commit	5b3b8a90638c49fc7ddcace69a85989c1053f1ab	[log] [tgz]
author	Dongjoon Hyun <dhyun@apple.com>	Fri May 10 15:48:08 2024 -0700
committer	Dongjoon Hyun <dhyun@apple.com>	Fri May 10 15:48:08 2024 -0700
tree	df49a9edf47b217bdea2c8b77f8101fed8b46464
parent	726ef8aa66ea6e56b739f3b16f99e457a0febb81 [diff]

[SPARK-48236][BUILD] Add `commons-lang:commons-lang:2.6` back to support legacy Hive UDF jars

### What changes were proposed in this pull request?

This PR aims to add `commons-lang:commons-lang:2.6` back to support legacy Hive UDF jars . This is a partial revert of SPARK-47018 .

### Why are the changes needed?

Recently, we dropped `commons-lang:commons-lang` during Hive upgrade.
- #46468

However, only Apache Hive 2.3.10 or 4.0.0 dropped it. In other words, Hive 2.0.0 ~ 2.3.9 and Hive 3.0.0 ~ 3.1.3 requires it. As a result, all existing  UDF jars built against those versions requires `commons-lang:commons-lang` still.

- https://github.com/apache/hive/pull/4892

For example, Apache Hive 3.1.3 code:
- https://github.com/apache/hive/blob/af7059e2bdc8b18af42e0b7f7163b923a0bfd424/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFTrim.java#L21
```
import org.apache.commons.lang.StringUtils;
```

- https://github.com/apache/hive/blob/af7059e2bdc8b18af42e0b7f7163b923a0bfd424/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFTrim.java#L42
```
return StringUtils.strip(val, " ");
```

As a result, Maven CIs are broken.
- https://github.com/apache/spark/actions/runs/9032639456/job/24825599546 (Maven / Java 17)
- https://github.com/apache/spark/actions/runs/9033374547/job/24835284769 (Maven / Java 21)

The root cause is that the existing test UDF jar `hive-test-udfs.jar` was built from old Hive (before 2.3.10) libraries which requires `commons-lang:commons-lang:2.6`.
```
HiveUDFDynamicLoadSuite:
- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (UDF
20:21:25.129 WARN org.apache.spark.SparkContext: The JAR file:///home/runner/work/spark/spark/sql/hive/src/test/noclasspath/hive-test-udfs.jar at spark://localhost:33327/jars/hive-test-udfs.jar has been added already. Overwriting of added jar is not supported in the current version.

*** RUN ABORTED ***
A needed class was not found. This could be due to an error in your runpath. Missing class: org/apache/commons/lang/StringUtils
  java.lang.NoClassDefFoundError: org/apache/commons/lang/StringUtils
  at org.apache.hadoop.hive.contrib.udf.example.GenericUDFTrim2.performOp(GenericUDFTrim2.java:43)
  at org.apache.hadoop.hive.ql.udf.generic.GenericUDFBaseTrim.evaluate(GenericUDFBaseTrim.java:75)
  at org.apache.hadoop.hive.ql.udf.generic.GenericUDF.initializeAndFoldConstants(GenericUDF.java:170)
  at org.apache.spark.sql.hive.HiveGenericUDFEvaluator.returnInspector$lzycompute(hiveUDFEvaluators.scala:118)
  at org.apache.spark.sql.hive.HiveGenericUDFEvaluator.returnInspector(hiveUDFEvaluators.scala:117)
  at org.apache.spark.sql.hive.HiveGenericUDF.dataType$lzycompute(hiveUDFs.scala:132)
  at org.apache.spark.sql.hive.HiveGenericUDF.dataType(hiveUDFs.scala:132)
  at org.apache.spark.sql.hive.HiveUDFExpressionBuilder$.makeHiveFunctionExpression(HiveSessionStateBuilder.scala:184)
  at org.apache.spark.sql.hive.HiveUDFExpressionBuilder$.$anonfun$makeExpression$1(HiveSessionStateBuilder.scala:164)
  at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:185)
  ...
  Cause: java.lang.ClassNotFoundException: org.apache.commons.lang.StringUtils
  at java.base/java.net.URLClassLoader.findClass(URLClassLoader.java:445)
  at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:593)
  at java.base/java.lang.ClassLoader.loadClass(ClassLoader.java:526)
  at org.apache.hadoop.hive.contrib.udf.example.GenericUDFTrim2.performOp(GenericUDFTrim2.java:43)
  at org.apache.hadoop.hive.ql.udf.generic.GenericUDFBaseTrim.evaluate(GenericUDFBaseTrim.java:75)
  at org.apache.hadoop.hive.ql.udf.generic.GenericUDF.initializeAndFoldConstants(GenericUDF.java:170)
  at org.apache.spark.sql.hive.HiveGenericUDFEvaluator.returnInspector$lzycompute(hiveUDFEvaluators.scala:118)
  at org.apache.spark.sql.hive.HiveGenericUDFEvaluator.returnInspector(hiveUDFEvaluators.scala:117)
  at org.apache.spark.sql.hive.HiveGenericUDF.dataType$lzycompute(hiveUDFs.scala:132)
  at org.apache.spark.sql.hive.HiveGenericUDF.dataType(hiveUDFs.scala:132)
  ...
```

### Does this PR introduce _any_ user-facing change?

To support the existing customer UDF jars.

### How was this patch tested?

Manually.

```
$ build/mvn -Dtest=none -DwildcardSuites=org.apache.spark.sql.hive.HiveUDFDynamicLoadSuite test
...
HiveUDFDynamicLoadSuite:
14:21:56.034 WARN org.apache.hadoop.hive.metastore.ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 2.3.0

14:21:56.035 WARN org.apache.hadoop.hive.metastore.ObjectStore: setMetaStoreSchemaVersion called but recording version is disabled: version = 2.3.0, comment = Set by MetaStore dongjoon127.0.0.1

14:21:56.041 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database default, returning NoSuchObjectException

- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (UDF
14:21:57.576 WARN org.apache.spark.SparkContext: The JAR file:///Users/dongjoon/APACHE/spark-merge/sql/hive/src/test/noclasspath/hive-test-udfs.jar at spark://localhost:55526/jars/hive-test-udfs.jar has been added already. Overwriting of added jar is not supported in the current version.

- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (GENERIC_UDF
14:21:58.314 WARN org.apache.spark.SparkContext: The JAR file:///Users/dongjoon/APACHE/spark-merge/sql/hive/src/test/noclasspath/hive-test-udfs.jar at spark://localhost:55526/jars/hive-test-udfs.jar has been added already. Overwriting of added jar is not supported in the current version.

- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (GENERIC_UDAF
14:21:58.943 WARN org.apache.spark.SparkContext: The JAR file:///Users/dongjoon/APACHE/spark-merge/sql/hive/src/test/noclasspath/hive-test-udfs.jar at spark://localhost:55526/jars/hive-test-udfs.jar has been added already. Overwriting of added jar is not supported in the current version.

- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (UDAF
14:21:59.333 WARN org.apache.hadoop.hive.ql.session.SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.

14:21:59.364 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist

14:21:59.370 WARN org.apache.hadoop.hive.metastore.HiveMetaStore: Location: file:/Users/dongjoon/APACHE/spark-merge/sql/hive/target/tmp/warehouse-49291492-9d48-4360-a354-ace73a2c76ce/src specified for non-external table:src

14:21:59.718 WARN org.apache.hadoop.hive.metastore.ObjectStore: Failed to get database global_temp, returning NoSuchObjectException

14:21:59.770 WARN org.apache.spark.SparkContext: The JAR file:///Users/dongjoon/APACHE/spark-merge/sql/hive/src/test/noclasspath/hive-test-udfs.jar at spark://localhost:55526/jars/hive-test-udfs.jar has been added already. Overwriting of added jar is not supported in the current version.

- Spark should be able to run Hive UDF using jar regardless of current thread context classloader (GENERIC_UDTF
14:22:00.403 WARN org.apache.hadoop.hive.common.FileUtils: File file:/Users/dongjoon/APACHE/spark-merge/sql/hive/target/tmp/warehouse-49291492-9d48-4360-a354-ace73a2c76ce/src does not exist; Force to delete it.

14:22:00.404 ERROR org.apache.hadoop.hive.common.FileUtils: Failed to delete file:/Users/dongjoon/APACHE/spark-merge/sql/hive/target/tmp/warehouse-49291492-9d48-4360-a354-ace73a2c76ce/src

14:22:00.441 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist

14:22:00.453 WARN org.apache.hadoop.hive.ql.session.SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.

14:22:00.537 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist

Run completed in 8 seconds, 612 milliseconds.
Total number of tests run: 5
Suites: completed 2, aborted 0
Tests: succeeded 5, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```

### Was this patch authored or co-authored using generative AI tooling?

Closes #46528 from dongjoon-hyun/SPARK-48236.

Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

5 files changed

tree: df49a9edf47b217bdea2c8b77f8101fed8b46464

README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

./build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

More detailed documentation is available from the project site, at “Building Spark”.

For general development tips, including info on developing Spark using an IDE, see “Useful Developer Tools”.

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1,000,000,000:

scala> spark.range(1000 * 1000 * 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, “yarn” to run on YARN, and “local” to run locally with one thread, or “local[N]” to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at “Specifying the Hadoop Version and Enabling YARN” for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.