ca2ba4fe64
### What changes were proposed in this pull request?
This patch fixes the behavior of ProgressReporter which always overwrite the value of "updated" of state operator to 0 if there's no new data. The behavior is correct only when we copy the state progress from "previous" executed plan, meaning no batch has been run. (Nonzero value of "updated" would be odd if batch didn't run, so it was correct.)
It was safe to assume no data is no batch, but SPARK-24156 enables empty data can run the batch if Spark needs to deal with watermark. After the patch, it only overwrites the value if both two conditions are met: 1) no data 2) no batch.
### Why are the changes needed?
Currently Spark doesn't reflect correct metrics when empty batch is run and this patch fixes it.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Modified UT. Note that FlatMapGroupsWithState increases the value of "updated" when state rows are removed.
Also manually tested via below query (not a simple query to test with spark-shell, as you'll meet closure issue in spark-shell while playing with state func):
> query
```
case class RunningCount(count: Long)
object TestFlatMapGroupsWithState {
def main(args: Array[String]): Unit = {
import org.apache.spark.sql.SparkSession
val ss = SparkSession
.builder()
.appName("TestFlatMapGroupsWithState")
.getOrCreate()
ss.conf.set("spark.sql.shuffle.partitions", "5")
import ss.implicits._
val stateFunc = (key: String, values: Iterator[String], state: GroupState[RunningCount]) => {
if (state.hasTimedOut) {
// End users are not restricted to remove the state here - they can update the
// state as well. For example, event time session window would have list of
// sessions here and it cannot remove entire state.
state.update(RunningCount(-1))
Iterator((key, "-1"))
} else {
val count = state.getOption.map(_.count).getOrElse(0L) + values.size
state.update(RunningCount(count))
state.setTimeoutDuration("1 seconds")
Iterator((key, count.toString))
}
}
implicit val sqlContext = ss.sqlContext
val inputData = MemoryStream[String]
val result = inputData
.toDF()
.as[String]
.groupByKey { v => v }
.flatMapGroupsWithState(OutputMode.Append(), GroupStateTimeout.ProcessingTimeTimeout())(stateFunc)
val query = result
.writeStream
.format("memory")
.option("queryName", "test")
.outputMode("append")
.trigger(Trigger.ProcessingTime("5 second"))
.start()
Thread.sleep(1000)
var chIdx: Long = 0
while (true) {
(chIdx to chIdx + 4).map { idx => inputData.addData(idx.toString) }
chIdx += 5
// intentionally sleep much more than trigger to enable "empty" batch
Thread.sleep(10 * 1000)
}
}
}
```
> before the patch (batch 3 which was an "empty" batch)
```
{
"id":"de945a5c-882b-4dae-aa58-cb8261cbaf9e",
"runId":"f1eb6d0d-3cd5-48b2-a03b-5e989b6c151b",
"name":"test",
"timestamp":"2019-11-18T07:00:25.005Z",
"batchId":3,
"numInputRows":0,
"inputRowsPerSecond":0.0,
"processedRowsPerSecond":0.0,
"durationMs":{
"addBatch":1664,
"getBatch":0,
"latestOffset":0,
"queryPlanning":29,
"triggerExecution":1789,
"walCommit":51
},
"stateOperators":[
{
"numRowsTotal":10,
"numRowsUpdated":0,
"memoryUsedBytes":5130,
"customMetrics":{
"loadedMapCacheHitCount":15,
"loadedMapCacheMissCount":0,
"stateOnCurrentVersionSizeBytes":2722
}
}
],
"sources":[
{
"description":"MemoryStream[value#1]",
"startOffset":9,
"endOffset":9,
"numInputRows":0,
"inputRowsPerSecond":0.0,
"processedRowsPerSecond":0.0
}
],
"sink":{
"description":"MemorySink",
"numOutputRows":5
}
}
```
> after the patch (batch 3 which was an "empty" batch)
```
{
"id":"7cb41623-6b9a-408e-ae02-6796ec636fa0",
"runId":"17847710-ddfe-45f5-a7ab-b160e149382f",
"name":"test",
"timestamp":"2019-11-18T07:02:25.005Z",
"batchId":3,
"numInputRows":0,
"inputRowsPerSecond":0.0,
"processedRowsPerSecond":0.0,
"durationMs":{
"addBatch":1196,
"getBatch":0,
"latestOffset":0,
"queryPlanning":30,
"triggerExecution":1333,
"walCommit":46
},
"stateOperators":[
{
"numRowsTotal":10,
"numRowsUpdated":5,
"memoryUsedBytes":5130,
"customMetrics":{
"loadedMapCacheHitCount":15,
"loadedMapCacheMissCount":0,
"stateOnCurrentVersionSizeBytes":2722
}
}
],
"sources":[
{
"description":"MemoryStream[value#1]",
"startOffset":9,
"endOffset":9,
"numInputRows":0,
"inputRowsPerSecond":0.0,
"processedRowsPerSecond":0.0
}
],
"sink":{
"description":"MemorySink",
"numOutputRows":5
}
}
```
"numRowsUpdated" is `0` in "stateOperators" before applying the patch which is "wrong", as we "update" the state when timeout occurs. After applying the patch, it correctly represents the "numRowsUpdated" as `5` in "stateOperators".
Closes #25987 from HeartSaVioR/SPARK-29314.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Burak Yavuz <brkyvz@gmail.com>
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| common | ||
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| core | ||
| data | ||
| dev | ||
| docs | ||
| examples | ||
| external | ||
| graphx | ||
| hadoop-cloud | ||
| launcher | ||
| licenses | ||
| licenses-binary | ||
| mllib | ||
| mllib-local | ||
| project | ||
| python | ||
| R | ||
| repl | ||
| resource-managers | ||
| sbin | ||
| sql | ||
| streaming | ||
| tools | ||
| .asf.yaml | ||
| .gitattributes | ||
| .gitignore | ||
| appveyor.yml | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| LICENSE-binary | ||
| NOTICE | ||
| NOTICE-binary | ||
| pom.xml | ||
| README.md | ||
| scalastyle-config.xml | ||
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.