c8779d9dfc
### What changes were proposed in this pull request? Force to initialize Hadoop VersionInfo in HiveExternalCatalog to make sure Hive can get the Hadoop version when using the isolated classloader. ### Why are the changes needed? This is a regression in Spark 3.0.0 because we switched the default Hive execution version from 1.2.1 to 2.3.7. Spark allows the user to set `spark.sql.hive.metastore.jars` to specify jars to access Hive Metastore. These jars are loaded by the isolated classloader. Because we also share Hadoop classes with the isolated classloader, the user doesn't need to add Hadoop jars to `spark.sql.hive.metastore.jars`, which means when we are using the isolated classloader, hadoop-common jar is not available in this case. If Hadoop VersionInfo is not initialized before we switch to the isolated classloader, and we try to initialize it using the isolated classloader (the current thread context classloader), it will fail and report `Unknown` which causes Hive to throw the following exception: ``` java.lang.RuntimeException: Illegal Hadoop Version: Unknown (expected A.B.* format) at org.apache.hadoop.hive.shims.ShimLoader.getMajorVersion(ShimLoader.java:147) at org.apache.hadoop.hive.shims.ShimLoader.loadShims(ShimLoader.java:122) at org.apache.hadoop.hive.shims.ShimLoader.getHadoopShims(ShimLoader.java:88) at org.apache.hadoop.hive.metastore.ObjectStore.getDataSourceProps(ObjectStore.java:377) at org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:268) at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:76) at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:136) at org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:58) at org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:67) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:517) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:482) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:544) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:370) at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:78) at org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:84) at org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:219) at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:67) at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:423) at org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1548) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104) at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3080) at org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3108) at org.apache.hadoop.hive.ql.metadata.Hive.getAllFunctions(Hive.java:3349) at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:217) at org.apache.hadoop.hive.ql.metadata.Hive.registerAllFunctionsOnce(Hive.java:204) at org.apache.hadoop.hive.ql.metadata.Hive.<init>(Hive.java:331) at org.apache.hadoop.hive.ql.metadata.Hive.get(Hive.java:292) at org.apache.hadoop.hive.ql.metadata.Hive.getInternal(Hive.java:262) at org.apache.hadoop.hive.ql.metadata.Hive.get(Hive.java:247) at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:543) at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:511) at org.apache.spark.sql.hive.client.HiveClientImpl.newState(HiveClientImpl.scala:175) at org.apache.spark.sql.hive.client.HiveClientImpl.<init>(HiveClientImpl.scala:128) at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:423) at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:301) at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:431) at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:324) at org.apache.spark.sql.hive.HiveExternalCatalog.client$lzycompute(HiveExternalCatalog.scala:72) at org.apache.spark.sql.hive.HiveExternalCatalog.client(HiveExternalCatalog.scala:71) at org.apache.spark.sql.hive.client.HadoopVersionInfoSuite.$anonfun$new$1(HadoopVersionInfoSuite.scala:63) at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85) at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83) ``` Technically, This is indeed an issue of Hadoop VersionInfo which has been fixed: https://issues.apache.org/jira/browse/HADOOP-14067. But since we are still supporting old Hadoop versions, we should fix it. Why this issue starts to happen in Spark 3.0.0? In Spark 2.4.x, we use Hive 1.2.1 by default. It will trigger `VersionInfo` initialization in the static codes of `Hive` class. This will happen when we load `HiveClientImpl` class because `HiveClientImpl.clent` method refers to `Hive` class. At this moment, the thread context classloader is not using the isolcated classloader, so it can access hadoop-common jar on the classpath and initialize it correctly. In Spark 3.0.0, we use Hive 2.3.7. The static codes of `Hive` class are not accessing `VersionInfo` because of the change in https://issues.apache.org/jira/browse/HIVE-11657. Instead, accessing `VersionInfo` happens when creating a `Hive` object (See the above stack trace). This happens here https://github.com/apache/spark/blob/v3.0.0/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala#L260. But we switch to the isolated classloader before calling `HiveClientImpl.client` (See https://github.com/apache/spark/blob/v3.0.0/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveClientImpl.scala#L283). This is exactly what I mentioned above: `If Hadoop VersionInfo is not initialized before we switch to the isolated classloader, and we try to initialize it using the isolated classloader (the current thread context classloader), it will fail` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? The new regression test added in this PR. Note that the new UT doesn't fail with the default profiles (-Phadoop-3.2) because it's already fixed at Hadoop 3.1. Please use the following to verify this. ``` build/sbt -Phadoop-2.7 -Phive "hive/testOnly *.HadoopVersionInfoSuite" ``` Closes #29059 from zsxwing/SPARK-32256. Authored-by: Shixiong Zhu <zsxwing@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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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, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
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 a mesos:// or 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.