ff5115c3ac
BasicWriteStatsTracker to probe for a custom Xattr if the size of the generated file is 0 bytes; if found and parseable use that as the declared length of the output. The matching Hadoop patch in HADOOP-17414: * Returns all S3 object headers as XAttr attributes prefixed "header." * Sets the custom header x-hadoop-s3a-magic-data-length to the length of the data in the marker file. As a result, spark job tracking will correctly report the amount of data uploaded and yet to materialize. ### Why are the changes needed? Now that S3 is consistent, it's a lot easier to use the S3A "magic" committer which redirects a file written to `dest/__magic/job_0011/task_1245/__base/year=2020/output.avro` to its final destination `dest/year=2020/output.avro` , adding a zero byte marker file at the end and a json file `dest/__magic/job_0011/task_1245/__base/year=2020/output.avro.pending` containing all the information for the job committer to complete the upload. But: the write tracker statictics don't show progress as they measure the length of the created file, find the marker file and report 0 bytes. By probing for a specific HTTP header in the marker file and parsing that if retrieved, the real progress can be reported. There's a matching change in Hadoop [https://github.com/apache/hadoop/pull/2530](https://github.com/apache/hadoop/pull/2530) which adds getXAttr API support to the S3A connector and returns the headers; the magic committer adds the relevant attributes. If the FS being probed doesn't support the XAttr API, the header is missing or the value not a positive long then the size of 0 is returned. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? New tests in BasicWriteTaskStatsTrackerSuite which use a filter FS to implement getXAttr on top of LocalFS; this is used to explore the set of options: * no XAttr API implementation (existing tests; what callers would see with most filesystems) * no attribute found (HDFS, ABFS without the attribute) * invalid data of different forms All of these return Some(0) as file length. The Hadoop PR verifies XAttr implementation in S3A and that the commit protocol attaches the header to the files. External downstream testing has done the full hadoop+spark end to end operation, with manual review of logs to verify that the data was successfully collected from the attribute. Closes #30714 from steveloughran/cdpd/SPARK-33739-magic-commit-tracking-master. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Thomas Graves <tgraves@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.