e086951349
### What changes were proposed in this pull request? Currently the uploadBlockSync api in BlockTransferService always succeeds irrespective of whether the BlockManager was able to successfully replicate a block on peer block manager or not. This PR makes sure that the NettyBlockRpcServer invokes onFailure callback when it is not able to replicate the block to itself because of any reason. The onFailure callback makes sure that the BlockTransferService on client side gets the failure and retry replication the Block on some other BlockManager. ### Why are the changes needed? Currently the Spark Block replication retry logic is not working correctly. It doesn't retry on other Block managers even when replication fails on 1 of the peers. A user can cache an DataFrame with different replication factor. Ex - df.persist(StorageLevel.MEMORY_ONLY_2) - This will cache each partition at two different BlockManagers. When a DataFrame partition is computed first time, it is firstly stored locally on the local BlockManager and then it is replicated to other block managers based on replication factor config. The replication of block to other block managers might fail because of memory/network etc issues and so there is already provision to retry the replication on some other peer based on "spark.storage.maxReplicationFailures" config, Currently when this replication fails, the client does not know about the failure and so it doesn't retry on other peers. This PR fixes this issue. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Added Unit Test. Closes #27539 from prakharjain09/bm_replicate. Authored-by: Prakhar Jain <prakharjain09@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.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.