06d5b173b6
## What changes were proposed in this pull request? When a streaming query has multiple file streams, and there is a batch where one of the file streams dont have data in that batch, then if the query has to restart from that, it will throw the following error. ``` java.lang.IllegalStateException: batch 1 doesn't exist at org.apache.spark.sql.execution.streaming.HDFSMetadataLog$.verifyBatchIds(HDFSMetadataLog.scala:300) at org.apache.spark.sql.execution.streaming.FileStreamSourceLog.get(FileStreamSourceLog.scala:120) at org.apache.spark.sql.execution.streaming.FileStreamSource.getBatch(FileStreamSource.scala:181) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets$2.apply(MicroBatchExecution.scala:294) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets$2.apply(MicroBatchExecution.scala:291) at scala.collection.Iterator$class.foreach(Iterator.scala:891) at scala.collection.AbstractIterator.foreach(Iterator.scala:1334) at scala.collection.IterableLike$class.foreach(IterableLike.scala:72) at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$populateStartOffsets(MicroBatchExecution.scala:291) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:178) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:175) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:175) at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:251) at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:61) at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:175) at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56) at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:169) at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295) at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:205) ``` Existing `HDFSMetadata.verifyBatchIds` threw error whenever the `batchIds` list was empty. In the context of `FileStreamSource.getBatch` (where verify is called) and `FileStreamSourceLog` (subclass of `HDFSMetadata`), this is usually okay because, in a streaming query with one file stream, the `batchIds` can never be empty: - A batch is planned only when the `FileStreamSourceLog` has seen new offset (that is, there are new data files). - So `FileStreamSource.getBatch` will be called on X to Y where X will always be > Y. This calls internally`HDFSMetadata.verifyBatchIds (X+1, Y)` with X+1-Y ids. For example.,`FileStreamSource.getBatch(4, 5)` will call `verify(batchIds = Seq(5), start = 5, end = 5)`. However, the invariant of X > Y is not true when there are two file stream sources, as a batch may be planned even when only one of the file streams has data. So one of the file stream may not have data, which can call `FileStreamSource.getBatch(X, X)` -> `verify(batchIds = Seq.empty, start = X+1, end = X)` -> failure. Note that `FileStreamSource.getBatch(X, X)` gets called **only when restarting a query in a batch where a file source did not have data**. This is because in normal planning of batches, `MicroBatchExecution` avoids calling `FileStreamSource.getBatch(X, X)` when offset X has not changed. However, when restarting a stream at such a batch, `MicroBatchExecution.populateStartOffsets()` calls `FileStreamSource.getBatch(X, X)` (DataSource V1 hack to initialize the source with last known offsets) thus hitting this issue. The minimum solution here is to skip verification when `FileStreamSource.getBatch(X, X)`. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. Closes #23557 from tdas/SPARK-26629. Authored-by: Tathagata Das <tathagata.das1565@gmail.com> Signed-off-by: Shixiong Zhu <zsxwing@gmail.com> |
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Apache Spark
Spark is a fast and general cluster computing system for Big Data. 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 Spark 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.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". 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 1000:
scala> sc.parallelize(1 to 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 1000:
>>> sc.parallelize(range(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.