table, to avoid inferring schema again at read path. However, there is one special case: overlapped columns between data and partition. For this case, it breaks the assumption of table schema that there is on ovelap between data and partition schema, and partition columns should be at the end. The result is, for Spark 2.1, the table scan has incorrect schema that puts partition columns at the end. For Spark 2.2, we add a check in CatalogTable to validate table schema, which fails at this case.
To fix this issue, a simple and safe approach is to fallback to old behavior when overlapeed columns detected, i.e. store empty schema in metastore.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19579 from cloud-fan/bug2.
## What changes were proposed in this pull request?
Adjust Spark download in test to use Apache mirrors and respect its load balancer, and use Spark 2.1.2. This follows on a recent PMC list thread about removing the cloudfront download rather than update it further.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#19564 from srowen/SPARK-21936.2.
## What changes were proposed in this pull request?
During [SPARK-21912](https://issues.apache.org/jira/browse/SPARK-21912), we skipped testing 'ADD COLUMNS' on ORC tables due to ORC limitation. Since [SPARK-21929](https://issues.apache.org/jira/browse/SPARK-21929) is resolved now, we can test both `ORC` and `PARQUET` completely.
## How was this patch tested?
Pass the updated test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19562 from dongjoon-hyun/SPARK-21912-2.
## What changes were proposed in this pull request?
When [SPARK-19261](https://issues.apache.org/jira/browse/SPARK-19261) implements `ALTER TABLE ADD COLUMNS`, ORC data source is omitted due to SPARK-14387, SPARK-16628, and SPARK-18355. Now, those issues are fixed and Spark 2.3 is [using Spark schema to read ORC table instead of ORC file schema](e6e36004af). This PR enables `ALTER TABLE ADD COLUMNS` for ORC data source.
## How was this patch tested?
Pass the updated and added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19545 from dongjoon-hyun/SPARK-21929.
## What changes were proposed in this pull request?
Simplifies the test cases that were added in the PR https://github.com/apache/spark/pull/18270.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19546 from gatorsmile/backportSPARK-21055.
## What changes were proposed in this pull request?
This is a follow-up PR of https://github.com/apache/spark/pull/17633.
This PR is to add a conf `spark.sql.hive.advancedPartitionPredicatePushdown.enabled`, which can be used to turn the enhancement off.
## How was this patch tested?
Add a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19547 from gatorsmile/Spark20331FollowUp.
## What changes were proposed in this pull request?
spark does not support grouping__id, it has grouping_id() instead.
But it is not convenient for hive user to change to spark-sql
so this pr is to replace grouping__id with grouping_id()
hive user need not to alter their scripts
## How was this patch tested?
test with SQLQuerySuite.scala
Author: CenYuhai <yuhai.cen@ele.me>
Closes#18270 from cenyuhai/SPARK-21055.
Hive delegation tokens are only needed when the Spark driver has no access
to the kerberos TGT. That happens only in two situations:
- when using a proxy user
- when using cluster mode without a keytab
This change modifies the Hive provider so that it only generates delegation
tokens in those situations, and tweaks the YARN AM so that it makes the proper
user visible to the Hive code when running with keytabs, so that the TGT
can be used instead of a delegation token.
The effect of this change is that now it's possible to initialize multiple,
non-concurrent SparkContext instances in the same JVM. Before, the second
invocation would fail to fetch a new Hive delegation token, which then could
make the second (or third or...) application fail once the token expired.
With this change, the TGT will be used to authenticate to the HMS instead.
This change also avoids polluting the current logged in user's credentials
when launching applications. The credentials are copied only when running
applications as a proxy user. This makes it possible to implement SPARK-11035
later, where multiple threads might be launching applications, and each app
should have its own set of credentials.
Tested by verifying HDFS and Hive access in following scenarios:
- client and cluster mode
- client and cluster mode with proxy user
- client and cluster mode with principal / keytab
- long-running cluster app with principal / keytab
- pyspark app that creates (and stops) multiple SparkContext instances
through its lifetime
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19509 from vanzin/SPARK-22290.
## What changes were proposed in this pull request?
This PR aims to improve **StatisticsSuite** to test `convertMetastore` configuration properly. Currently, some test logic in `test statistics of LogicalRelation converted from Hive serde tables` depends on the default configuration. New test case is shorter and covers both(true/false) cases explicitly.
This test case was previously modified by SPARK-17410 and SPARK-17284 in Spark 2.3.0.
- a2460be9c3 (diff-1c464c86b68c2d0b07e73b7354e74ce7R443)
## How was this patch tested?
Pass the Jenkins with the improved test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19500 from dongjoon-hyun/SPARK-22280.
## What changes were proposed in this pull request?
This PR aims to
- Rename `OrcRelation` to `OrcFileFormat` object.
- Replace `OrcRelation.ORC_COMPRESSION` with `org.apache.orc.OrcConf.COMPRESS`. Since [SPARK-21422](https://issues.apache.org/jira/browse/SPARK-21422), we can use `OrcConf.COMPRESS` instead of Hive's.
```scala
// The references of Hive's classes will be minimized.
val ORC_COMPRESSION = "orc.compress"
```
## How was this patch tested?
Pass the Jenkins with the existing and updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19502 from dongjoon-hyun/SPARK-22282.
## What changes were proposed in this pull request?
`BasicWriteTaskStatsTracker.getFileSize()` to catch `FileNotFoundException`, log info and then return 0 as a file size.
This ensures that if a newly created file isn't visible due to the store not always having create consistency, the metric collection doesn't cause the failure.
## How was this patch tested?
New test suite included, `BasicWriteTaskStatsTrackerSuite`. This not only checks the resilience to missing files, but verifies the existing logic as to how file statistics are gathered.
Note that in the current implementation
1. if you call `Tracker..getFinalStats()` more than once, the file size count will increase by size of the last file. This could be fixed by clearing the filename field inside `getFinalStats()` itself.
2. If you pass in an empty or null string to `Tracker.newFile(path)` then IllegalArgumentException is raised, but only in `getFinalStats()`, rather than in `newFile`. There's a test for this behaviour in the new suite, as it verifies that only FNFEs get swallowed.
Author: Steve Loughran <stevel@hortonworks.com>
Closes#18979 from steveloughran/cloud/SPARK-21762-missing-files-in-metrics.
## What changes were proposed in this pull request?
Before Hive 2.0, ORC File schema has invalid column names like `_col1` and `_col2`. This is a well-known limitation and there are several Apache Spark issues with `spark.sql.hive.convertMetastoreOrc=true`. This PR ignores ORC File schema and use Spark schema.
## How was this patch tested?
Pass the newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19470 from dongjoon-hyun/SPARK-18355.
## What changes were proposed in this pull request?
Due to optimizer removing some unnecessary aliases, the logical and physical plan may have different output attribute ids. FileFormatWriter should handle this when creating the physical sort node.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19483 from cloud-fan/bug2.
## What changes were proposed in this pull request?
The method `deterministic` is frequently called in optimizer.
Refactor `deterministic` as lazy value, in order to avoid redundant computations.
## How was this patch tested?
Simple benchmark test over TPC-DS queries, run time from query string to optimized plan(continuous 20 runs, and get the average of last 5 results):
Before changes: 12601 ms
After changes: 11993ms
This is 4.8% performance improvement.
Also run test with Unit test.
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19478 from gengliangwang/deterministicAsLazyVal.
## What changes were proposed in this pull request?
In https://github.com/apache/spark/pull/18064, we allowed `RunnableCommand` to have children in order to fix some UI issues. Then we made `InsertIntoXXX` commands take the input `query` as a child, when we do the actual writing, we just pass the physical plan to the writer(`FileFormatWriter.write`).
However this is problematic. In Spark SQL, optimizer and planner are allowed to change the schema names a little bit. e.g. `ColumnPruning` rule will remove no-op `Project`s, like `Project("A", Scan("a"))`, and thus change the output schema from "<A: int>" to `<a: int>`. When it comes to writing, especially for self-description data format like parquet, we may write the wrong schema to the file and cause null values at the read path.
Fortunately, in https://github.com/apache/spark/pull/18450 , we decided to allow nested execution and one query can map to multiple executions in the UI. This releases the major restriction in #18604 , and now we don't have to take the input `query` as child of `InsertIntoXXX` commands.
So the fix is simple, this PR partially revert #18064 and make `InsertIntoXXX` commands leaf nodes again.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19474 from cloud-fan/bug.
## What changes were proposed in this pull request?
This is a follow-up of #19384.
In the previous pr, only definitions of the config names were modified, but we also need to modify the names in runtime or tests specified as string literal.
## How was this patch tested?
Existing tests but modified the config names.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19462 from ueshin/issues/SPARK-22159/fup1.
## What changes were proposed in this pull request?
In this PR we make a few changes to the list hive partitions code, to make the code more extensible.
The following changes are made:
1. In `HiveClientImpl.getPartitions()`, call `client.getPartitions` instead of `shim.getAllPartitions` when `spec` is empty;
2. In `HiveTableScanExec`, previously we always call `listPartitionsByFilter` if the config `metastorePartitionPruning` is enabled, but actually, we'd better call `listPartitions` if `partitionPruningPred` is empty;
3. We should use sessionCatalog instead of SharedState.externalCatalog in `HiveTableScanExec`.
## How was this patch tested?
Tested by existing test cases since this is code refactor, no regression or behavior change is expected.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#19444 from jiangxb1987/hivePartitions.
## What changes were proposed in this pull request?
Users could hit `java.lang.NullPointerException` when the tables were created by Hive and the table's owner is `null` that are got from Hive metastore. `DESC EXTENDED` failed with the error:
> SQLExecutionException: java.lang.NullPointerException at scala.collection.immutable.StringOps$.length$extension(StringOps.scala:47) at scala.collection.immutable.StringOps.length(StringOps.scala:47) at scala.collection.IndexedSeqOptimized$class.isEmpty(IndexedSeqOptimized.scala:27) at scala.collection.immutable.StringOps.isEmpty(StringOps.scala:29) at scala.collection.TraversableOnce$class.nonEmpty(TraversableOnce.scala:111) at scala.collection.immutable.StringOps.nonEmpty(StringOps.scala:29) at org.apache.spark.sql.catalyst.catalog.CatalogTable.toLinkedHashMap(interface.scala:300) at org.apache.spark.sql.execution.command.DescribeTableCommand.describeFormattedTableInfo(tables.scala:565) at org.apache.spark.sql.execution.command.DescribeTableCommand.run(tables.scala:543) at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:66) at
## How was this patch tested?
Added a unit test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19395 from gatorsmile/desc.
## What changes were proposed in this pull request?
The underlying tables of persistent views are not refreshed when users issue the REFRESH TABLE command against the persistent views.
## How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19405 from gatorsmile/refreshView.
## What changes were proposed in this pull request?
Reading ORC files containing special characters like '%' fails with a FileNotFoundException.
This PR aims to fix the problem.
## How was this patch tested?
Added UT.
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19368 from mgaido91/SPARK-22146.
## What changes were proposed in this pull request?
During TestHiveSparkSession.reset(), which is called after each TestHiveSingleton suite, we now delete and recreate the Hive warehouse directory.
## How was this patch tested?
Ran full suite of tests locally, verified that they pass.
Author: Greg Owen <greg@databricks.com>
Closes#19341 from GregOwen/SPARK-22120.
## What changes were proposed in this pull request?
This PR proposes to remove `assume` in `Utils.resolveURIs` and replace `assume` to `assert` in `Utils.resolveURI` in the test cases in `UtilsSuite`.
It looks `Utils.resolveURIs` supports multiple but also single paths as input. So, it looks not meaningful to check if the input has `,`.
For the test for `Utils.resolveURI`, I replaced it to `assert` because it looks taking single path and in order to prevent future mistakes when adding more tests here.
For `assume` in `HiveDDLSuite`, it looks it should be `assert` to test at the last
## How was this patch tested?
Fixed unit tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19332 from HyukjinKwon/SPARK-22093.
## What changes were proposed in this pull request?
While running bin/spark-sql, we will reuse cliSessionState, but the Hive configurations generated here just points to a dummy meta store which actually should be the real one. And the warehouse is determined later in SharedState, HiveClient should respect this config changing in this case too.
## How was this patch tested?
existing ut
cc cloud-fan jiangxb1987
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#19068 from yaooqinn/SPARK-21428-FOLLOWUP.
## What changes were proposed in this pull request?
This PR tries to download Spark for each test run, to make sure each test run is absolutely isolated.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19265 from cloud-fan/test.
## What changes were proposed in this pull request?
As reported in https://issues.apache.org/jira/browse/SPARK-22047 , HiveExternalCatalogVersionsSuite is failing frequently, let's disable this test suite to unblock other PRs, I'm looking into the root cause.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19264 from cloud-fan/test.
## What changes were proposed in this pull request?
This PR is clean the codes in https://github.com/apache/spark/pull/18975
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19225 from gatorsmile/refactorSPARK-4131.
## What changes were proposed in this pull request?
When reading column descriptions from hive catalog, we currently populate the metadata for all types to record the raw hive type string. In terms of processing , we need this additional metadata information for CHAR/VARCHAR types or complex type containing the CHAR/VARCHAR types.
Its a minor cleanup. I haven't created a JIRA for it.
## How was this patch tested?
Test added in HiveMetastoreCatalogSuite
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#19215 from dilipbiswal/column_metadata.
## What changes were proposed in this pull request?
The code is already merged to master:
https://github.com/apache/spark/pull/18975
This is a following up PR to merge HiveTmpFile.scala to SaveAsHiveFile.
## How was this patch tested?
Build successfully
Author: Jane Wang <janewang@fb.com>
Closes#19221 from janewangfb/merge_savehivefile_hivetmpfile.
## What changes were proposed in this pull request?
this PR describe remove the import class that are unused.
## How was this patch tested?
N/A
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19131 from heary-cao/unuse_import.
## What changes were proposed in this pull request?
This PR implements the sql feature:
INSERT OVERWRITE [LOCAL] DIRECTORY directory1
[ROW FORMAT row_format] [STORED AS file_format]
SELECT ... FROM ...
## How was this patch tested?
Added new unittests and also pulled the code to fb-spark so that we could test writing to hdfs directory.
Author: Jane Wang <janewang@fb.com>
Closes#18975 from janewangfb/port_local_directory.
## What changes were proposed in this pull request?
`HiveExternalCatalog` is a semi-public interface. When creating tables, `HiveExternalCatalog` converts the table metadata to hive table format and save into hive metastore. It's very import to guarantee backward compatibility here, i.e., tables created by previous Spark versions should still be readable in newer Spark versions.
Previously we find backward compatibility issues manually, which is really easy to miss bugs. This PR introduces a test framework to automatically test `HiveExternalCatalog` backward compatibility, by downloading Spark binaries with different versions, and create tables with these Spark versions, and read these tables with current Spark version.
## How was this patch tested?
test-only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes#19148 from cloud-fan/test.
## What changes were proposed in this pull request?
Tables should be dropped after use in unit tests.
## How was this patch tested?
N/A
Author: liuxian <liu.xian3@zte.com.cn>
Closes#19155 from 10110346/droptable.
Since ScalaTest 3.0.0, `org.scalatest.concurrent.Timeouts` is deprecated.
This PR replaces the deprecated one with `org.scalatest.concurrent.TimeLimits`.
```scala
-import org.scalatest.concurrent.Timeouts._
+import org.scalatest.concurrent.TimeLimits._
```
Pass the existing test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19150 from dongjoon-hyun/SPARK-21939.
Change-Id: I1a1b07f1b97e51e2263dfb34b7eaaa099b2ded5e
## What changes were proposed in this pull request?
Currently, users meet job abortions while creating or altering ORC/Parquet tables with invalid column names. We had better prevent this by raising **AnalysisException** with a guide to use aliases instead like Paquet data source tables.
**BEFORE**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:28:21 ERROR Utils: Aborting task
java.lang.IllegalArgumentException: Error: : expected at the position 8 of 'struct<a b:int>' but ' ' is found.
17/09/04 13:28:21 ERROR FileFormatWriter: Job job_20170904132821_0001 aborted.
17/09/04 13:28:21 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
org.apache.spark.SparkException: Task failed while writing rows.
```
**AFTER**
```scala
scala> sql("CREATE TABLE orc1 USING ORC AS SELECT 1 `a b`")
17/09/04 13:27:40 ERROR CreateDataSourceTableAsSelectCommand: Failed to write to table orc1
org.apache.spark.sql.AnalysisException: Attribute name "a b" contains invalid character(s) among " ,;{}()\n\t=". Please use alias to rename it.;
```
## How was this patch tested?
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19124 from dongjoon-hyun/SPARK-21912.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19119 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
Supporting moving tables across different database in HiveClient `alterTable`
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19104 from gatorsmile/alterTable.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
## What changes were proposed in this pull request?
Creates `SQLMetricsTestUtils` for the utility functions of both Hive-specific and the other SQLMetrics test cases.
Also, move two SQLMetrics test cases from sql/hive to sql/core.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19092 from gatorsmile/rewriteSQLMetrics.
## What changes were proposed in this pull request?
This PR aims to support `spark.sql.orc.compression.codec` like Parquet's `spark.sql.parquet.compression.codec`. Users can use SQLConf to control ORC compression, too.
## How was this patch tested?
Pass the Jenkins with new and updated test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19055 from dongjoon-hyun/SPARK-21839.
## What changes were proposed in this pull request?
`org.apache.spark.deploy.RPackageUtilsSuite`
```
- jars without manifest return false *** FAILED *** (109 milliseconds)
java.io.IOException: Unable to delete file: C:\projects\spark\target\tmp\1500266936418-0\dep1-c.jar
```
`org.apache.spark.deploy.SparkSubmitSuite`
```
- download one file to local *** FAILED *** (16 milliseconds)
java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2630198944759847458.jar
- download list of files to local *** FAILED *** (0 milliseconds)
java.net.URISyntaxException: Illegal character in authority at index 6: s3a://C:\projects\spark\target\tmp\test2783551769392880031.jar
```
`org.apache.spark.scheduler.ReplayListenerSuite`
```
- Replay compressed inprogress log file succeeding on partial read (156 milliseconds)
Exception encountered when attempting to run a suite with class name:
org.apache.spark.scheduler.ReplayListenerSuite *** ABORTED *** (1 second, 391 milliseconds)
java.io.IOException: Failed to delete: C:\projects\spark\target\tmp\spark-8f3cacd6-faad-4121-b901-ba1bba8025a0
- End-to-end replay *** FAILED *** (62 milliseconds)
java.io.IOException: No FileSystem for scheme: C
- End-to-end replay with compression *** FAILED *** (110 milliseconds)
java.io.IOException: No FileSystem for scheme: C
```
`org.apache.spark.sql.hive.StatisticsSuite`
```
- SPARK-21079 - analyze table with location different than that of individual partitions *** FAILED *** (875 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
- SPARK-21079 - analyze partitioned table with only a subset of partitions visible *** FAILED *** (47 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
```
**Note:** this PR does not fix:
`org.apache.spark.deploy.SparkSubmitSuite`
```
- launch simple application with spark-submit with redaction *** FAILED *** (172 milliseconds)
java.util.NoSuchElementException: next on empty iterator
```
I can't reproduce this on my Windows machine but looks appearntly consistently failed on AppVeyor. This one is unclear to me yet and hard to debug so I did not include this one for now.
**Note:** it looks there are more instances but it is hard to identify them partly due to flakiness and partly due to swarming logs and errors. Will probably go one more time if it is fine.
## How was this patch tested?
Manually via AppVeyor:
**Before**
- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/8t8ra3lrljuir7q4
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/taquy84yudjjen64
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/24omrfn2k0xfa9xq
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/771-windows-fix/job/2079y1plgj76dc9l
**After**
- `org.apache.spark.deploy.RPackageUtilsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/3803dbfn89ne1164
- `org.apache.spark.deploy.SparkSubmitSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/m5l350dp7u9a4xjr
- `org.apache.spark.scheduler.ReplayListenerSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/565vf74pp6bfdk18
- `org.apache.spark.sql.hive.StatisticsSuite`: https://ci.appveyor.com/project/spark-test/spark/build/775-windows-fix/job/qm78tsk8c37jb6s4
Jenkins tests are required and AppVeyor tests will be triggered.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18971 from HyukjinKwon/windows-fixes.
## What changes were proposed in this pull request?
We should make codegen fallback of expressions configurable. So far, it is always on. We might hide it when our codegen have compilation bugs. Thus, we should also disable the codegen fallback when running test cases.
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19062 from gatorsmile/fallbackCodegen.
## What changes were proposed in this pull request?
Add trait UserDefinedExpression to identify user-defined functions.
UDF can be expensive. In optimizer we may need to avoid executing UDF multiple times.
E.g.
```scala
table.select(UDF as 'a).select('a, ('a + 1) as 'b)
```
If UDF is expensive in this case, optimizer should not collapse the project to
```scala
table.select(UDF as 'a, (UDF+1) as 'b)
```
Currently UDF classes like PythonUDF, HiveGenericUDF are not defined in catalyst.
This PR is to add a new trait to make it easier to identify user-defined functions.
## How was this patch tested?
Unit test
Author: Wang Gengliang <ltnwgl@gmail.com>
Closes#19064 from gengliangwang/UDFType.
## What changes were proposed in this pull request?
[SPARK-19025](https://github.com/apache/spark/pull/16869) removes SQLBuilder, so we don't need the following in HiveCompatibilitySuite.
```scala
// Ensures that the plans generation use metastore relation and not OrcRelation
// Was done because SqlBuilder does not work with plans having logical relation
TestHive.setConf(HiveUtils.CONVERT_METASTORE_ORC, false)
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19043 from dongjoon-hyun/SPARK-21831.
## What changes were proposed in this pull request?
After [SPARK-19025](https://github.com/apache/spark/pull/16869), there is no need to keep SQLBuilderTest.
ExpressionSQLBuilderSuite is the only place to use it.
This PR aims to remove SQLBuilderTest.
## How was this patch tested?
Pass the updated `ExpressionSQLBuilderSuite`.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19044 from dongjoon-hyun/SPARK-21832.
## What changes were proposed in this pull request?
All streaming logical plans will now have isStreaming set. This involved adding isStreaming as a case class arg in a few cases, since a node might be logically streaming depending on where it came from.
## How was this patch tested?
Existing unit tests - no functional change is intended in this PR.
Author: Jose Torres <joseph-torres@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18973 from joseph-torres/SPARK-21765.
## What changes were proposed in this pull request?
For Hive-serde tables, we always respect the schema stored in Hive metastore, because the schema could be altered by the other engines that share the same metastore. Thus, we always trust the metastore-controlled schema for Hive-serde tables when the schemas are different (without considering the nullability and cases). However, in some scenarios, Hive metastore also could INCORRECTLY overwrite the schemas when the serde and Hive metastore built-in serde are different.
The proposed solution is to introduce a table-specific option for such scenarios. For a specific table, users can make Spark always respect Spark-inferred/controlled schema instead of trusting metastore-controlled schema. By default, we trust Hive metastore-controlled schema.
## How was this patch tested?
Added a cross-version test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19003 from gatorsmile/respectSparkSchema.
## What changes were proposed in this pull request?
This PR is to enable users to create persistent Scala UDAF (that extends UserDefinedAggregateFunction).
```SQL
CREATE FUNCTION myDoubleAvg AS 'test.org.apache.spark.sql.MyDoubleAvg'
```
Before this PR, Spark UDAF only can be registered through the API `spark.udf.register(...)`
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18700 from gatorsmile/javaUDFinScala.
## What changes were proposed in this pull request?
We do not have any Hive-specific parser. It does not make sense to keep a parser-specific test suite `HiveDDLCommandSuite.scala` in the Hive package. This PR is to remove it.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19015 from gatorsmile/combineDDL.
For Hive tables, the current "replace the schema" code is the correct
path, except that an exception in that path should result in an error, and
not in retrying in a different way.
For data source tables, Spark may generate a non-compatible Hive table;
but for that to work with Hive 2.1, the detection of data source tables needs
to be fixed in the Hive client, to also consider the raw tables used by code
such as `alterTableSchema`.
Tested with existing and added unit tests (plus internal tests with a 2.1 metastore).
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18849 from vanzin/SPARK-21617.
## What changes were proposed in this pull request?
Fix a typo in test.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19005 from viirya/SPARK-21721-followup.
## What changes were proposed in this pull request?
Added support for ANALYZE TABLE [db_name].tablename PARTITION (partcol1[=val1], partcol2[=val2], ...) COMPUTE STATISTICS [NOSCAN] SQL command to calculate total number of rows and size in bytes for a subset of partitions. Calculated statistics are stored in Hive Metastore as user-defined properties attached to partition objects. Property names are the same as the ones used to store table-level statistics: spark.sql.statistics.totalSize and spark.sql.statistics.numRows.
When partition specification contains all partition columns with values, the command collects statistics for a single partition that matches the specification. When some partition columns are missing or listed without their values, the command collects statistics for all partitions which match a subset of partition column values specified.
For example, table t has 4 partitions with the following specs:
* Partition1: (ds='2008-04-08', hr=11)
* Partition2: (ds='2008-04-08', hr=12)
* Partition3: (ds='2008-04-09', hr=11)
* Partition4: (ds='2008-04-09', hr=12)
'ANALYZE TABLE t PARTITION (ds='2008-04-09', hr=11)' command will collect statistics only for partition 3.
'ANALYZE TABLE t PARTITION (ds='2008-04-09')' command will collect statistics for partitions 3 and 4.
'ANALYZE TABLE t PARTITION (ds, hr)' command will collect statistics for all four partitions.
When the optional parameter NOSCAN is specified, the command doesn't count number of rows and only gathers size in bytes.
The statistics gathered by ANALYZE TABLE command can be fetched using DESC EXTENDED [db_name.]tablename PARTITION command.
## How was this patch tested?
Added tests.
Author: Masha Basmanova <mbasmanova@fb.com>
Closes#18421 from mbasmanova/mbasmanova-analyze-partition.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/projects/SPARK/issues/SPARK-21739
This issue is caused by introducing TimeZoneAwareExpression.
When the **Cast** expression converts something into TimestampType, it should be resolved with setting `timezoneId`. In general, it is resolved in LogicalPlan phase.
However, there are still some places that use Cast expression statically to convert datatypes without setting `timezoneId`. In such cases, `NoSuchElementException: None.get` will be thrown for TimestampType.
This PR is proposed to fix the issue. We have checked the whole project and found two such usages(i.e., in`TableReader` and `HiveTableScanExec`).
## How was this patch tested?
unit test
Author: donnyzone <wellfengzhu@gmail.com>
Closes#18960 from DonnyZone/spark-21739.
## What changes were proposed in this pull request?
Decimal is a logical type of AVRO. We need to ensure the support of Hive's AVRO serde works well in Spark
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18977 from gatorsmile/addAvroTest.
## What changes were proposed in this pull request?
This pr sorted output attributes on their name and exprId in `AttributeSet.toSeq` to make the order consistent. If the order is different, spark possibly generates different code and then misses cache in `CodeGenerator`, e.g., `GenerateColumnAccessor` generates code depending on an input attribute order.
## How was this patch tested?
Added tests in `AttributeSetSuite` and manually checked if the cache worked well in the given query of the JIRA.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18959 from maropu/SPARK-18394.
## What changes were proposed in this pull request?
When running IntelliJ, we are unable to capture the exception of memory leak detection.
> org.apache.spark.executor.Executor: Managed memory leak detected
Explicitly setting `spark.unsafe.exceptionOnMemoryLeak` in SparkConf when building the SparkSession, instead of reading it from system properties.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18967 from gatorsmile/setExceptionOnMemoryLeak.
## What changes were proposed in this pull request?
Set isolated to false while using builtin hive jars and `SessionState.get` returns a `CliSessionState` instance.
## How was this patch tested?
1 Unit Tests
2 Manually verified: `hive.exec.strachdir` was only created once because of reusing cliSessionState
```java
➜ spark git:(SPARK-21428) ✗ bin/spark-sql --conf spark.sql.hive.metastore.jars=builtin
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.Shell).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/07/16 23:59:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/07/16 23:59:27 INFO HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore
17/07/16 23:59:27 INFO ObjectStore: ObjectStore, initialize called
17/07/16 23:59:28 INFO Persistence: Property hive.metastore.integral.jdo.pushdown unknown - will be ignored
17/07/16 23:59:28 INFO Persistence: Property datanucleus.cache.level2 unknown - will be ignored
17/07/16 23:59:29 INFO ObjectStore: Setting MetaStore object pin classes with hive.metastore.cache.pinobjtypes="Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order"
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:30 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MFieldSchema" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MOrder" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:31 INFO MetaStoreDirectSql: Using direct SQL, underlying DB is DERBY
17/07/16 23:59:31 INFO ObjectStore: Initialized ObjectStore
17/07/16 23:59:31 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
17/07/16 23:59:31 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
17/07/16 23:59:32 INFO HiveMetaStore: Added admin role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: Added public role in metastore
17/07/16 23:59:32 INFO HiveMetaStore: No user is added in admin role, since config is empty
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_all_databases
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_all_databases
17/07/16 23:59:32 INFO HiveMetaStore: 0: get_functions: db=default pat=*
17/07/16 23:59:32 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_functions: db=default pat=*
17/07/16 23:59:32 INFO Datastore: The class "org.apache.hadoop.hive.metastore.model.MResourceUri" is tagged as "embedded-only" so does not have its own datastore table.
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/beea7261-221a-4711-89e8-8b12a9d37370_resources
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created local directory: /var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/Kent/beea7261-221a-4711-89e8-8b12a9d37370
17/07/16 23:59:32 INFO SessionState: Created HDFS directory: /tmp/hive/Kent/beea7261-221a-4711-89e8-8b12a9d37370/_tmp_space.db
17/07/16 23:59:32 INFO SparkContext: Running Spark version 2.3.0-SNAPSHOT
17/07/16 23:59:32 INFO SparkContext: Submitted application: SparkSQL::10.0.0.8
17/07/16 23:59:32 INFO SecurityManager: Changing view acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls to: Kent
17/07/16 23:59:32 INFO SecurityManager: Changing view acls groups to:
17/07/16 23:59:32 INFO SecurityManager: Changing modify acls groups to:
17/07/16 23:59:32 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(Kent); groups with view permissions: Set(); users with modify permissions: Set(Kent); groups with modify permissions: Set()
17/07/16 23:59:33 INFO Utils: Successfully started service 'sparkDriver' on port 51889.
17/07/16 23:59:33 INFO SparkEnv: Registering MapOutputTracker
17/07/16 23:59:33 INFO SparkEnv: Registering BlockManagerMaster
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
17/07/16 23:59:33 INFO DiskBlockManager: Created local directory at /private/var/folders/k2/04p4k4ws73l6711h_mz2_tq00000gn/T/blockmgr-9cfae28a-01e9-4c73-a1f1-f76fa52fc7a5
17/07/16 23:59:33 INFO MemoryStore: MemoryStore started with capacity 366.3 MB
17/07/16 23:59:33 INFO SparkEnv: Registering OutputCommitCoordinator
17/07/16 23:59:33 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/07/16 23:59:33 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://10.0.0.8:4040
17/07/16 23:59:33 INFO Executor: Starting executor ID driver on host localhost
17/07/16 23:59:33 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 51890.
17/07/16 23:59:33 INFO NettyBlockTransferService: Server created on 10.0.0.8:51890
17/07/16 23:59:33 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
17/07/16 23:59:33 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMasterEndpoint: Registering block manager 10.0.0.8:51890 with 366.3 MB RAM, BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:33 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, 10.0.0.8, 51890, None)
17/07/16 23:59:34 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('file:/Users/Kent/Documents/spark/spark-warehouse').
17/07/16 23:59:34 INFO SharedState: Warehouse path is 'file:/Users/Kent/Documents/spark/spark-warehouse'.
17/07/16 23:59:34 INFO HiveUtils: Initializing HiveMetastoreConnection version 1.2.1 using Spark classes.
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: default
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: default
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO HiveMetaStore: 0: get_database: global_temp
17/07/16 23:59:34 INFO audit: ugi=Kent ip=unknown-ip-addr cmd=get_database: global_temp
17/07/16 23:59:34 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
17/07/16 23:59:34 INFO HiveClientImpl: Warehouse location for Hive client (version 1.2.2) is /user/hive/warehouse
17/07/16 23:59:34 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint
spark-sql>
```
cc cloud-fan gatorsmile
Author: Kent Yao <yaooqinn@hotmail.com>
Author: hzyaoqin <hzyaoqin@corp.netease.com>
Closes#18648 from yaooqinn/SPARK-21428.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
## What changes were proposed in this pull request?
This is a follow-up of https://github.com/apache/spark/pull/15900 , to fix one more bug:
When table schema is empty and need to be inferred at runtime, we should not resolve parent plans before the schema has been inferred, or the parent plans will be resolved against an empty schema and may get wrong result for something like `select *`
The fix logic is: introduce `UnresolvedCatalogRelation` as a placeholder. Then we replace it with `LogicalRelation` or `HiveTableRelation` during analysis, so that it's guaranteed that we won't resolve parent plans until the schema has been inferred.
## How was this patch tested?
regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18907 from cloud-fan/bug.
## What changes were proposed in this pull request?
We put staging path to delete into the deleteOnExit cache of `FileSystem` in case of the path can't be successfully removed. But when we successfully remove the path, we don't remove it from the cache. We should do it to avoid continuing grow the cache size.
## How was this patch tested?
Added a test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18934 from viirya/SPARK-21721.
## What changes were proposed in this pull request?
This patch removes the unused SessionCatalog.getTableMetadataOption and ExternalCatalog. getTableOption.
## How was this patch tested?
Removed the test case.
Author: Reynold Xin <rxin@databricks.com>
Closes#18912 from rxin/remove-getTableOption.
## What changes were proposed in this pull request?
This patch introduces an internal interface for tracking metrics and/or statistics on data on the fly, as it is being written to disk during a `FileFormatWriter` job and partially reimplements SPARK-20703 in terms of it.
The interface basically consists of 3 traits:
- `WriteTaskStats`: just a tag for classes that represent statistics collected during a `WriteTask`
The only constraint it adds is that the class should be `Serializable`, as instances of it will be collected on the driver from all executors at the end of the `WriteJob`.
- `WriteTaskStatsTracker`: a trait for classes that can actually compute statistics based on tuples that are processed by a given `WriteTask` and eventually produce a `WriteTaskStats` instance.
- `WriteJobStatsTracker`: a trait for classes that act as containers of `Serializable` state that's necessary for instantiating `WriteTaskStatsTracker` on executors and finally process the resulting collection of `WriteTaskStats`, once they're gathered back on the driver.
Potential future use of this interface is e.g. CBO stats maintenance during `INSERT INTO table ... ` operations.
## How was this patch tested?
Existing tests for SPARK-20703 exercise the new code: `hive/SQLMetricsSuite`, `sql/JavaDataFrameReaderWriterSuite`, etc.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#18884 from adrian-ionescu/write-stats-tracker-api.
## What changes were proposed in this pull request?
This PR is to add the spark version info in the table metadata. When creating the table, this value is assigned. It can help users find which version of Spark was used to create the table.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18709 from gatorsmile/addVersion.
## What changes were proposed in this pull request?
Remove duplicate test-jar:test spark-sql dependency from Hive module; move test-jar dependencies together logically. This generates a big warning at the start of the Maven build otherwise.
## How was this patch tested?
Existing build. No functional changes here.
Author: Sean Owen <sowen@cloudera.com>
Closes#18858 from srowen/DupeSqlTestDep.
## What changes were proposed in this pull request?
This pr (follow-up of #18772) used `UnresolvedSubqueryColumnAliases` for `visitTableName` in `AstBuilder`, which is a new unresolved `LogicalPlan` implemented in #18185.
## How was this patch tested?
Existing tests
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18857 from maropu/SPARK-20963-FOLLOWUP.
## What changes were proposed in this pull request?
When we use `bin/spark-sql` command configuring `--conf spark.hadoop.foo=bar`, the `SparkSQLCliDriver` initializes an instance of hiveconf, it does not add `foo->bar` to it.
this pr gets `spark.hadoop.*` properties from sysProps to this hiveconf
## How was this patch tested?
UT
Author: hzyaoqin <hzyaoqin@corp.netease.com>
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#18668 from yaooqinn/SPARK-21451.
## What changes were proposed in this pull request?
OneRowRelation is the only plan that is a case object, which causes some issues with makeCopy using a 0-arg constructor. This patch changes it from a case object to a case class.
This blocks SPARK-21619.
## How was this patch tested?
Should be covered by existing test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18839 from rxin/SPARK-21634.
## What changes were proposed in this pull request?
In case of datasource tables (when they are stored in non-hive compatible way) , the schema information is recorded as table properties in hive meta-store. The alterTableStats method needs to get the schema information from table properties for data source tables before recording the column level statistics. Currently, we don't get the correct schema information and fail with java.util.NoSuchElement exception.
## How was this patch tested?
A new test case is added in StatisticsSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes#18804 from dilipbiswal/datasource_stats.
## What changes were proposed in this pull request?
Long values can be passed to `rangeBetween` as range frame boundaries, but we silently convert it to Int values, this can cause wrong results and we should fix this.
Further more, we should accept any legal literal values as range frame boundaries. In this PR, we make it possible for Long values, and make accepting other DataTypes really easy to add.
This PR is mostly based on Herman's previous amazing work: 596f53c339
After this been merged, we can close#16818 .
## How was this patch tested?
Add new tests in `DataFrameWindowFunctionsSuite` and `TypeCoercionSuite`.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18540 from jiangxb1987/rangeFrame.
## What changes were proposed in this pull request?
It's a follow-up of https://github.com/apache/spark/pull/18689 , which forgot to remove a useless test.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18716 from cloud-fan/test.
## What changes were proposed in this pull request?
DirectParquetOutputCommitter was removed from Spark as it was deemed unsafe to use. We however still have some code to generate warning. This patch removes those code as well.
This is kind of a follow-up of https://github.com/apache/spark/pull/16796
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18689 from cloud-fan/minor.
## What changes were proposed in this pull request?
When we list partitions from hive metastore with a partial partition spec, we are expecting exact matching according to the partition values. However, hive treats dot specially and match any single character for dot. We should do an extra filter to drop unexpected partitions.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18671 from cloud-fan/hive.
## What changes were proposed in this pull request?
Address scapegoat warnings for:
- BigDecimal double constructor
- Catching NPE
- Finalizer without super
- List.size is O(n)
- Prefer Seq.empty
- Prefer Set.empty
- reverse.map instead of reverseMap
- Type shadowing
- Unnecessary if condition.
- Use .log1p
- Var could be val
In some instances like Seq.empty, I avoided making the change even where valid in test code to keep the scope of the change smaller. Those issues are concerned with performance and it won't matter for tests.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18635 from srowen/Scapegoat1.
### What changes were proposed in this pull request?
The current SQLConf messages of `spark.sql.hive.convertMetastoreParquet` and `spark.sql.hive.convertMetastoreOrc` are not very clear to end users. This PR is to improve them.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18657 from gatorsmile/msgUpdates.
### What changes were proposed in this pull request?
The build-in functions `input_file_name`, `input_file_block_start`, `input_file_block_length` do not support more than one sources, like what Hive does. Currently, Spark does not block it and the outputs are ambiguous/non-deterministic. It could be from any side.
```
hive> select *, INPUT__FILE__NAME FROM t1, t2;
FAILED: SemanticException Column INPUT__FILE__NAME Found in more than One Tables/Subqueries
```
This PR blocks it and issues an error.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18580 from gatorsmile/inputFileName.
## What changes were proposed in this pull request?
- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17150 from srowen/SPARK-19810.
## What changes were proposed in this pull request?
Add sql test for window functions, also remove uncecessary test cases in `WindowQuerySuite`.
## How was this patch tested?
Added `window.sql` and the corresponding output file.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#18591 from jiangxb1987/window.
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-20331)
## What changes were proposed in this pull request?
Spark 2.1 introduced scalable support for Hive tables with huge numbers of partitions. Key to leveraging this support is the ability to prune unnecessary table partitions to answer queries. Spark supports a subset of the class of partition pruning predicates that the Hive metastore supports. If a user writes a query with a partition pruning predicate that is *not* supported by Spark, Spark falls back to loading all partitions and pruning client-side. We want to broaden Spark's current partition pruning predicate pushdown capabilities.
One of the key missing capabilities is support for disjunctions. For example, for a table partitioned by date, writing a query with a predicate like
date = 20161011 or date = 20161014
will result in Spark fetching all partitions. For a table partitioned by date and hour, querying a range of hours across dates can be quite difficult to accomplish without fetching all partition metadata.
The current partition pruning support supports only comparisons against literals. We can expand that to foldable expressions by evaluating them at planning time.
We can also implement support for the "IN" comparison by expanding it to a sequence of "OR"s.
## How was this patch tested?
The `HiveClientSuite` and `VersionsSuite` were refactored and simplified to make Hive client-based, version-specific testing more modular and conceptually simpler. There are now two Hive test suites: `HiveClientSuite` and `HivePartitionFilteringSuite`. These test suites have a single-argument constructor taking a `version` parameter. As such, these test suites cannot be run by themselves. Instead, they have been bundled into "aggregation" test suites which run each suite for each Hive client version. These aggregation suites are called `HiveClientSuites` and `HivePartitionFilteringSuites`. The `VersionsSuite` and `HiveClientSuite` have been refactored into each of these aggregation suites, respectively.
`HiveClientSuite` and `HivePartitionFilteringSuite` subclass a new abstract class, `HiveVersionSuite`. `HiveVersionSuite` collects functionality related to testing a single Hive version and overrides relevant test suite methods to display version-specific information.
A new trait, `HiveClientVersions`, has been added with a sequence of Hive test versions.
Author: Michael Allman <michael@videoamp.com>
Closes#17633 from mallman/spark-20331-enhanced_partition_pruning_pushdown.
## What changes were proposed in this pull request?
This pr made it more consistent to handle column name duplication. In the current master, error handling is different when hitting column name duplication:
```
// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#12, a#13.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Duplicate column(s) : "a" found, cannot save to JSON format;
at org.apache.spark.sql.execution.datasources.json.JsonDataSource.checkConstraints(JsonDataSource.scala:81)
at org.apache.spark.sql.execution.datasources.json.JsonDataSource.inferSchema(JsonDataSource.scala:63)
at org.apache.spark.sql.execution.datasources.json.JsonFileFormat.inferSchema(JsonFileFormat.scala:57)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#41, a#42.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
// If `inferSchema` is true, a CSV format is duplicate-safe (See SPARK-16896)
scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
| 1| 1|
+---+---+
// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be: a#110, a#111.;
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
```
When this patch applied, the results change to;
```
// json
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("""{"a":1, "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("json").schema(schema).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
scala> spark.read.format("json").load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
// csv
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq("a,a", "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
scala> spark.read.format("csv").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
+---+---+
| a0| a1|
+---+---+
| 1| 1|
+---+---+
// parquet
scala> val schema = StructType(StructField("a", IntegerType) :: StructField("a", IntegerType) :: Nil)
scala> Seq((1, 1)).toDF("a", "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
scala> spark.read.format("parquet").schema(schema).option("header", false).load("/tmp/data").show
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in datasource: "a";
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtil.scala:47)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtil.scala:33)
at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:186)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:368)
```
## How was this patch tested?
Added tests in `DataFrameReaderWriterSuite` and `SQLQueryTestSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17758 from maropu/SPARK-20460.
## What changes were proposed in this pull request?
We should be able to store zero size and row count after analyzing empty table.
This pr also enhances the test cases for re-analyzing tables.
## How was this patch tested?
Added a new test case and enhanced some test cases.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#18292 from wzhfy/analyzeNewColumn.
## What changes were proposed in this pull request?
Remove time metrics since it seems no way to measure it in non per-row tracking.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18558 from viirya/SPARK-20703-followup.
## What changes were proposed in this pull request?
1. move `StatisticsCollectionTestBase` to a separate file.
2. move some test cases to `StatisticsCollectionSuite` so that `hive/StatisticsSuite` only keeps tests that need hive support.
3. clear up some test cases.
## How was this patch tested?
Existing tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18545 from wzhfy/cleanStatSuites.
## What changes were proposed in this pull request?
Right now in the UI, after SPARK-20213, we can show the operations to write data out. However, there is no way to associate metrics with data writes. We should show relative metrics on the operations.
#### Supported commands
This change supports updating metrics for file-based data writing operations, including `InsertIntoHadoopFsRelationCommand`, `InsertIntoHiveTable`.
Supported metrics:
* number of written files
* number of dynamic partitions
* total bytes of written data
* total number of output rows
* average writing data out time (ms)
* (TODO) min/med/max number of output rows per file/partition
* (TODO) min/med/max bytes of written data per file/partition
#### Commands not supported
`InsertIntoDataSourceCommand`, `SaveIntoDataSourceCommand`:
The two commands uses DataSource APIs to write data out, i.e., the logic of writing data out is delegated to the DataSource implementations, such as `InsertableRelation.insert` and `CreatableRelationProvider.createRelation`. So we can't obtain metrics from delegated methods for now.
`CreateHiveTableAsSelectCommand`, `CreateDataSourceTableAsSelectCommand` :
The two commands invokes other commands to write data out. The invoked commands can even write to non file-based data source. We leave them as future TODO.
#### How to update metrics of writing files out
A `RunnableCommand` which wants to update metrics, needs to override its `metrics` and provide the metrics data structure to `ExecutedCommandExec`.
The metrics are prepared during the execution of `FileFormatWriter`. The callback function passed to `FileFormatWriter` will accept the metrics and update accordingly.
There is a metrics updating function in `RunnableCommand`. In runtime, the function will be bound to the spark context and `metrics` of `ExecutedCommandExec` and pass to `FileFormatWriter`.
## How was this patch tested?
Updated unit tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18159 from viirya/SPARK-20703-2.
## What changes were proposed in this pull request?
Support register Java UDAFs in PySpark so that user can use Java UDAF in PySpark. Besides that I also add api in `UDFRegistration`
## How was this patch tested?
Unit test is added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#17222 from zjffdu/SPARK-19439.
## What changes were proposed in this pull request?
support to create [temporary] function with the keyword 'OR REPLACE' and 'IF NOT EXISTS'
## How was this patch tested?
manual test and added test cases
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: ouyangxiaochen <ou.yangxiaochen@zte.com.cn>
Closes#17681 from ouyangxiaochen/spark-419.
## What changes were proposed in this pull request?
Looking at the code in `SessionCatalog.registerFunction`, the parameter `ignoreIfExists` is a wrong name. When `ignoreIfExists` is true, we will override the function if it already exists. So `overrideIfExists` should be the corrected name.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18510 from cloud-fan/minor.
## What changes were proposed in this pull request?
It is strange that we will get "table not found" error if **the first sql** uses upper case table names, when developers write tests with `TestHiveSingleton`, **although case insensitivity**. This is because in `TestHiveQueryExecution`, test tables are loaded based on exact matching instead of case sensitivity.
## How was this patch tested?
Added a new test case.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18504 from wzhfy/testHive.
## What changes were proposed in this pull request?
We currently implement statistics propagation directly in logical plan. Given we already have two different implementations, it'd make sense to actually decouple the two and add stats propagation using mixin. This would reduce the coupling between logical plan and statistics handling.
This can also be a powerful pattern in the future to add additional properties (e.g. constraints).
## How was this patch tested?
Should be covered by existing test cases.
Author: Reynold Xin <rxin@databricks.com>
Closes#18479 from rxin/stats-trait.
## What changes were proposed in this pull request?
Update stats after the following data changing commands:
- InsertIntoHadoopFsRelationCommand
- InsertIntoHiveTable
- LoadDataCommand
- TruncateTableCommand
- AlterTableSetLocationCommand
- AlterTableDropPartitionCommand
## How was this patch tested?
Added new test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18334 from wzhfy/changeStatsForOperation.
## What changes were proposed in this pull request?
Invalidate spark's stats after data changing commands:
- InsertIntoHadoopFsRelationCommand
- InsertIntoHiveTable
- LoadDataCommand
- TruncateTableCommand
- AlterTableSetLocationCommand
- AlterTableDropPartitionCommand
## How was this patch tested?
Added test cases.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#18449 from wzhfy/removeStats.
## What changes were proposed in this pull request?
If someone creates a HiveSession, the planner in `IncrementalExecution` doesn't take into account the Hive scan strategies. This causes joins of Streaming DataFrame's with Hive tables to fail.
## How was this patch tested?
Regression test
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#18426 from brkyvz/hive-join.
## What changes were proposed in this pull request?
Builds failed due to the recent [merge](b449a1d6aa). This is because [PR#18309](https://github.com/apache/spark/pull/18309) needed update after [this patch](b803b66a81) was merged.
## How was this patch tested?
N/A
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18415 from wzhfy/hotfixStats.
## What changes were proposed in this pull request?
Storage URI of a partitioned table may or may not point to a directory under which individual partitions are stored. In fact, individual partitions may be located in totally unrelated directories. Before this change, ANALYZE TABLE table COMPUTE STATISTICS command calculated total size of a table by adding up sizes of files found under table's storage URI. This calculation could produce 0 if partitions are stored elsewhere.
This change uses storage URIs of individual partitions to calculate the sizes of all partitions of a table and adds these up to produce the total size of a table.
CC: wzhfy
## How was this patch tested?
Added unit test.
Ran ANALYZE TABLE xxx COMPUTE STATISTICS on a partitioned Hive table and verified that sizeInBytes is calculated correctly. Before this change, the size would be zero.
Author: Masha Basmanova <mbasmanova@fb.com>
Closes#18309 from mbasmanova/mbasmanova-analyze-part-table.
## What changes were proposed in this pull request?
After wiring `SQLConf` in logical plan ([PR 18299](https://github.com/apache/spark/pull/18299)), we can remove the need of passing `conf` into `def stats` and `def computeStats`.
## How was this patch tested?
Covered by existing tests, plus some modified existing tests.
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18391 from wzhfy/removeConf.
## What changes were proposed in this pull request?
Currently we have several tens of test sqls in catalyst will fail at `SimpleAnalyzer.checkAnalysis`, we should make sure they are valid.
This PR makes the following changes:
1. Apply `checkAnalysis` on plans that tests `Optimizer` rules, but don't require the testcases for `Parser`/`Analyzer` pass `checkAnalysis`;
2. Fix testcases for `Optimizer` that would have fall.
## How was this patch tested?
Apply `SimpleAnalyzer.checkAnalysis` on plans in `PlanTest.comparePlans`, update invalid test cases.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Author: jiangxingbo <jiangxb1987@gmail.com>
Closes#15417 from jiangxb1987/cptest.
## What changes were proposed in this pull request?
This is a regression in Spark 2.2. In Spark 2.2, we introduced a new way to resolve persisted view: https://issues.apache.org/jira/browse/SPARK-18209 , but this makes the persisted view non case-preserving because we store the schema in hive metastore directly. We should follow data source table and store schema in table properties.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18360 from cloud-fan/view.
### What changes were proposed in this pull request?
Before the PR, Spark is unable to read the partitioned table created by Spark 2.1 when the table schema does not put the partitioning column at the end of the schema.
[assert(partitionFields.map(_.name) == partitionColumnNames)](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala#L234-L236)
When reading the table metadata from the metastore, we also need to reorder the columns.
### How was this patch tested?
Added test cases to check both Hive-serde and data source tables.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18295 from gatorsmile/reorderReadSchema.
## What changes were proposed in this pull request?
After PruneFileSourcePartitions rule, It needs reset table's statistics because PruneFileSourcePartitions can filter some unnecessary partitions. So the statistics need to be changed.
## How was this patch tested?
add unit test.
Author: lianhuiwang <lianhuiwang09@gmail.com>
Closes#18205 from lianhuiwang/SPARK-20986.
## What changes were proposed in this pull request?
Currently, hive's stats are read into `CatalogStatistics`, while spark's stats are also persisted through `CatalogStatistics`. As a result, hive's stats can be unexpectedly propagated into spark' stats.
For example, for a catalog table, we read stats from hive, e.g. "totalSize" and put it into `CatalogStatistics`. Then, by using "ALTER TABLE" command, we will store the stats in `CatalogStatistics` into metastore as spark's stats (because we don't know whether it's from spark or not). But spark's stats should be only generated by "ANALYZE" command. This is unexpected from this command.
Secondly, now that we have spark's stats in metastore, after inserting new data, although hive updated "totalSize" in metastore, we still cannot get the right `sizeInBytes` in `CatalogStatistics`, because we respect spark's stats (should not exist) over hive's stats.
A running example is shown in [JIRA](https://issues.apache.org/jira/browse/SPARK-21031).
To fix this, we add a new method `alterTableStats` to store spark's stats, and let `alterTable` keep existing stats.
## How was this patch tested?
Added new tests.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18248 from wzhfy/separateHiveStats.
### What changes were proposed in this pull request?
Currently, the unquoted string of a function identifier is being used as the function identifier in the function registry. This could cause the incorrect the behavior when users use `.` in the function names. This PR is to take the `FunctionIdentifier` as the identifier in the function registry.
- Add one new function `createOrReplaceTempFunction` to `FunctionRegistry`
```Scala
final def createOrReplaceTempFunction(name: String, builder: FunctionBuilder): Unit
```
### How was this patch tested?
Add extra test cases to verify the inclusive bug fixes.
Author: Xiao Li <gatorsmile@gmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18142 from gatorsmile/fuctionRegistry.
## What changes were proposed in this pull request?
After [SPARK-20067](https://issues.apache.org/jira/browse/SPARK-20067), `DESCRIBE` and `DESCRIBE EXTENDED` shows the following result. This is incompatible with Spark 2.1.1. This PR removes the column header line in case of those command.
**MASTER** and **BRANCH-2.2**
```scala
scala> sql("desc t").show(false)
+----------+---------+-------+
|col_name |data_type|comment|
+----------+---------+-------+
|# col_name|data_type|comment|
|a |int |null |
+----------+---------+-------+
```
**SPARK 2.1.1** and **this PR**
```scala
scala> sql("desc t").show(false)
+--------+---------+-------+
|col_name|data_type|comment|
+--------+---------+-------+
|a |int |null |
+--------+---------+-------+
```
## How was this patch tested?
Pass the Jenkins with the updated test suites.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18203 from dongjoon-hyun/SPARK-20954.
There could be test failures because DataStorageStrategy, HiveMetastoreCatalog and also HiveSchemaInferenceSuite were exposed to guava library by directly accessing SessionCatalog's tableRelationCacheg. These failures occur when guava shading is in place.
## What changes were proposed in this pull request?
This change removes those guava exposures by introducing new methods in SessionCatalog and also changing DataStorageStrategy, HiveMetastoreCatalog and HiveSchemaInferenceSuite so that they use those proxy methods.
## How was this patch tested?
Unit tests passed after applying these changes.
Author: Reza Safi <rezasafi@cloudera.com>
Closes#18148 from rezasafi/branch-2.2.
(cherry picked from commit 1388fdd707)
## What changes were proposed in this pull request?
Usually when using explain cost command, users want to see the stats of plan. Since stats is only showed in optimized plan, it is more direct and convenient to include only optimized plan and physical plan in the output.
## How was this patch tested?
Enhanced existing test.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18190 from wzhfy/simplifyExplainCost.
## What changes were proposed in this pull request?
Currently the `DataFrameWriter` operations have several problems:
1. non-file-format data source writing action doesn't show up in the SQL tab in Spark UI
2. file-format data source writing action shows a scan node in the SQL tab, without saying anything about writing. (streaming also have this issue, but not fixed in this PR)
3. Spark SQL CLI actions don't show up in the SQL tab.
This PR fixes all of them, by refactoring the `ExecuteCommandExec` to make it have children.
close https://github.com/apache/spark/pull/17540
## How was this patch tested?
existing tests.
Also test the UI manually. For a simple command: `Seq(1 -> "a").toDF("i", "j").write.parquet("/tmp/qwe")`
before this PR:
<img width="266" alt="qq20170523-035840 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326050/24e18ba2-3f6c-11e7-8817-6dd275bf6ac5.png">
after this PR:
<img width="287" alt="qq20170523-035708 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326054/2ad7f460-3f6c-11e7-8053-d68325beb28f.png">
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18064 from cloud-fan/execution.
### What changes were proposed in this pull request?
We are unable to call the function registered in the not-current database.
```Scala
sql("CREATE DATABASE dAtABaSe1")
sql(s"CREATE FUNCTION dAtABaSe1.test_avg AS '${classOf[GenericUDAFAverage].getName}'")
sql("SELECT dAtABaSe1.test_avg(1)")
```
The above code returns an error:
```
Undefined function: 'dAtABaSe1.test_avg'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 1 pos 7
```
This PR is to fix the above issue.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#18146 from gatorsmile/qualifiedFunction.
## What changes were proposed in this pull request?
Now, we have two sources of statistics, i.e. Spark's stats and Hive's stats. Spark's stats is generated by running "analyze" command in Spark. Once it's available, we respect this stats over Hive's.
This pr is to clearly document in related code the mechanism to choose between these two sources of stats.
## How was this patch tested?
Not related.
Author: Zhenhua Wang <wzh_zju@163.com>
Closes#18105 from wzhfy/cboSwitchStats.
## What changes were proposed in this pull request?
This pr added parsing rules to support table column aliases in FROM clause.
## How was this patch tested?
Added tests in `PlanParserSuite`, `SQLQueryTestSuite`, and `PlanParserSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18079 from maropu/SPARK-20841.
## What changes were proposed in this pull request?
It is reported that there is performance downgrade when applying ML pipeline for dataset with many columns but few rows.
A big part of the performance downgrade comes from some operations (e.g., `select`) on DataFrame/Dataset which re-create new DataFrame/Dataset with a new `LogicalPlan`. The cost can be ignored in the usage of SQL, normally.
However, it's not rare to chain dozens of pipeline stages in ML. When the query plan grows incrementally during running those stages, the total cost spent on re-creation of DataFrame grows too. In particular, the `Analyzer` will go through the big query plan even most part of it is analyzed.
By eliminating part of the cost, the time to run the example code locally is reduced from about 1min to about 30 secs.
In particular, the time applying the pipeline locally is mostly spent on calling transform of the 137 `Bucketizer`s. Before the change, each call of `Bucketizer`'s transform can cost about 0.4 sec. So the total time spent on all `Bucketizer`s' transform is about 50 secs. After the change, each call only costs about 0.1 sec.
<del>We also make `boundEnc` as lazy variable to reduce unnecessary running time.</del>
### Performance improvement
The codes and datasets provided by Barry Becker to re-produce this issue and benchmark can be found on the JIRA.
Before this patch: about 1 min
After this patch: about 20 secs
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17770 from viirya/SPARK-20392.
## What changes were proposed in this pull request?
This PR proposes two things:
- A follow up for SPARK-19707 (Improving the invalid path check for sc.addJar on Windows as well).
```
org.apache.spark.SparkContextSuite:
- add jar with invalid path *** FAILED *** (32 milliseconds)
2 was not equal to 1 (SparkContextSuite.scala:309)
...
```
- Fix path vs URI related test failures on Windows.
```
org.apache.spark.storage.LocalDirsSuite:
- SPARK_LOCAL_DIRS override also affects driver *** FAILED *** (0 milliseconds)
new java.io.File("/NONEXISTENT_PATH").exists() was true (LocalDirsSuite.scala:50)
...
- Utils.getLocalDir() throws an exception if any temporary directory cannot be retrieved *** FAILED *** (15 milliseconds)
Expected exception java.io.IOException to be thrown, but no exception was thrown. (LocalDirsSuite.scala:64)
...
```
```
org.apache.spark.sql.hive.HiveSchemaInferenceSuite:
- orc: schema should be inferred and saved when INFER_AND_SAVE is specified *** FAILED *** (203 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-dae61ab3-a851-4dd3-bf4e-be97c501f254
...
- parquet: schema should be inferred and saved when INFER_AND_SAVE is specified *** FAILED *** (203 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-fa3aff89-a66e-4376-9a37-2a9b87596939
...
- orc: schema should be inferred but not stored when INFER_ONLY is specified *** FAILED *** (141 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-fb464e59-b049-481b-9c75-f53295c9fc2c
...
- parquet: schema should be inferred but not stored when INFER_ONLY is specified *** FAILED *** (125 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-9487568e-80a4-42b3-b0a5-d95314c4ccbc
...
- orc: schema should not be inferred when NEVER_INFER is specified *** FAILED *** (156 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-0d2dfa45-1b0f-4958-a8be-1074ed0135a
...
- parquet: schema should not be inferred when NEVER_INFER is specified *** FAILED *** (547 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-6d95d64e-613e-4a59-a0f6-d198c5aa51ee
...
```
```
org.apache.spark.sql.execution.command.DDLSuite:
- create temporary view using *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-3881d9ca-561b-488d-90b9-97587472b853 mp;
...
- insert data to a data source table which has a non-existing location should succeed *** FAILED *** (109 milliseconds)
file:/C:projectsspark%09arget%09mpspark-4cad3d19-6085-4b75-b407-fe5e9d21df54 did not equal file:///C:/projects/spark/target/tmp/spark-4cad3d19-6085-4b75-b407-fe5e9d21df54 (DDLSuite.scala:1869)
...
- insert into a data source table with a non-existing partition location should succeed *** FAILED *** (94 milliseconds)
file:/C:projectsspark%09arget%09mpspark-4b52e7de-e3aa-42fd-95d4-6d4d58d1d95d did not equal file:///C:/projects/spark/target/tmp/spark-4b52e7de-e3aa-42fd-95d4-6d4d58d1d95d (DDLSuite.scala:1910)
...
- read data from a data source table which has a non-existing location should succeed *** FAILED *** (93 milliseconds)
file:/C:projectsspark%09arget%09mpspark-f8c281e2-08c2-4f73-abbf-f3865b702c34 did not equal file:///C:/projects/spark/target/tmp/spark-f8c281e2-08c2-4f73-abbf-f3865b702c34 (DDLSuite.scala:1937)
...
- read data from a data source table with non-existing partition location should succeed *** FAILED *** (110 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- create datasource table with a non-existing location *** FAILED *** (94 milliseconds)
file:/C:projectsspark%09arget%09mpspark-387316ae-070c-4e78-9b78-19ebf7b29ec8 did not equal file:///C:/projects/spark/target/tmp/spark-387316ae-070c-4e78-9b78-19ebf7b29ec8 (DDLSuite.scala:1982)
...
- CTAS for external data source table with a non-existing location *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- CTAS for external data source table with a existed location *** FAILED *** (15 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- data source table:partition column name containing a b *** FAILED *** (125 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- data source table:partition column name containing a:b *** FAILED *** (143 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- data source table:partition column name containing a%b *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- data source table:partition column name containing a,b *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- location uri contains a b for datasource table *** FAILED *** (94 milliseconds)
file:/C:projectsspark%09arget%09mpspark-5739cda9-b702-4e14-932c-42e8c4174480a%20b did not equal file:///C:/projects/spark/target/tmp/spark-5739cda9-b702-4e14-932c-42e8c4174480/a%20b (DDLSuite.scala:2084)
...
- location uri contains a:b for datasource table *** FAILED *** (78 milliseconds)
file:/C:projectsspark%09arget%09mpspark-9bdd227c-840f-4f08-b7c5-4036638f098da:b did not equal file:///C:/projects/spark/target/tmp/spark-9bdd227c-840f-4f08-b7c5-4036638f098d/a:b (DDLSuite.scala:2084)
...
- location uri contains a%b for datasource table *** FAILED *** (78 milliseconds)
file:/C:projectsspark%09arget%09mpspark-62bb5f1d-fa20-460a-b534-cb2e172a3640a%25b did not equal file:///C:/projects/spark/target/tmp/spark-62bb5f1d-fa20-460a-b534-cb2e172a3640/a%25b (DDLSuite.scala:2084)
...
- location uri contains a b for database *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- location uri contains a:b for database *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- location uri contains a%b for database *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
```
```
org.apache.spark.sql.hive.execution.HiveDDLSuite:
- create hive table with a non-existing location *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- CTAS for external hive table with a non-existing location *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- CTAS for external hive table with a existed location *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- partition column name of parquet table containing a b *** FAILED *** (156 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- partition column name of parquet table containing a:b *** FAILED *** (94 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- partition column name of parquet table containing a%b *** FAILED *** (125 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- partition column name of parquet table containing a,b *** FAILED *** (110 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
- partition column name of hive table containing a b *** FAILED *** (15 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- partition column name of hive table containing a:b *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- partition column name of hive table containing a%b *** FAILED *** (16 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- partition column name of hive table containing a,b *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- hive table: location uri contains a b *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- hive table: location uri contains a:b *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
- hive table: location uri contains a%b *** FAILED *** (0 milliseconds)
org.apache.spark.sql.AnalysisException: org.apache.hadoop.hive.ql.metadata.HiveException: MetaException(message:java.lang.IllegalArgumentException: Can not create a Path from an empty string);
...
```
```
org.apache.spark.sql.sources.PathOptionSuite:
- path option also exist for write path *** FAILED *** (94 milliseconds)
file:/C:projectsspark%09arget%09mpspark-2870b281-7ac0-43d6-b6b6-134e01ab6fdc did not equal file:///C:/projects/spark/target/tmp/spark-2870b281-7ac0-43d6-b6b6-134e01ab6fdc (PathOptionSuite.scala:98)
...
```
```
org.apache.spark.sql.CachedTableSuite:
- SPARK-19765: UNCACHE TABLE should un-cache all cached plans that refer to this table *** FAILED *** (110 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
...
```
```
org.apache.spark.sql.execution.DataSourceScanExecRedactionSuite:
- treeString is redacted *** FAILED *** (250 milliseconds)
"file:/C:/projects/spark/target/tmp/spark-3ecc1fa4-3e76-489c-95f4-f0b0500eae28" did not contain "C:\projects\spark\target\tmp\spark-3ecc1fa4-3e76-489c-95f4-f0b0500eae28" (DataSourceScanExecRedactionSuite.scala:46)
...
```
## How was this patch tested?
Tested via AppVeyor for each and checked it passed once each. These should be retested via AppVeyor in this PR.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17987 from HyukjinKwon/windows-20170515.
### What changes were proposed in this pull request?
After we adding a new field `stats` into `CatalogTable`, we should not expose Hive-specific Stats metadata to `MetastoreRelation`. It complicates all the related codes. It also introduces a bug in `SHOW CREATE TABLE`. The statistics-related table properties should be skipped by `SHOW CREATE TABLE`, since it could be incorrect in the newly created table. See the Hive JIRA: https://issues.apache.org/jira/browse/HIVE-13792
Also fix the issue to fill Hive-generated RowCounts to our stats.
This PR is to handle Hive-specific Stats metadata in `HiveClientImpl`.
### How was this patch tested?
Added a few test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#14971 from gatorsmile/showCreateTableNew.
### What changes were proposed in this pull request?
Currently, we have a bug when we specify `IF NOT EXISTS` in `INSERT OVERWRITE` data source tables. For example, given a query:
```SQL
INSERT OVERWRITE TABLE $tableName partition (b=2, c=3) IF NOT EXISTS SELECT 9, 10
```
we will get the following error:
```
unresolved operator 'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true;;
'InsertIntoTable Relation[a#425,d#426,b#427,c#428] parquet, Map(b -> Some(2), c -> Some(3)), true, true
+- Project [cast(9#423 as int) AS a#429, cast(10#424 as int) AS d#430]
+- Project [9 AS 9#423, 10 AS 10#424]
+- OneRowRelation$
```
This PR is to fix the issue to follow the behavior of Hive serde tables
> INSERT OVERWRITE will overwrite any existing data in the table or partition unless IF NOT EXISTS is provided for a partition
### How was this patch tested?
Modified an existing test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18050 from gatorsmile/insertPartitionIfNotExists.
## What changes were proposed in this pull request?
When an expression for `df.filter()` has many nodes (e.g. 400), the size of Java bytecode for the generated Java code is more than 64KB. It produces an Java exception. As a result, the execution fails.
This PR continues to execute by calling `Expression.eval()` disabling code generation if an exception has been caught.
## How was this patch tested?
Add a test suite into `DataFrameSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#17087 from kiszk/SPARK-19372.
## What changes were proposed in this pull request?
Hive allows inserting data to bucketed table without guaranteeing bucketed and sorted-ness based on these two configs : `hive.enforce.bucketing` and `hive.enforce.sorting`.
What does this PR achieve ?
- Spark will disallow users from writing outputs to hive bucketed tables by default (given that output won't adhere with Hive's semantics).
- IF user still wants to write to hive bucketed table, the only resort is to use `hive.enforce.bucketing=false` and `hive.enforce.sorting=false` which means user does NOT care about bucketing guarantees.
Changes done in this PR:
- Extract table's bucketing information in `HiveClientImpl`
- While writing table info to metastore, `HiveClientImpl` now populates the bucketing information in the hive `Table` object
- `InsertIntoHiveTable` allows inserts to bucketed table only if both `hive.enforce.bucketing` and `hive.enforce.sorting` are `false`
Ability to create bucketed tables will enable adding test cases to Spark while I add more changes related to hive bucketing support. Design doc for hive hive bucketing support : https://docs.google.com/document/d/1a8IDh23RAkrkg9YYAeO51F4aGO8-xAlupKwdshve2fc/edit#
## How was this patch tested?
- Added test for creating bucketed and sorted table.
- Added test to ensure that INSERTs fail if strict bucket / sort is enforced
- Added test to ensure that INSERTs can go through if strict bucket / sort is NOT enforced
- Added test to validate that bucketing information shows up in output of DESC FORMATTED
- Added test to ensure that `SHOW CREATE TABLE` works for hive bucketed tables
Author: Tejas Patil <tejasp@fb.com>
Closes#17644 from tejasapatil/SPARK-17729_create_bucketed_table.
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-20594
## What changes were proposed in this pull request?
The staging directory should be a child directory starts with "." to avoid being deleted before moving staging directory to table directory if we set hive.exec.stagingdir under the table directory.
## How was this patch tested?
Added unit tests
Author: zuotingbing <zuo.tingbing9@zte.com.cn>
Closes#17858 from zuotingbing/spark-stagingdir.
## What changes were proposed in this pull request?
Fix canonicalization for different filter orders in `HiveTableScanExec`.
## How was this patch tested?
Added a new test case.
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17962 from wzhfy/canonicalizeHiveTableScanExec.
## What changes were proposed in this pull request?
Remove uses of scala.language.reflectiveCalls that are either unnecessary or probably resulting in more complex code. This turned out to be less significant than I thought, but, still worth a touch-up.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17949 from srowen/SPARK-20554.
## What changes were proposed in this pull request?
So far, we do not drop all the cataloged objects after each package. Sometimes, we might hit strange test case errors because the previous test suite did not drop the cataloged/temporary objects (tables/functions/database). At least, we can first clean up the environment when completing the package of `sql/core` and `sql/hive`.
## How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17908 from gatorsmile/reset.
## What changes were proposed in this pull request?
This change allows timestamps in parquet-based hive table to behave as a "floating time", without a timezone, as timestamps are for other file formats. If the storage timezone is the same as the session timezone, this conversion is a no-op. When data is read from a hive table, the table property is *always* respected. This allows spark to not change behavior when reading old data, but read newly written data correctly (whatever the source of the data is).
Spark inherited the original behavior from Hive, but Hive is also updating behavior to use the same scheme in HIVE-12767 / HIVE-16231.
The default for Spark remains unchanged; created tables do not include the new table property.
This will only apply to hive tables; nothing is added to parquet metadata to indicate the timezone, so data that is read or written directly from parquet files will never have any conversions applied.
## How was this patch tested?
Added a unit test which creates tables, reads and writes data, under a variety of permutations (different storage timezones, different session timezones, vectorized reading on and off).
Author: Imran Rashid <irashid@cloudera.com>
Closes#16781 from squito/SPARK-12297.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
### What changes were proposed in this pull request?
This is a follow-up of enabling test cases in DDLSuite with Hive Metastore. It consists of the following remaining tasks:
- Run all the `alter table` and `drop table` DDL tests against data source tables when using Hive metastore.
- Do not run any `alter table` and `drop table` DDL test against Hive serde tables when using InMemoryCatalog.
- Reenable `alter table: set serde partition` and `alter table: set serde` tests for Hive serde tables.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17524 from gatorsmile/cleanupDDLSuite.
### What changes were proposed in this pull request?
```SQL
hive> create table t1(`a,` string);
OK
Time taken: 1.399 seconds
hive> create table t2(`a,` string, b string);
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.RuntimeException: MetaException(message:org.apache.hadoop.hive.serde2.SerDeException org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe: columns has 3 elements while columns.types has 2 elements!)
hive> create table t2(`a,` string, b string) stored as parquet;
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.IllegalArgumentException: ParquetHiveSerde initialization failed. Number of column name and column type differs. columnNames = [a, , b], columnTypes = [string, string]
```
It has a bug in Hive metastore.
When users do not provide alias name in the SELECT query, we call `toPrettySQL` to generate the alias name. For example, the string `get_json_object(jstring, '$.f1')` will be the alias name for the function call in the statement
```SQL
SELECT key, get_json_object(jstring, '$.f1') FROM tempView
```
Above is not an issue for the SELECT query statements. However, for CTAS, we hit the issue due to a bug in Hive metastore. Hive metastore does not like the column names containing commas and returned a confusing error message, like:
```
17/04/26 23:12:56 ERROR [hive.log(397) -- main]: error in initSerDe: org.apache.hadoop.hive.serde2.SerDeException org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe: columns has 2 elements while columns.types has 1 elements!
org.apache.hadoop.hive.serde2.SerDeException: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe: columns has 2 elements while columns.types has 1 elements!
```
Thus, this PR is to block users to create a table in Hive metastore when the table table has a column containing commas in the name.
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17781 from gatorsmile/blockIllegalColumnNames.
### What changes were proposed in this pull request?
`spark.catalog.listTables` and `spark.catalog.getTable` does not work if we are unable to retrieve table metadata due to any reason (e.g., table serde class is not accessible or the table type is not accepted by Spark SQL). After this PR, the APIs still return the corresponding Table without the description and tableType)
### How was this patch tested?
Added a test case
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17730 from gatorsmile/listTables.
## What changes were proposed in this pull request?
It is often useful to be able to track changes to the `ExternalCatalog`. This PR makes the `ExternalCatalog` emit events when a catalog object is changed. Events are fired before and after the change.
The following events are fired per object:
- Database
- CreateDatabasePreEvent: event fired before the database is created.
- CreateDatabaseEvent: event fired after the database has been created.
- DropDatabasePreEvent: event fired before the database is dropped.
- DropDatabaseEvent: event fired after the database has been dropped.
- Table
- CreateTablePreEvent: event fired before the table is created.
- CreateTableEvent: event fired after the table has been created.
- RenameTablePreEvent: event fired before the table is renamed.
- RenameTableEvent: event fired after the table has been renamed.
- DropTablePreEvent: event fired before the table is dropped.
- DropTableEvent: event fired after the table has been dropped.
- Function
- CreateFunctionPreEvent: event fired before the function is created.
- CreateFunctionEvent: event fired after the function has been created.
- RenameFunctionPreEvent: event fired before the function is renamed.
- RenameFunctionEvent: event fired after the function has been renamed.
- DropFunctionPreEvent: event fired before the function is dropped.
- DropFunctionPreEvent: event fired after the function has been dropped.
The current events currently only contain the names of the object modified. We add more events, and more details at a later point.
A user can monitor changes to the external catalog by adding a listener to the Spark listener bus checking for `ExternalCatalogEvent`s using the `SparkListener.onOtherEvent` hook. A more direct approach is add listener directly to the `ExternalCatalog`.
## How was this patch tested?
Added the `ExternalCatalogEventSuite`.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17710 from hvanhovell/SPARK-20420.
## What changes were proposed in this pull request?
A cast expression with a resolved time zone is not equal to a cast expression without a resolved time zone. The `ResolveAggregateFunction` assumed that these expression were the same, and would fail to resolve `HAVING` clauses which contain a `Cast` expression.
This is in essence caused by the fact that a `TimeZoneAwareExpression` can be resolved without a set time zone. This PR fixes this, and makes a `TimeZoneAwareExpression` unresolved as long as it has no TimeZone set.
## How was this patch tested?
Added a regression test to the `SQLQueryTestSuite.having` file.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17641 from hvanhovell/SPARK-20329.
### What changes were proposed in this pull request?
The session catalog caches some persistent functions in the `FunctionRegistry`, so there can be duplicates. Our Catalog API `listFunctions` does not handle it.
It would be better if `SessionCatalog` API can de-duplciate the records, instead of doing it by each API caller. In `FunctionRegistry`, our functions are identified by the unquoted string. Thus, this PR is try to parse it using our parser interface and then de-duplicate the names.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17646 from gatorsmile/showFunctions.
### What changes were proposed in this pull request?
```JAVA
/**
* Certain optimizations should not be applied if UDF is not deterministic.
* Deterministic UDF returns same result each time it is invoked with a
* particular input. This determinism just needs to hold within the context of
* a query.
*
* return true if the UDF is deterministic
*/
boolean deterministic() default true;
```
Based on the definition of [UDFType](https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/UDFType.java#L42-L50), when Hive UDF's children are non-deterministic, Hive UDF is also non-deterministic.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17635 from gatorsmile/udfDeterministic.
## What changes were proposed in this pull request?
Currently when estimating predicates like col > literal or col = literal, we will update min or max in column stats based on literal value. However, literal value is of Catalyst type (internal type), while min/max is of external type. Then for the next predicate, we again need to do type conversion to compare and update column stats. This is awkward and causes many unnecessary conversions in estimation.
To solve this, we use Catalyst type for min/max in `ColumnStat`. Note that the persistent format in metastore is still of external type, so there's no inconsistency for statistics in metastore.
This pr also fixes a bug for boolean type in `IN` condition.
## How was this patch tested?
The changes for ColumnStat are covered by existing tests.
For bug fix, a new test for boolean type in IN condition is added
Author: wangzhenhua <wangzhenhua@huawei.com>
Closes#17630 from wzhfy/refactorColumnStat.
### What changes were proposed in this pull request?
Session catalog API `createTempFunction` is being used by Hive build-in functions, persistent functions, and temporary functions. Thus, the name is confusing. This PR is to rename it by `registerFunction`. Also we can move construction of `FunctionBuilder` and `ExpressionInfo` into the new `registerFunction`, instead of duplicating the logics everywhere.
In the next PRs, the remaining Function-related APIs also need cleanups.
### How was this patch tested?
Existing test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17615 from gatorsmile/cleanupCreateTempFunction.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
Like `Expression`, `QueryPlan` should also have a `semanticHash` method, then we can put plans to a hash map and look it up fast. This PR refactors `QueryPlan` to follow `Expression` and put all the normalization logic in `QueryPlan.canonicalized`, so that it's very natural to implement `semanticHash`.
follow-up: improve `CacheManager` to leverage this `semanticHash` and speed up plan lookup, instead of iterating all cached plans.
## How was this patch tested?
existing tests. Note that we don't need to test the `semanticHash` method, once the existing tests prove `sameResult` is correct, we are good.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17541 from cloud-fan/plan-semantic.
## What changes were proposed in this pull request?
This error message doesn't get properly formatted because of a missing `s`. Currently the error looks like:
```
Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line"
```
(note the literal `$current` instead of the interpolated value)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Vijay Ramesh <vramesh@demandbase.com>
Closes#17572 from vijaykramesh/master.
## What changes were proposed in this pull request?
Currently `LogicalRelation` has a `expectedOutputAttributes` parameter, which makes it hard to reason about what the actual output is. Like other leaf nodes, `LogicalRelation` should also take `output` as a parameter, to simplify the logic
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#17552 from cloud-fan/minor.
### What changes were proposed in this pull request?
This PR is to unify and clean up the outputs of `DESC EXTENDED/FORMATTED` and `SHOW TABLE EXTENDED` by moving the logics into the Catalog interface. The output formats are improved. We also add the missing attributes. It impacts the DDL commands like `SHOW TABLE EXTENDED`, `DESC EXTENDED` and `DESC FORMATTED`.
In addition, by following what we did in Dataset API `printSchema`, we can use `treeString` to show the schema in the more readable way.
Below is the current way:
```
Schema: STRUCT<`a`: STRING (nullable = true), `b`: INT (nullable = true), `c`: STRING (nullable = true), `d`: STRING (nullable = true)>
```
After the change, it should look like
```
Schema: root
|-- a: string (nullable = true)
|-- b: integer (nullable = true)
|-- c: string (nullable = true)
|-- d: string (nullable = true)
```
### How was this patch tested?
`describe.sql` and `show-tables.sql`
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17394 from gatorsmile/descFollowUp.
## What changes were proposed in this pull request?
This patch implements `listPartitionsByFilter()` for `InMemoryCatalog` and thus resolves an outstanding TODO causing the `PruneFileSourcePartitions` optimizer rule not to apply when "spark.sql.catalogImplementation" is set to "in-memory" (which is the default).
The change is straightforward: it extracts the code for further filtering of the list of partitions returned by the metastore's `getPartitionsByFilter()` out from `HiveExternalCatalog` into `ExternalCatalogUtils` and calls this new function from `InMemoryCatalog` on the whole list of partitions.
Now that this method is implemented we can always pass the `CatalogTable` to the `DataSource` in `FindDataSourceTable`, so that the latter is resolved to a relation with a `CatalogFileIndex`, which is what the `PruneFileSourcePartitions` rule matches for.
## How was this patch tested?
Ran existing tests and added new test for `listPartitionsByFilter` in `ExternalCatalogSuite`, which is subclassed by both `InMemoryCatalogSuite` and `HiveExternalCatalogSuite`.
Author: Adrian Ionescu <adrian@databricks.com>
Closes#17510 from adrian-ionescu/InMemoryCatalog.
### What changes were proposed in this pull request?
`ParquetConversions` and `OrcConversions` should be treated as regular `Analyzer` rules. It is not reasonable to be part of `HiveSessionCatalog`. This PR also combines two rules `ParquetConversions` and `OrcConversions` to build a new rule `RelationConversions `.
After moving these two rules out of HiveSessionCatalog, the next step is to clean up, rename and move `HiveMetastoreCatalog` because it is not related to the hive package any more.
### How was this patch tested?
The existing test cases
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17484 from gatorsmile/cleanup.
…adoc
## What changes were proposed in this pull request?
Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0).
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes#17417 from jaceklaskowski/window-expression-scaladoc.
## What changes were proposed in this pull request?
This pr added `StructType.fromDDL` to convert a DDL format string into `StructType` for defining schemas in `functions.from_json`.
## How was this patch tested?
Added tests in `JsonFunctionsSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17406 from maropu/SPARK-20009.
## What changes were proposed in this pull request?
Bugfix from [SPARK-19540.](https://github.com/apache/spark/pull/16826)
Cloning SessionState does not clone query execution listeners, so cloned session is unable to listen to events on queries.
## How was this patch tested?
- Unit test
Author: Kunal Khamar <kkhamar@outlook.com>
Closes#17379 from kunalkhamar/clone-bugfix.
## What changes were proposed in this pull request?
Commit ea361165e1 moved most of the logic from the SessionState classes into an accompanying builder. This makes the existence of the `HiveSessionState` redundant. This PR removes the `HiveSessionState`.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17457 from hvanhovell/SPARK-20126.
## What changes were proposed in this pull request?
The current SessionState initialization code path is quite complex. A part of the creation is done in the SessionState companion objects, a part of the creation is one inside the SessionState class, and a part is done by passing functions.
This PR refactors this code path, and consolidates SessionState initialization into a builder class. This SessionState will not do any initialization and just becomes a place holder for the various Spark SQL internals. This also lays the ground work for two future improvements:
1. This provides us with a start for removing the `HiveSessionState`. Removing the `HiveSessionState` would also require us to move resource loading into a separate class, and to (re)move metadata hive.
2. This makes it easier to customize the Spark Session. Currently you will need to create a custom version of the builder. I have added hooks to facilitate this. A future step will be to create a semi stable API on top of this.
## How was this patch tested?
Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes#17433 from hvanhovell/SPARK-20100.
### What changes were proposed in this pull request?
This is a follow-up for the PR: https://github.com/apache/spark/pull/17311
- For safety, use `sessionState` to get the user name, instead of calling `SessionState.get()` in the function `toHiveTable`.
- Passing `user names` instead of `conf` when calling `toHiveTable`.
### How was this patch tested?
N/A
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17405 from gatorsmile/user.
