1a042cc414
### What changes were proposed in this pull request? TL;DR: - This PR completes the support of archives in Spark itself instead of Yarn-only - It makes `--archives` option work in other cluster modes too and adds `spark.archives` configuration. - After this PR, PySpark users can leverage Conda to ship Python packages together as below: ```python conda create -y -n pyspark_env -c conda-forge pyarrow==2.0.0 pandas==1.1.4 conda-pack==0.5.0 conda activate pyspark_env conda pack -f -o pyspark_env.tar.gz PYSPARK_DRIVER_PYTHON=python PYSPARK_PYTHON=./environment/bin/python pyspark --archives pyspark_env.tar.gz#environment ``` - Issue a warning that undocumented and hidden behavior of partial archive handling in `spark.files` / `SparkContext.addFile` will be deprecated, and users can use `spark.archives` and `SparkContext.addArchive`. This PR proposes to add Spark's native `--archives` in Spark submit, and `spark.archives` configuration. Currently, both are supported only in Yarn mode: ```bash ./bin/spark-submit --help ``` ``` Options: ... Spark on YARN only: --queue QUEUE_NAME The YARN queue to submit to (Default: "default"). --archives ARCHIVES Comma separated list of archives to be extracted into the working directory of each executor. ``` This `archives` feature is useful often when you have to ship a directory and unpack into executors. One example is native libraries to use e.g. JNI. Another example is to ship Python packages together by Conda environment. Especially for Conda, PySpark currently does not have a nice way to ship a package that works in general, please see also https://hyukjin-spark.readthedocs.io/en/stable/user_guide/python_packaging.html#using-zipped-virtual-environment (PySpark new documentation demo for 3.1.0). The neatest way is arguably to use Conda environment by shipping zipped Conda environment but this is currently dependent on this archive feature. NOTE that we are able to use `spark.files` by relying on its undocumented behaviour that untars `tar.gz` but I don't think we should document such ways and promote people to more rely on it. Also, note that this PR does not target to add the feature parity of `spark.files.overwrite`, `spark.files.useFetchCache`, etc. yet. I documented that this is an experimental feature as well. ### Why are the changes needed? To complete the feature parity, and to provide a better support of shipping Python libraries together with Conda env. ### Does this PR introduce _any_ user-facing change? Yes, this makes `--archives` works in Spark instead of Yarn-only, and adds a new configuration `spark.archives`. ### How was this patch tested? I added unittests. Also, manually tested in standalone cluster, local-cluster, and local modes. Closes #30486 from HyukjinKwon/native-archive. Authored-by: HyukjinKwon <gurwls223@apache.org> 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.