## What changes were proposed in this pull request?
The Spark SQL test framework needs to support 2 kinds of tests:
1. tests inside Spark to test Spark itself (extends `SparkFunSuite`)
2. test outside of Spark to test Spark applications (introduced at b57ed2245c)
The class hierarchy of the major testing traits:
`PlanTestBase`, `SQLTestUtilsBase` and `SharedSparkSession` intentionally don't extend `SparkFunSuite`, so that they can be used for tests outside of Spark. Tests in Spark should extends `QueryTest` and/or `SharedSQLContext` in most cases.
However, the name is a little confusing. As a result, some test suites extend `SharedSparkSession` instead of `SharedSQLContext`. `SharedSparkSession` doesn't work well with `SparkFunSuite` as it doesn't have the special handling of thread auditing in `SharedSQLContext`. For example, you will see a warning starting with `===== POSSIBLE THREAD LEAK IN SUITE` when you run `DataFrameSelfJoinSuite`.
This PR proposes to rename `SharedSparkSession` to `SharedSparkSessionBase`, and rename `SharedSQLContext` to `SharedSparkSession`.
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#25463 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[SPARK-28163](https://issues.apache.org/jira/browse/SPARK-28163) fixed a bug and during the analysis we've concluded it would be more robust to use `CaseInsensitiveMap` inside Kafka source. This case less lower/upper case problem would rise in the future.
Please note this PR doesn't intend to solve any kind of actual problem but finish the concept added in [SPARK-28163](https://issues.apache.org/jira/browse/SPARK-28163) (in a fix PR I didn't want to add too invasive changes). In this PR I've changed `Map[String, String]` to `CaseInsensitiveMap[String]` to enforce the usage. These are the main use-cases:
* `contains` => `CaseInsensitiveMap` solves it
* `get...` => `CaseInsensitiveMap` solves it
* `filter` => keys must be converted to lowercase because there is no guarantee that the incoming map has such key set
* `find` => keys must be converted to lowercase because there is no guarantee that the incoming map has such key set
* passing parameters to Kafka consumer/producer => keys must be converted to lowercase because there is no guarantee that the incoming map has such key set
## How was this patch tested?
Existing unit tests.
Closes#25418 from gaborgsomogyi/SPARK-28695.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The mapping of Spark schema to Avro schema is many-to-many. (See https://spark.apache.org/docs/latest/sql-data-sources-avro.html#supported-types-for-spark-sql---avro-conversion)
The default schema mapping might not be exactly what users want. For example, by default, a "string" column is always written as "string" Avro type, but users might want to output the column as "enum" Avro type.
With PR https://github.com/apache/spark/pull/21847, Spark supports user-specified schema in the batch writer.
For the function `to_avro`, we should support user-specified output schema as well.
## How was this patch tested?
Unit test.
Closes#25419 from gengliangwang/to_avro.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR fixed typos in comments and replace the explicit type with '<>' for Java 8+.
## How was this patch tested?
Manually tested.
Closes#25338 from younggyuchun/younggyu.
Authored-by: younggyu chun <younggyuchun@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There are "unsafe" conversions in the Kafka connector.
`CaseInsensitiveStringMap` comes in which is then converted the following way:
```
...
options.asScala.toMap
...
```
The main problem with this is that such case it looses its case insensitive nature
(case insensitive map is converting the key to lower case when get/contains called).
In this PR I'm using `CaseInsensitiveMap` to solve this problem.
## How was this patch tested?
Existing + additional unit tests.
Closes#24967 from gaborgsomogyi/SPARK-28163.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`KafkaOffsetRangeCalculator.getRanges` may drop offsets due to round off errors. The test added in this PR is one example.
This PR rewrites the logic in `KafkaOffsetRangeCalculator.getRanges` to ensure it never drops offsets.
## How was this patch tested?
The regression test.
Closes#25237 from zsxwing/fix-range.
Authored-by: Shixiong Zhu <zsxwing@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This PR change `CalendarIntervalType`'s readable string representation from `calendarinterval` to `interval`.
## How was this patch tested?
Existing UT
Closes#25225 from wangyum/SPARK-28469.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
PostgreSQL doesn't have `TINYINT`, which would map directly, but `SMALLINT`s are sufficient for uni-directional translation.
A side-effect of this fix is that `AggregatedDialect` is now usable with multiple dialects targeting `jdbc:postgresql`, as `PostgresDialect.getJDBCType` no longer throws (for which reason backporting this fix would be lovely):
1217996f15/sql/core/src/main/scala/org/apache/spark/sql/jdbc/AggregatedDialect.scala (L42)
`dialects.flatMap` currently throws on the first attempt to get a JDBC type preventing subsequent dialects in the chain from providing an alternative.
## How was this patch tested?
Unit tests.
Closes#24845 from mojodna/postgres-byte-type-mapping.
Authored-by: Seth Fitzsimmons <seth@mojodna.net>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
#25010 breaks the integration test suite due to the changing the user-facing exception like the following. This PR fixes the integration test suite.
```scala
- require(
- decimalVal.precision <= precision,
- s"Decimal precision ${decimalVal.precision} exceeds max precision $precision")
+ if (decimalVal.precision > precision) {
+ throw new ArithmeticException(
+ s"Decimal precision ${decimalVal.precision} exceeds max precision $precision")
+ }
```
## How was this patch tested?
Manual test.
```
$ build/mvn install -DskipTests
$ build/mvn -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.12 test
```
Closes#25165 from dongjoon-hyun/SPARK-28201.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR aims to correct mappings in `MsSqlServerDialect`. `ShortType` is mapped to `SMALLINT` and `FloatType` is mapped to `REAL` per [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017) respectively.
ShortType and FloatTypes are not correctly mapped to right JDBC types when using JDBC connector. This results in tables and spark data frame being created with unintended types. The issue was observed when validating against SQLServer.
Refer [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017 ) for guidance on mappings between SQLServer, JDBC and Java. Note that java "Short" type should be mapped to JDBC "SMALLINT" and java Float should be mapped to JDBC "REAL".
Some example issue that can happen because of wrong mappings
- Write from df with column type results in a SQL table of with column type as INTEGER as opposed to SMALLINT.Thus a larger table that expected.
- Read results in a dataframe with type INTEGER as opposed to ShortType
- ShortType has a problem in both the the write and read path
- FloatTypes only have an issue with read path. In the write path Spark data type 'FloatType' is correctly mapped to JDBC equivalent data type 'Real'. But in the read path when JDBC data types need to be converted to Catalyst data types ( getCatalystType) 'Real' gets incorrectly gets mapped to 'DoubleType' rather than 'FloatType'.
Refer #28151 which contained this fix as one part of a larger PR. Following PR #28151 discussion it was decided to file seperate PRs for each of the fixes.
## How was this patch tested?
UnitTest added in JDBCSuite.scala and these were tested.
Integration test updated and passed in MsSqlServerDialect.scala
E2E test done with SQLServer
Closes#25146 from shivsood/float_short_type_fix.
Authored-by: shivsood <shivsood@microsoft.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch proposes moving all Trigger implementations to `Triggers.scala`, to avoid exposing these implementations to the end users and let end users only deal with `Trigger.xxx` static methods. This fits the intention of deprecation of `ProcessingTIme`, and we agree to move others without deprecation as this patch will be shipped in major version (Spark 3.0.0).
## How was this patch tested?
UTs modified to work with newly introduced class.
Closes#24996 from HeartSaVioR/SPARK-28199.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There are some hardcoded configs, using config entry to replace them.
## How was this patch tested?
Existing UT
Closes#25059 from WangGuangxin/ConfigEntry.
Authored-by: wangguangxin.cn <wangguangxin.cn@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
At the moment Kafka delegation tokens are fetched through `AdminClient` but there is no possibility to add custom configuration parameters. In [options](https://spark.apache.org/docs/2.4.3/structured-streaming-kafka-integration.html#kafka-specific-configurations) there is already a possibility to add custom configurations.
In this PR I've added similar this possibility to `AdminClient`.
## How was this patch tested?
Existing + added unit tests.
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#24875 from gaborgsomogyi/SPARK-28055.
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?
`DirectKafkaStreamSuite.offset recovery from kafka` commits offsets to Kafka with `Consumer.commitAsync` API (and then reads it back). Since this API is asynchronous it may send notifications late(or not at all). The actual test makes the assumption if the data sent and collected then the offset must be committed as well. This is not true.
In this PR I've made the following modifications:
* Wait for async offset commit before context stopped
* Added commit succeed log to see whether it arrived at all
* Using `ConcurrentHashMap` for committed offsets because 2 threads are using the variable (`JobGenerator` and `ScalaTest...`)
## How was this patch tested?
Existing unit test in a loop + jenkins runs.
Closes#25100 from gaborgsomogyi/SPARK-28335.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Migrate Avro to File source V2.
## How was this patch tested?
Unit test
Closes#25017 from gengliangwang/avroV2.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This pr upgrades Postgres docker image for integration tests.
## How was this patch tested?
manual tests:
```
./build/mvn install -DskipTests
./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.12
```
Closes#25050 from wangyum/SPARK-28248.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This patch adds missing UT which tests the changed behavior of original patch #24942.
## How was this patch tested?
Newly added UT.
Closes#24999 from HeartSaVioR/SPARK-28142-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
According to the documentation `groupIdPrefix` should be available for `streaming and batch`.
It is not the case because the batch part is missing.
In this PR I've added:
* Structured Streaming test for v1 and v2 to cover `groupIdPrefix`
* Batch test for v1 and v2 to cover `groupIdPrefix`
* Added `groupIdPrefix` usage in batch
## How was this patch tested?
Additional + existing unit tests.
Closes#25030 from gaborgsomogyi/SPARK-28232.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Kafka batch data source is using v1 at the moment. In the PR I've migrated to v2. Majority of the change is moving code.
What this PR contains:
* useV1Sources usage fixed in `DataFrameReader` and `DataFrameWriter`
* `KafkaBatch` added to handle DSv2 batch reading
* `KafkaBatchWrite` added to handle DSv2 batch writing
* `KafkaBatchPartitionReader` extracted to share between batch and microbatch
* `KafkaDataWriter` extracted to share between batch, microbatch and continuous
* Batch related source/sink tests are now executing on v1 and v2 connectors
* Couple of classes hidden now, functions moved + couple of minor fixes
## How was this patch tested?
Existing + added unit tests.
Closes#24738 from gaborgsomogyi/SPARK-23098.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This patch addresses a missing spot which Map should be passed as CaseInsensitiveStringMap - KafkaContinuousStream seems to be only the missed one.
Before this fix, it has a relevant bug where `pollTimeoutMs` is always set to default value, as the value of `KafkaSourceProvider.CONSUMER_POLL_TIMEOUT` is `kafkaConsumer.pollTimeoutMs` which key-lowercased map has been provided as `sourceOptions`.
## How was this patch tested?
N/A.
Closes#24942 from HeartSaVioR/MINOR-use-case-insensitive-map-for-kafka-continuous-source.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When using `from_avro` to deserialize avro data to catalyst StructType format, if `ConvertToLocalRelation` is applied at the time, `from_avro` produces only the last value (overriding previous values).
The cause is `AvroDeserializer` reuses output row for StructType. Normally, it should be fine in Spark SQL. But `ConvertToLocalRelation` just uses `InterpretedProjection` to project local rows. `InterpretedProjection` creates new row for each output thro, it includes the same nested row object from `AvroDeserializer`. By the end, converted local relation has only last value.
I think there're two possible options:
1. Make `AvroDeserializer` output new row for StructType.
2. Use `InterpretedMutableProjection` in `ConvertToLocalRelation` and call `copy()` on output rows.
Option 2 is chose because previously `ConvertToLocalRelation` also creates new rows, this `InterpretedMutableProjection` + `copy()` shoudn't bring too much performance penalty. `ConvertToLocalRelation` should be arguably less critical, compared with `AvroDeserializer`.
## How was this patch tested?
Added test.
Closes#24805 from viirya/SPARK-27798.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Kafka parameters are logged at several places and the following parameters has to be redacted:
* Delegation token
* `ssl.truststore.password`
* `ssl.keystore.password`
* `ssl.key.password`
This PR contains:
* Spark central redaction framework used to redact passwords (`spark.redaction.regex`)
* Custom redaction added to handle `sasl.jaas.config` (delegation token)
* Redaction code added into consumer/producer code
* Test refactor
## How was this patch tested?
Existing + additional unit tests.
Closes#24627 from gaborgsomogyi/SPARK-27748.
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 PR aims to fix an issue on a union avro type with more than one non-null value (for instance `["string", "null", "int"]`) whose the deserialization to a DataFrame would throw a `java.lang.ArrayIndexOutOfBoundsException`. The issue was that the `fieldWriter` relied on the index from the avro schema before nulls were filtered out.
## How was this patch tested?
A test for the case of multiple non-null values was added and the tests were run using sbt by running `testOnly org.apache.spark.sql.avro.AvroSuite`
Closes#24722 from gcmerz/master.
Authored-by: Gabbi Merz <gmerz@palantir.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
When the data is read from the sources, the catalyst schema is always nullable. Since Avro uses Union type to represent nullable, when any non-nullable avro file is read and then written out, the schema will always be changed.
This PR provides a solution for users to keep the Avro schema without being forced to use Union type.
## How was this patch tested?
One test is added.
Closes#24682 from dbtsai/avroNull.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Use https URL for CRAN repo (and for a Scala download in a Dockerfile)
## How was this patch tested?
Existing tests.
Closes#24664 from srowen/SPARK-27794.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Remove obsolete spark-mesos Dockerfile example. This isn't tested and apparently hasn't been updated in 4 years.
## How was this patch tested?
N/A
Closes#24667 from srowen/SPARK-27796.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Spark Avro reader supports reading avro files with provided schema with different field orderings. However, the avro writer doesn't support this feature. This PR enables the Spark avro writer with this feature.
## How was this patch tested?
New test is added.
Closes#24635 from dbtsai/avroFix.
Lead-authored-by: DB Tsai <d_tsai@apple.com>
Co-authored-by: Brian Lindblom <blindblom@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Kafka related Spark parameters has to start with `spark.kafka.` and not with `spark.sql.`. Because of this I've renamed `spark.sql.kafkaConsumerCache.capacity`.
Since Kafka consumer caching is not documented I've added this also.
## How was this patch tested?
Existing + added unit test.
```
cd docs
SKIP_API=1 jekyll build
```
and manual webpage check.
Closes#24590 from gaborgsomogyi/SPARK-27687.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This removes usage of `Traversable`, which is removed in Scala 2.13. This is mostly an internal change, except for the change in the `SparkConf.setAll` method. See additional comments below.
## How was this patch tested?
Existing tests.
Closes#24584 from srowen/SPARK-27680.
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?
[SPARK-27343](https://issues.apache.org/jira/projects/SPARK/issues/SPARK-27343)
Based on the previous PR: https://github.com/apache/spark/pull/24270
Extracting parameters , building the objects of ConfigEntry.
For example:
for the parameter "spark.kafka.producer.cache.timeout",we build
```
private[kafka010] val PRODUCER_CACHE_TIMEOUT =
ConfigBuilder("spark.kafka.producer.cache.timeout")
.doc("The expire time to remove the unused producers.")
.timeConf(TimeUnit.MILLISECONDS)
.createWithDefaultString("10m")
```
Closes#24574 from hehuiyuan/hehuiyuan-patch-9.
Authored-by: hehuiyuan <hehuiyuan@ZBMAC-C02WD3K5H.local>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
### Background:
The data source option `pathGlobFilter` is introduced for Binary file format: https://github.com/apache/spark/pull/24354 , which can be used for filtering file names, e.g. reading `.png` files only while there is `.json` files in the same directory.
### Proposal:
Make the option `pathGlobFilter` as a general option for all file sources. The path filtering should happen in the path globbing on Driver.
### Motivation:
Filtering the file path names in file scan tasks on executors is kind of ugly.
### Impact:
1. The splitting of file partitions will be more balanced.
2. The metrics of file scan will be more accurate.
3. Users can use the option for reading other file sources.
## How was this patch tested?
Unit tests
Closes#24518 from gengliangwang/globFilter.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
To move DS v2 to the catalyst module, we can't make v2 offset rely on v1 offset, as v1 offset is in sql/core.
## How was this patch tested?
existing tests
Closes#24538 from cloud-fan/offset.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
For historical reasons, structured streaming still has some old way of use
`spark.network.timeout`
, even though
`org.apache.spark.internal.config.Network.NETWORK_TIMEOUT`
is now available.
## How was this patch tested?
Exists UT.
Closes#24545 from beliefer/unify-spark-network-timeout.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The actual implementation doesn't support multi-cluster Kafka connection with delegation token. In this PR I've added this functionality.
What this PR contains:
* New way of configuration
* Multiple delegation token obtain/store/use functionality
* Documentation
* The change works on DStreams also
## How was this patch tested?
Existing + additional unit tests.
Additionally tested on cluster.
Test scenario:
* 2 * 4 node clusters
* The 4-4 nodes are in different kerberos realms
* Cross-Realm trust between the 2 realms
* Yarn
* Kafka broker version 2.1.0
* security.protocol = SASL_SSL
* sasl.mechanism = SCRAM-SHA-512
* Artificial exceptions during processing
* Source reads from realm1 sink writes to realm2
Kafka broker settings:
* delegation.token.expiry.time.ms=600000 (10 min)
* delegation.token.max.lifetime.ms=1200000 (20 min)
* delegation.token.expiry.check.interval.ms=300000 (5 min)
Closes#24305 from gaborgsomogyi/SPARK-27294.
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?
Fixed the `spark-<version>-yarn-shuffle.jar` artifact packaging to shade the native netty libraries:
- shade the `META-INF/native/libnetty_*` native libraries when packagin
the yarn shuffle service jar. This is required as netty library loader
derives that based on shaded package name.
- updated the `org/spark_project` shade package prefix to `org/sparkproject`
(i.e. removed underscore) as the former breaks the netty native lib loading.
This was causing the yarn external shuffle service to fail
when spark.shuffle.io.mode=EPOLL
## How was this patch tested?
Manual tests
Closes#24502 from amuraru/SPARK-27610_master.
Authored-by: Adi Muraru <amuraru@adobe.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
`BaseStreamingSource` and `BaseStreamingSink` is used to unify v1 and v2 streaming data source API in some code paths.
This PR removes these 2 interfaces, and let the v1 API extend v2 API to keep API compatibility.
The motivation is https://github.com/apache/spark/pull/24416 . We want to move data source v2 to catalyst module, but `BaseStreamingSource` and `BaseStreamingSink` are in sql/core.
## How was this patch tested?
existing tests
Closes#24471 from cloud-fan/streaming.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
**Description from JIRA**
For the JDBC option `query`, we use the identifier name to start with underscore: s"(${subquery}) _SPARK_GEN_JDBC_SUBQUERY_NAME${curId.getAndIncrement()}". This is not supported by Oracle.
The Oracle doesn't seem to support identifier name to start with non-alphabet character (unless it is quoted) and has length restrictions as well. [link](https://docs.oracle.com/cd/B19306_01/server.102/b14200/sql_elements008.htm)
In this PR, the generated alias name 'SPARK_GEN_JDBC_SUBQUERY_NAME<int value>' is fixed to remove "_" prefix and also the alias name is shortened to not exceed the identifier length limit.
## How was this patch tested?
Tests are added for MySql, Postgress, Oracle and DB2 to ensure enough coverage.
Closes#24532 from dilipbiswal/SPARK-27596.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
There is a MemorySink v2 already so v1 can be removed. In this PR I've removed it completely.
What this PR contains:
* V1 memory sink removal
* V2 memory sink renamed to become the only implementation
* Since DSv2 sends exceptions in a chained format (linking them with cause field) I've made python side compliant
* Adapted all the tests
## How was this patch tested?
Existing unit tests.
Closes#24403 from gaborgsomogyi/SPARK-23014.
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?
I want to get rid of as much use of `scala.language.existentials` as possible for 3.0. It's a complicated language feature that generates warnings unless this value is imported. It might even be on the way out of Scala: https://contributors.scala-lang.org/t/proposal-to-remove-existential-types-from-the-language/2785
For Spark, it comes up mostly where the code plays fast and loose with generic types, not the advanced situations you'll often see referenced where this feature is explained. For example, it comes up in cases where a function returns something like `(String, Class[_])`. Scala doesn't like matching this to any other instance of `(String, Class[_])` because doing so requires inferring the existence of some type that satisfies both. Seems obvious if the generic type is a wildcard, but, not technically something Scala likes to let you get away with.
This is a large PR, and it only gets rid of _most_ instances of `scala.language.existentials`. The change should be all compile-time and shouldn't affect APIs or logic.
Many of the changes simply touch up sloppiness about generic types, making the known correct value explicit in the code.
Some fixes involve being more explicit about the existence of generic types in methods. For instance, `def foo(arg: Class[_])` seems innocent enough but should really be declared `def foo[T](arg: Class[T])` to let Scala select and fix a single type when evaluating calls to `foo`.
For kind of surprising reasons, this comes up in places where code evaluates a tuple of things that involve a generic type, but is OK if the two parts of the tuple are evaluated separately.
One key change was altering `Utils.classForName(...): Class[_]` to the more correct `Utils.classForName[T](...): Class[T]`. This caused a number of small but positive changes to callers that otherwise had to cast the result.
In several tests, `Dataset[_]` was used where `DataFrame` seems to be the clear intent.
Finally, in a few cases in MLlib, the return type `this.type` was used where there are no subclasses of the class that uses it. This really isn't needed and causes issues for Scala reasoning about the return type. These are just changed to be concrete classes as return types.
After this change, we have only a few classes that still import `scala.language.existentials` (because modifying them would require extensive rewrites to fix) and no build warnings.
## How was this patch tested?
Existing tests.
Closes#24431 from srowen/SPARK-27536.
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?
Change spark-token-provider-kafka-0-10 dependency on spark-core to be provided
## How was this patch tested?
Ran existing unit tests
Closes#24384 from koertkuipers/feat-kafka-token-provider-fix-deps.
Authored-by: Koert Kuipers <koert@tresata.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/24012 , to add the corresponding capabilities for streaming.
## How was this patch tested?
existing tests
Closes#24129 from cloud-fan/capability.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Right now Kafka source v2 doesn't support null values. The issue is in org.apache.spark.sql.kafka010.KafkaRecordToUnsafeRowConverter.toUnsafeRow which doesn't handle null values.
## How was this patch tested?
add new unit tests
Closes#24441 from uncleGen/SPARK-27494.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Kind of related to https://github.com/gatorsmile/spark/pull/5 - let's update genjavadoc to see if it generates fewer spurious javadoc errors to begin with.
## How was this patch tested?
Existing docs build
Closes#24443 from srowen/genjavadoc013.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
`Krb5LoginModule` supports debug parameter which is not yet supported from Spark side. This configuration makes it easier to debug authentication issues against Kafka.
In this PR `Krb5LoginModule` debug flag controlled by either `sun.security.krb5.debug` or `com.ibm.security.krb5.Krb5Debug`.
Additionally found some hardcoded values like `ssl.truststore.location`, etc... which could be error prone if Kafka changes it so in such cases Kafka define used.
## How was this patch tested?
Existing + additional unit tests + on cluster.
Closes#24204 from gaborgsomogyi/SPARK-27270.
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?
Fix build warnings -- see some details below.
But mostly, remove use of postfix syntax where it causes warnings without the `scala.language.postfixOps` import. This is mostly in expressions like "120000 milliseconds". Which, I'd like to simplify to things like "2.minutes" anyway.
## How was this patch tested?
Existing tests.
Closes#24314 from srowen/SPARK-27404.
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?
I found a lot scattered config in `Kafka` streaming.I think should arrange these config in unified position.
## How was this patch tested?
No need UT.
Closes#24267 from beliefer/arrange-scattered-streaming-kafka-config.
Authored-by: gengjiaan <gengjiaan@360.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Use Single Abstract Method syntax where possible (and minor related cleanup). Comments below. No logic should change here.
## How was this patch tested?
Existing tests.
Closes#24241 from srowen/SPARK-27323.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Update Oracle docker image name.
## How was this patch tested?
./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.12
Closes#24086 from lipzhu/SPARK-27155.
Authored-by: Zhu, Lipeng <lipzhu@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove Scala 2.11 support in build files and docs, and in various parts of code that accommodated 2.11. See some targeted comments below.
## How was this patch tested?
Existing tests.
Closes#23098 from srowen/SPARK-26132.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
