Remove spark-tag's compile-scope dependency (and, indirectly, spark-core's compile-scope transitive-dependency) on scalatest by splitting test-oriented tags into spark-tags' test JAR.
Alternative to #16303.
Author: Ryan Williams <ryan.blake.williams@gmail.com>
Closes#16311 from ryan-williams/tt.
## What changes were proposed in this pull request?
When KafkaSource fails on Kafka errors, we should create a new consumer to retry rather than using the existing broken one because it's possible that the broken one will fail again.
This PR also assigns a new group id to the new created consumer for a possible race condition: the broken consumer cannot talk with the Kafka cluster in `close` but the new consumer can talk to Kafka cluster. I'm not sure if this will happen or not. Just for safety to avoid that the Kafka cluster thinks there are two consumers with the same group id in a short time window. (Note: CachedKafkaConsumer doesn't need this fix since `assign` never uses the group id.)
## How was this patch tested?
In https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/70370/console , it ran this flaky test 120 times and all passed.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16282 from zsxwing/kafka-fix.
## What changes were proposed in this pull request?
Disable KafkaSourceStressForDontFailOnDataLossSuite for now.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16275 from zsxwing/ignore-flaky-test.
## What changes were proposed in this pull request?
- Changed FileStreamSource to use new FileStreamSourceOffset rather than LongOffset. The field is named as `logOffset` to make it more clear that this is a offset in the file stream log.
- Fixed bug in FileStreamSourceLog, the field endId in the FileStreamSourceLog.get(startId, endId) was not being used at all. No test caught it earlier. Only my updated tests caught it.
Other minor changes
- Dont use batchId in the FileStreamSource, as calling it batch id is extremely miss leading. With multiple sources, it may happen that a new batch has no new data from a file source. So offset of FileStreamSource != batchId after that batch.
## How was this patch tested?
Updated unit test.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16205 from tdas/SPARK-18776.
Based on an informal survey, users find this option easier to understand / remember.
Author: Michael Armbrust <michael@databricks.com>
Closes#16182 from marmbrus/renameRecentProgress.
## What changes were proposed in this pull request?
Fixed the following failures:
```
org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 3745 times over 1.0000790851666665 minutes. Last failure message: assertion failed: failOnDataLoss-0 not deleted after timeout.
```
```
sbt.ForkMain$ForkError: org.apache.spark.sql.streaming.StreamingQueryException: Query query-66 terminated with exception: null
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runBatches(StreamExecution.scala:252)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:146)
Caused by: sbt.ForkMain$ForkError: java.lang.NullPointerException: null
at java.util.ArrayList.addAll(ArrayList.java:577)
at org.apache.kafka.clients.Metadata.getClusterForCurrentTopics(Metadata.java:257)
at org.apache.kafka.clients.Metadata.update(Metadata.java:177)
at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.handleResponse(NetworkClient.java:605)
at org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.maybeHandleCompletedReceive(NetworkClient.java:582)
at org.apache.kafka.clients.NetworkClient.handleCompletedReceives(NetworkClient.java:450)
at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:269)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.clientPoll(ConsumerNetworkClient.java:360)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:224)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:192)
at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.awaitPendingRequests(ConsumerNetworkClient.java:260)
at org.apache.kafka.clients.consumer.internals.AbstractCoordinator.ensureActiveGroup(AbstractCoordinator.java:222)
at org.apache.kafka.clients.consumer.internals.ConsumerCoordinator.ensurePartitionAssignment(ConsumerCoordinator.java:366)
at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:978)
at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:938)
at
...
```
## How was this patch tested?
Tested in #16048 by running many times.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16109 from zsxwing/fix-kafka-flaky-test.
## What changes were proposed in this pull request?
Maven compilation seem to not allow resource is sql/test to be easily referred to in kafka-0-10-sql tests. So moved the kafka-source-offset-version-2.1.0 from sql test resources to kafka-0-10-sql test resources.
## How was this patch tested?
Manually ran maven test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16183 from tdas/SPARK-18671-1.
## What changes were proposed in this pull request?
To be able to restart StreamingQueries across Spark version, we have already made the logs (offset log, file source log, file sink log) use json. We should added tests with actual json files in the Spark such that any incompatible changes in reading the logs is immediately caught. This PR add tests for FileStreamSourceLog, FileStreamSinkLog, and OffsetSeqLog.
## How was this patch tested?
new unit tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#16128 from tdas/SPARK-18671.
## What changes were proposed in this pull request?
This patch bumps master branch version to 2.2.0-SNAPSHOT.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#16126 from rxin/SPARK-18695.
This PR separates the status of a `StreamingQuery` into two separate APIs:
- `status` - describes the status of a `StreamingQuery` at this moment, including what phase of processing is currently happening and if data is available.
- `recentProgress` - an array of statistics about the most recent microbatches that have executed.
A recent progress contains the following information:
```
{
"id" : "2be8670a-fce1-4859-a530-748f29553bb6",
"name" : "query-29",
"timestamp" : 1479705392724,
"inputRowsPerSecond" : 230.76923076923077,
"processedRowsPerSecond" : 10.869565217391303,
"durationMs" : {
"triggerExecution" : 276,
"queryPlanning" : 3,
"getBatch" : 5,
"getOffset" : 3,
"addBatch" : 234,
"walCommit" : 30
},
"currentWatermark" : 0,
"stateOperators" : [ ],
"sources" : [ {
"description" : "KafkaSource[Subscribe[topic-14]]",
"startOffset" : {
"topic-14" : {
"2" : 0,
"4" : 1,
"1" : 0,
"3" : 0,
"0" : 0
}
},
"endOffset" : {
"topic-14" : {
"2" : 1,
"4" : 2,
"1" : 0,
"3" : 0,
"0" : 1
}
},
"numRecords" : 3,
"inputRowsPerSecond" : 230.76923076923077,
"processedRowsPerSecond" : 10.869565217391303
} ]
}
```
Additionally, in order to make it possible to correlate progress updates across restarts, we change the `id` field from an integer that is unique with in the JVM to a `UUID` that is globally unique.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#15954 from marmbrus/queryProgress.
## What changes were proposed in this pull request?
Ignore the flaky test to unblock other PRs while I'm debugging it.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#16051 from zsxwing/ignore-flaky-kafka-test.
## What changes were proposed in this pull request?
Changed Kafka timestamp column type to TimestampType.
## How was this patch tested?
`test("Kafka column types")`.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15969 from zsxwing/SPARK-18530.
## What changes were proposed in this pull request?
This PR adds `CachedKafkaConsumer.getAndIgnoreLostData` to handle corner cases of `failOnDataLoss=false`.
It also resolves [SPARK-18529](https://issues.apache.org/jira/browse/SPARK-18529) after refactoring codes: Timeout will throw a TimeoutException.
## How was this patch tested?
Because I cannot find any way to manually control the Kafka server to clean up logs, it's impossible to write unit tests for each corner case. Therefore, I just created `test("stress test for failOnDataLoss=false")` which should cover most of corner cases.
I also modified some existing tests to test for both `failOnDataLoss=false` and `failOnDataLoss=true` to make sure it doesn't break existing logic.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15820 from zsxwing/failOnDataLoss.
## What changes were proposed in this pull request?
Currently we use java serialization for the WAL that stores the offsets contained in each batch. This has two main issues:
It can break across spark releases (though this is not the only thing preventing us from upgrading a running query)
It is unnecessarily opaque to the user.
I'd propose we require offsets to provide a user readable serialization and use that instead. JSON is probably a good option.
## How was this patch tested?
Tests were added for KafkaSourceOffset in [KafkaSourceOffsetSuite](external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSourceOffsetSuite.scala) and for LongOffset in [OffsetSuite](sql/core/src/test/scala/org/apache/spark/sql/streaming/OffsetSuite.scala)
Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.
zsxwing marmbrus
Author: Tyson Condie <tcondie@gmail.com>
Author: Tyson Condie <tcondie@clash.local>
Closes#15626 from tcondie/spark-8360.
## What changes were proposed in this pull request?
Added test to check whether default starting offset in latest
## How was this patch tested?
new unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15778 from tdas/SPARK-18283.
## What changes were proposed in this pull request?
Increase poll timeout to try and address flaky test
## How was this patch tested?
Ran existing unit tests
Author: cody koeninger <cody@koeninger.org>
Closes#15737 from koeninger/SPARK-18212.
## What changes were proposed in this pull request?
maxOffsetsPerTrigger option for rate limiting, proportionally based on volume of different topicpartitions.
## How was this patch tested?
Added unit test
Author: cody koeninger <cody@koeninger.org>
Closes#15527 from koeninger/SPARK-17813.
## What changes were proposed in this pull request?
startingOffsets takes specific per-topicpartition offsets as a json argument, usable with any consumer strategy
assign with specific topicpartitions as a consumer strategy
## How was this patch tested?
Unit tests
Author: cody koeninger <cody@koeninger.org>
Closes#15504 from koeninger/SPARK-17812.
## What changes were proposed in this pull request?
The newly implemented Structured Streaming `KafkaSource` did calculate the preferred locations for each topic partition, but didn't offer this information through RDD's `getPreferredLocations` method. So here propose to add this method in `KafkaSourceRDD`.
## How was this patch tested?
Manual verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#15545 from jerryshao/SPARK-17999.
## What changes were proposed in this pull request?
Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing
Specifically, this PR adds the following public APIs changes.
### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)
- `StreamingQueryStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by all the sources
- processingRate - Current rate (rows/sec) at which the query is processing data from
all the sources
- ~~outputRate~~ - *Does not work with wholestage codegen*
- latency - Current average latency between the data being available in source and the sink writing the corresponding output
- sourceStatuses: Array[SourceStatus] - Current statuses of the sources
- sinkStatus: SinkStatus - Current status of the sink
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- latencies - getOffset, getBatch, full trigger, wal writes
- timestamps - trigger start, finish, after getOffset, after getBatch
- numRows - input, output, state total/updated rows for aggregations
- `SourceStatus` has the following important fields
- inputRate - Current rate (rows/sec) at which data is being generated by the source
- processingRate - Current rate (rows/sec) at which the query is processing data from the source
- triggerStatus - Low-level detailed status of the last completed/currently active trigger
- Python API for `StreamingQuery.status()`
### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
- Branch 2.0 should have it deprecated, master should have it removed.
**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
- Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)
- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.
- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.
- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.
## How was this patch tested?
Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.
Metrics also manually tested using Ganglia sink
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#15307 from tdas/SPARK-17731.
## What changes were proposed in this pull request?
Because `KafkaConsumer.poll(0)` may update the partition offsets, this PR just calls `seekToBeginning` to manually set the earliest offsets for the KafkaSource initial offsets.
## How was this patch tested?
Existing tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15397 from zsxwing/SPARK-17834.
## What changes were proposed in this pull request?
Generate the sql test jar to fix the maven build
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#15368 from zsxwing/sql-test-jar.
## What changes were proposed in this pull request?
This PR adds a new project ` external/kafka-0-10-sql` for Structured Streaming Kafka source.
It's based on the design doc: https://docs.google.com/document/d/19t2rWe51x7tq2e5AOfrsM9qb8_m7BRuv9fel9i0PqR8/edit?usp=sharing
tdas did most of work and part of them was inspired by koeninger's work.
### Introduction
The Kafka source is a structured streaming data source to poll data from Kafka. The schema of reading data is as follows:
Column | Type
---- | ----
key | binary
value | binary
topic | string
partition | int
offset | long
timestamp | long
timestampType | int
The source can deal with deleting topics. However, the user should make sure there is no Spark job processing the data when deleting a topic.
### Configuration
The user can use `DataStreamReader.option` to set the following configurations.
Kafka Source's options | value | default | meaning
------ | ------- | ------ | -----
startingOffset | ["earliest", "latest"] | "latest" | The start point when a query is started, either "earliest" which is from the earliest offset, or "latest" which is just from the latest offset. Note: This only applies when a new Streaming query is started, and that resuming will always pick up from where the query left off.
failOnDataLost | [true, false] | true | Whether to fail the query when it's possible that data is lost (e.g., topics are deleted, or offsets are out of range). This may be a false alarm. You can disable it when it doesn't work as you expected.
subscribe | A comma-separated list of topics | (none) | The topic list to subscribe. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
subscribePattern | Java regex string | (none) | The pattern used to subscribe the topic. Only one of "subscribe" and "subscribeParttern" options can be specified for Kafka source.
kafka.consumer.poll.timeoutMs | long | 512 | The timeout in milliseconds to poll data from Kafka in executors
fetchOffset.numRetries | int | 3 | Number of times to retry before giving up fatch Kafka latest offsets.
fetchOffset.retryIntervalMs | long | 10 | milliseconds to wait before retrying to fetch Kafka offsets
Kafka's own configurations can be set via `DataStreamReader.option` with `kafka.` prefix, e.g, `stream.option("kafka.bootstrap.servers", "host:port")`
### Usage
* Subscribe to 1 topic
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1")
.load()
```
* Subscribe to multiple topics
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribe", "topic1,topic2")
.load()
```
* Subscribe to a pattern
```Scala
spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "host:port")
.option("subscribePattern", "topic.*")
.load()
```
## How was this patch tested?
The new unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Shixiong Zhu <zsxwing@gmail.com>
Author: cody koeninger <cody@koeninger.org>
Closes#15102 from zsxwing/kafka-source.
