## What changes were proposed in this pull request?
Fix the integer overflow issue in rateLimit.
## How was this patch tested?
Pass the Jenkins with newly added UT for the possible case where integer could be overflowed.
Closes#23666 from linehrr/master.
Authored-by: ryne.yang <ryne.yang@acuityads.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Following https://github.com/apache/spark/pull/23430, this PR does the API refactor for continuous read, w.r.t. the [doc](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
The major changes:
1. rename `XXXContinuousReadSupport` to `XXXContinuousStream`
2. at the beginning of continuous streaming execution, convert `StreamingRelationV2` to `StreamingDataSourceV2Relation` directly, instead of `StreamingExecutionRelation`.
3. remove all the hacks as we have finished all the read side API refactor
## How was this patch tested?
existing tests
Closes#23619 from cloud-fan/continuous.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
There are ugly provided dependencies inside core for the following:
* Hive
* Kafka
In this PR I've extracted them out. This PR contains the following:
* Token providers are now loaded with service loader
* Hive token provider moved to hive project
* Kafka token provider extracted into a new project
## How was this patch tested?
Existing + newly added unit tests.
Additionally tested on cluster.
Closes#23499 from gaborgsomogyi/SPARK-26254.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Following https://github.com/apache/spark/pull/23086, this PR does the API refactor for micro-batch read, w.r.t. the [doc](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
The major changes:
1. rename `XXXMicroBatchReadSupport` to `XXXMicroBatchReadStream`
2. implement `TableProvider`, `Table`, `ScanBuilder` and `Scan` for streaming sources
3. at the beginning of micro-batch streaming execution, convert `StreamingRelationV2` to `StreamingDataSourceV2Relation` directly, instead of `StreamingExecutionRelation`.
followup:
support operator pushdown for stream sources
## How was this patch tested?
existing tests
Closes#23430 from cloud-fan/micro-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This patch adds the check to verify consumer group id is given correctly when custom group id is provided to Kafka parameter.
## How was this patch tested?
Modified UT.
Closes#23544 from HeartSaVioR/SPARK-26350-follow-up-actual-verification-on-UT.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This PR allows the user to override `kafka.group.id` for better monitoring or security. The user needs to make sure there are not multiple queries or sources using the same group id.
It also fixes a bug that the `groupIdPrefix` option cannot be retrieved.
## How was this patch tested?
The new added unit tests.
Closes#23301 from zsxwing/SPARK-26350.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This PR upgrades Mockito from 1.10.19 to 2.23.4. The following changes are required.
- Replace `org.mockito.Matchers` with `org.mockito.ArgumentMatchers`
- Replace `anyObject` with `any`
- Replace `getArgumentAt` with `getArgument` and add type annotation.
- Use `isNull` matcher in case of `null` is invoked.
```scala
saslHandler.channelInactive(null);
- verify(handler).channelInactive(any(TransportClient.class));
+ verify(handler).channelInactive(isNull());
```
- Make and use `doReturn` wrapper to avoid [SI-4775](https://issues.scala-lang.org/browse/SI-4775)
```scala
private def doReturn(value: Any) = org.mockito.Mockito.doReturn(value, Seq.empty: _*)
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23452 from dongjoon-hyun/SPARK-26536.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Use of `ProcessingTime` class was deprecated in favor of `Trigger.ProcessingTime` in Spark 2.2. And, [SPARK-21464](https://issues.apache.org/jira/browse/SPARK-21464) minimized it at 2.2.1. Recently, it grows again in test suites. This PR aims to clean up newly introduced deprecation warnings for Spark 3.0.
## How was this patch tested?
Pass the Jenkins with existing tests and manually check the warnings.
Closes#23367 from dongjoon-hyun/SPARK-26428.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Due to [KAFKA-7703](https://issues.apache.org/jira/browse/KAFKA-7703), Kafka may return an earliest offset when we are request a latest offset. This will cause Spark to reprocess data.
As per suggestion in KAFKA-7703, we put a position call between poll and seekToEnd to block the fetch request triggered by `poll` before calling `seekToEnd`.
In addition, to avoid other unknown issues, we also use the previous known offsets to audit the latest offsets returned by Kafka. If we find some incorrect offsets (a latest offset is less than an offset in `knownOffsets`), we will retry at most `maxOffsetFetchAttempts` times.
## How was this patch tested?
Jenkins
Closes#23324 from zsxwing/SPARK-26267.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
SinkProgress should report similar properties like SourceProgress as long as they are available for given Sink. Count of written rows is metric availble for all Sinks. Since relevant progress information is with respect to commited rows, ideal object to carry this info is WriterCommitMessage. For brevity the implementation will focus only on Sinks with API V2 and on Micro Batch mode. Implemention for Continuous mode will be provided at later date.
### Before
```
{"description":"org.apache.spark.sql.kafka010.KafkaSourceProvider3c0bd317"}
```
### After
```
{"description":"org.apache.spark.sql.kafka010.KafkaSourceProvider3c0bd317","numOutputRows":5000}
```
### This PR is related to:
- https://issues.apache.org/jira/browse/SPARK-24647
- https://issues.apache.org/jira/browse/SPARK-21313
## How was this patch tested?
Existing and new unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21919 from vackosar/feature/SPARK-24933-numOutputRows.
Lead-authored-by: Vaclav Kosar <admin@vaclavkosar.com>
Co-authored-by: Kosar, Vaclav: Functions Transformation <Vaclav.Kosar@barclayscapital.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
As Kafka delegation token added logic into ConfigUpdater it would be good to test it.
This PR contains the following changes:
* ConfigUpdater extracted to a separate file and renamed to KafkaConfigUpdater
* mockito-core dependency added to kafka-0-10-sql
* Unit tests added
## How was this patch tested?
Existing + new unit tests + on cluster.
Closes#23321 from gaborgsomogyi/SPARK-26371.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
remove a redundant `KafkaWriter.validateQuery` call in `KafkaSourceProvider `
## How was this patch tested?
Just removing duplicate codes, so I just build and run unit tests.
Closes#23309 from JasonWayne/SPARK-26360.
Authored-by: jasonwayne <wuwenjie0102@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When Kafka delegation token obtained, SCRAM `sasl.mechanism` has to be configured for authentication. This can be configured on the related source/sink which is inconvenient from user perspective. Such granularity is not required and this configuration can be implemented with one central parameter.
In this PR `spark.kafka.sasl.token.mechanism` added to configure this centrally (default: `SCRAM-SHA-512`).
## How was this patch tested?
Existing unit tests + on cluster.
Closes#23274 from gaborgsomogyi/SPARK-26322.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
spark.kafka.sasl.kerberos.service.name is an optional parameter but most of the time value `kafka` has to be set. As I've written in the jira the following reasoning is behind:
* Kafka's configuration guide suggest the same value: https://kafka.apache.org/documentation/#security_sasl_kerberos_brokerconfig
* It would be easier for spark users by providing less configuration
* Other streaming engines are doing the same
In this PR I've changed the parameter from optional to `WithDefault` and set `kafka` as default value.
## How was this patch tested?
Available unit tests + on cluster.
Closes#23254 from gaborgsomogyi/SPARK-26304.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This is the first step of the data source v2 API refactor [proposal](https://docs.google.com/document/d/1uUmKCpWLdh9vHxP7AWJ9EgbwB_U6T3EJYNjhISGmiQg/edit?usp=sharing)
It adds the new API for batch read, without removing the old APIs, as they are still needed for streaming sources.
More concretely, it adds
1. `TableProvider`, works like an anonymous catalog
2. `Table`, represents a structured data set.
3. `ScanBuilder` and `Scan`, a logical represents of data source scan
4. `Batch`, a physical representation of data source batch scan.
## How was this patch tested?
existing tests
Closes#23086 from cloud-fan/refactor-batch.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
It adds kafka delegation token support for structured streaming. Please see the relevant [SPIP](https://docs.google.com/document/d/1ouRayzaJf_N5VQtGhVq9FURXVmRpXzEEWYHob0ne3NY/edit?usp=sharing)
What this PR contains:
* Configuration parameters for the feature
* Delegation token fetching from broker
* Usage of token through dynamic JAAS configuration
* Minor refactoring in the existing code
What this PR doesn't contain:
* Documentation changes because design can change
## How was this patch tested?
Existing tests + added small amount of additional unit tests.
Because it's an external service integration mainly tested on cluster.
* 4 node cluster
* Kafka broker version 1.1.0
* Topic with 4 partitions
* security.protocol = SASL_SSL
* sasl.mechanism = SCRAM-SHA-256
An example of obtaining a token:
```
18/10/01 01:07:49 INFO kafka010.TokenUtil: TOKENID HMAC OWNER RENEWERS ISSUEDATE EXPIRYDATE MAXDATE
18/10/01 01:07:49 INFO kafka010.TokenUtil: D1-v__Q5T_uHx55rW16Jwg [hidden] User:user [] 2018-10-01T01:07 2018-10-02T01:07 2018-10-08T01:07
18/10/01 01:07:49 INFO security.KafkaDelegationTokenProvider: Get token from Kafka: Kind: KAFKA_DELEGATION_TOKEN, Service: kafka.server.delegation.token, Ident: 44 31 2d 76 5f 5f 51 35 54 5f 75 48 78 35 35 72 57 31 36 4a 77 67
```
An example token usage:
```
18/10/01 01:08:07 INFO kafka010.KafkaSecurityHelper: Scram JAAS params: org.apache.kafka.common.security.scram.ScramLoginModule required tokenauth=true serviceName="kafka" username="D1-v__Q5T_uHx55rW16Jwg" password="[hidden]";
18/10/01 01:08:07 INFO kafka010.KafkaSourceProvider: Delegation token detected, using it for login.
```
Closes#22598 from gaborgsomogyi/SPARK-25501.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Allow the Spark Structured Streaming user to specify the prefix of the consumer group (group.id), compared to force consumer group ids of the form `spark-kafka-source-*`
## How was this patch tested?
Unit tests provided by Spark (backwards compatible change, i.e., user can optionally use the functionality)
`mvn test -pl external/kafka-0-10`
Closes#23103 from zouzias/SPARK-26121.
Authored-by: Anastasios Zouzias <anastasios@sqooba.io>
Signed-off-by: cody koeninger <cody@koeninger.org>
## What changes were proposed in this pull request?
This is a followup of #23099 . After upgrading to Kafka 2.1.0, maven test fails due to Zookeeper test dependency while sbt test succeeds.
- [sbt test on master branch](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-sbt-hadoop-2.7/5203/)
- [maven test on master branch](https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7/5653/)
The root cause is that the embedded Kafka server is using [Zookeepr 3.4.7 API](https://zookeeper.apache.org/doc/r3.4.7/api/org/apache/zookeeper/AsyncCallback.MultiCallback.html
) while Apache Spark provides Zookeeper 3.4.6. This PR adds a test dependency.
```
KafkaMicroBatchV2SourceSuite:
*** RUN ABORTED ***
...
org.apache.spark.sql.kafka010.KafkaTestUtils.setupEmbeddedKafkaServer(KafkaTestUtils.scala:123)
...
Cause: java.lang.ClassNotFoundException: org.apache.zookeeper.AsyncCallback$MultiCallback
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at kafka.zk.KafkaZkClient$.apply(KafkaZkClient.scala:1693)
at kafka.server.KafkaServer.createZkClient$1(KafkaServer.scala:348)
at kafka.server.KafkaServer.initZkClient(KafkaServer.scala:372)
at kafka.server.KafkaServer.startup(KafkaServer.scala:202)
at org.apache.spark.sql.kafka010.KafkaTestUtils.$anonfun$setupEmbeddedKafkaServer$2(KafkaTestUtils.scala:120)
at org.apache.spark.sql.kafka010.KafkaTestUtils.$anonfun$setupEmbeddedKafkaServer$2$adapted(KafkaTestUtils.scala:116)
...
```
## How was this patch tested?
Pass the maven Jenkins test.
Closes#23119 from dongjoon-hyun/SPARK-25954-2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In this test, i have reduced the test time to 20 secs from 1 minute while reducing the sleep time from 1 sec to 100 milliseconds.
With this change, i was able to run the test in 20+ seconds consistently on my laptop. I would like see if it passes in jenkins consistently.
## How was this patch tested?
Its a test fix.
Closes#22900 from dilipbiswal/SPARK-25618.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
`Literal.value` should have a value a value corresponding to `dataType`. This pr added code to verify it and fixed the existing tests to do so.
## How was this patch tested?
Modified the existing tests.
Closes#22724 from maropu/SPARK-25734.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Specify `kafka.max.block.ms` to 10 seconds while creating the kafka writer. In the absence of this overridden config, by default it uses a default time out of 60 seconds.
With this change the test completes in close to 10 seconds as opposed to 1 minute.
## How was this patch tested?
This is a test fix.
Closes#22671 from dilipbiswal/SPARK-25615.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR fixes the Scala-2.12 build error due to ambiguity in `foreachBatch` test cases.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/428/console
```scala
[error] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:102: ambiguous reference to overloaded definition,
[error] both method foreachBatch in class DataStreamWriter of type (function: org.apache.spark.api.java.function.VoidFunction2[org.apache.spark.sql.Dataset[Int],Long])org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] and method foreachBatch in class DataStreamWriter of type (function: (org.apache.spark.sql.Dataset[Int], Long) => Unit)org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] match argument types ((org.apache.spark.sql.Dataset[Int], Any) => Unit)
[error] ds.writeStream.foreachBatch((_, _) => {}).trigger(Trigger.Continuous("1 second")).start()
[error] ^
[error] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7-ubuntu-scala-2.12/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:106: ambiguous reference to overloaded definition,
[error] both method foreachBatch in class DataStreamWriter of type (function: org.apache.spark.api.java.function.VoidFunction2[org.apache.spark.sql.Dataset[Int],Long])org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] and method foreachBatch in class DataStreamWriter of type (function: (org.apache.spark.sql.Dataset[Int], Long) => Unit)org.apache.spark.sql.streaming.DataStreamWriter[Int]
[error] match argument types ((org.apache.spark.sql.Dataset[Int], Any) => Unit)
[error] ds.writeStream.foreachBatch((_, _) => {}).partitionBy("value").start()
[error] ^
```
## How was this patch tested?
Manual.
Since this failure occurs in Scala-2.12 profile and test cases, Jenkins will not test this. We need to build with Scala-2.12 and run the tests.
Closes#22649 from dongjoon-hyun/SPARK-SCALA212.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
`FetchedData.reset` should reset `_nextOffsetInFetchedData` and `_offsetAfterPoll`. Otherwise it will cause inconsistent cached data and may make Kafka connector return wrong results.
## How was this patch tested?
The new unit test.
Closes#22507 from zsxwing/fix-kafka-reset.
Lead-authored-by: Shixiong Zhu <zsxwing@gmail.com>
Co-authored-by: Shixiong Zhu <shixiong@databricks.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
For self-join/self-union, Spark will produce a physical plan which has multiple `DataSourceV2ScanExec` instances referring to the same `ReadSupport` instance. In this case, the streaming source is indeed scanned multiple times, and the `numInputRows` metrics should be counted for each scan.
Actually we already have 2 test cases to verify the behavior:
1. `StreamingQuerySuite.input row calculation with same V2 source used twice in self-join`
2. `KafkaMicroBatchSourceSuiteBase.ensure stream-stream self-join generates only one offset in log and correct metrics`.
However, in these 2 tests, the expected result is different, which is super confusing. It turns out that, the first test doesn't trigger exchange reuse, so the source is scanned twice. The second test triggers exchange reuse, and the source is scanned only once.
This PR proposes to improve these 2 tests, to test with/without exchange reuse.
## How was this patch tested?
test only change
Closes#22402 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR ensures to call `super.afterAll()` in `override afterAll()` method for test suites.
* Some suites did not call `super.afterAll()`
* Some suites may call `super.afterAll()` only under certain condition
* Others never call `super.afterAll()`.
This PR also ensures to call `super.beforeAll()` in `override beforeAll()` for test suites.
## How was this patch tested?
Existing UTs
Closes#22337 from kiszk/SPARK-25338.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Fix unused imports & outdated comments on `kafka-0-10-sql` module. (Found while I was working on [SPARK-23539](https://github.com/apache/spark/pull/22282))
## How was this patch tested?
Existing unit tests.
Closes#22342 from dongjinleekr/feature/fix-kafka-sql-trivials.
Authored-by: Lee Dongjin <dongjin@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Revert SPARK-24863 (#21819) and SPARK-24748 (#21721) as per discussion in #21721. We will revisit them when the data source v2 APIs are out.
## How was this patch tested?
Jenkins
Closes#22334 from zsxwing/revert-SPARK-24863-SPARK-24748.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
As the user uses Kafka transactions to write data, the offsets in Kafka will be non-consecutive. It will contains some transaction (commit or abort) markers. In addition, if the consumer's `isolation.level` is `read_committed`, `poll` will not return aborted messages either. Hence, we will see non-consecutive offsets in the date returned by `poll`. However, as `seekToEnd` may move the offset point to these missing offsets, there are 4 possible corner cases we need to support:
- The whole batch contains no data messages
- The first offset in a batch is not a committed data message
- The last offset in a batch is not a committed data message
- There is a gap in the middle of a batch
They are all covered by the new unit tests.
## How was this patch tested?
The new unit tests.
Closes#22042 from zsxwing/kafka-transaction-read.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
Fix flaky synchronization in Kafka tests - we need to use the scan config that was persisted rather than reconstructing it to identify the stream's current configuration.
We caught most instances of this in the original PR, but this one slipped through.
## How was this patch tested?
n/a
Closes#22245 from jose-torres/fixflake.
Authored-by: Jose Torres <torres.joseph.f+github@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This is a follow up PR for #22207 to fix a potential flaky test. `processAllAvailable` doesn't work for continuous processing so we should not use it for a continuous query.
## How was this patch tested?
Jenkins.
Closes#22230 from zsxwing/SPARK-25214-2.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
When there are missing offsets, Kafka v2 source may return duplicated records when `failOnDataLoss=false` because it doesn't skip missing offsets.
This PR fixes the issue and also adds regression tests for all Kafka readers.
## How was this patch tested?
New tests.
Closes#22207 from zsxwing/SPARK-25214.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This pr proposed to show RDD/relation names in RDD/Hive table scan nodes.
This change made these names show up in the webUI and explain results.
For example;
```
scala> sql("CREATE TABLE t(c1 int) USING hive")
scala> sql("INSERT INTO t VALUES(1)")
scala> spark.table("t").explain()
== Physical Plan ==
Scan hive default.t [c1#8], HiveTableRelation `default`.`t`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [c1#8]
^^^^^^^^^^^
```
<img width="212" alt="spark-pr-hive" src="https://user-images.githubusercontent.com/692303/44501013-51264c80-a6c6-11e8-94f8-0704aee83bb6.png">
Closes#20226
## How was this patch tested?
Added tests in `DataFrameSuite`, `DatasetSuite`, and `HiveExplainSuite`
Closes#22153 from maropu/pr20226.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: Tejas Patil <tejasp@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The race condition that caused test failure is between 2 threads.
- The MicrobatchExecution thread that processes inputs to produce answers and then generates progress events.
- The test thread that generates some input data, checked the answer and then verified the query generated progress event.
The synchronization structure between these threads is as follows
1. MicrobatchExecution thread, in every batch, does the following in order.
a. Processes batch input to generate answer.
b. Signals `awaitProgressLockCondition` to wake up threads waiting for progress using `awaitOffset`
c. Generates progress event
2. Test execution thread
a. Calls `awaitOffset` to wait for progress, which waits on `awaitProgressLockCondition`.
b. As soon as `awaitProgressLockCondition` is signaled, it would move on the in the test to check answer.
c. Finally, it would verify the last generated progress event.
What can happen is the following sequence of events: 2a -> 1a -> 1b -> 2b -> 2c -> 1c.
In other words, the progress event may be generated after the test tries to verify it.
The solution has two steps.
1. Signal the waiting thread after the progress event has been generated, that is, after `finishTrigger()`.
2. Increase the timeout of `awaitProgressLockCondition.await(100 ms)` to a large value.
This latter is to ensure that test thread for keeps waiting on `awaitProgressLockCondition`until the MicroBatchExecution thread explicitly signals it. With the existing small timeout of 100ms the following sequence can occur.
- MicroBatchExecution thread updates committed offsets
- Test thread waiting on `awaitProgressLockCondition` accidentally times out after 100 ms, finds that the committed offsets have been updated, therefore returns from `awaitOffset` and moves on to the progress event tests.
- MicroBatchExecution thread then generates progress event and signals. But the test thread has already attempted to verify the event and failed.
By increasing the timeout to large (e.g., `streamingTimeoutMs = 60 seconds`, similar to `awaitInitialization`), this above type of race condition is also avoided.
## How was this patch tested?
Ran locally many times.
Closes#22182 from tdas/SPARK-25184.
Authored-by: Tathagata Das <tathagata.das1565@gmail.com>
Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
## What changes were proposed in this pull request?
Improve the data source v2 API according to the [design doc](https://docs.google.com/document/d/1DDXCTCrup4bKWByTalkXWgavcPdvur8a4eEu8x1BzPM/edit?usp=sharing)
summary of the changes
1. rename `ReadSupport` -> `DataSourceReader` -> `InputPartition` -> `InputPartitionReader` to `BatchReadSupportProvider` -> `BatchReadSupport` -> `InputPartition`/`PartitionReaderFactory` -> `PartitionReader`. Similar renaming also happens at streaming and write APIs.
2. create `ScanConfig` to store query specific information like operator pushdown result, streaming offsets, etc. This makes batch and streaming `ReadSupport`(previouslly named `DataSourceReader`) immutable. All other methods take `ScanConfig` as input, which implies applying operator pushdown and getting streaming offsets happen before all other things(get input partitions, report statistics, etc.).
3. separate `InputPartition` to `InputPartition` and `PartitionReaderFactory`. This is a natural separation, data splitting and reading are orthogonal and we should not mix them in one interfaces. This also makes the naming consistent between read and write API: `PartitionReaderFactory` vs `DataWriterFactory`.
4. separate the batch and streaming interfaces. Sometimes it's painful to force the streaming interface to extend batch interface, as we may need to override some batch methods to return false, or even leak the streaming concept to batch API(e.g. `DataWriterFactory#createWriter(partitionId, taskId, epochId)`)
Some follow-ups we should do after this PR (tracked by https://issues.apache.org/jira/browse/SPARK-25186 ):
1. Revisit the life cycle of `ReadSupport` instances. Currently I keep it same as the previous `DataSourceReader`, i.e. the life cycle is bound to the batch/stream query. This fits streaming very well but may not be perfect for batch source. We can also consider to let `ReadSupport.newScanConfigBuilder` take `DataSourceOptions` as parameter, if we decide to change the life cycle.
2. Add `WriteConfig`. This is similar to `ScanConfig` and makes the write API more flexible. But it's only needed when we add the `replaceWhere` support, and it needs to change the streaming execution engine for this new concept, which I think is better to be done in another PR.
3. Refine the document. This PR adds/changes a lot of document and it's very likely that some people may have better ideas.
4. Figure out the life cycle of `CustomMetrics`. It looks to me that it should be bound to a `ScanConfig`, but we need to change `ProgressReporter` to get the `ScanConfig`. Better to be done in another PR.
5. Better operator pushdown API. This PR keeps the pushdown API as it was, i.e. using the `SupportsPushdownXYZ` traits. We can design a better API using build pattern, but this is a complicated design and deserves an individual JIRA ticket and design doc.
6. Improve the continuous streaming engine to only create a new `ScanConfig` when re-configuring.
7. Remove `SupportsPushdownCatalystFilter`. This is actually not a must-have for file source, we can change the hive partition pruning to use the public `Filter`.
## How was this patch tested?
existing tests.
Closes#22009 from cloud-fan/redesign.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This builds on top of SPARK-24748 to report 'offset lag' as a custom metrics for Kafka structured streaming source.
This lag is the difference between the latest offsets in Kafka the time the metrics is reported (just after a micro-batch completes) and the latest offset Spark has processed. It can be 0 (or close to 0) if spark keeps up with the rate at which messages are ingested into Kafka topics in steady state. This measures how far behind the spark source has fallen behind (per partition) and can aid in tuning the application.
## How was this patch tested?
Existing and new unit tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21819 from arunmahadevan/SPARK-24863.
Authored-by: Arun Mahadevan <arunm@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
KafkaContinuousSinkSuite leaks a Kafka cluster because both KafkaSourceTest and KafkaContinuousSinkSuite create a Kafka cluster but `afterAll` only shuts down one cluster. This leaks a Kafka cluster and causes that some Kafka thread crash and kill JVM when SBT is trying to clean up tests.
This PR fixes the leak and also adds a shut down hook to detect Kafka cluster leak.
In additions, it also fixes `AdminClient` leak and cleans up cached producers (When a record is writtn using a producer, the producer will keep refreshing the topic and I don't find an API to clear it except closing the producer) to eliminate the following annoying logs:
```
8/13 15:34:42.568 kafka-admin-client-thread | adminclient-4 WARN NetworkClient: [AdminClient clientId=adminclient-4] Connection to node 0 could not be established. Broker may not be available.
18/08/13 15:34:42.570 kafka-admin-client-thread | adminclient-6 WARN NetworkClient: [AdminClient clientId=adminclient-6] Connection to node 0 could not be established. Broker may not be available.
18/08/13 15:34:42.606 kafka-admin-client-thread | adminclient-8 WARN NetworkClient: [AdminClient clientId=adminclient-8] Connection to node -1 could not be established. Broker may not be available.
18/08/13 15:34:42.729 kafka-producer-network-thread | producer-797 WARN NetworkClient: [Producer clientId=producer-797] Connection to node -1 could not be established. Broker may not be available.
18/08/13 15:34:42.906 kafka-producer-network-thread | producer-1598 WARN NetworkClient: [Producer clientId=producer-1598] Connection to node 0 could not be established. Broker may not be available.
```
I also reverted b5eb54244e introduced by #22097 since it doesn't help.
## How was this patch tested?
Jenkins
Closes#22106 from zsxwing/SPARK-25116.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
I'm still seeing the Kafka tests failed randomly due to `kafka.zookeeper.ZooKeeperClientTimeoutException: Timed out waiting for connection while in state: CONNECTING`. I checked the test output and saw zookeeper picked up an ipv6 address. Most details can be found in https://issues.apache.org/jira/browse/KAFKA-7193
This PR just uses `127.0.0.1` rather than `localhost` to make sure zookeeper will never use an ipv6 address.
## How was this patch tested?
Jenkins
Closes#22097 from zsxwing/fix-zookeeper-connect.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Shixiong Zhu <zsxwing@gmail.com>
## What changes were proposed in this pull request?
This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.
## How was this patch tested?
N/A
Closes#22040 from kiszk/spellcheck1.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
A follow up of #21118
Since we use `InternalRow` in the read API of data source v2, we should do the same thing for the write API.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#21948 from cloud-fan/row-write.
## What changes were proposed in this pull request?
This small fix adds a `consumer.release()` call to `KafkaSourceRDD` in the case where we've retrieved offsets from Kafka, but the `fromOffset` is equal to the `lastOffset`, meaning there is no new data to read for a particular topic partition. Up until now, we'd just return an empty iterator without closing the consumer which would cause a FD leak.
If accepted, this pull request should be merged into master as well.
## How was this patch tested?
Haven't ran any specific tests, would love help on how to test methods running inside `RDD.compute`.
Author: Yuval Itzchakov <yuval.itzchakov@clicktale.com>
Closes#21997 from YuvalItzchakov/master.
## What changes were proposed in this pull request?
Increase ZK timeout and harmonize configs across Kafka tests to resol…ve potentially flaky test failure
## How was this patch tested?
Existing tests
Author: Sean Owen <srowen@gmail.com>
Closes#21995 from srowen/SPARK-18057.3.
## What changes were proposed in this pull request?
Update to kafka 2.0.0 in streaming-kafka module, and remove override for Scala 2.12. It won't compile for 2.12 otherwise.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21955 from srowen/SPARK-18057.2.
## What changes were proposed in this pull request?
This PR addresses issues 2,3 in this [document](https://docs.google.com/document/d/1fbkjEL878witxVQpOCbjlvOvadHtVjYXeB-2mgzDTvk).
* We modified the closure cleaner to identify closures that are implemented via the LambdaMetaFactory mechanism (serializedLambdas) (issue2).
* We also fix the issue due to scala/bug#11016. There are two options for solving the Unit issue, either add () at the end of the closure or use the trick described in the doc. Otherwise overloading resolution does not work (we are not going to eliminate either of the methods) here. Compiler tries to adapt to Unit and makes these two methods candidates for overloading, when there is polymorphic overloading there is no ambiguity (that is the workaround implemented). This does not look that good but it serves its purpose as we need to support two different uses for method: `addTaskCompletionListener`. One that passes a TaskCompletionListener and one that passes a closure that is wrapped with a TaskCompletionListener later on (issue3).
Note: regarding issue 1 in the doc the plan is:
> Do Nothing. Don’t try to fix this as this is only a problem for Java users who would want to use 2.11 binaries. In that case they can cast to MapFunction to be able to utilize lambdas. In Spark 3.0.0 the API should be simplified so that this issue is removed.
## How was this patch tested?
This was manually tested:
```./dev/change-scala-version.sh 2.12
./build/mvn -DskipTests -Pscala-2.12 clean package
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.serializer.ProactiveClosureSerializationSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.util.ClosureCleanerSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.streaming.DStreamClosureSuite -Dtest=None```
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21930 from skonto/scala2.12-sup.
