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### What changes were proposed in this pull request? This PR introduces a new protected method in `SparkFunSuite` which is only called when the test failed and can be used to collect logs for failed test. By this PR it is implemented in the Kubernetes tests by `KubernetesSuite` class where it collects all the POD logs and logs them out. This unfortunately cannot be realized with a simple "after" method as in the "after" method the test outcome is not available. Moreover this PR removes the `appLocator` as a method argument as `appLocator` is available as a member variable. ### Why are the changes needed? Currently both the driver and executors logs are lost. In [developer-tools](https://spark.apache.org/developer-tools.html) there is a hint: "Getting logs from the pods and containers directly is an exercise left to the reader." But when the test is executed by Jenkins and a failure happened we really need the POD logs to analyze problem. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? By integration testing. I have checked what would happen if one test fails, the output would be: ``` 21/02/14 11:05:34.261 ScalaTest-main-running-KubernetesSuite INFO KubernetesSuite: ===== EXTRA LOGS FOR THE FAILED TEST 21/02/14 11:05:34.278 ScalaTest-main-running-KubernetesSuite INFO KubernetesSuite: BEGIN driver POD log ++ id -u + myuid=185 ++ id -g + mygid=0 + set +e ++ getent passwd 185 + uidentry= + set -e + '[' -z '' ']' + '[' -w /etc/passwd ']' + echo '185❌185:0:anonymous uid:/opt/spark:/bin/false' + SPARK_CLASSPATH=':/opt/spark/jars/*' + env + grep SPARK_JAVA_OPT_ + sort -t_ -k4 -n + sed 's/[^=]*=\(.*\)/\1/g' + readarray -t SPARK_EXECUTOR_JAVA_OPTS + '[' -n '' ']' + '[' -z ']' + '[' -z ']' + '[' -n '' ']' + '[' -z ']' + '[' -z x ']' + SPARK_CLASSPATH='/opt/spark/conf::/opt/spark/jars/*' + case "$1" in + shift 1 + CMD=("$SPARK_HOME/bin/spark-submit" --conf "spark.driver.bindAddress=$SPARK_DRIVER_BIND_ADDRESS" --deploy-mode client "$") + exec /usr/bin/tini -s -- /opt/spark/bin/spark-submit --conf spark.driver.bindAddress=172.17.0.3 --deploy-mode client --properties-file /opt/spark/conf/spark.properties --class org.apache.spark.deploy.PythonRunner local:///opt/spark/tests/decommissioning.py 21/02/14 10:02:28 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Starting decom test Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties 21/02/14 10:02:29 INFO SparkContext: Running Spark version 3.2.0-SNAPSHOT 21/02/14 10:02:29 INFO ResourceUtils: ============================================================== 21/02/14 10:02:29 INFO ResourceUtils: No custom resources configured for spark.driver. 21/02/14 10:02:29 INFO ResourceUtils: ============================================================== ... 21/02/14 10:03:17 INFO ShutdownHookManager: Deleting directory /var/data/spark-fa6961ed-a2c1-444c-bfeb-20e63ba0b5cf/spark-ab4b0287-6e24-4b39-837e-9b0b62c1f26f 21/02/14 10:03:17 INFO ShutdownHookManager: Deleting directory /tmp/spark-d6b11e7d-6a03-4a1d-8559-37cb853319bf 21/02/14 11:05:34.279 ScalaTest-main-running-KubernetesSuite INFO KubernetesSuite: END driver POD log ``` Closes #31561 from attilapiros/SPARK-34426. Authored-by: “attilapiros” <piros.attila.zsolt@gmail.com> Signed-off-by: Jungtaek Lim <kabhwan.opensource@gmail.com> |
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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.