## What changes were proposed in this pull request?
Add kafka source and sink for continuous processing. This involves two small changes to the execution engine:
* Bring data reader close() into the normal data reader thread to avoid thread safety issues.
* Fix up the semantics of the RECONFIGURING StreamExecution state. State updates are now atomic, and we don't have to deal with swallowing an exception.
## How was this patch tested?
new unit tests
Author: Jose Torres <jose@databricks.com>
Closes#20096 from jose-torres/continuous-kafka.
## What changes were proposed in this pull request?
This PR cleans up a few Java linter errors for Apache Spark 2.3 release.
## How was this patch tested?
```bash
$ dev/lint-java
Using `mvn` from path: /usr/local/bin/mvn
Checkstyle checks passed.
```
We can see the result from [Travis CI](https://travis-ci.org/dongjoon-hyun/spark/builds/322470787), too.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#20101 from dongjoon-hyun/fix-java-lint.
## What changes were proposed in this pull request?
Kinesis client can resume from a specified timestamp while creating a stream. We should have option to pass a timestamp in config to allow kinesis to resume from the given timestamp.
The patch introduces a new `KinesisInitialPositionInStream` that takes the `InitialPositionInStream` with the `timestamp` information that can be used to resume kinesis fetches from the provided timestamp.
## How was this patch tested?
Unit Tests
cc : budde brkyvz
Author: Yash Sharma <ysharma@atlassian.com>
Closes#18029 from yssharma/ysharma/kcl_resume.
## What changes were proposed in this pull request?
Some users depend on source compatibility with the org.apache.spark.sql.execution.streaming.Offset class. Although this is not a stable interface, we can keep it in place for now to simplify upgrades to 2.3.
Author: Jose Torres <jose@databricks.com>
Closes#20012 from joseph-torres/binary-compat.
## What changes were proposed in this pull request?
This PR provides DataSourceV2 API support for structured streaming, including new pieces needed to support continuous processing [SPARK-20928]. High level summary:
- DataSourceV2 includes new mixins to support micro-batch and continuous reads and writes. For reads, we accept an optional user specified schema rather than using the ReadSupportWithSchema model, because doing so would severely complicate the interface.
- DataSourceV2Reader includes new interfaces to read a specific microbatch or read continuously from a given offset. These follow the same setter pattern as the existing Supports* mixins so that they can work with SupportsScanUnsafeRow.
- DataReader (the per-partition reader) has a new subinterface ContinuousDataReader only for continuous processing. This reader has a special method to check progress, and next() blocks for new input rather than returning false.
- Offset, an abstract representation of position in a streaming query, is ported to the public API. (Each type of reader will define its own Offset implementation.)
- DataSourceV2Writer has a new subinterface ContinuousWriter only for continuous processing. Commits to this interface come tagged with an epoch number, as the execution engine will continue to produce new epoch commits as the task continues indefinitely.
Note that this PR does not propose to change the existing DataSourceV2 batch API, or deprecate the existing streaming source/sink internal APIs in spark.sql.execution.streaming.
## How was this patch tested?
Toy implementations of the new interfaces with unit tests.
Author: Jose Torres <jose@databricks.com>
Closes#19925 from joseph-torres/continuous-api.
## What changes were proposed in this pull request?
In the previous PRs, https://github.com/apache/spark/pull/17832 and https://github.com/apache/spark/pull/17835 , we convert `TIMESTAMP WITH TIME ZONE` and `TIME WITH TIME ZONE` to `TIMESTAMP` for all the JDBC sources. However, this conversion could be risky since it does not respect our SQL configuration `spark.sql.session.timeZone`.
In addition, each vendor might have different semantics for these two types. For example, Postgres simply returns `TIMESTAMP` types for `TIMESTAMP WITH TIME ZONE`. For such supports, we should do it case by case. This PR reverts the general support of `TIMESTAMP WITH TIME ZONE` and `TIME WITH TIME ZONE` for JDBC sources, except ORACLE Dialect.
When supporting the ORACLE's `TIMESTAMP WITH TIME ZONE`, we only support it when the JVM default timezone is the same as the user-specified configuration `spark.sql.session.timeZone` (whose default is the JVM default timezone). Now, we still treat `TIMESTAMP WITH TIME ZONE` as `TIMESTAMP` when fetching the values via the Oracle JDBC connector, whose client converts the timestamp values with time zone to the timestamp values using the local JVM default timezone (a test case is added to `OracleIntegrationSuite.scala` in this PR for showing the behavior). Thus, to avoid any future behavior change, we will not support it if JVM default timezone is different from `spark.sql.session.timeZone`
No regression because the previous two PRs were just merged to be unreleased master branch.
## How was this patch tested?
Added the test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes#19939 from gatorsmile/timezoneUpdate.
…a-2.12 and JDK9
## What changes were proposed in this pull request?
Some compile error after upgrading to scala-2.12
```javascript
spark_source/core/src/main/scala/org/apache/spark/executor/Executor.scala:455: ambiguous reference to overloaded definition, method limit in class ByteBuffer of type (x$1: Int)java.nio.ByteBuffer
method limit in class Buffer of type ()Int
match expected type ?
val resultSize = serializedDirectResult.limit
error
```
The limit method was moved from ByteBuffer to the superclass Buffer and it can no longer be called without (). The same reason for position method.
```javascript
/home/zly/prj/oss/jdk9_HOS_SOURCE/spark_source/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformationExec.scala:427: ambiguous reference to overloaded definition, [error] both method putAll in class Properties of type (x$1: java.util.Map[_, _])Unit [error] and method putAll in class Hashtable of type (x$1: java.util.Map[_ <: Object, _ <: Object])Unit [error] match argument types (java.util.Map[String,String])
[error] props.putAll(outputSerdeProps.toMap.asJava)
[error] ^
```
This is because the key type is Object instead of String which is unsafe.
## How was this patch tested?
running tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: kellyzly <kellyzly@126.com>
Closes#19854 from kellyzly/SPARK-22660.
## What changes were proposed in this pull request?
This PR fixes the conversion error when reads data from a PostgreSQL table that contains columns of `uuid[]`, `inet[]` and `cidr[]` data types.
For example, create a table with the uuid[] data type, and insert the test data.
```SQL
CREATE TABLE users
(
id smallint NOT NULL,
name character varying(50),
user_ids uuid[],
PRIMARY KEY (id)
)
INSERT INTO users ("id", "name","user_ids")
VALUES (1, 'foo', ARRAY
['7be8aaf8-650e-4dbb-8186-0a749840ecf2'
,'205f9bfc-018c-4452-a605-609c0cfad228']::UUID[]
)
```
Then it will throw the following exceptions when trying to load the data.
```
java.lang.ClassCastException: [Ljava.util.UUID; cannot be cast to [Ljava.lang.String;
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:459)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:458)
...
```
## How was this patch tested?
Added test in `PostgresIntegrationSuite`.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#19567 from jmchung/SPARK-22291.
TIMESTAMP (-101), BINARY_DOUBLE (101) and BINARY_FLOAT (100) are handled in OracleDialect
## What changes were proposed in this pull request?
When a oracle table contains columns whose type is BINARY_FLOAT or BINARY_DOUBLE, spark sql fails to load a table with SQLException
```
java.sql.SQLException: Unsupported type 101
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$getCatalystType(JdbcUtils.scala:235)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$8.apply(JdbcUtils.scala:292)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$8.apply(JdbcUtils.scala:292)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:291)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:64)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.<init>(JDBCRelation.scala:113)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:47)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:306)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:146)
```
## How was this patch tested?
I updated a UT which covers type conversion test for types (-101, 100, 101), on top of that I tested this change against actual table with those columns and it was able to read and write to the table.
Author: Kohki Nishio <taroplus@me.com>
Closes#19548 from taroplus/oracle_sql_types_101.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
Enable Scala 2.12 REPL. Fix most remaining issues with 2.12 compilation and warnings, including:
- Selecting Kafka 0.10.1+ for Scala 2.12 and patching over a minor API difference
- Fixing lots of "eta expansion of zero arg method deprecated" warnings
- Resolving the SparkContext.sequenceFile implicits compile problem
- Fixing an odd but valid jetty-server missing dependency in hive-thriftserver
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#19307 from srowen/Scala212.
## What changes were proposed in this pull request?
Auto generated Oracle schema some times not we expect:
- `number(1)` auto mapped to BooleanType, some times it's not we expect, per [SPARK-20921](https://issues.apache.org/jira/browse/SPARK-20921).
- `number` auto mapped to Decimal(38,10), It can't read big data, per [SPARK-20427](https://issues.apache.org/jira/browse/SPARK-20427).
This PR fix this issue by custom schema as follows:
```scala
val props = new Properties()
props.put("customSchema", "ID decimal(38, 0), N1 int, N2 boolean")
val dfRead = spark.read.schema(schema).jdbc(jdbcUrl, "tableWithCustomSchema", props)
dfRead.show()
```
or
```sql
CREATE TEMPORARY VIEW tableWithCustomSchema
USING org.apache.spark.sql.jdbc
OPTIONS (url '$jdbcUrl', dbTable 'tableWithCustomSchema', customSchema'ID decimal(38, 0), N1 int, N2 boolean')
```
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18266 from wangyum/SPARK-20427.
## What changes were proposed in this pull request?
Put Kafka 0.8 support behind a kafka-0-8 profile.
## How was this patch tested?
Existing tests, but, until PR builder and Jenkins configs are updated the effect here is to not build or test Kafka 0.8 support at all.
Author: Sean Owen <sowen@cloudera.com>
Closes#19134 from srowen/SPARK-21893.
## What changes were proposed in this pull request?
this PR describe remove the import class that are unused.
## How was this patch tested?
N/A
Author: caoxuewen <cao.xuewen@zte.com.cn>
Closes#19131 from heary-cao/unuse_import.
…build; fix some things that will be warnings or errors in 2.12; restore Scala 2.12 profile infrastructure
## What changes were proposed in this pull request?
This change adds back the infrastructure for a Scala 2.12 build, but does not enable it in the release or Python test scripts.
In order to make that meaningful, it also resolves compile errors that the code hits in 2.12 only, in a way that still works with 2.11.
It also updates dependencies to the earliest minor release of dependencies whose current version does not yet support Scala 2.12. This is in a sense covered by other JIRAs under the main umbrella, but implemented here. The versions below still work with 2.11, and are the _latest_ maintenance release in the _earliest_ viable minor release.
- Scalatest 2.x -> 3.0.3
- Chill 0.8.0 -> 0.8.4
- Clapper 1.0.x -> 1.1.2
- json4s 3.2.x -> 3.4.2
- Jackson 2.6.x -> 2.7.9 (required by json4s)
This change does _not_ fully enable a Scala 2.12 build:
- It will also require dropping support for Kafka before 0.10. Easy enough, just didn't do it yet here
- It will require recreating `SparkILoop` and `Main` for REPL 2.12, which is SPARK-14650. Possible to do here too.
What it does do is make changes that resolve much of the remaining gap without affecting the current 2.11 build.
## How was this patch tested?
Existing tests and build. Manually tested with `./dev/change-scala-version.sh 2.12` to verify it compiles, modulo the exceptions above.
Author: Sean Owen <sowen@cloudera.com>
Closes#18645 from srowen/SPARK-14280.
During profiling of a structured streaming application with Kafka as the source, I came across this exception:
This is a 1 minute sample, which caused 106K `NonLocalReturnControl` exceptions to be thrown.
This happens because `CachedKafkaConsumer.get` is ran inside:
`private def runUninterruptiblyIfPossible[T](body: => T): T`
Where `body: => T` is the `get` method. Turning the method into a function means that in order to escape the `while` loop defined in `get` the runtime has to do dirty tricks which involve throwing the above exception.
## What changes were proposed in this pull request?
Instead of using `return` (which is generally not recommended in Scala), we place the result of the `fetchData` method inside a local variable and use a boolean flag to indicate the status of fetching data, which we monitor as our predicate to the `while` loop.
## How was this patch tested?
I've ran the `KafkaSourceSuite` to make sure regression passes. Since the exception isn't visible from user code, there is no way (at least that I could think of) to add this as a test to the existing suite.
Author: Yuval Itzchakov <yuval.itzchakov@clicktale.com>
Closes#19059 from YuvalItzchakov/master.
## What changes were proposed in this pull request?
All streaming logical plans will now have isStreaming set. This involved adding isStreaming as a case class arg in a few cases, since a node might be logically streaming depending on where it came from.
## How was this patch tested?
Existing unit tests - no functional change is intended in this PR.
Author: Jose Torres <joseph-torres@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18973 from joseph-torres/SPARK-21765.
## What changes were proposed in this pull request?
The previous PR(https://github.com/apache/spark/pull/19000) removed filter pushdown verification, This PR add them back.
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19002 from wangyum/SPARK-21790-follow-up.
## What changes were proposed in this pull request?
[SPARK-17701](https://github.com/apache/spark/pull/18600/files#diff-b9f96d092fb3fea76bcf75e016799678L77) removed `metadata` function, this PR removed the Docker-based Integration module that has been relevant to `SparkPlan.metadata`.
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19000 from wangyum/SPARK-21709.
This version fixes a few issues in the import order checker; it provides
better error messages, and detects more improper ordering (thus the need
to change a lot of files in this patch). The main fix is that it correctly
complains about the order of packages vs. classes.
As part of the above, I moved some "SparkSession" import in ML examples
inside the "$example on$" blocks; that didn't seem consistent across
different source files to start with, and avoids having to add more on/off blocks
around specific imports.
The new scalastyle also seems to have a better header detector, so a few
license headers had to be updated to match the expected indentation.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18943 from vanzin/SPARK-21731.
## What changes were proposed in this pull request?
This pr updated `lz4-java` to the latest (v1.4.0) and removed custom `LZ4BlockInputStream`. We currently use custom `LZ4BlockInputStream` to read concatenated byte stream in shuffle. But, this functionality has been implemented in the latest lz4-java (https://github.com/lz4/lz4-java/pull/105). So, we might update the latest to remove the custom `LZ4BlockInputStream`.
Major diffs between the latest release and v1.3.0 in the master are as follows (62f7547abb...6d4693f562);
- fixed NPE in XXHashFactory similarly
- Don't place resources in default package to support shading
- Fixes ByteBuffer methods failing to apply arrayOffset() for array-backed
- Try to load lz4-java from java.library.path, then fallback to bundled
- Add ppc64le binary
- Add s390x JNI binding
- Add basic LZ4 Frame v1.5.0 support
- enable aarch64 support for lz4-java
- Allow unsafeInstance() for ppc64le archiecture
- Add unsafeInstance support for AArch64
- Support 64-bit JNI build on Solaris
- Avoid over-allocating a buffer
- Allow EndMark to be incompressible for LZ4FrameInputStream.
- Concat byte stream
## How was this patch tested?
Existing tests.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18883 from maropu/SPARK-21276.
## What changes were proposed in this pull request?
The examples and docs for Spark-Kinesis integrations use the deprecated KinesisUtils. We should update the docs to use the KinesisInputDStream builder to create DStreams.
## How was this patch tested?
The patch primarily updates the documents. The patch will also need to make changes to the Spark-Kinesis examples. The examples need to be tested.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#18071 from yssharma/ysharma/kinesis_docs.
## What changes were proposed in this pull request?
Currently spark-streaming-kafka-0-10 has a dependency on the full kafka distribution (but only uses and requires the kafka-clients library).
The PR fixes that (the library only depends on kafka-clients), and the tests depend on the full kafka.
## How was this patch tested?
All existing tests still pass.
Author: Tim Van Wassenhove <github@timvw.be>
Closes#18353 from timvw/master.
## What changes were proposed in this pull request?
Address scapegoat warnings for:
- BigDecimal double constructor
- Catching NPE
- Finalizer without super
- List.size is O(n)
- Prefer Seq.empty
- Prefer Set.empty
- reverse.map instead of reverseMap
- Type shadowing
- Unnecessary if condition.
- Use .log1p
- Var could be val
In some instances like Seq.empty, I avoided making the change even where valid in test code to keep the scope of the change smaller. Those issues are concerned with performance and it won't matter for tests.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#18635 from srowen/Scapegoat1.
## What changes were proposed in this pull request?
- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17150 from srowen/SPARK-19810.
## What changes were proposed in this pull request?
Move `compileValue` method in JDBCRDD to JdbcDialect, and override the `compileValue` method in OracleDialect to rewrite the Oracle-specific timestamp and date literals in where clause.
## How was this patch tested?
An integration test has been added.
Author: Rui Zha <zrdt713@gmail.com>
Author: Zharui <zrdt713@gmail.com>
Closes#18451 from SharpRay/extend-compileValue-to-dialects.
## What changes were proposed in this pull request?
This PR is to revert some code changes in the read path of https://github.com/apache/spark/pull/14377. The original fix is https://github.com/apache/spark/pull/17830
When merging this PR, please give the credit to gaborfeher
## How was this patch tested?
Added a test case to OracleIntegrationSuite.scala
Author: Gabor Feher <gabor.feher@lynxanalytics.com>
Author: gatorsmile <gatorsmile@gmail.com>
Closes#18408 from gatorsmile/OracleType.
This patch adds DB2 specific data type mappings for decfloat, real, xml , and timestamp with time zone (DB2Z specific type) types on read and for byte, short data types on write to the to jdbc data source DB2 dialect. Default mapping does not work for these types when reading/writing from DB2 database.
Added docker test, and a JDBC unit test case.
Author: sureshthalamati <suresh.thalamati@gmail.com>
Closes#9162 from sureshthalamati/db2dialect_enhancements-spark-10655.
## What changes were proposed in this pull request?
Add a new property `spark.streaming.kafka.consumer.cache.enabled` that allows users to enable or disable the cache for Kafka consumers. This property can be especially handy in cases where issues like SPARK-19185 get hit, for which there isn't a solution committed yet. By default, the cache is still on, so this change doesn't change any out-of-box behavior.
## How was this patch tested?
Running unit tests
Author: Mark Grover <mark@apache.org>
Author: Mark Grover <grover.markgrover@gmail.com>
Closes#18234 from markgrover/spark-19185.
## What changes were proposed in this pull request?
Currently the `DataFrameWriter` operations have several problems:
1. non-file-format data source writing action doesn't show up in the SQL tab in Spark UI
2. file-format data source writing action shows a scan node in the SQL tab, without saying anything about writing. (streaming also have this issue, but not fixed in this PR)
3. Spark SQL CLI actions don't show up in the SQL tab.
This PR fixes all of them, by refactoring the `ExecuteCommandExec` to make it have children.
close https://github.com/apache/spark/pull/17540
## How was this patch tested?
existing tests.
Also test the UI manually. For a simple command: `Seq(1 -> "a").toDF("i", "j").write.parquet("/tmp/qwe")`
before this PR:
<img width="266" alt="qq20170523-035840 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326050/24e18ba2-3f6c-11e7-8817-6dd275bf6ac5.png">
after this PR:
<img width="287" alt="qq20170523-035708 2x" src="https://cloud.githubusercontent.com/assets/3182036/26326054/2ad7f460-3f6c-11e7-8053-d68325beb28f.png">
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18064 from cloud-fan/execution.
## What changes were proposed in this pull request?
In summary, cost of recreating a KafkaProducer for writing every batch is high as it starts a lot threads and make connections and then closes them. A KafkaProducer instance is promised to be thread safe in Kafka docs. Reuse of KafkaProducer instance while writing via multiple threads is encouraged.
Furthermore, I have performance improvement of 10x in latency, with this patch.
### These are times that addBatch took in ms. Without applying this patch
### These are times that addBatch took in ms. After applying this patch
## How was this patch tested?
Running distributed benchmarks comparing runs with this patch and without it.
Added relevant unit tests.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Closes#17308 from ScrapCodes/cached-kafka-producer.
## What changes were proposed in this pull request?
Add `ThreadUtils.awaitReady` similar to `ThreadUtils.awaitResult` and disallow `Await.ready`.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17763 from zsxwing/awaitready.
## What changes were proposed in this pull request?
The pull requests proposes to remove the hardcoded values for Amazon Kinesis - MIN_RETRY_WAIT_TIME_MS, MAX_RETRIES.
This change is critical for kinesis checkpoint recovery when the kinesis backed rdd is huge.
Following happens in a typical kinesis recovery :
- kinesis throttles large number of requests while recovering
- retries in case of throttling are not able to recover due to the small wait period
- kinesis throttles per second, the wait period should be configurable for recovery
The patch picks the spark kinesis configs from:
- spark.streaming.kinesis.retry.wait.time
- spark.streaming.kinesis.retry.max.attempts
Jira : https://issues.apache.org/jira/browse/SPARK-20140
## How was this patch tested?
Modified the KinesisBackedBlockRDDSuite.scala to run kinesis tests with the modified configurations. Wasn't able to test the patch with actual throttling.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#17467 from yssharma/ysharma/spark-kinesis-retries.
## What changes were proposed in this pull request?
This PR proposes to fix the lint-breaks as below:
```
[ERROR] src/main/java/org/apache/spark/unsafe/Platform.java:[51] (regexp) RegexpSingleline: No trailing whitespace allowed.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[45,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[62,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[78,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[92,25] (naming) MethodName: Method name 'ProcessingTime' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/main/scala/org/apache/spark/sql/streaming/Trigger.java:[102,25] (naming) MethodName: Method name 'Once' must match pattern '^[a-z][a-z0-9][a-zA-Z0-9_]*$'.
[ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisInputDStreamBuilderSuite.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.api.java.JavaDStream.
```
after:
```
dev/lint-java
Checkstyle checks passed.
```
[Test Result](https://travis-ci.org/ConeyLiu/spark/jobs/229666169)
## How was this patch tested?
Travis CI
Author: Xianyang Liu <xianyang.liu@intel.com>
Closes#17890 from ConeyLiu/codestyle.
### What changes were proposed in this pull request?
This PR is to support JDBC data type TIME WITH TIME ZONE. It can be converted to TIMESTAMP
In addition, before this PR, for unsupported data types, we simply output the type number instead of the type name.
```
java.sql.SQLException: Unsupported type 2014
```
After this PR, the message is like
```
java.sql.SQLException: Unsupported type TIMESTAMP_WITH_TIMEZONE
```
- Also upgrade the H2 version to `1.4.195` which has the type fix for "TIMESTAMP WITH TIMEZONE". However, it is not fully supported. Thus, we capture the exception, but we still need it to partially test the support of "TIMESTAMP WITH TIMEZONE", because Docker tests are not regularly run.
### How was this patch tested?
Added test cases.
Author: Xiao Li <gatorsmile@gmail.com>
Closes#17835 from gatorsmile/h2.
## What changes were proposed in this pull request?
SparkSQL can now read from a database table with column type [TIMESTAMP WITH TIME ZONE](https://docs.oracle.com/javase/8/docs/api/java/sql/Types.html#TIMESTAMP_WITH_TIMEZONE).
## How was this patch tested?
Tested against Oracle database.
JoshRosen, you seem to know the class, would you look at this? Thanks!
Author: Jannik Arndt <jannik@jannikarndt.de>
Closes#17832 from JannikArndt/spark-20557-timestamp-with-timezone.
## What changes were proposed in this pull request?
I checked the logs of https://amplab.cs.berkeley.edu/jenkins/job/spark-branch-2.2-test-maven-hadoop-2.7/47/ and found it took several seconds to create Kafka internal topic `__consumer_offsets`. As Kafka creates this topic lazily, the topic creation happens in the first test `deserialization of initial offset with Spark 2.1.0` and causes it timeout.
This PR changes `offsets.topic.num.partitions` from the default value 50 to 1 to make creating `__consumer_offsets` (50 partitions -> 1 partition) much faster.
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17863 from zsxwing/fix-kafka-flaky-test.
## What changes were proposed in this pull request?
We didn't enforce analyzed plans in Spark 2.1 when writing out to Kafka.
## How was this patch tested?
New unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bill Chambers <bill@databricks.com>
Closes#17804 from anabranch/SPARK-20496-2.
## What changes were proposed in this pull request?
Cancel a batch Kafka query but one of task cannot be cancelled, and rerun the same DataFrame may cause ConcurrentModificationException because it may launch two tasks sharing the same group id.
This PR always create a new consumer when `reuseKafkaConsumer = false` to avoid ConcurrentModificationException. It also contains other minor fixes.
## How was this patch tested?
Jenkins.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17752 from zsxwing/kafka-fix.
## What changes were proposed in this pull request?
This PR changes Executor's threads to `UninterruptibleThread` so that we can use `runUninterruptibly` in `CachedKafkaConsumer`. However, this is just best effort to avoid hanging forever. If the user uses`CachedKafkaConsumer` in another thread (e.g., create a new thread or Future), the potential hang may still happen.
## How was this patch tested?
The new added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#17761 from zsxwing/int.
## What changes were proposed in this pull request?
The spark-kinesis testcases use the KinesisUtils.createStream which are deprecated now. Modify the testcases to use the recommended KinesisInputDStream.builder instead.
This change will also enable the testcases to automatically use the session tokens automatically.
## How was this patch tested?
All the existing testcases work fine as expected with the changes.
https://issues.apache.org/jira/browse/SPARK-20189
Author: Yash Sharma <ysharma@atlassian.com>
Closes#17506 from yssharma/ysharma/cleanup_kinesis_testcases.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
For large trigger intervals (e.g. 10 minutes), if a batch takes 11 minutes, then it will wait for 9 mins before starting the next batch. This does not make sense. The processing time based trigger policy should be to do process batches as fast as possible, but no faster than 1 in every trigger interval. If batches are taking longer than trigger interval anyways, then no point waiting extra trigger interval.
In this PR, I modified the ProcessingTimeExecutor to do so. Another minor change I did was to extract our StreamManualClock into a separate class so that it can be used outside subclasses of StreamTest. For example, ProcessingTimeExecutorSuite does not need to create any context for testing, just needs the StreamManualClock.
## How was this patch tested?
Added new unit tests to comprehensively test this behavior.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17525 from tdas/SPARK-20209.
## What changes were proposed in this pull request?
- Add new KinesisDStream.scala containing KinesisDStream.Builder class
- Add KinesisDStreamBuilderSuite test suite
- Make KinesisInputDStream ctor args package private for testing
- Add JavaKinesisDStreamBuilderSuite test suite
- Add args to KinesisInputDStream and KinesisReceiver for optional
service-specific auth (Kinesis, DynamoDB and CloudWatch)
## How was this patch tested?
Added ```KinesisDStreamBuilderSuite``` to verify builder class works as expected
Author: Adam Budde <budde@amazon.com>
Closes#17250 from budde/KinesisStreamBuilder.
## What changes were proposed in this pull request?
An additional trigger and trigger executor that will execute a single trigger only. One can use this OneTime trigger to have more control over the scheduling of triggers.
In addition, this patch requires an optimization to StreamExecution that logs a commit record at the end of successfully processing a batch. This new commit log will be used to determine the next batch (offsets) to process after a restart, instead of using the offset log itself to determine what batch to process next after restart; using the offset log to determine this would process the previously logged batch, always, thus not permitting a OneTime trigger feature.
## How was this patch tested?
A number of existing tests have been revised. These tests all assumed that when restarting a stream, the last batch in the offset log is to be re-processed. Given that we now have a commit log that will tell us if that last batch was processed successfully, the results/assumptions of those tests needed to be revised accordingly.
In addition, a OneTime trigger test was added to StreamingQuerySuite, which tests:
- The semantics of OneTime trigger (i.e., on start, execute a single batch, then stop).
- The case when the commit log was not able to successfully log the completion of a batch before restart, which would mean that we should fall back to what's in the offset log.
- A OneTime trigger execution that results in an exception being thrown.
marmbrus tdas zsxwing
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Tyson Condie <tcondie@gmail.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#17219 from tcondie/stream-commit.
## Problem
There are several places where we write out version identifiers in various logs for structured streaming (usually `v1`). However, in the places where we check for this, we throw a confusing error message.
## What changes were proposed in this pull request?
This patch made two major changes:
1. added a `parseVersion(...)` method, and based on this method, fixed the following places the way they did version checking (no other place needed to do this checking):
```
HDFSMetadataLog
- CompactibleFileStreamLog ------------> fixed with this patch
- FileStreamSourceLog ---------------> inherited the fix of `CompactibleFileStreamLog`
- FileStreamSinkLog -----------------> inherited the fix of `CompactibleFileStreamLog`
- OffsetSeqLog ------------------------> fixed with this patch
- anonymous subclass in KafkaSource ---> fixed with this patch
```
2. changed the type of `FileStreamSinkLog.VERSION`, `FileStreamSourceLog.VERSION` etc. from `String` to `Int`, so that we can identify newer versions via `version > 1` instead of `version != "v1"`
- note this didn't break any backwards compatibility -- we are still writing out `"v1"` and reading back `"v1"`
## Exception message with this patch
```
java.lang.IllegalStateException: Failed to read log file /private/var/folders/nn/82rmvkk568sd8p3p8tb33trw0000gn/T/spark-86867b65-0069-4ef1-b0eb-d8bd258ff5b8/0. UnsupportedLogVersion: maximum supported log version is v1, but encountered v99. The log file was produced by a newer version of Spark and cannot be read by this version. Please upgrade.
at org.apache.spark.sql.execution.streaming.HDFSMetadataLog.get(HDFSMetadataLog.scala:202)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite$$anonfun$3$$anonfun$apply$mcV$sp$2.apply(OffsetSeqLogSuite.scala:78)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite$$anonfun$3$$anonfun$apply$mcV$sp$2.apply(OffsetSeqLogSuite.scala:75)
at org.apache.spark.sql.test.SQLTestUtils$class.withTempDir(SQLTestUtils.scala:133)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite.withTempDir(OffsetSeqLogSuite.scala:26)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite$$anonfun$3.apply$mcV$sp(OffsetSeqLogSuite.scala:75)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite$$anonfun$3.apply(OffsetSeqLogSuite.scala:75)
at org.apache.spark.sql.execution.streaming.OffsetSeqLogSuite$$anonfun$3.apply(OffsetSeqLogSuite.scala:75)
at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
```
## How was this patch tested?
unit tests
Author: Liwei Lin <lwlin7@gmail.com>
Closes#17070 from lw-lin/better-msg.
