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## What changes were proposed in this pull request?
After unifying the CREATE TABLE syntax in https://github.com/apache/spark/pull/16296, it's pretty easy to support creating hive table with `DataFrameWriter` and `Catalog` now.
This PR basically just removes the hive provider check in `DataFrameWriter.saveAsTable` and `Catalog.createExternalTable`, and add tests.
## How was this patch tested?
new tests in `HiveDDLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16487 from cloud-fan/hive-table.
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## What changes were proposed in this pull request?
`OutputWriterFactory`/`OutputWriter` are internal interfaces and we can remove some unnecessary APIs:
1. `OutputWriterFactory.newWriter(path: String)`: no one calls it and no one implements it.
2. `OutputWriter.write(row: Row)`: during execution we only call `writeInternal`, which is weird as `OutputWriter` is already an internal interface. We should rename `writeInternal` to `write` and remove `def write(row: Row)` and it's related converter code. All implementations should just implement `def write(row: InternalRow)`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16479 from cloud-fan/hive-writer.
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tables
## What changes were proposed in this pull request?
Today we have different syntax to create data source or hive serde tables, we should unify them to not confuse users and step forward to make hive a data source.
Please read https://issues.apache.org/jira/secure/attachment/12843835/CREATE-TABLE.pdf for details.
TODO(for follow-up PRs):
1. TBLPROPERTIES is not added to the new syntax, we should decide if we wanna add it later.
2. `SHOW CREATE TABLE` should be updated to use the new syntax.
3. we should decide if we wanna change the behavior of `SET LOCATION`.
## How was this patch tested?
new tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16296 from cloud-fan/create-table.
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DataFrameWriter.saveAsTable
## What changes were proposed in this pull request?
When we append data to a partitioned table with `DataFrameWriter.saveAsTable`, there are 2 issues:
1. doesn't work when the partition has custom location.
2. will recover all partitions
This PR fixes them by moving the special partition handling code from `DataSourceAnalysis` to `InsertIntoHadoopFsRelationCommand`, so that the `DataFrameWriter.saveAsTable` code path can also benefit from it.
## How was this patch tested?
newly added regression tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16460 from cloud-fan/append.
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## What changes were proposed in this pull request?
Now all aggregation functions support partial aggregate, we can remove the `supportsPartual` flag in `AggregateFunction`
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16461 from cloud-fan/partial.
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Partitioned Tables in InMemoryCatalog
### What changes were proposed in this pull request?
The data in the managed table should be deleted after table is dropped. However, if the partition location is not under the location of the partitioned table, it is not deleted as expected. Users can specify any location for the partition when they adding a partition.
This PR is to delete partition location when dropping managed partitioned tables stored in `InMemoryCatalog`.
### How was this patch tested?
Added test cases for both HiveExternalCatalog and InMemoryCatalog
Author: gatorsmile <gatorsmile@gmail.com>
Closes #16448 from gatorsmile/unsetSerdeProp.
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## What changes were proposed in this pull request?
This PR proposes to check pep8 against all other Python scripts and fix the errors as below:
```bash
./dev/create-release/generate-contributors.py
./dev/create-release/releaseutils.py
./dev/create-release/translate-contributors.py
./dev/lint-python
./python/docs/epytext.py
./examples/src/main/python/mllib/decision_tree_classification_example.py
./examples/src/main/python/mllib/decision_tree_regression_example.py
./examples/src/main/python/mllib/gradient_boosting_classification_example.py
./examples/src/main/python/mllib/gradient_boosting_regression_example.py
./examples/src/main/python/mllib/linear_regression_with_sgd_example.py
./examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py
./examples/src/main/python/mllib/naive_bayes_example.py
./examples/src/main/python/mllib/random_forest_classification_example.py
./examples/src/main/python/mllib/random_forest_regression_example.py
./examples/src/main/python/mllib/svm_with_sgd_example.py
./examples/src/main/python/streaming/network_wordjoinsentiments.py
./sql/hive/src/test/resources/data/scripts/cat.py
./sql/hive/src/test/resources/data/scripts/cat_error.py
./sql/hive/src/test/resources/data/scripts/doubleescapedtab.py
./sql/hive/src/test/resources/data/scripts/dumpdata_script.py
./sql/hive/src/test/resources/data/scripts/escapedcarriagereturn.py
./sql/hive/src/test/resources/data/scripts/escapednewline.py
./sql/hive/src/test/resources/data/scripts/escapedtab.py
./sql/hive/src/test/resources/data/scripts/input20_script.py
./sql/hive/src/test/resources/data/scripts/newline.py
```
## How was this patch tested?
- `./python/docs/epytext.py`
```bash
cd ./python/docs $$ make html
```
- pep8 check (Python 2.7 / Python 3.3.6)
```
./dev/lint-python
```
- `./dev/merge_spark_pr.py` (Python 2.7 only / Python 3.3.6 not working)
```bash
python -m doctest -v ./dev/merge_spark_pr.py
```
- `./dev/create-release/releaseutils.py` `./dev/create-release/generate-contributors.py` `./dev/create-release/translate-contributors.py` (Python 2.7 only / Python 3.3.6 not working)
```bash
python generate-contributors.py
python translate-contributors.py
```
- Examples (Python 2.7 / Python 3.3.6)
```bash
./bin/spark-submit examples/src/main/python/mllib/decision_tree_classification_example.py
./bin/spark-submit examples/src/main/python/mllib/decision_tree_regression_example.py
./bin/spark-submit examples/src/main/python/mllib/gradient_boosting_classification_example.py
./bin/spark-submit examples/src/main/python/mllib/gradient_boosting_regression_example.p
./bin/spark-submit examples/src/main/python/mllib/random_forest_classification_example.py
./bin/spark-submit examples/src/main/python/mllib/random_forest_regression_example.py
```
- Examples (Python 2.7 only / Python 3.3.6 not working)
```
./bin/spark-submit examples/src/main/python/mllib/linear_regression_with_sgd_example.py
./bin/spark-submit examples/src/main/python/mllib/logistic_regression_with_lbfgs_example.py
./bin/spark-submit examples/src/main/python/mllib/naive_bayes_example.py
./bin/spark-submit examples/src/main/python/mllib/svm_with_sgd_example.py
```
- `sql/hive/src/test/resources/data/scripts/*.py` (Python 2.7 / Python 3.3.6 within suggested changes)
Manually tested only changed ones.
- `./dev/github_jira_sync.py` (Python 2.7 only / Python 3.3.6 not working)
Manually tested this after disabling actually adding comments and links.
And also via Jenkins tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #16405 from HyukjinKwon/minor-pep8.
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## What changes were proposed in this pull request?
This PR proposes to fix the test failures due to different format of paths on Windows.
Failed tests are as below:
```
ColumnExpressionSuite:
- input_file_name, input_file_block_start, input_file_block_length - FileScanRDD *** FAILED *** (187 milliseconds)
"file:///C:/projects/spark/target/tmp/spark-0b21b963-6cfa-411c-8d6f-e6a5e1e73bce/part-00001-c083a03a-e55e-4b05-9073-451de352d006.snappy.parquet" did not contain "C:\projects\spark\target\tmp\spark-0b21b963-6cfa-411c-8d6f-e6a5e1e73bce" (ColumnExpressionSuite.scala:545)
- input_file_name, input_file_block_start, input_file_block_length - HadoopRDD *** FAILED *** (172 milliseconds)
"file:/C:/projects/spark/target/tmp/spark-5d0afa94-7c2f-463b-9db9-2e8403e2bc5f/part-00000-f6530138-9ad3-466d-ab46-0eeb6f85ed0b.txt" did not contain "C:\projects\spark\target\tmp\spark-5d0afa94-7c2f-463b-9db9-2e8403e2bc5f" (ColumnExpressionSuite.scala:569)
- input_file_name, input_file_block_start, input_file_block_length - NewHadoopRDD *** FAILED *** (156 milliseconds)
"file:/C:/projects/spark/target/tmp/spark-a894c7df-c74d-4d19-82a2-a04744cb3766/part-00000-29674e3f-3fcf-4327-9b04-4dab1d46338d.txt" did not contain "C:\projects\spark\target\tmp\spark-a894c7df-c74d-4d19-82a2-a04744cb3766" (ColumnExpressionSuite.scala:598)
```
```
DataStreamReaderWriterSuite:
- source metadataPath *** FAILED *** (62 milliseconds)
org.mockito.exceptions.verification.junit.ArgumentsAreDifferent: Argument(s) are different! Wanted:
streamSourceProvider.createSource(
org.apache.spark.sql.SQLContext3b04133b,
"C:\projects\spark\target\tmp\streaming.metadata-b05db6ae-c8dc-4ce4-b0d9-1eb8c84876c0/sources/0",
None,
"org.apache.spark.sql.streaming.test",
Map()
);
-> at org.apache.spark.sql.streaming.test.DataStreamReaderWriterSuite$$anonfun$12.apply$mcV$sp(DataStreamReaderWriterSuite.scala:374)
Actual invocation has different arguments:
streamSourceProvider.createSource(
org.apache.spark.sql.SQLContext3b04133b,
"/C:/projects/spark/target/tmp/streaming.metadata-b05db6ae-c8dc-4ce4-b0d9-1eb8c84876c0/sources/0",
None,
"org.apache.spark.sql.streaming.test",
Map()
);
```
```
GlobalTempViewSuite:
- CREATE GLOBAL TEMP VIEW USING *** FAILED *** (110 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-960398ba-a0a1-45f6-a59a-d98533f9f519;
```
```
CreateTableAsSelectSuite:
- CREATE TABLE USING AS SELECT *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create a table, drop it and create another one with the same name *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create table using as select - with partitioned by *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- create table using as select - with non-zero buckets *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveMetadataCacheSuite:
- partitioned table is cached when partition pruning is true *** FAILED *** (532 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);
- partitioned table is cached when partition pruning is false *** FAILED *** (297 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);
```
```
MultiDatabaseSuite:
- createExternalTable() to non-default database - with USE *** FAILED *** (954 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-0839d9a7-5e29-467a-9e3e-3e4cd618ee09;
- createExternalTable() to non-default database - without USE *** FAILED *** (500 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c7e24d73-1d8f-45e8-ab7d-53a83087aec3;
- invalid database name and table names *** FAILED *** (31 milliseconds)
"Path does not exist: file:/C:projectsspark arget mpspark-15a2a494-3483-4876-80e5-ec396e704b77;" did not contain "`t:a` is not a valid name for tables/databases. Valid names only contain alphabet characters, numbers and _." (MultiDatabaseSuite.scala:296)
```
```
OrcQuerySuite:
- SPARK-8501: Avoids discovery schema from empty ORC files *** 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);
- Verify the ORC conversion parameter: CONVERT_METASTORE_ORC *** FAILED *** (78 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);
- converted ORC table supports resolving mixed case field *** FAILED *** (297 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);
```
```
HadoopFsRelationTest - JsonHadoopFsRelationSuite, OrcHadoopFsRelationSuite, ParquetHadoopFsRelationSuite, SimpleTextHadoopFsRelationSuite:
- Locality support for FileScanRDD *** FAILED *** (15 milliseconds)
java.lang.IllegalArgumentException: Wrong FS: file://C:\projects\spark\target\tmp\spark-383d1f13-8783-47fd-964d-9c75e5eec50f, expected: file:///
```
```
HiveQuerySuite:
- CREATE TEMPORARY FUNCTION *** FAILED *** (0 milliseconds)
java.net.MalformedURLException: For input string: "%5Cprojects%5Cspark%5Csql%5Chive%5Ctarget%5Cscala-2.11%5Ctest-classes%5CTestUDTF.jar"
- ADD FILE command *** FAILED *** (500 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\sql\hive\target\scala-2.11\test-classes\data\files\v1.txt
- ADD JAR command 2 *** FAILED *** (110 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilessample.json;
```
```
PruneFileSourcePartitionsSuite:
- PruneFileSourcePartitions should not change the output of LogicalRelation *** 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);
```
```
HiveCommandSuite:
- LOAD DATA LOCAL *** FAILED *** (109 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilesemployee.dat;
- LOAD DATA *** FAILED *** (93 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 15: C:projectsspark arget mpemployee.dat7496657117354281006.tmp
- Truncate Table *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: C:projectssparksqlhive argetscala-2.11 est-classesdatafilesemployee.dat;
```
```
HiveExternalCatalogBackwardCompatibilitySuite:
- make sure we can read table created by old version of Spark *** FAILED *** (0 milliseconds)
"[/C:/projects/spark/target/tmp/]spark-0554d859-74e1-..." did not equal "[C:\projects\spark\target\tmp\]spark-0554d859-74e1-..." (HiveExternalCatalogBackwardCompatibilitySuite.scala:213)
org.scalatest.exceptions.TestFailedException
- make sure we can alter table location created by old version of Spark *** FAILED *** (110 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 15: C:projectsspark arget mpspark-0e9b2c5f-49a1-4e38-a32a-c0ab1813a79f
```
```
ExternalCatalogSuite:
- create/drop/rename partitions should create/delete/rename the directory *** FAILED *** (610 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 2: C:\projects\spark\target\tmp\spark-4c24f010-18df-437b-9fed-990c6f9adece
```
```
SQLQuerySuite:
- describe functions - temporary user defined functions *** FAILED *** (16 milliseconds)
java.net.URISyntaxException: Illegal character in opaque part at index 22: C:projectssparksqlhive argetscala-2.11 est-classesTestUDTF.jar
- specifying database name for a temporary table is not allowed *** FAILED *** (125 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-a34c9814-a483-43f2-be29-37f616b6df91;
```
```
PartitionProviderCompatibilitySuite:
- convert partition provider to hive with repair table *** FAILED *** (281 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-ee5fc96d-8c7d-4ebf-8571-a1d62736473e;
- when partition management is enabled, new tables have partition provider hive *** FAILED *** (187 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-803ad4d6-3e8c-498d-9ca5-5cda5d9b2a48;
- when partition management is disabled, new tables have no partition provider *** FAILED *** (172 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-c9fda9e2-4020-465f-8678-52cd72d0a58f;
- when partition management is disabled, we preserve the old behavior even for new tables *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget
mpspark-f4a518a6-c49d-43d3-b407-0ddd76948e13;
- insert overwrite partition of legacy datasource table *** FAILED *** (188 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-f4a518a6-c49d-43d3-b407-0ddd76948e79;
- insert overwrite partition of new datasource table overwrites just partition *** FAILED *** (219 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-6ba3a88d-6f6c-42c5-a9f4-6d924a0616ff;
- SPARK-18544 append with saveAsTable - partition management true *** FAILED *** (173 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-cd234a6d-9cb4-4d1d-9e51-854ae9543bbd;
- SPARK-18635 special chars in partition values - partition management true *** FAILED *** (2 seconds, 967 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-18635 special chars in partition values - partition management false *** FAILED *** (62 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-18659 insert overwrite table with lowercase - partition management true *** FAILED *** (63 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-18544 append with saveAsTable - partition management false *** FAILED *** (266 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-18659 insert overwrite table files - partition management false *** FAILED *** (63 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-18659 insert overwrite table with lowercase - partition management false *** FAILED *** (78 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);
- sanity check table setup *** FAILED *** (31 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);
- insert into partial dynamic partitions *** 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);
- insert into fully dynamic partitions *** FAILED *** (62 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);
- insert into static partition *** FAILED *** (78 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);
- overwrite partial dynamic partitions *** FAILED *** (63 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);
- overwrite fully dynamic partitions *** 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);
- overwrite static partition *** FAILED *** (63 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);
```
```
MetastoreDataSourcesSuite:
- check change without refresh *** FAILED *** (203 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-00713fe4-ca04-448c-bfc7-6c5e9a2ad2a1;
- drop, change, recreate *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-2030a21b-7d67-4385-a65b-bb5e2bed4861;
- SPARK-15269 external data source table creation *** FAILED *** (78 milliseconds)
org.apache.spark.sql.AnalysisException: Path does not exist: file:/C:projectsspark arget mpspark-4d50fd4a-14bc-41d6-9232-9554dd233f86;
- CTAS *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS with IF NOT EXISTS *** FAILED *** (109 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS: persisted partitioned bucketed data source table *** FAILED *** (0 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- SPARK-15025: create datasource table with path with select *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- CTAS: persisted partitioned data source table *** FAILED *** (47 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveMetastoreCatalogSuite:
- Persist non-partitioned parquet relation into metastore as managed table using CTAS *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
- Persist non-partitioned orc relation into metastore as managed table using CTAS *** FAILED *** (16 milliseconds)
java.lang.IllegalArgumentException: Can not create a Path from an empty string
```
```
HiveUDFSuite:
- SPARK-11522 select input_file_name from non-parquet table *** 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);
```
```
QueryPartitionSuite:
- SPARK-13709: reading partitioned Avro table with nested schema *** FAILED *** (250 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);
```
```
ParquetHiveCompatibilitySuite:
- simple primitives *** 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);
- SPARK-10177 timestamp *** 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);
- array *** 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);
- map *** 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);
- struct *** 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);
- SPARK-16344: array of struct with a single field named 'array_element' *** 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);
```
## How was this patch tested?
Manually tested via AppVeyor.
```
ColumnExpressionSuite:
- input_file_name, input_file_block_start, input_file_block_length - FileScanRDD (234 milliseconds)
- input_file_name, input_file_block_start, input_file_block_length - HadoopRDD (235 milliseconds)
- input_file_name, input_file_block_start, input_file_block_length - NewHadoopRDD (203 milliseconds)
```
```
DataStreamReaderWriterSuite:
- source metadataPath (63 milliseconds)
```
```
GlobalTempViewSuite:
- CREATE GLOBAL TEMP VIEW USING (436 milliseconds)
```
```
CreateTableAsSelectSuite:
- CREATE TABLE USING AS SELECT (171 milliseconds)
- create a table, drop it and create another one with the same name (422 milliseconds)
- create table using as select - with partitioned by (141 milliseconds)
- create table using as select - with non-zero buckets (125 milliseconds)
```
```
HiveMetadataCacheSuite:
- partitioned table is cached when partition pruning is true (3 seconds, 211 milliseconds)
- partitioned table is cached when partition pruning is false (1 second, 781 milliseconds)
```
```
MultiDatabaseSuite:
- createExternalTable() to non-default database - with USE (797 milliseconds)
- createExternalTable() to non-default database - without USE (640 milliseconds)
- invalid database name and table names (62 milliseconds)
```
```
OrcQuerySuite:
- SPARK-8501: Avoids discovery schema from empty ORC files (703 milliseconds)
- Verify the ORC conversion parameter: CONVERT_METASTORE_ORC (750 milliseconds)
- converted ORC table supports resolving mixed case field (625 milliseconds)
```
```
HadoopFsRelationTest - JsonHadoopFsRelationSuite, OrcHadoopFsRelationSuite, ParquetHadoopFsRelationSuite, SimpleTextHadoopFsRelationSuite:
- Locality support for FileScanRDD (296 milliseconds)
```
```
HiveQuerySuite:
- CREATE TEMPORARY FUNCTION (125 milliseconds)
- ADD FILE command (250 milliseconds)
- ADD JAR command 2 (609 milliseconds)
```
```
PruneFileSourcePartitionsSuite:
- PruneFileSourcePartitions should not change the output of LogicalRelation (359 milliseconds)
```
```
HiveCommandSuite:
- LOAD DATA LOCAL (1 second, 829 milliseconds)
- LOAD DATA (1 second, 735 milliseconds)
- Truncate Table (1 second, 641 milliseconds)
```
```
HiveExternalCatalogBackwardCompatibilitySuite:
- make sure we can read table created by old version of Spark (32 milliseconds)
- make sure we can alter table location created by old version of Spark (125 milliseconds)
- make sure we can rename table created by old version of Spark (281 milliseconds)
```
```
ExternalCatalogSuite:
- create/drop/rename partitions should create/delete/rename the directory (625 milliseconds)
```
```
SQLQuerySuite:
- describe functions - temporary user defined functions (31 milliseconds)
- specifying database name for a temporary table is not allowed (390 milliseconds)
```
```
PartitionProviderCompatibilitySuite:
- convert partition provider to hive with repair table (813 milliseconds)
- when partition management is enabled, new tables have partition provider hive (562 milliseconds)
- when partition management is disabled, new tables have no partition provider (344 milliseconds)
- when partition management is disabled, we preserve the old behavior even for new tables (422 milliseconds)
- insert overwrite partition of legacy datasource table (750 milliseconds)
- SPARK-18544 append with saveAsTable - partition management true (985 milliseconds)
- SPARK-18635 special chars in partition values - partition management true (3 seconds, 328 milliseconds)
- SPARK-18635 special chars in partition values - partition management false (2 seconds, 891 milliseconds)
- SPARK-18659 insert overwrite table with lowercase - partition management true (750 milliseconds)
- SPARK-18544 append with saveAsTable - partition management false (656 milliseconds)
- SPARK-18659 insert overwrite table files - partition management false (922 milliseconds)
- SPARK-18659 insert overwrite table with lowercase - partition management false (469 milliseconds)
- sanity check table setup (937 milliseconds)
- insert into partial dynamic partitions (2 seconds, 985 milliseconds)
- insert into fully dynamic partitions (1 second, 937 milliseconds)
- insert into static partition (1 second, 578 milliseconds)
- overwrite partial dynamic partitions (7 seconds, 561 milliseconds)
- overwrite fully dynamic partitions (1 second, 766 milliseconds)
- overwrite static partition (1 second, 797 milliseconds)
```
```
MetastoreDataSourcesSuite:
- check change without refresh (610 milliseconds)
- drop, change, recreate (437 milliseconds)
- SPARK-15269 external data source table creation (297 milliseconds)
- CTAS with IF NOT EXISTS (437 milliseconds)
- CTAS: persisted partitioned bucketed data source table (422 milliseconds)
- SPARK-15025: create datasource table with path with select (265 milliseconds)
- CTAS (438 milliseconds)
- CTAS with IF NOT EXISTS (469 milliseconds)
- CTAS: persisted partitioned bucketed data source table (406 milliseconds)
```
```
HiveMetastoreCatalogSuite:
- Persist non-partitioned parquet relation into metastore as managed table using CTAS (406 milliseconds)
- Persist non-partitioned orc relation into metastore as managed table using CTAS (313 milliseconds)
```
```
HiveUDFSuite:
- SPARK-11522 select input_file_name from non-parquet table (3 seconds, 144 milliseconds)
```
```
QueryPartitionSuite:
- SPARK-13709: reading partitioned Avro table with nested schema (1 second, 67 milliseconds)
```
```
ParquetHiveCompatibilitySuite:
- simple primitives (745 milliseconds)
- SPARK-10177 timestamp (375 milliseconds)
- array (407 milliseconds)
- map (409 milliseconds)
- struct (437 milliseconds)
- SPARK-16344: array of struct with a single field named 'array_element' (391 milliseconds)
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #16397 from HyukjinKwon/SPARK-18922-paths.
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## What changes were proposed in this pull request?
The `CreateDataSourceTableAsSelectCommand` is quite complex now, as it has a lot of work to do if the table already exists:
1. throw exception if we don't want to ignore it.
2. do some check and adjust the schema if we want to append data.
3. drop the table and create it again if we want to overwrite.
The work 2 and 3 should be done by analyzer, so that we can also apply it to hive tables.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #15996 from cloud-fan/append.
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primitive array in DataFrame
## What changes were proposed in this pull request?
This PR reduces runtime overhead of a program the creates an primitive array in DataFrame by using the similar approach to #15044. Generated code performs boxing operation in an assignment from InternalRow to an `Object[]` temporary array (at Lines 051 and 061 in the generated code before without this PR). If we know that type of array elements is primitive, we apply the following optimizations:
1. Eliminate a pair of `isNullAt()` and a null assignment
2. Allocate an primitive array instead of `Object[]` (eliminate boxing operations)
3. Create `UnsafeArrayData` by using `UnsafeArrayWriter` to keep a primitive array in a row format instead of doing non-lightweight operations in constructor of `GenericArrayData`
The PR also performs the same things for `CreateMap`.
Here are performance results of [DataFrame programs](https://github.com/kiszk/spark/blob/6bf54ec5e227689d69f6db991e9ecbc54e153d0a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/PrimitiveArrayBenchmark.scala#L83-L112) by up to 17.9x over without this PR.
```
Without SPARK-16043
OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 4.4.11-200.fc22.x86_64
Intel Xeon E3-12xx v2 (Ivy Bridge)
Read a primitive array in DataFrame: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 3805 / 4150 0.0 507308.9 1.0X
Double 3593 / 3852 0.0 479056.9 1.1X
With SPARK-16043
Read a primitive array in DataFrame: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Int 213 / 271 0.0 28387.5 1.0X
Double 204 / 223 0.0 27250.9 1.0X
```
Note : #15780 is enabled for these measurements
An motivating example
``` java
val df = sparkContext.parallelize(Seq(0.0d, 1.0d), 1).toDF
df.selectExpr("Array(value + 1.1d, value + 2.2d)").show
```
Generated code without this PR
``` java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private scala.collection.Iterator inputadapter_input;
/* 009 */ private UnsafeRow serializefromobject_result;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 012 */ private Object[] project_values;
/* 013 */ private UnsafeRow project_result;
/* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder;
/* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter project_arrayWriter;
/* 017 */
/* 018 */ public GeneratedIterator(Object[] references) {
/* 019 */ this.references = references;
/* 020 */ }
/* 021 */
/* 022 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */ partitionIndex = index;
/* 024 */ this.inputs = inputs;
/* 025 */ inputadapter_input = inputs[0];
/* 026 */ serializefromobject_result = new UnsafeRow(1);
/* 027 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 028 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 029 */ this.project_values = null;
/* 030 */ project_result = new UnsafeRow(1);
/* 031 */ this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 32);
/* 032 */ this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 1);
/* 033 */ this.project_arrayWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter();
/* 034 */
/* 035 */ }
/* 036 */
/* 037 */ protected void processNext() throws java.io.IOException {
/* 038 */ while (inputadapter_input.hasNext()) {
/* 039 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 040 */ double inputadapter_value = inputadapter_row.getDouble(0);
/* 041 */
/* 042 */ final boolean project_isNull = false;
/* 043 */ this.project_values = new Object[2];
/* 044 */ boolean project_isNull1 = false;
/* 045 */
/* 046 */ double project_value1 = -1.0;
/* 047 */ project_value1 = inputadapter_value + 1.1D;
/* 048 */ if (false) {
/* 049 */ project_values[0] = null;
/* 050 */ } else {
/* 051 */ project_values[0] = project_value1;
/* 052 */ }
/* 053 */
/* 054 */ boolean project_isNull4 = false;
/* 055 */
/* 056 */ double project_value4 = -1.0;
/* 057 */ project_value4 = inputadapter_value + 2.2D;
/* 058 */ if (false) {
/* 059 */ project_values[1] = null;
/* 060 */ } else {
/* 061 */ project_values[1] = project_value4;
/* 062 */ }
/* 063 */
/* 064 */ final ArrayData project_value = new org.apache.spark.sql.catalyst.util.GenericArrayData(project_values);
/* 065 */ this.project_values = null;
/* 066 */ project_holder.reset();
/* 067 */
/* 068 */ project_rowWriter.zeroOutNullBytes();
/* 069 */
/* 070 */ if (project_isNull) {
/* 071 */ project_rowWriter.setNullAt(0);
/* 072 */ } else {
/* 073 */ // Remember the current cursor so that we can calculate how many bytes are
/* 074 */ // written later.
/* 075 */ final int project_tmpCursor = project_holder.cursor;
/* 076 */
/* 077 */ if (project_value instanceof UnsafeArrayData) {
/* 078 */ final int project_sizeInBytes = ((UnsafeArrayData) project_value).getSizeInBytes();
/* 079 */ // grow the global buffer before writing data.
/* 080 */ project_holder.grow(project_sizeInBytes);
/* 081 */ ((UnsafeArrayData) project_value).writeToMemory(project_holder.buffer, project_holder.cursor);
/* 082 */ project_holder.cursor += project_sizeInBytes;
/* 083 */
/* 084 */ } else {
/* 085 */ final int project_numElements = project_value.numElements();
/* 086 */ project_arrayWriter.initialize(project_holder, project_numElements, 8);
/* 087 */
/* 088 */ for (int project_index = 0; project_index < project_numElements; project_index++) {
/* 089 */ if (project_value.isNullAt(project_index)) {
/* 090 */ project_arrayWriter.setNullDouble(project_index);
/* 091 */ } else {
/* 092 */ final double project_element = project_value.getDouble(project_index);
/* 093 */ project_arrayWriter.write(project_index, project_element);
/* 094 */ }
/* 095 */ }
/* 096 */ }
/* 097 */
/* 098 */ project_rowWriter.setOffsetAndSize(0, project_tmpCursor, project_holder.cursor - project_tmpCursor);
/* 099 */ }
/* 100 */ project_result.setTotalSize(project_holder.totalSize());
/* 101 */ append(project_result);
/* 102 */ if (shouldStop()) return;
/* 103 */ }
/* 104 */ }
/* 105 */ }
```
Generated code with this PR
``` java
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private scala.collection.Iterator inputadapter_input;
/* 009 */ private UnsafeRow serializefromobject_result;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder;
/* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter;
/* 012 */ private UnsafeArrayData project_arrayData;
/* 013 */ private UnsafeRow project_result;
/* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder;
/* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter project_arrayWriter;
/* 017 */
/* 018 */ public GeneratedIterator(Object[] references) {
/* 019 */ this.references = references;
/* 020 */ }
/* 021 */
/* 022 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */ partitionIndex = index;
/* 024 */ this.inputs = inputs;
/* 025 */ inputadapter_input = inputs[0];
/* 026 */ serializefromobject_result = new UnsafeRow(1);
/* 027 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0);
/* 028 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1);
/* 029 */
/* 030 */ project_result = new UnsafeRow(1);
/* 031 */ this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 32);
/* 032 */ this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 1);
/* 033 */ this.project_arrayWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeArrayWriter();
/* 034 */
/* 035 */ }
/* 036 */
/* 037 */ protected void processNext() throws java.io.IOException {
/* 038 */ while (inputadapter_input.hasNext()) {
/* 039 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 040 */ double inputadapter_value = inputadapter_row.getDouble(0);
/* 041 */
/* 042 */ byte[] project_array = new byte[32];
/* 043 */ project_arrayData = new UnsafeArrayData();
/* 044 */ Platform.putLong(project_array, 16, 2);
/* 045 */ project_arrayData.pointTo(project_array, 16, 32);
/* 046 */
/* 047 */ boolean project_isNull1 = false;
/* 048 */
/* 049 */ double project_value1 = -1.0;
/* 050 */ project_value1 = inputadapter_value + 1.1D;
/* 051 */ if (false) {
/* 052 */ project_arrayData.setNullAt(0);
/* 053 */ } else {
/* 054 */ project_arrayData.setDouble(0, project_value1);
/* 055 */ }
/* 056 */
/* 057 */ boolean project_isNull4 = false;
/* 058 */
/* 059 */ double project_value4 = -1.0;
/* 060 */ project_value4 = inputadapter_value + 2.2D;
/* 061 */ if (false) {
/* 062 */ project_arrayData.setNullAt(1);
/* 063 */ } else {
/* 064 */ project_arrayData.setDouble(1, project_value4);
/* 065 */ }
/* 066 */ project_holder.reset();
/* 067 */
/* 068 */ // Remember the current cursor so that we can calculate how many bytes are
/* 069 */ // written later.
/* 070 */ final int project_tmpCursor = project_holder.cursor;
/* 071 */
/* 072 */ if (project_arrayData instanceof UnsafeArrayData) {
/* 073 */ final int project_sizeInBytes = ((UnsafeArrayData) project_arrayData).getSizeInBytes();
/* 074 */ // grow the global buffer before writing data.
/* 075 */ project_holder.grow(project_sizeInBytes);
/* 076 */ ((UnsafeArrayData) project_arrayData).writeToMemory(project_holder.buffer, project_holder.cursor);
/* 077 */ project_holder.cursor += project_sizeInBytes;
/* 078 */
/* 079 */ } else {
/* 080 */ final int project_numElements = project_arrayData.numElements();
/* 081 */ project_arrayWriter.initialize(project_holder, project_numElements, 8);
/* 082 */
/* 083 */ for (int project_index = 0; project_index < project_numElements; project_index++) {
/* 084 */ if (project_arrayData.isNullAt(project_index)) {
/* 085 */ project_arrayWriter.setNullDouble(project_index);
/* 086 */ } else {
/* 087 */ final double project_element = project_arrayData.getDouble(project_index);
/* 088 */ project_arrayWriter.write(project_index, project_element);
/* 089 */ }
/* 090 */ }
/* 091 */ }
/* 092 */
/* 093 */ project_rowWriter.setOffsetAndSize(0, project_tmpCursor, project_holder.cursor - project_tmpCursor);
/* 094 */ project_result.setTotalSize(project_holder.totalSize());
/* 095 */ append(project_result);
/* 096 */ if (shouldStop()) return;
/* 097 */ }
/* 098 */ }
/* 099 */ }
```
## How was this patch tested?
Added unit tests into `DataFrameComplexTypeSuite`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes #13909 from kiszk/SPARK-16213.
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## What changes were proposed in this pull request?
When we describe a table, we only wanna see the information of this table, not read it, so it's ok even if the format class is not present at the classpath.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16388 from cloud-fan/hive.
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## What changes were proposed in this pull request?
Currently we implement `Aggregator` with `DeclarativeAggregate`, which will serialize/deserialize the buffer object every time we process an input.
This PR implements `Aggregator` with `TypedImperativeAggregate` and avoids to serialize/deserialize buffer object many times. The benchmark shows we get about 2 times speed up.
For simple buffer object that doesn't need serialization, we still go with `DeclarativeAggregate`, to avoid performance regression.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16383 from cloud-fan/aggregator.
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convert it to Statistics in relations
## What changes were proposed in this pull request?
Statistics in LogicalPlan should use attributes to refer to columns rather than column names, because two columns from two relations can have the same column name. But CatalogTable doesn't have the concepts of attribute or broadcast hint in Statistics. Therefore, putting Statistics in CatalogTable is confusing.
We define a different statistic structure in CatalogTable, which is only responsible for interacting with metastore, and is converted to statistics in LogicalPlan when it is used.
## How was this patch tested?
add test cases
Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>
Closes #16323 from wzhfy/nameToAttr.
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sources implementing FileFormat
## What changes were proposed in this pull request?
This PR cleans up duplicated checking for file paths in implemented data sources and prevent to attempt to list twice in ORC data source.
https://github.com/apache/spark/pull/14585 handles a problem for the partition column name having `_` and the issue itself is resolved correctly. However, it seems the data sources implementing `FileFormat` are validating the paths duplicately. Assuming from the comment in `CSVFileFormat`, `// TODO: Move filtering.`, I guess we don't have to check this duplicately.
Currently, this seems being filtered in `PartitioningAwareFileIndex.shouldFilterOut` and`PartitioningAwareFileIndex.isDataPath`. So, `FileFormat.inferSchema` will always receive leaf files. For example, running to codes below:
``` scala
spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
spark.read.parquet("/tmp/parquet")
```
gives the paths below without directories but just valid data files:
``` bash
/tmp/parquet/_col=0/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet
/tmp/parquet/_col=1/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet
/tmp/parquet/_col=2/part-r-00000-25de2b50-225a-4bcf-a2bc-9eb9ed407ef6.snappy.parquet
...
```
to `FileFormat.inferSchema`.
## How was this patch tested?
Unit test added in `HadoopFsRelationTest` and related existing tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14627 from HyukjinKwon/SPARK-16975.
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### What changes were proposed in this pull request?
Currently, we only have a SQL interface for recovering all the partitions in the directory of a table and update the catalog. `MSCK REPAIR TABLE` or `ALTER TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to remember `MSCK` and have no clue what it means)
After the new "Scalable Partition Handling", the table repair becomes much more important for making visible the data in the created data source partitioned table.
Thus, this PR is to add it into the Catalog interface. After this PR, users can repair the table by
```Scala
spark.catalog.recoverPartitions("testTable")
```
### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #16356 from gatorsmile/repairTable.
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when append data to an existing table
## What changes were proposed in this pull request?
When we append data to an existing table with `DataFrameWriter.saveAsTable`, we will do various checks to make sure the appended data is consistent with the existing data.
However, we get the information of the existing table by matching the table relation, instead of looking at the table metadata. This is error-prone, e.g. we only check the number of columns for `HadoopFsRelation`, we forget to check bucketing, etc.
This PR refactors the error checking by looking at the metadata of the existing table, and fix several bugs:
* SPARK-18899: We forget to check if the specified bucketing matched the existing table, which may lead to a problematic table that has different bucketing in different data files.
* SPARK-18912: We forget to check the number of columns for non-file-based data source table
* SPARK-18913: We don't support append data to a table with special column names.
## How was this patch tested?
new regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16313 from cloud-fan/bug1.
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of getDatabase
## What changes were proposed in this pull request?
It's weird that we use `Hive.getDatabase` to check the existence of a database, while Hive has a `databaseExists` interface.
What's worse, `Hive.getDatabase` will produce an error message if the database doesn't exist, which is annoying when we only want to check the database existence.
This PR fixes this and use `Hive.databaseExists` to check database existence.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16332 from cloud-fan/minor.
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## What changes were proposed in this pull request?
As the scenario describe in [SPARK-18700](https://issues.apache.org/jira/browse/SPARK-18700), when cachedDataSourceTables invalided, the coming few queries will fetch all FileStatus in listLeafFiles function. In the condition of table has many partitions, these jobs will occupy much memory of driver finally may cause driver OOM.
In this patch, add StripedLock for each table's relation in cache not for the whole cachedDataSourceTables, each table's load cache operation protected by it.
## How was this patch tested?
Add a multi-thread access table test in `PartitionedTablePerfStatsSuite` and check it only loading once using metrics in `HiveCatalogMetrics`
Author: xuanyuanking <xyliyuanjian@gmail.com>
Closes #16135 from xuanyuanking/SPARK-18700.
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Insertion/CTAS of Hive serde Tables
### What changes were proposed in this pull request?
Below are the files/directories generated for three inserts againsts a Hive table:
```
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-29_149_4298858301766472202-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_454_6445008511655931341-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/._SUCCESS.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/_SUCCESS
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.hive-staging_hive_2016-12-03_20-56-30_722_3388423608658711001-1/-ext-10000/part-00000
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/part-00000
```
The first 18 files are temporary. We do not drop it until the end of JVM termination. If JVM does not appropriately terminate, these temporary files/directories will not be dropped.
Only the last two files are needed, as shown below.
```
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/.part-00000.crc
/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/spark-41eaa5ce-0288-471e-bba1-09cc482813ff/part-00000
```
The temporary files/directories could accumulate a lot when we issue many inserts, since each insert generats at least six files. This could eat a lot of spaces and slow down the JVM termination. When the JVM does not terminates approprately, the files might not be dropped.
This PR is to drop the created staging files and temporary data files after each insert/CTAS.
### How was this patch tested?
Added a test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes #16134 from gatorsmile/deleteFiles.
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## What changes were proposed in this pull request?
Before hive 1.1, when inserting into a table, hive will create the staging directory under a common scratch directory. After the writing is finished, hive will simply empty the table directory and move the staging directory to it.
After hive 1.1, hive will create the staging directory under the table directory, and when moving staging directory to table directory, hive will still empty the table directory, but will exclude the staging directory there.
In `InsertIntoHiveTable`, we simply copy the code from hive 1.2, which means we will always create the staging directory under the table directory, no matter what the hive version is. This causes problems if the hive version is prior to 1.1, because the staging directory will be removed by hive when hive is trying to empty the table directory.
This PR copies the code from hive 0.13, so that we have 2 branches to create staging directory. If hive version is prior to 1.1, we'll go to the old style branch(i.e. create the staging directory under a common scratch directory), else, go to the new style branch(i.e. create the staging directory under the table directory)
## How was this patch tested?
new test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16104 from cloud-fan/hive-0.13.
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The value of the "isSrcLocal" parameter passed to Hive's loadTable and
loadPartition methods needs to be set according to the user query (e.g.
"LOAD DATA LOCAL"), and not the current code that tries to guess what
it should be.
For existing versions of Hive the current behavior is probably ok, but
some recent changes in the Hive code changed the semantics slightly,
making code that sets "isSrcLocal" to "true" incorrectly to do the
wrong thing. It would end up moving the parent directory of the files
into the final location, instead of the file themselves, resulting
in a table that cannot be read.
I modified HiveCommandSuite so that existing "LOAD DATA" tests are run
both in local and non-local mode, since the semantics are slightly different.
The tests include a few new checks to make sure the semantics follow
what Hive describes in its documentation.
Tested with existing unit tests and also ran some Hive integration tests
with a version of Hive containing the changes that surfaced the problem.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes #16179 from vanzin/SPARK-18752.
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## What changes were proposed in this pull request?
Make stateful udf as nondeterministic
## How was this patch tested?
Add new test cases with both Stateful and Stateless UDF.
Without the patch, the test cases will throw exception:
1 did not equal 10
ScalaTestFailureLocation: org.apache.spark.sql.hive.execution.HiveUDFSuite$$anonfun$21 at (HiveUDFSuite.scala:501)
org.scalatest.exceptions.TestFailedException: 1 did not equal 10
at org.scalatest.Assertions$class.newAssertionFailedException(Assertions.scala:500)
at org.scalatest.FunSuite.newAssertionFailedException(FunSuite.scala:1555)
...
Author: Zhan Zhang <zhanzhang@fb.com>
Closes #16068 from zhzhan/state.
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(Link to Jira issue: https://issues.apache.org/jira/browse/SPARK-18572)
## What changes were proposed in this pull request?
Currently Spark answers the `SHOW PARTITIONS` command by fetching all of the table's partition metadata from the external catalog and constructing partition names therefrom. The Hive client has a `getPartitionNames` method which is many times faster for this purpose, with the performance improvement scaling with the number of partitions in a table.
To test the performance impact of this PR, I ran the `SHOW PARTITIONS` command on two Hive tables with large numbers of partitions. One table has ~17,800 partitions, and the other has ~95,000 partitions. For the purposes of this PR, I'll call the former table `table1` and the latter table `table2`. I ran 5 trials for each table with before-and-after versions of this PR. The results are as follows:
Spark at bdc8153, `SHOW PARTITIONS table1`, times in seconds:
7.901
3.983
4.018
4.331
4.261
Spark at bdc8153, `SHOW PARTITIONS table2`
(Timed out after 10 minutes with a `SocketTimeoutException`.)
Spark at this PR, `SHOW PARTITIONS table1`, times in seconds:
3.801
0.449
0.395
0.348
0.336
Spark at this PR, `SHOW PARTITIONS table2`, times in seconds:
5.184
1.63
1.474
1.519
1.41
Taking the best times from each trial, we get a 12x performance improvement for a table with ~17,800 partitions and at least a 426x improvement for a table with ~95,000 partitions. More significantly, the latter command doesn't even complete with the current code in master.
This is actually a patch we've been using in-house at VideoAmp since Spark 1.1. It's made all the difference in the practical usability of our largest tables. Even with tables with about 1,000 partitions there's a performance improvement of about 2-3x.
## How was this patch tested?
I added a unit test to `VersionsSuite` which tests that the Hive client's `getPartitionNames` method returns the correct number of partitions.
Author: Michael Allman <michael@videoamp.com>
Closes #15998 from mallman/spark-18572-list_partition_names.
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all files for table
## What changes were proposed in this pull request?
Even though in 2.1 creating a partitioned datasource table will not populate the partition data by default (until the user issues MSCK REPAIR TABLE), it seems we still scan the filesystem for no good reason.
We should avoid doing this when the user specifies a schema.
## How was this patch tested?
Perf stat tests.
Author: Eric Liang <ekl@databricks.com>
Closes #16090 from ericl/spark-18661.
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## What changes were proposed in this pull request?
Two bugs are addressed here
1. INSERT OVERWRITE TABLE sometime crashed when catalog partition management was enabled. This was because when dropping partitions after an overwrite operation, the Hive client will attempt to delete the partition files. If the entire partition directory was dropped, this would fail. The PR fixes this by adding a flag to control whether the Hive client should attempt to delete files.
2. The static partition spec for OVERWRITE TABLE was not correctly resolved to the case-sensitive original partition names. This resulted in the entire table being overwritten if you did not correctly capitalize your partition names.
cc yhuai cloud-fan
## How was this patch tested?
Unit tests. Surprisingly, the existing overwrite table tests did not catch these edge cases.
Author: Eric Liang <ekl@databricks.com>
Closes #16088 from ericl/spark-18659.
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## What changes were proposed in this pull request?
This replaces uses of `TextOutputFormat` with an `OutputStream`, which will either write directly to the filesystem or indirectly via a compressor (if so configured). This avoids intermediate buffering.
The inverse of this (reading directly from a stream) is necessary for streaming large JSON records (when `wholeFile` is enabled) so I wanted to keep the read and write paths symmetric.
## How was this patch tested?
Existing unit tests.
Author: Nathan Howell <nhowell@godaddy.com>
Closes #16089 from NathanHowell/SPARK-18658.
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## What changes were proposed in this pull request?
In Spark 2.1, we make Hive serde tables case-preserving by putting the table metadata in table properties, like what we did for data source table. However, we should not put table provider, as it will break forward compatibility. e.g. if we create a Hive serde table with Spark 2.1, using `sql("create table test stored as parquet as select 1")`, we will fail to read it with Spark 2.0, as Spark 2.0 mistakenly treat it as data source table because there is a `provider` entry in table properties.
Logically Hive serde table's provider is always hive, we don't need to store it in table properties, this PR removes it.
## How was this patch tested?
manually test the forward compatibility issue.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16080 from cloud-fan/hive.
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## What changes were proposed in this pull request?
Due to confusion between URI vs paths, in certain cases we escape partition values too many times, which causes some Hive client operations to fail or write data to the wrong location. This PR fixes at least some of these cases.
To my understanding this is how values, filesystem paths, and URIs interact.
- Hive stores raw (unescaped) partition values that are returned to you directly when you call listPartitions.
- Internally, we convert these raw values to filesystem paths via `ExternalCatalogUtils.[un]escapePathName`.
- In some circumstances we store URIs instead of filesystem paths. When a path is converted to a URI via `path.toURI`, the escaped partition values are further URI-encoded. This means that to get a path back from a URI, you must call `new Path(new URI(uriTxt))` in order to decode the URI-encoded string.
- In `CatalogStorageFormat` we store URIs as strings. This makes it easy to forget to URI-decode the value before converting it into a path.
- Finally, the Hive client itself uses mostly Paths for representing locations, and only URIs occasionally.
In the future we should probably clean this up, perhaps by dropping use of URIs when unnecessary. We should also try fixing escaping for partition names as well as values, though names are unlikely to contain special characters.
cc mallman cloud-fan yhuai
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes #16071 from ericl/spark-18635.
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## What changes were proposed in this pull request?
Spark SQL only has `StringType`, when reading hive table with varchar column, we will read that column as `StringType`. However, we still need to use varchar `ObjectInspector` to read varchar column in hive table, which means we need to know the actual column type at hive side.
In Spark 2.1, after https://github.com/apache/spark/pull/14363 , we parse hive type string to catalyst type, which means the actual column type at hive side is erased. Then we may use string `ObjectInspector` to read varchar column and fail.
This PR keeps the original hive column type string in the metadata of `StructField`, and use it when we convert it to a hive column.
## How was this patch tested?
newly added regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16060 from cloud-fan/varchar.
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Support for Column Names and Comments
### What changes were proposed in this pull request?
Spark SQL supports Unicode characters for column names when specified within backticks(`). When the Hive support is enabled, the version of the Hive metastore must be higher than 0.12, See the JIRA: https://issues.apache.org/jira/browse/HIVE-6013 Hive metastore supports Unicode characters for column names since 0.13.
In Spark SQL, table comments, and view comments always allow Unicode characters without backticks.
BTW, a separate PR has been submitted for database and table name validation because we do not support Unicode characters in these two cases.
### How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15255 from gatorsmile/unicodeSupport.
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## What changes were proposed in this pull request?
`AggregateFunction` currently implements `ImplicitCastInputTypes` (which enables implicit input type casting). There are actually quite a few situations in which we don't need this, or require more control over our input. A recent example is the aggregate for `CountMinSketch` which should only take string, binary or integral types inputs.
This PR removes `ImplicitCastInputTypes` from the `AggregateFunction` and makes a case-by-case decision on what kind of input validation we should use.
## How was this patch tested?
Refactoring only. Existing tests.
Author: Herman van Hovell <hvanhovell@databricks.com>
Closes #16066 from hvanhovell/SPARK-18632.
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## What changes were proposed in this pull request?
We failed to properly propagate table metadata for existing tables for the saveAsTable command. This caused a downstream component to think the table was MANAGED, writing data to the wrong location.
## How was this patch tested?
Unit test that fails before the patch.
Author: Eric Liang <ekl@databricks.com>
Closes #15983 from ericl/spark-18544.
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ObjectHashAggregateExec
## What changes were proposed in this pull request?
This PR fixes a random OOM issue occurred while running `ObjectHashAggregateSuite`.
This issue can be steadily reproduced under the following conditions:
1. The aggregation must be evaluated using `ObjectHashAggregateExec`;
2. There must be an input column whose data type involves `ArrayType` (an input column of `MapType` may even cause SIGSEGV);
3. Sort-based aggregation fallback must be triggered during evaluation.
The root cause is that while falling back to sort-based aggregation, we must sort and feed already evaluated partial aggregation buffers living in the hash map to the sort-based aggregator using an external sorter. However, the underlying mutable byte buffer of `UnsafeRow`s produced by the iterator of the external sorter is reused and may get overwritten when the iterator steps forward. After the last entry is consumed, the byte buffer points to a block of uninitialized memory filled by `5a`. Therefore, while reading an `UnsafeArrayData` out of the `UnsafeRow`, `5a5a5a5a` is treated as array size and triggers a memory allocation for a ridiculously large array and immediately blows up the JVM with an OOM.
To fix this issue, we only need to add `.copy()` accordingly.
## How was this patch tested?
New regression test case added in `ObjectHashAggregateSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes #15976 from liancheng/investigate-oom.
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## What changes were proposed in this pull request?
Implement percentile SQL function. It computes the exact percentile(s) of expr at pc with range in [0, 1].
## How was this patch tested?
Add a new testsuite `PercentileSuite` to test percentile directly.
Updated related testcases in `ExpressionToSQLSuite`.
Author: jiangxingbo <jiangxb1987@gmail.com>
Author: 蒋星博 <jiangxingbo@meituan.com>
Author: jiangxingbo <jiangxingbo@meituan.com>
Closes #14136 from jiangxb1987/percentile.
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## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/15704 will fail if we use int literal in `DROP PARTITION`, and we have reverted it in branch-2.1.
This PR reverts it in master branch, and add a regression test for it, to make sure the master branch is healthy.
## How was this patch tested?
new regression test
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16036 from cloud-fan/revert.
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older version of spark
## What changes were proposed in this pull request?
In Spark 2.1, we did a lot of refactor for `HiveExternalCatalog` and related code path. These refactor may introduce external behavior changes and break backward compatibility. e.g. http://issues.apache.org/jira/browse/SPARK-18464
To avoid future compatibility problems of `HiveExternalCatalog`, this PR dumps some typical table metadata from tables created by 2.0, and test if they can recognized by current version of Spark.
## How was this patch tested?
test only change
Author: Wenchen Fan <wenchen@databricks.com>
Closes #16003 from cloud-fan/test.
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### What changes were proposed in this pull request?
Currently, the name validation checks are limited to table creation. It is enfored by Analyzer rule: `PreWriteCheck`.
However, table renaming and database creation have the same issues. It makes more sense to do the checks in `SessionCatalog`. This PR is to add it into `SessionCatalog`.
### How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes #16018 from gatorsmile/nameValidate.
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## What changes were proposed in this pull request?
The current implementation of column stats uses the base64 encoding of the internal UnsafeRow format to persist statistics (in table properties in Hive metastore). This is an internal format that is not stable across different versions of Spark and should NOT be used for persistence. In addition, it would be better if statistics stored in the catalog is human readable.
This pull request introduces the following changes:
1. Created a single ColumnStat class to for all data types. All data types track the same set of statistics.
2. Updated the implementation for stats collection to get rid of the dependency on internal data structures (e.g. InternalRow, or storing DateType as an int32). For example, previously dates were stored as a single integer, but are now stored as java.sql.Date. When we implement the next steps of CBO, we can add code to convert those back into internal types again.
3. Documented clearly what JVM data types are being used to store what data.
4. Defined a simple Map[String, String] interface for serializing and deserializing column stats into/from the catalog.
5. Rearranged the method/function structure so it is more clear what the supported data types are, and also moved how stats are generated into ColumnStat class so they are easy to find.
## How was this patch tested?
Removed most of the original test cases created for column statistics, and added three very simple ones to cover all the cases. The three test cases validate:
1. Roundtrip serialization works.
2. Behavior when analyzing non-existent column or unsupported data type column.
3. Result for stats collection for all valid data types.
Also moved parser related tests into a parser test suite and added an explicit serialization test for the Hive external catalog.
Author: Reynold Xin <rxin@databricks.com>
Closes #15959 from rxin/SPARK-18522.
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PartitionedTablePerfStatsSuite
## What changes were proposed in this pull request?
This would help catch accidental O(n) calls to the hive client as in https://issues.apache.org/jira/browse/SPARK-18507
## How was this patch tested?
Checked that the test fails before https://issues.apache.org/jira/browse/SPARK-18507 was patched. cc cloud-fan
Author: Eric Liang <ekl@databricks.com>
Closes #15985 from ericl/spark-18545.
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serde table
### What changes were proposed in this pull request?
In Spark 2.0, `SaveAsTable` does not work when the target table is a Hive serde table, but Spark 1.6 works.
**Spark 1.6**
``` Scala
scala> sql("create table sample.sample stored as SEQUENCEFILE as select 1 as key, 'abc' as value")
res2: org.apache.spark.sql.DataFrame = []
scala> val df = sql("select key, value as value from sample.sample")
df: org.apache.spark.sql.DataFrame = [key: int, value: string]
scala> df.write.mode("append").saveAsTable("sample.sample")
scala> sql("select * from sample.sample").show()
+---+-----+
|key|value|
+---+-----+
| 1| abc|
| 1| abc|
+---+-----+
```
**Spark 2.0**
``` Scala
scala> df.write.mode("append").saveAsTable("sample.sample")
org.apache.spark.sql.AnalysisException: Saving data in MetastoreRelation sample, sample
is not supported.;
```
So far, we do not plan to support it in Spark 2.1 due to the risk. Spark 1.6 works because it internally uses insertInto. But, if we change it back it will break the semantic of saveAsTable (this method uses by-name resolution instead of using by-position resolution used by insertInto). More extra changes are needed to support `hive` as a `format` in DataFrameWriter.
Instead, users should use insertInto API. This PR corrects the error messages. Users can understand how to bypass it before we support it in a separate PR.
### How was this patch tested?
Test cases are added
Author: gatorsmile <gatorsmile@gmail.com>
Closes #15926 from gatorsmile/saveAsTableFix5.
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when table doesn't exist
## What changes were proposed in this pull request?
While this behavior is debatable, consider the following use case:
```sql
UNCACHE TABLE foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
The command above fails the first time you run it. But I want to run the command above over and over again, and I don't want to change my code just for the first run of it.
The issue is that subsequent `CACHE TABLE` commands do not overwrite the existing table.
Now we can do:
```sql
UNCACHE TABLE IF EXISTS foo;
CACHE TABLE foo AS
SELECT * FROM bar
```
## How was this patch tested?
Unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes #15896 from brkyvz/uncache.
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## What changes were proposed in this pull request?
This PR adds the support for `UserDefinedType` when writing out instead of throwing `ClassCastException` in ORC data source.
In more details, `OrcStruct` is being created based on string from`DataType.catalogString`. For user-defined type, it seems it returns `sqlType.simpleString` for `catalogString` by default[1]. However, during type-dispatching to match the output with the schema, it tries to cast to, for example, `StructType`[2].
So, running the codes below (`MyDenseVector` was borrowed[3]) :
``` scala
val data = Seq((1, new UDT.MyDenseVector(Array(0.25, 2.25, 4.25))))
val udtDF = data.toDF("id", "vectors")
udtDF.write.orc("/tmp/test.orc")
```
ends up throwing an exception as below:
```
java.lang.ClassCastException: org.apache.spark.sql.UDT$MyDenseVectorUDT cannot be cast to org.apache.spark.sql.types.ArrayType
at org.apache.spark.sql.hive.HiveInspectors$class.wrapperFor(HiveInspectors.scala:381)
at org.apache.spark.sql.hive.orc.OrcSerializer.wrapperFor(OrcFileFormat.scala:164)
...
```
So, this PR uses `UserDefinedType.sqlType` during finding the correct converter when writing out in ORC data source.
[1]https://github.com/apache/spark/blob/dfdcab00c7b6200c22883baa3ebc5818be09556f/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala#L95
[2]https://github.com/apache/spark/blob/d2dc8c4a162834818190ffd82894522c524ca3e5/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L326
[3]https://github.com/apache/spark/blob/2bfed1a0c5be7d0718fd574a4dad90f4f6b44be7/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala#L38-L70
## How was this patch tested?
Unit tests in `OrcQuerySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #15361 from HyukjinKwon/SPARK-17765.
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columns when doing a simple count
## What changes were proposed in this pull request?
When reading zero columns (e.g., count(*)) from ORC or any other format that uses HiveShim, actually set the read column list to empty for Hive to use.
## How was this patch tested?
Query correctness is handled by existing unit tests. I'm happy to add more if anyone can point out some case that is not covered.
Reduction in data read can be verified in the UI when built with a recent version of Hadoop say:
```
build/mvn -Pyarn -Phadoop-2.7 -Dhadoop.version=2.7.0 -Phive -DskipTests clean package
```
However the default Hadoop 2.2 that is used for unit tests does not report actual bytes read and instead just full file sizes (see FileScanRDD.scala line 80). Therefore I don't think there is a good way to add a unit test for this.
I tested with the following setup using above build options
```
case class OrcData(intField: Long, stringField: String)
spark.range(1,1000000).map(i => OrcData(i, s"part-$i")).toDF().write.format("orc").save("orc_test")
sql(
s"""CREATE EXTERNAL TABLE orc_test(
| intField LONG,
| stringField STRING
|)
|STORED AS ORC
|LOCATION '${System.getProperty("user.dir") + "/orc_test"}'
""".stripMargin)
```
## Results
query | Spark 2.0.2 | this PR
---|---|---
`sql("select count(*) from orc_test").collect`|4.4 MB|199.4 KB
`sql("select intField from orc_test").collect`|743.4 KB|743.4 KB
`sql("select * from orc_test").collect`|4.4 MB|4.4 MB
Author: Andrew Ray <ray.andrew@gmail.com>
Closes #15898 from aray/sql-orc-no-col.
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depend on the location of default database
## What changes were proposed in this pull request?
The current semantic of the warehouse config:
1. it's a static config, which means you can't change it once your spark application is launched.
2. Once a database is created, its location won't change even the warehouse path config is changed.
3. default database is a special case, although its location is fixed, but the locations of tables created in it are not. If a Spark app starts with warehouse path B(while the location of default database is A), then users create a table `tbl` in default database, its location will be `B/tbl` instead of `A/tbl`. If uses change the warehouse path config to C, and create another table `tbl2`, its location will still be `B/tbl2` instead of `C/tbl2`.
rule 3 doesn't make sense and I think we made it by mistake, not intentionally. Data source tables don't follow rule 3 and treat default database like normal ones.
This PR fixes hive serde tables to make it consistent with data source tables.
## How was this patch tested?
HiveSparkSubmitSuite
Author: Wenchen Fan <wenchen@databricks.com>
Closes #15812 from cloud-fan/default-db.
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## What changes were proposed in this pull request?
Before Spark 2.1, users can create an external data source table without schema, and we will infer the table schema at runtime. In Spark 2.1, we decided to infer the schema when the table was created, so that we don't need to infer it again and again at runtime.
This is a good improvement, but we should still respect and support old tables which doesn't store table schema in metastore.
## How was this patch tested?
regression test.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #15900 from cloud-fan/hive-catalog.
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partial aggregation support
## What changes were proposed in this pull request?
While being evaluated in Spark SQL, Hive UDAFs don't support partial aggregation. This PR migrates `HiveUDAFFunction`s to `TypedImperativeAggregate`, which already provides partial aggregation support for aggregate functions that may use arbitrary Java objects as aggregation states.
The following snippet shows the effect of this PR:
```scala
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax
sql(s"CREATE FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
spark.range(100).createOrReplaceTempView("t")
// A query using both Spark SQL native `max` and Hive `max`
sql(s"SELECT max(id), hive_max(id) FROM t").explain()
```
Before this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax7475f57e), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- *Range (0, 100, step=1, splits=Some(1))
```
After this PR:
```
== Physical Plan ==
SortAggregate(key=[], functions=[max(id#1L), default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- Exchange SinglePartition
+- SortAggregate(key=[], functions=[partial_max(id#1L), partial_default.hive_max(default.hive_max, HiveFunctionWrapper(org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax,org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax5e18a6a7), id#1L, false, 0, 0)])
+- *Range (0, 100, step=1, splits=Some(1))
```
The tricky part of the PR is mostly about updating and passing around aggregation states of `HiveUDAFFunction`s since the aggregation state of a Hive UDAF may appear in three different forms. Let's take a look at the testing `MockUDAF` added in this PR as an example. This UDAF computes the count of non-null values together with the count of nulls of a given column. Its aggregation state may appear as the following forms at different time:
1. A `MockUDAFBuffer`, which is a concrete subclass of `GenericUDAFEvaluator.AggregationBuffer`
The form used by Hive UDAF API. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.iterate()` to update an existing aggregation state with new input values.
- Calling `GenericUDAFEvaluator.terminate()` to get the final aggregated value from an existing aggregation state.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The existing aggregation state to be updated must be in this form.
Conversions:
- To form 2:
`GenericUDAFEvaluator.terminatePartial()`
- To form 3:
Convert to form 2 first, and then to 3.
2. An `Object[]` array containing two `java.lang.Long` values.
The form used to interact with Hive's `ObjectInspector`s. This form is required by the following scenarios:
- Calling `GenericUDAFEvaluator.terminatePartial()` to convert an existing aggregation state in form 1 to form 2.
- Calling `GenericUDAFEvaluator.merge()` to merge other aggregation states into an existing aggregation state.
The input aggregation state must be in this form.
Conversions:
- To form 1:
No direct method. Have to create an empty `AggregationBuffer` and merge it into the empty buffer.
- To form 3:
`unwrapperFor()`/`unwrap()` method of `HiveInspectors`
3. The byte array that holds data of an `UnsafeRow` with two `LongType` fields.
The form used by Spark SQL to shuffle partial aggregation results. This form is required because `TypedImperativeAggregate` always asks its subclasses to serialize their aggregation states into a byte array.
Conversions:
- To form 1:
Convert to form 2 first, and then to 1.
- To form 2:
`wrapperFor()`/`wrap()` method of `HiveInspectors`
Here're some micro-benchmark results produced by the most recent master and this PR branch.
Master:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 339 / 372 3.1 323.2 1.0X
w/ groupBy 503 / 529 2.1 479.7 0.7X
```
This PR:
```
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
w/o groupBy 116 / 126 9.0 110.8 1.0X
w/ groupBy 151 / 159 6.9 144.0 0.8X
```
Benchmark code snippet:
```scala
test("Hive UDAF benchmark") {
val N = 1 << 20
sparkSession.sql(s"CREATE TEMPORARY FUNCTION hive_max AS '${classOf[GenericUDAFMax].getName}'")
val benchmark = new Benchmark(
name = "hive udaf vs spark af",
valuesPerIteration = N,
minNumIters = 5,
warmupTime = 5.seconds,
minTime = 5.seconds,
outputPerIteration = true
)
benchmark.addCase("w/o groupBy") { _ =>
sparkSession.range(N).agg("id" -> "hive_max").collect()
}
benchmark.addCase("w/ groupBy") { _ =>
sparkSession.range(N).groupBy($"id" % 10).agg("id" -> "hive_max").collect()
}
benchmark.run()
sparkSession.sql(s"DROP TEMPORARY FUNCTION IF EXISTS hive_max")
}
```
## How was this patch tested?
New test suite `HiveUDAFSuite` is added.
Author: Cheng Lian <lian@databricks.com>
Closes #15703 from liancheng/partial-agg-hive-udaf.
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## What changes were proposed in this pull request?
This PR aims to improve DataSource option keys to be more case-insensitive
DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url.
```scala
val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
df.write.format("jdbc")
.option("UrL", url1)
.option("dbtable", "TEST.SAVETEST")
.options(properties.asScala)
.save()
```
This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside.
## How was this patch tested?
Pass the Jenkins test with newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #15884 from dongjoon-hyun/SPARK-18433.
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## What changes were proposed in this pull request?
it's weird that every session can set its own warehouse path at runtime, we should forbid it and make it a static conf.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #15825 from cloud-fan/warehouse.
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## What changes were proposed in this pull request?
This PR aims to support `comparators`, e.g. '<', '<=', '>', '>=', again in Apache Spark 2.0 for backward compatibility.
**Spark 1.6**
``` scala
scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)")
res0: org.apache.spark.sql.DataFrame = [result: string]
scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')")
res1: org.apache.spark.sql.DataFrame = [result: string]
```
**Spark 2.0**
``` scala
scala> sql("CREATE TABLE sales(id INT) PARTITIONED BY (country STRING, quarter STRING)")
res0: org.apache.spark.sql.DataFrame = []
scala> sql("ALTER TABLE sales DROP PARTITION (country < 'KR')")
org.apache.spark.sql.catalyst.parser.ParseException:
mismatched input '<' expecting {')', ','}(line 1, pos 42)
```
After this PR, it's supported.
## How was this patch tested?
Pass the Jenkins test with a newly added testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #15704 from dongjoon-hyun/SPARK-17732-2.
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for LIMIT
## What changes were proposed in this pull request?
Currently, `SQLBuilder` handles `LIMIT` by always adding `LIMIT` at the end of the generated subSQL. It makes `RuntimeException`s like the following. This PR adds a parenthesis always except `SubqueryAlias` is used together with `LIMIT`.
**Before**
``` scala
scala> sql("CREATE TABLE tbl(id INT)")
scala> sql("CREATE VIEW v1(id2) AS SELECT id FROM tbl LIMIT 2")
java.lang.RuntimeException: Failed to analyze the canonicalized SQL: ...
```
**After**
``` scala
scala> sql("CREATE TABLE tbl(id INT)")
scala> sql("CREATE VIEW v1(id2) AS SELECT id FROM tbl LIMIT 2")
scala> sql("SELECT id2 FROM v1")
res4: org.apache.spark.sql.DataFrame = [id2: int]
```
**Fixed cases in this PR**
The following two cases are the detail query plans having problematic SQL generations.
1. `SELECT * FROM (SELECT id FROM tbl LIMIT 2)`
Please note that **FROM SELECT** part of the generated SQL in the below. When we don't use '()' for limit, this fails.
```scala
# Original logical plan:
Project [id#1]
+- GlobalLimit 2
+- LocalLimit 2
+- Project [id#1]
+- MetastoreRelation default, tbl
# Canonicalized logical plan:
Project [gen_attr_0#1 AS id#4]
+- SubqueryAlias tbl
+- Project [gen_attr_0#1]
+- GlobalLimit 2
+- LocalLimit 2
+- Project [gen_attr_0#1]
+- SubqueryAlias gen_subquery_0
+- Project [id#1 AS gen_attr_0#1]
+- SQLTable default, tbl, [id#1]
# Generated SQL:
SELECT `gen_attr_0` AS `id` FROM (SELECT `gen_attr_0` FROM SELECT `gen_attr_0` FROM (SELECT `id` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0 LIMIT 2) AS tbl
```
2. `SELECT * FROM (SELECT id FROM tbl TABLESAMPLE (2 ROWS))`
Please note that **((~~~) AS gen_subquery_0 LIMIT 2)** in the below. When we use '()' for limit on `SubqueryAlias`, this fails.
```scala
# Original logical plan:
Project [id#1]
+- Project [id#1]
+- GlobalLimit 2
+- LocalLimit 2
+- MetastoreRelation default, tbl
# Canonicalized logical plan:
Project [gen_attr_0#1 AS id#4]
+- SubqueryAlias tbl
+- Project [gen_attr_0#1]
+- GlobalLimit 2
+- LocalLimit 2
+- SubqueryAlias gen_subquery_0
+- Project [id#1 AS gen_attr_0#1]
+- SQLTable default, tbl, [id#1]
# Generated SQL:
SELECT `gen_attr_0` AS `id` FROM (SELECT `gen_attr_0` FROM ((SELECT `id` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0 LIMIT 2)) AS tbl
```
## How was this patch tested?
Pass the Jenkins test with a newly added test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #15546 from dongjoon-hyun/SPARK-17982.
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