### What changes were proposed in this pull request?
This PR replaces `withTimeZone` defined and used in `OracleIntegrationSuite` with `DateTimeTestUtils.withDefaultTimeZone` which is defined as a utility method.
### Why are the changes needed?
Both methods are semantically the same so it might be better to use the utility one.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
`OracleIntegrationSuite` passes.
Closes#31465 from sarutak/oracle-timezone-util.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR added tests for some non-array types in PostgreSQL.
PostgreSQL supports wide range of types (https://www.postgresql.org/docs/13/datatype.html) and `PostgresIntegrationSuite` contains tests for some types but ones for the following types are missing.
* bit varying
* point
* line
* lseg
* box
* path
* polygon
* circle
* pg_lsn
* macaddr
* macaddr8
* numeric
* pg_snapshot
* real
* time
* timestamp
* tsquery
* tsvector
* txid_snapshot
* xml
NOTE: Handling money types can be buggy so this PR doesn't add tests for those types.
### Why are the changes needed?
To ensure those types work with Spark well.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Extended `PostgresIntegrationSuite`.
Closes#31456 from sarutak/test-for-some-types-postgresql.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR proposes to expose the number of total paths in Utils.buildLocationMetadata(), with relaxing space usage a bit (around 10+ chars).
Suppose the first 2 of 5 paths are only fit to the threshold, the outputs between the twos are below:
* before the change: `[path1, path2]`
* after the change: `(5 paths)[path1, path2, ...]`
### Why are the changes needed?
SPARK-31793 silently truncates the paths hence end users can't indicate how many paths are truncated, and even more, whether paths are truncated or not.
### Does this PR introduce _any_ user-facing change?
Yes, the location metadata will also show how many paths are truncated (not shown), instead of silently truncated.
### How was this patch tested?
Modified UTs
Closes#31464 from HeartSaVioR/SPARK-34339.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Due to user-experience (confusing to Spark users - java.sql.Time using milliseconds vs Spark using microseconds; and user losing useful functions like hour(), minute(), etc on the column), we have decided to revert back to use TimestampType but this time we will enforce the hour to be consistently across system timezone (via offset manipulation) and date part fixed to zero epoch.
Full Discussion with Wenchen Fan Wenchen Fan regarding this ticket is here https://github.com/apache/spark/pull/30902#discussion_r569186823
### Why are the changes needed?
Revert and improvement to sql.Time handling
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit tests and integration tests
Closes#31473 from saikocat/SPARK-34357.
Authored-by: Hoa <hoameomu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Improve the error messages for incompatibilities between Avro and Catalyst schemas. First, make `AvroSerializer` more similar to `AvroDeserializer` in printing out contextual information such as hierarchical field names. Standardize exception messages in both serializer and deserializer to always include such contextual information, and include a top-level exception which shows the full schemas which were being parsed when the incompatibility was found. Both now print out the hierarchical name for both the Avro and Catalyst fields, since they may be different due to case sensitivity and Avro union handling.
### Why are the changes needed?
The error messages in this type of failure scenario are very lacking in information on the write path (`AvroSerializer`). Below are two examples of messages that provide insufficient information to determine what went wrong (lacking in field names, context about the overall schema structure, etc.).
```
org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst type IntegerType to Avro type "float".
org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst type StructType(StructField(bar,IntegerType,true)) to Avro type {"type":"record","name":"test","fields":[{"name":"NOTbar","type":["null","int"],"default":null}]}.
```
The error messages currently existing in `AvroDeserializer` are much better, but still not very internally consistent, and it would be better if they were consistent with the newly added exception messages in `AvroSerializer`.
### Does this PR introduce _any_ user-facing change?
Error messages when there are incompatibilities between Avro and Catalyst schemas will be greatly improved on when writing Avro data using the `avroSchema` option, a little bit improved when reading Avro data, and much more consistent between the two.
Below is an example of a new message. See `AvroSerdeSuite` for more examples.
```
org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst type STRUCT<`foo`: STRUCT<`bar`: INT>> to Avro type {"type":"record","name":"top","fields":[{"name":"foo","type":"int"}]}
at org.apache.spark.sql.avro.AvroSerializer.liftedTree1$1(AvroSerializer.scala:83)
...
Caused by: org.apache.spark.sql.avro.IncompatibleSchemaException: Cannot convert Catalyst field 'foo' to Avro field 'foo' because schema is incompatible (sqlType = STRUCT<`bar`: INT>, avroType = "int")
at org.apache.spark.sql.avro.AvroSerializer.newConverter(AvroSerializer.scala:230)
...
```
### How was this patch tested?
New unit test suite, `AvroSerdeSuite`, was added to test corner cases on `AvroSerializer` and `AvroDeserializer` and verify that the exception messages are as expected. Existing tests in `AvroSuite` also continue to pass, with modifications in places where assertions were made about the exceptions that would be thrown.
Closes#31333 from xkrogen/xkrogen-SPARK-34182-avro-serde-errormessages.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change `AvroSuite."Ignore corrupt Avro file if flag IGNORE_CORRUPT_FILES"` to use `episodesAvro`, which is loaded as a resource using the classloader, instead of trying to read `episodes.avro` directly from a relative file path.
### Why are the changes needed?
This is the proper way to read resource files, and currently this test will fail when called from my IntelliJ IDE, though it will succeed when called from Maven/sbt, presumably due to different working directory handling.
### Does this PR introduce _any_ user-facing change?
No, unit test only.
### How was this patch tested?
Previous failure from IntelliJ:
```
Source 'src/test/resources/episodes.avro' does not exist
java.io.FileNotFoundException: Source 'src/test/resources/episodes.avro' does not exist
at org.apache.commons.io.FileUtils.checkFileRequirements(FileUtils.java:1405)
at org.apache.commons.io.FileUtils.copyFile(FileUtils.java:1072)
at org.apache.commons.io.FileUtils.copyFile(FileUtils.java:1040)
at org.apache.spark.sql.avro.AvroSuite.$anonfun$new$34(AvroSuite.scala:397)
at org.apache.spark.sql.avro.AvroSuite.$anonfun$new$34$adapted(AvroSuite.scala:388)
```
Now it succeeds.
Closes#31332 from xkrogen/xkrogen-SPARK-34231-avrosuite-testfix.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Using a function like `.mkString` or `.getLines` directly on a `scala.io.Source` opened by `fromFile`, `fromURL`, `fromURI ` will leak the underlying file handle, this pr use the `Utils.tryWithResource` method wrap the `BufferedSource` to ensure these `BufferedSource` closed.
### Why are the changes needed?
Avoid file handle leak.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31323 from LuciferYang/source-not-closed.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to fix Avro data source to use the decimal precision and scale of file schema.
### Why are the changes needed?
The decimal value should be interpreted with its original precision and scale. Otherwise, it returns incorrect result like the following. The schema mismatch happens when we use `userSpecifiedSchema` or there are multiple files with inconsistent schema or HiveMetastore schema is updated by the user.
```scala
scala> sql("SELECT 3.14 a").write.format("avro").save("/tmp/avro")
scala> spark.read.schema("a DECIMAL(4, 3)").format("avro").load("/tmp/avro").show
+-----+
| a|
+-----+
|0.314|
+-----+
```
### Does this PR introduce _any_ user-facing change?
Yes, this will return correct result.
### How was this patch tested?
Pass the CI with the newly added test case.
Closes#31329 from dongjoon-hyun/SPARK-34229.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Make the field name matching between Avro and Catalyst schemas, on both the reader and writer paths, respect the global SQL settings for case sensitivity (i.e. case-insensitive by default). `AvroSerializer` and `AvroDeserializer` share a common utility in `AvroUtils` to search for an Avro field to match a given Catalyst field.
### Why are the changes needed?
Spark SQL is normally case-insensitive (by default), but currently when `AvroSerializer` and `AvroDeserializer` perform matching between Catalyst schemas and Avro schemas, the matching is done in a case-sensitive manner. So for example the following will fail:
```scala
val avroSchema =
"""
|{
| "type" : "record",
| "name" : "test_schema",
| "fields" : [
| {"name": "foo", "type": "int"},
| {"name": "BAR", "type": "int"}
| ]
|}
""".stripMargin
val df = Seq((1, 3), (2, 4)).toDF("FOO", "bar")
df.write.option("avroSchema", avroSchema).format("avro").save(savePath)
```
The same is true on the read path, if we assume `testAvro` has been written using the schema above, the below will fail to match the fields:
```scala
df.read.schema(new StructType().add("FOO", IntegerType).add("bar", IntegerType))
.format("avro").load(testAvro)
```
### Does this PR introduce _any_ user-facing change?
When reading Avro data, or writing Avro data using the `avroSchema` option, field matching will be performed with case sensitivity respecting the global SQL settings.
### How was this patch tested?
New tests added to `AvroSuite` to validate the case sensitivity logic in an end-to-end manner through the SQL engine.
Closes#31201 from xkrogen/xkrogen-SPARK-34133-avro-serde-casesensitivity-errormessages.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the following integration suites to reflect the change of SPARK-33888.
* PostgresIntegrationSuite
* MySQLIntegrationSuite
* MsSqlServerIntegrationSuite
* DB2IntegrationSuite
### Why are the changes needed?
Those suites doesn't pass currently in `master` branch.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Those suites pass.
Closes#31274 from sarutak/fix-integration-suites.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch uses the available offset range obtained during polling Kafka records to do offset validation check.
### Why are the changes needed?
We support non-consecutive offsets for Kafka since 2.4.0. In `fetchRecord`, we do offset validation by checking if the offset is in available offset range. But currently we obtain latest available offset range to do the check. It looks not correct as the available offset range could be changed during the batch, so the available offset range is different than the one when we polling the records from Kafka.
It is possible that an offset is valid when polling, but at the time we do the above check, it is out of latest available offset range. We will wrongly consider it as data loss case and fail the query or drop the record.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This should pass existing unit tests.
This is hard to have unit test as the Kafka producer and the consumer is asynchronous. Further, we also need to make the offset out of new available offset range.
Closes#31275 from viirya/SPARK-34187.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR fixes the issue that reading tables which contain spatial datatypes from MS SQL Server fails.
MS SQL server supports two non-standard spatial JDBC types, `geometry` and `geography` but Spark SQL can't treat them
```
java.sql.SQLException: Unrecognized SQL type -157
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getCatalystType(JdbcUtils.scala:251)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$getSchema$1(JdbcUtils.scala:321)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:321)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:226)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:35)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:364)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:366)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:355)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:355)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:240)
at org.apache.spark.sql.DataFrameReader.jdbc(DataFrameReader.scala:381)
```
Considering the [data type mapping](https://docs.microsoft.com/ja-jp/sql/connect/jdbc/using-basic-data-types?view=sql-server-ver15) says, I think those spatial types can be mapped to Catalyst's `BinaryType`.
### Why are the changes needed?
To provide better support.
### Does this PR introduce _any_ user-facing change?
Yes. MS SQL Server users can use `geometry` and `geography` types in datasource tables.
### How was this patch tested?
New test case added to `MsSqlServerIntegrationSuite`.
Closes#31283 from sarutak/mssql-spatial-types.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the regression bug brought by SPARK-33888 (#30902).
After that PR merged, `PostgresDIalect#getCatalystType` throws Exception for array types.
```
[info] - Type mapping for various types *** FAILED *** (551 milliseconds)
[info] java.util.NoSuchElementException: key not found: scale
[info] at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:106)
[info] at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:104)
[info] at org.apache.spark.sql.types.Metadata.get(Metadata.scala:111)
[info] at org.apache.spark.sql.types.Metadata.getLong(Metadata.scala:51)
[info] at org.apache.spark.sql.jdbc.PostgresDialect$.getCatalystType(PostgresDialect.scala:43)
[info] at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:321)
```
### Why are the changes needed?
To fix the regression bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed the test case `SPARK-22291: Conversion error when transforming array types of uuid, inet and cidr to StingType in PostgreSQL` in `PostgresIntegrationSuite` passed.
I also confirmed whether all the `v2.*IntegrationSuite` pass because this PR changed them and they passed.
Closes#31262 from sarutak/fix-postgres-dialect-regression.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Update Avro dependency to version 1.10.1
### Why are the changes needed?
To catch up multiple improvements of Avro as well as fix security issues on transitive dependencies.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Since there were no API changes required we just run the tests
Closes#31232 from iemejia/SPARK-27733-avro-upgrade.
Authored-by: Ismaël Mejía <iemejia@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR is the 3rd try to upgrade Scala 2.12.x in order to see the feasibility.
- https://github.com/apache/spark/pull/27929 (Upgrade Scala to 2.12.11, wangyum )
- https://github.com/apache/spark/pull/30940 (Upgrade Scala to 2.12.12, viirya )
`silencer` library is updated accordingly. And, Kafka version upgrade is required because it fails like the following.
```
[info] KafkaDataConsumerSuite:
[info] org.apache.spark.streaming.kafka010.KafkaDataConsumerSuite *** ABORTED *** (1 second, 580 milliseconds)
[info] java.lang.NoClassDefFoundError: scala/math/Ordering$$anon$7
[info] at kafka.api.ApiVersion$.orderingByVersion(ApiVersion.scala:45)
```
### Why are the changes needed?
Apache Spark was stuck to 2.12.10 due to the regression in Scala 2.12.11 and 2.12.12. This will bring all the bug fixes.
- https://github.com/scala/scala/releases/tag/v2.12.13
- https://github.com/scala/scala/releases/tag/v2.12.12
- https://github.com/scala/scala/releases/tag/v2.12.11
### Does this PR introduce _any_ user-facing change?
Yes, but this is a bug-fixed version.
### How was this patch tested?
Pass the CIs.
Closes#31223 from dongjoon-hyun/SPARK-31168.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This:
1. switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x.
2. upgrade built-in version for Hadoop 3.x to Hadoop 3.2.2
Note that for Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
Hadoop 3.2.2 is released with new features and bug fixes, so it's good for the Spark community to adopt it. However, latest Hadoop versions starting from Hadoop 3.2.1 have upgraded to use Guava 27+. In order to resolve Guava conflicts, this takes the approach by switching to shaded client jars provided by Hadoop. This also has the benefits of avoid pulling other 3rd party dependencies from Hadoop side so as to avoid more potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#30701 from sunchao/test-hadoop-3.2.2.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Some local variables are declared as `var`, but they are never reassigned and should be declared as `val`, so this pr turn these from `var` to `val` except for `mockito` related cases.
### Why are the changes needed?
Use `val` instead of `var` when possible.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#31142 from LuciferYang/SPARK-33346.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
There are some redundant collection conversion can be removed, for version compatibility, clean up these with Scala-2.13 profile.
### Why are the changes needed?
Remove redundant collection conversion
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test `core`, `graphx`, `mllib`, `mllib-local`, `sql`, `yarn`,`kafka-0-10` in Scala 2.13 passed
Closes#31125 from LuciferYang/SPARK-34068.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
`HadoopDelegationTokenManager.isServiceEnabled` is quite a time consuming operation which is called in `KafkaTokenUtil.needTokenUpdate` often which slowed down Kafka processing heavily. SPARK-33635 changed the if condition in order to overcome this issue when no delegation token is used but in case of delegation token usage the problem still exists. In this PR I'm caching the `HadoopDelegationTokenManager.isServiceEnabled` result in the `KafkaDataConsumer` instances which solves the issue. There would be another solution, namely caching the result inside `HadoopDelegationTokenManager` but since it's an object function and several application is running inside a JVM, different `SparkConf` instances will arrive. Caching the result per `SparkConf` instance would be an overkill.
### Why are the changes needed?
Kafka stream processing is slow with delegation token.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
* Existing unit tests
* In Kafka to Kafka live query I've double checked that `HadoopDelegationTokenManager.isServiceEnabled` call executed only when new `KafkaDataConsumer` created (new delegation token arrives or task failure).
Closes#31154 from gaborgsomogyi/SPARK-34090.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is basically a followup of https://github.com/apache/spark/pull/14332.
Calling `map` alone might leave it not executed due to lazy evaluation, e.g.)
```
scala> val foo = Seq(1,2,3)
foo: Seq[Int] = List(1, 2, 3)
scala> foo.map(println)
1
2
3
res0: Seq[Unit] = List((), (), ())
scala> foo.view.map(println)
res1: scala.collection.SeqView[Unit,Seq[_]] = SeqViewM(...)
scala> foo.view.foreach(println)
1
2
3
```
We should better use `foreach` to make sure it's executed where the output is unused or `Unit`.
### Why are the changes needed?
To prevent the potential issues by not executing `map`.
### Does this PR introduce _any_ user-facing change?
No, the current codes look not causing any problem for now.
### How was this patch tested?
I found these item by running IntelliJ inspection, double checked one by one, and fixed them. These should be all instances across the codebase ideally.
Closes#31110 from HyukjinKwon/SPARK-34059.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Liang-Chi Hsieh <viirya@gmail.com>
### What changes were proposed in this pull request?
Kafka delegation token is obtained with `AdminClient` where security settings can be set. Keystore and trustrore type however can't be set. In this PR I've added these new configurations. This can be useful when the type is different. A good example is to make Spark FIPS compliant where the default JKS is not accepted.
### Why are the changes needed?
Missing configurations.
### Does this PR introduce _any_ user-facing change?
Yes, adding 2 additional config parameters.
### How was this patch tested?
Existing + modified unit tests + simple Kafka to Kafka app on cluster.
Closes#31070 from gaborgsomogyi/SPARK-34032.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR proposes to adjust the order of check in KafkaTokenUtil.needTokenUpdate, so that short-circuit applies on the non-delegation token cases (insecure + secured without delegation token) and remedies the performance regression heavily.
### Why are the changes needed?
There's a serious performance regression between Spark 2.4 vs Spark 3.0 on read path against Kafka data source.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually ran a reproducer (https://github.com/codegorillauk/spark-kafka-read with modification to just count instead of writing to Kafka topic) with measuring the time.
> the branch applying the change with adding measurement
https://github.com/HeartSaVioR/spark/commits/debug-SPARK-33635-v3.0.1
> the branch only adding measurement
https://github.com/HeartSaVioR/spark/commits/debug-original-ver-SPARK-33635-v3.0.1
> the result (before the fix)
count: 10280000
Took 41.634007047 secs
21/01/06 13:16:07 INFO KafkaDataConsumer: debug ver. 17-original
21/01/06 13:16:07 INFO KafkaDataConsumer: Total time taken to retrieve: 82118 ms
> the result (after the fix)
count: 10280000
Took 7.964058475 secs
21/01/06 13:08:22 INFO KafkaDataConsumer: debug ver. 17
21/01/06 13:08:22 INFO KafkaDataConsumer: Total time taken to retrieve: 987 ms
Closes#31056 from HeartSaVioR/SPARK-33635.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch proposes to add latest offset to source progress for streaming queries.
### Why are the changes needed?
Currently we record start and end offsets per source in streaming process. Latest offset is an important information for streaming process but the progress lacks of this info. We can use it to track the process lag and adjust streaming queries. We should add latest offset to source progress.
### Does this PR introduce _any_ user-facing change?
Yes, for new metric about latest source offset in source progress.
### How was this patch tested?
Unit test. Manually test in Spark cluster:
```
"description" : "KafkaV2[Subscribe[page_view_events]]",
"startOffset" : {
"page_view_events" : {
"2" : 582370921,
"4" : 391910836,
"1" : 631009201,
"3" : 406601346,
"0" : 195799112
}
},
"endOffset" : {
"page_view_events" : {
"2" : 583764414,
"4" : 392338002,
"1" : 632183480,
"3" : 407101489,
"0" : 197304028
}
},
"latestOffset" : {
"page_view_events" : {
"2" : 589852545,
"4" : 394204277,
"1" : 637313869,
"3" : 409286602,
"0" : 203878962
}
},
"numInputRows" : 4999997,
"inputRowsPerSecond" : 29287.70501405811,
```
Closes#30988 from viirya/latest-offset.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes an incorrect condition when comparing offset range size and min partition config.
### Why are the changes needed?
When calculating offset ranges, we consider `minPartitions` configuration. If `minPartitions` is not set or is less than or equal the size of given ranges, it means there are enough partitions at Kafka so we don't need to split offsets to satisfy min partition requirement. But the current condition is `offsetRanges.size > minPartitions.get` and is not correct. Currently `getRanges` will split offsets in unnecessary case.
Besides, in non-split case, we can assign preferred executor location and reuse `KafkaConsumer`. So unnecessary splitting offset range will miss the chance to reuse `KafkaConsumer`.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Manual test in Spark cluster with Kafka.
Closes#30994 from viirya/ss-minor4.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This minor patch changes two variables where calling `fetchEarliestOffsets` to `lazy` because these values are not always necessary.
### Why are the changes needed?
To avoid unnecessary Kafka RPC calls.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes#30969 from viirya/ss-minor3.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch cleans up KafkaOffsetReader API document.
### Why are the changes needed?
KafkaOffsetReader API documents are duplicated among KafkaOffsetReaderConsumer and KafkaOffsetReaderAdmin. It seems to be good if the doc is centralized.
This also adds missing API doc too.
### Does this PR introduce _any_ user-facing change?
No, dev only.
### How was this patch tested?
Doc only.
Closes#30961 from viirya/SPARK-33932.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The test failures were due to machine being slow in Jenkins. We switched to Ubuntu 20 if I am not wrong.
Looks like all machines are functioning properly unlike the past, and the tests pass without a problem anymore.
This PR proposes to enable them back.
### Why are the changes needed?
To restore test coverage.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Jenkins jobs in this PR show the flakiness.
Closes#30798 from HyukjinKwon/do-not-merge-test.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
The Kafka offset reader which uses `AdminClient` still uses `UninterruptibleThread` to call it. Since there is no evidence that `AdminClient` suffers from similar issues like [KAFKA-1894](https://issues.apache.org/jira/browse/KAFKA-1894) I'm removing `UninterruptibleThread` usage. In order to put the `AdminClient` under stress and make sure it works I've created the following standalone application: https://github.com/gaborgsomogyi/kafka-admin-interruption
What this PR contains:
* Removed `UninterruptibleThread` from `KafkaOffsetReaderAdmin`
* Removed/modified comments which are not true
* Adapted `KafkaRelationSuite`
* Renamed `partitionsAssignedToConsumer` to `partitionsAssignedToAdmin`
### Why are the changes needed?
`KafkaOffsetReaderAdmin` doesn't need `UninterruptibleThread` usage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests + manually with simple Kafka to Kafka query.
Closes#30668 from gaborgsomogyi/SPARK-32910.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR fixes a build error of `OracleIntegrationSuite` with Scala 2.13.
### Why are the changes needed?
Build should pass with Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed that the build pass with the following command.
```
$ build/sbt -Pdocker-integration-tests -Pscala-2.13 "docker-integration-tests/test:compile"
```
Closes#30660 from sarutak/fix-docker-integration-tests-for-scala-2.13.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR proposes to fix a string interpolation in `CommandUtils.scala` and `KafkaDataConsumer.scala`.
### Why are the changes needed?
To fix a string interpolation bug.
### Does this PR introduce _any_ user-facing change?
Yes, the string will be correctly constructed.
### How was this patch tested?
Existing tests since they were used in exception/log messages.
Closes#30609 from imback82/fix_cache_str_interporlation.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to update `master` branch version to 3.2.0-SNAPSHOT.
### Why are the changes needed?
Start to prepare Apache Spark 3.2.0.
### Does this PR introduce _any_ user-facing change?
N/A.
### How was this patch tested?
Pass the CIs.
Closes#30606 from dongjoon-hyun/SPARK-3.2.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR extends the connection timeout to the DB server for DB2IntegrationSuite and its variants.
The container image ibmcom/db2 creates a database when it starts up.
The database creation can take over 2 minutes.
DB2IntegrationSuite and its variants use the container image but the connection timeout is set to 2 minutes so these suites almost always fail.
### Why are the changes needed?
To pass those suites.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed the suites pass with the following commands.
```
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.DB2IntegrationSuite"
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.v2.DB2IntegrationSuite"
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.DB2KrbIntegrationSuite"
Closes#30583 from sarutak/extend-timeout-for-db2.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR add an option to keep container after DockerJDBCIntegrationSuites (e.g. DB2IntegrationSuite, PostgresIntegrationSuite) finish.
By setting a system property `spark.test.docker.keepContainer` to `true`, we can use this option.
### Why are the changes needed?
If some error occur during the tests, it would be useful to keep the container for debug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed that the container is kept after the test by the following commands.
```
# With sbt
$ build/sbt -Dspark.test.docker.keepContainer=true -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
# With Maven
$ build/mvn -Dspark.test.docker.keepContainer=true -Pdocker-integration-tests -Phive -Phive-thriftserver -Dtest=none -DwildcardSuites=org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite test
$ docker container ls
```
I also confirmed that there are no regression for all the subclasses of `DockerJDBCIntegrationSuite` with sbt/Maven.
* MariaDBKrbIntegrationSuite
* DB2KrbIntegrationSuite
* PostgresKrbIntegrationSuite
* MySQLIntegrationSuite
* PostgresIntegrationSuite
* DB2IntegrationSuite
* MsSqlServerintegrationsuite
* OracleIntegrationSuite
* v2.MySQLIntegrationSuite
* v2.PostgresIntegrationSuite
* v2.DB2IntegrationSuite
* v2.MsSqlServerIntegrationSuite
* v2.OracleIntegrationSuite
NOTE: `DB2IntegrationSuite`, `v2.DB2IntegrationSuite` and `DB2KrbIntegrationSuite` can fail due to the too much short connection timeout. It's a separate issue and I'll fix it in #30583Closes#30601 from sarutak/keepContainer.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Add namespaces support in JDBC v2 Table Catalog by making ```JDBCTableCatalog``` extends```SupportsNamespaces```
### Why are the changes needed?
make v2 JDBC implementation complete
### Does this PR introduce _any_ user-facing change?
Yes. Add the following to ```JDBCTableCatalog```
- listNamespaces
- listNamespaces(String[] namespace)
- namespaceExists(String[] namespace)
- loadNamespaceMetadata(String[] namespace)
- createNamespace
- alterNamespace
- dropNamespace
### How was this patch tested?
Add new docker tests
Closes#30473 from huaxingao/name_space.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
remove USING _ in CREATE TABLE in JDBCTableCatalog docker tests
### Why are the changes needed?
Previously CREATE TABLE syntax forces users to specify a provider so we have to add a USING _ . Now the problem was fix and we need to remove it.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests
Closes#30599 from huaxingao/remove_USING.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c936) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes#30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Deprecated `KafkaConsumer.poll(long)` API calls may cause infinite wait in the driver. In this PR I've added a new `AdminClient` based offset fetching which is turned off by default. There is a new flag named `spark.sql.streaming.kafka.useDeprecatedOffsetFetching` (default: `true`) which can be set to `false` to reach the newly added functionality. The Structured Streaming migration guide contains more information what migration consideration must be done. Please see the following [doc](https://docs.google.com/document/d/1gAh0pKgZUgyqO2Re3sAy-fdYpe_SxpJ6DkeXE8R1P7E/edit?usp=sharing) for further details.
The PR contains the following changes:
* Added `AdminClient` based offset fetching
* GroupId prefix feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* GroupId override feature removed from driver but only in `AdminClient` based approach (`AdminClient` doesn't need any GroupId)
* Additional unit tests
* Code comment changes
* Minor bugfixes here and there
* Removed Kafka auto topic creation feature but only in `AdminClient` based approach (please see doc for rationale). In short, it's super hidden, not sure anybody ever used in production + error prone.
* Added documentation to `ss-migration-guide` and `structured-streaming-kafka-integration`
### Why are the changes needed?
Driver may hang forever.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Cluster test with simple Kafka topic to another topic query.
Documentation:
```
cd docs/
SKIP_API=1 jekyll build
```
Manual webpage check.
Closes#29729 from gaborgsomogyi/SPARK-32032.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
This PR changes mariadb_docker_entrypoint.sh to set the proper version automatically for mariadb-plugin-gssapi-server.
The proper version is based on the one of mariadb-server.
Also, this PR enables to use arbitrary docker image by setting the environment variable `MARIADB_CONTAINER_IMAGE_NAME`.
### Why are the changes needed?
For `MariaDBKrbIntegrationSuite`, the version of `mariadb-plugin-gssapi-server` is currently set to `10.5.5` in `mariadb_docker_entrypoint.sh` but it's no longer available in the official apt repository and `MariaDBKrbIntegrationSuite` doesn't pass for now.
It seems that only the most recent three versions are available for each major version and they are `10.5.6`, `10.5.7` and `10.5.8` for now.
Further, the release cycle of MariaDB seems to be very rapid (1 ~ 2 months) so I don't think it's a good idea to set to an specific version for `mariadb-plugin-gssapi-server`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Confirmed that `MariaDBKrbIntegrationSuite` passes with the following commands.
```
$ build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
```
In this case, we can see what version of `mariadb-plugin-gssapi-server` is going to be installed in the following container log message.
```
Installing mariadb-plugin-gssapi-server=1:10.5.8+maria~focal
```
Or, we can set MARIADB_CONTAINER_IMAGE_NAME for a specific version of MariaDB.
```
$ MARIADB_DOCKER_IMAGE_NAME=mariadb:10.5.6 build/sbt -Pdocker-integration-tests -Phive -Phive-thriftserver package "testOnly org.apache.spark.sql.jdbc.MariaDBKrbIntegrationSuite"
```
```
Installing mariadb-plugin-gssapi-server=1:10.5.6+maria~focal
```
Closes#30515 from sarutak/fix-MariaDBKrbIntegrationSuite.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
change
```
./build/sbt -Pdocker-integration-tests "testOnly *xxxIntegrationSuite"
```
to
```
./build/sbt -Pdocker-integration-tests "testOnly org.apache.spark.sql.jdbc.xxxIntegrationSuite"
```
### Why are the changes needed?
We only want to start v1 ```xxxIntegrationSuite```, not the newly added```v2.xxxIntegrationSuite```.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Manually checked
Closes#30448 from huaxingao/dockertest.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This pr add a new Scala compile arg to `pom.xml` to defense against new unused imports:
- `-Ywarn-unused-import` for Scala 2.12
- `-Wconf:cat=unused-imports:e` for Scala 2.13
The other fIles change are remove all unused imports in Spark code
### Why are the changes needed?
Cleanup code and add guarantee to defense against new unused imports
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30351 from LuciferYang/remove-imports-core-module.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In [SPARK-33139] we defined `setActionSession` and `clearActiveSession` as deprecated API, it turns out it is widely used, and after discussion, even if without this PR, it should work with unify view feature, it might only be a risk if user really abuse using these two API. So revert the PR is needed.
[SPARK-33139] has two commit, include a follow up. Revert them both.
### Why are the changes needed?
Revert.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT.
Closes#30367 from leanken/leanken-revert-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to fix typos in the sub-modules: graphx, external, and examples.
Split per holdenk https://github.com/apache/spark/pull/30323#issuecomment-725159710
NOTE: The misspellings have been reported at 706a726f87 (commitcomment-44064356)
### Why are the changes needed?
Misspelled words make it harder to read / understand content.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
No testing was performed
Closes#30326 from jsoref/spelling-graphx.
Authored-by: Josh Soref <jsoref@users.noreply.github.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Currently in JDBCTableCatalog, we ignore the table options when creating table.
```
// TODO (SPARK-32405): Apply table options while creating tables in JDBC Table Catalog
if (!properties.isEmpty) {
logWarning("Cannot create JDBC table with properties, these properties will be " +
"ignored: " + properties.asScala.map { case (k, v) => s"$k=$v" }.mkString("[", ", ", "]"))
}
```
### Why are the changes needed?
need to apply the table options when we create table
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
add new test
Closes#30154 from huaxingao/table_options.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
There are two similar compilation warnings about procedure-like declaration in Scala 2.13:
```
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala:70: procedure syntax is deprecated for constructors: add `=`, as in method definition
```
and
```
[WARNING] [Warn] /spark/core/src/main/scala/org/apache/spark/storage/BlockManagerDecommissioner.scala:211: procedure syntax is deprecated: instead, add `: Unit =` to explicitly declare `run`'s return type
```
this pr is the first part to resolve SPARK-33352：
- For constructors method definition add `=` to convert to function syntax
- For without `return type` methods definition add `: Unit =` to convert to function syntax
### Why are the changes needed?
Eliminate compilation warnings in Scala 2.13 and this change should be compatible with Scala 2.12
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Pass the Jenkins or GitHub Action
Closes#30255 from LuciferYang/SPARK-29392-FOLLOWUP.1.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Override the default SQL strings for:
ALTER TABLE RENAME COLUMN
ALTER TABLE UPDATE COLUMN NULLABILITY
in the following MsSQLServer JDBC dialect according to official documentation.
Write MsSqlServer integration tests for JDBC.
### Why are the changes needed?
To add the support for alter table when interacting with MSSql Server.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
added tests
Closes#30038 from ScrapCodes/mssql-dialect.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This change is to support user provided nullable Avro schema for data with non-nullable catalyst schema in Avro writing.
Without this change, when users try to use a nullable Avro schema to write data with a non-nullable catalyst schema, it will throw an `IncompatibleSchemaException` with a message like `Cannot convert Catalyst type StringType to Avro type ["null","string"]`. With this change it will assume that the data is non-nullable, log a warning message for the nullability difference and serialize the data to Avro format with the nullable Avro schema provided.
### Why are the changes needed?
This change is needed because sometimes our users do not have full control over the nullability of the Avro schemas they use, and this change provides them with the flexibility.
### Does this PR introduce _any_ user-facing change?
Yes. Users are allowed to use nullable Avro schemas for data with non-nullable catalyst schemas in Avro writing after the change.
### How was this patch tested?
Added unit tests.
Closes#30224 from bozhang2820/avro-nullable.
Authored-by: Bo Zhang <bo.zhang@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
This PR adds a check to RowReader#hasNextRow such that multiple calls to RowReader#hasNextRow with no intervening call to RowReader#nextRow will avoid consuming more than 1 record.
This PR also modifies RowReader#nextRow such that consecutive calls will return new rows (previously consecutive calls would return the same row).
### Why are the changes needed?
SPARK-32346 slightly refactored the AvroFileFormat and AvroPartitionReaderFactory to use a new iterator-like trait called AvroUtils#RowReader. RowReader#hasNextRow consumes a raw input record and stores the deserialized row for the next call to RowReader#nextRow. Unfortunately, sometimes hasNextRow is called twice before nextRow is called, resulting in a lost row.
For example (which assumes V1 Avro reader):
```scala
val df = spark.range(0, 25).toDF("index")
df.write.mode("overwrite").format("avro").save("index_avro")
val loaded = spark.read.format("avro").load("index_avro")
// The following will give the expected size
loaded.collect.size
// The following will give the wrong size
loaded.orderBy("index").collect.size
```
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added tests, which fail without the fix.
Closes#30221 from bersprockets/avro_iterator_play.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR renames some part of `Seq` in `PostgresIntegrationSuite` to `scala.collection.Seq`.
When I run `docker-integration-test`, I noticed that `PostgresIntegrationSuite` failed due to `ClassCastException`.
The reason is the same as what is resolved in SPARK-29292.
### Why are the changes needed?
To pass `docker-integration-test` for Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Ran `PostgresIntegrationSuite` fixed and confirmed it successfully finished.
Closes#30166 from sarutak/fix-toseq-postgresql.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Support rename column for mysql dialect.
### Why are the changes needed?
At the moment, it does not work for mysql version 5.x. So, we should throw proper exception for that case.
### Does this PR introduce _any_ user-facing change?
Yes, `column rename` with mysql dialect should work correctly.
### How was this patch tested?
Added tests for rename column.
Ran the tests to pass with both versions of mysql.
* `export MYSQL_DOCKER_IMAGE_NAME=mysql:5.7.31`
* `export MYSQL_DOCKER_IMAGE_NAME=mysql:8.0`
Closes#30142 from ScrapCodes/mysql-dialect-rename.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Override the default SQL strings in Postgres Dialect for:
- ALTER TABLE UPDATE COLUMN TYPE
- ALTER TABLE UPDATE COLUMN NULLABILITY
Add new docker integration test suite `jdbc/v2/PostgreSQLIntegrationSuite.scala`
### Why are the changes needed?
supports Postgres specific ALTER TABLE syntax.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Add new test `PostgreSQLIntegrationSuite`
Closes#30089 from huaxingao/postgres_docker.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Override the default SQL strings for:
ALTER TABLE UPDATE COLUMN TYPE
ALTER TABLE UPDATE COLUMN NULLABILITY
in the following MySQL JDBC dialect according to official documentation.
Write MySQL integration tests for JDBC.
### Why are the changes needed?
Improved code coverage and support mysql dialect for jdbc.
### Does this PR introduce _any_ user-facing change?
Yes, Support ALTER TABLE in JDBC v2 Table Catalog: add, update type and nullability of columns (MySQL dialect)
### How was this patch tested?
Added tests.
Closes#30025 from ScrapCodes/mysql-dialect.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This switches Spark to use shaded Hadoop clients, namely hadoop-client-api and hadoop-client-runtime, for Hadoop 3.x. For Hadoop 2.7, we'll still use the same modules such as hadoop-client.
In order to still keep default Hadoop profile to be hadoop-3.2, this defines the following Maven properties:
```
hadoop-client-api.artifact
hadoop-client-runtime.artifact
hadoop-client-minicluster.artifact
```
which default to:
```
hadoop-client-api
hadoop-client-runtime
hadoop-client-minicluster
```
but all switch to `hadoop-client` when the Hadoop profile is hadoop-2.7. A side affect from this is we'll import the same dependency multiple times. For this I have to disable Maven enforcer `banDuplicatePomDependencyVersions`.
Besides above, there are the following changes:
- explicitly add a few dependencies which are imported via transitive dependencies from Hadoop jars, but are removed from the shaded client jars.
- removed the use of `ProxyUriUtils.getPath` from `ApplicationMaster` which is a server-side/private API.
- modified `IsolatedClientLoader` to exclude `hadoop-auth` jars when Hadoop version is 3.x. This change should only matter when we're not sharing Hadoop classes with Spark (which is _mostly_ used in tests).
### Why are the changes needed?
This serves two purposes:
- to unblock Spark from upgrading to Hadoop 3.2.2/3.3.0+. Latest Hadoop versions have upgraded to use Guava 27+ and in order to adopt the latest Hadoop versions in Spark, we'll need to resolve the Guava conflicts. This takes the approach by switching to shaded client jars provided by Hadoop.
- avoid pulling 3rd party dependencies from Hadoop and avoid potential future conflicts.
### Does this PR introduce _any_ user-facing change?
When people use Spark with `hadoop-provided` option, they should make sure class path contains `hadoop-client-api` and `hadoop-client-runtime` jars. In addition, they may need to make sure these jars appear before other Hadoop jars in the order. Otherwise, classes may be loaded from the other non-shaded Hadoop jars and cause potential conflicts.
### How was this patch tested?
Relying on existing tests.
Closes#29843 from sunchao/SPARK-29250.
Authored-by: Chao Sun <sunchao@apple.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
### What changes were proposed in this pull request?
1. Add the common trait `CommonFileDataSourceSuite` with tests that can be executed for all built-in file-based datasources.
2. Add a test `CommonFileDataSourceSuite` to check that datasource options are propagated to underlying file systems as Hadoop configs.
3. Mix `CommonFileDataSourceSuite` to `AvroSuite`, `OrcSourceSuite`, `TextSuite`, `JsonSuite`, CSVSuite` and to `ParquetFileFormatSuite`.
4. Remove duplicated tests from `AvroSuite` and from `OrcSourceSuite`.
### Why are the changes needed?
To improve test coverage and test all built-in file-based datasources.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the affected test suites.
Closes#30067 from MaxGekk/ds-options-common-test.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession.
Change of the PR:
* add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API.
* by default, if user call these two API, it will throw exception
* add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage
* change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive
### Why are the changes needed?
Make SQLConf.get reliable and stable.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
* Add UT in SparkSessionBuilderSuite to test the legacy config
* Existing test
Closes#30042 from leanken/leanken-SPARK-33139.
Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Added a couple tests to `AvroSuite` and to `ParquetIOSuite` to check that the metadata key 'org.apache.spark.legacyDateTime' is written correctly depending on the SQL configs:
- spark.sql.legacy.avro.datetimeRebaseModeInWrite
- spark.sql.legacy.parquet.datetimeRebaseModeInWrite
This is a follow up https://github.com/apache/spark/pull/28137.
### Why are the changes needed?
1. To improve test coverage
2. To make sure that the metadata key is actually saved to Avro/Parquet files
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the added tests:
```
$ build/sbt "testOnly org.apache.spark.sql.execution.datasources.parquet.ParquetIOSuite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.AvroV1Suite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.AvroV2Suite"
```
Closes#30061 from MaxGekk/parquet-test-metakey.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
SPARK-32926 added a build test to GitHub Action for Scala 2.13 but it's only with Maven.
As SPARK-32873 reported, some compilation error happens only with SBT so I think we need to add another build test to GitHub Action for SBT.
Unfortunately, we don't have abundant resources for GitHub Actions so instead of just adding the new SBT job, let's replace the existing Maven job with the new SBT job for Scala 2.13.
### Why are the changes needed?
To ensure build test passes even with SBT for Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
GitHub Actions' job.
Closes#29958 from sarutak/add-sbt-job-for-scala-2.13.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
test-only - > testOnly in docs across the project.
### Why are the changes needed?
Since the sbt version is updated, the older way or running i.e. `test-only` is no longer valid.
### Does this PR introduce _any_ user-facing change?
docs update.
### How was this patch tested?
Manually.
Closes#30028 from ScrapCodes/fix-build/sbt-sample.
Authored-by: Prashant Sharma <prashsh1@in.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
- Override the default SQL strings in the DB2 Dialect for:
* ALTER TABLE UPDATE COLUMN TYPE
* ALTER TABLE UPDATE COLUMN NULLABILITY
- Add new docker integration test suite jdbc/v2/DB2IntegrationSuite.scala
### Why are the changes needed?
In SPARK-24907, we implemented JDBC v2 Table Catalog but it doesn't support some ALTER TABLE at the moment. This PR supports DB2 specific ALTER TABLE.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new integration test suite:
$ ./build/sbt -Pdocker-integration-tests "test-only *.DB2IntegrationSuite"
Closes#29972 from huaxingao/db2_docker.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In `AvroUtils`'s `inferSchema()`, propagate Hadoop config from DS options to underlying HDFS file system.
### Why are the changes needed?
There is a bug that when running:
```scala
spark.read.format("avro").options(conf).load(path)
```
The underlying file system will not receive the `conf` options.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
unit test added
Closes#29971 from yuningzh-db/avro_options.
Authored-by: Yuning Zhang <yuning.zhang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
1. Override the default SQL strings in the Oracle Dialect for:
- ALTER TABLE ADD COLUMN
- ALTER TABLE UPDATE COLUMN TYPE
- ALTER TABLE UPDATE COLUMN NULLABILITY
2. Add new docker integration test suite `jdbc/v2/OracleIntegrationSuite.scala`
### Why are the changes needed?
In SPARK-24907, we implemented JDBC v2 Table Catalog but it doesn't support some `ALTER TABLE` at the moment. This PR supports Oracle specific `ALTER TABLE`.
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?
By running new integration test suite:
```
$ ./build/sbt -Pdocker-integration-tests "test-only *.OracleIntegrationSuite"
```
Closes#29912 from MaxGekk/jdbcv2-oracle-alter-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR intends to update database versions below for integration tests;
- ibmcom/db2:11.5.0.0a => ibmcom/db2:11.5.4.0 in `DB2[Krb]IntegrationSuite`
- mysql:5.7.28 => mysql:5.7.31 in `MySQLIntegrationSuite`
- postgres:12.0 => postgres:13.0 in `Postgres[Krb]IntegrationSuite`
- mariadb:10.4 => mariadb:10.5 in `MariaDBKrbIntegrationSuite`
Also, this added environmental variables so that we can test with any database version and all the variables are as follows (see documents in the code for how to use all the variables);
- DB2_DOCKER_IMAGE_NAME
- MSSQLSERVER_DOCKER_IMAGE_NAME
- MYSQL_DOCKER_IMAGE_NAME
- POSTGRES_DOCKER_IMAGE_NAME
### Why are the changes needed?
To improve tests.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually checked.
Closes#29932 from maropu/UpdateIntegrationTests.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
At the moment only the baked in JDBC connection providers can be used but there is a need to support additional databases and use-cases. In this PR I'm proposing a new developer API name `JdbcConnectionProvider`. To show how an external JDBC connection provider can be implemented I've created an example [here](https://github.com/gaborgsomogyi/spark-jdbc-connection-provider).
The PR contains the following changes:
* Added connection provider developer API
* Made JDBC connection providers constructor to noarg => needed to load them w/ service loader
* Connection providers are now loaded w/ service loader
* Added tests to load providers independently
* Moved `SecurityConfigurationLock` into a central place because other areas will change global JVM security config
### Why are the changes needed?
No custom authentication possibility.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
* Existing + additional unit tests
* Docker integration tests
* Tested manually the newly created external JDBC connection provider
Closes#29024 from gaborgsomogyi/SPARK-32001.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This pr fix all 14 failed cases in `external/avro` module in Scala 2.13, the main change of this pr as follow:
- Manual call `toSeq` in `AvroDeserializer#newWriter` and `SchemaConverters#toSqlTypeHelper` method because the object type for case match is `ArrayBuffer` not `Seq` in Scala 2.13
- Specified `Seq` to `s.c.Seq` when we call `Row.get(i).asInstanceOf[Seq]` because the data maybe `mutable.ArraySeq` but `Seq` is `immutable.Seq` in Scala 2.13
### Why are the changes needed?
We need to support a Scala 2.13 build.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Scala 2.12: Pass the Jenkins or GitHub Action
- Scala 2.13: Pass 2.13 Build GitHub Action and do the following:
```
dev/change-scala-version.sh 2.13
mvn clean install -DskipTests -pl external/avro -Pscala-2.13 -am
mvn clean test -pl external/avro -Pscala-2.13
```
**Before**
```
Tests: succeeded 197, failed 14, canceled 0, ignored 2, pending 0
*** 14 TESTS FAILED ***
```
**After**
```
Tests: succeeded 211, failed 0, canceled 0, ignored 2, pending 0
All tests passed.
```
Closes#29801 from LuciferYang/fix-external-avro-213.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR fix code which causes error when build with sbt and Scala 2.13 like as follows.
```
[error] [warn] /home/kou/work/oss/spark-scala-2.13/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala:251: method with a single empty parameter list overrides method without any parameter list
[error] [warn] override def hasNext(): Boolean = requestOffset < part.untilOffset
[error] [warn]
[error] [warn] /home/kou/work/oss/spark-scala-2.13/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/KafkaRDD.scala:294: method with a single empty parameter list overrides method without any parameter list
[error] [warn] override def hasNext(): Boolean = okNext
```
More specifically, what this PR fixes are
* Methods which has an empty parameter list and overrides an method which has no parameter list.
```
override def hasNext(): Boolean = okNext
```
* Methods which has no parameter list and overrides an method which has an empty parameter list.
```
override def next: (Int, Double) = {
```
* Infix operator expression that the operator wraps.
```
3L * math.min(k, numFeatures) * math.min(k, numFeatures)
3L * math.min(k, numFeatures) * math.min(k, numFeatures) +
+ math.max(math.max(k, numFeatures), 4L * math.min(k, numFeatures)
math.max(math.max(k, numFeatures), 4L * math.min(k, numFeatures) *
* math.min(k, numFeatures) + 4L * math.min(k, numFeatures))
```
### Why are the changes needed?
For building Spark with sbt and Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
After this change and #29742 applied, compile passed with the following command.
```
build/sbt -Pscala-2.13 -Phive -Phive-thriftserver -Pyarn -Pkubernetes compile test:compile
```
Closes#29745 from sarutak/fix-code-for-sbt-and-spark-2.13.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR renames `SupportsStreamingUpdate` to `SupportsStreamingUpdateAsAppend` as the new interface name represents the actual behavior clearer. This PR also removes the `update()` method (so the interface is more likely a marker), as the implementations of `SupportsStreamingUpdateAsAppend` should support append mode by default, hence no need to trigger some flag on it.
### Why are the changes needed?
SupportsStreamingUpdate was intended to revive the functionality of Streaming update output mode for internal data sources, but despite the name, that interface isn't really used to do actual update on sink; all sinks are implementing this interface to do append, so strictly saying, it's just to support update as append. Renaming the interface would make it clear.
### Does this PR introduce _any_ user-facing change?
No, as the class is only for internal data sources.
### How was this patch tested?
Jenkins test will follow.
Closes#29693 from HeartSaVioR/SPARK-32831.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
When CSV/JSON datasources infer schema (e.g, `def inferSchema(files: Seq[FileStatus])`, they use the `files` along with the original options. `files` in `inferSchema` could have been deduced from the "path" option if the option was present, so this can cause issues (e.g., reading more data, listing the path again) since the "path" option is **added** to the `files`.
### Why are the changes needed?
The current behavior can cause the following issue:
```scala
class TestFileFilter extends PathFilter {
override def accept(path: Path): Boolean = path.getParent.getName != "p=2"
}
val path = "/tmp"
val df = spark.range(2)
df.write.json(path + "/p=1")
df.write.json(path + "/p=2")
val extraOptions = Map(
"mapred.input.pathFilter.class" -> classOf[TestFileFilter].getName,
"mapreduce.input.pathFilter.class" -> classOf[TestFileFilter].getName
)
// This works fine.
assert(spark.read.options(extraOptions).json(path).count == 2)
// The following with "path" option fails with the following:
// assertion failed: Conflicting directory structures detected. Suspicious paths
// file:/tmp
// file:/tmp/p=1
assert(spark.read.options(extraOptions).format("json").option("path", path).load.count() === 2)
```
### Does this PR introduce _any_ user-facing change?
Yes, the above failure doesn't happen and you get the consistent experience when you use `spark.read.csv(path)` or `spark.read.format("csv").option("path", path).load`.
### How was this patch tested?
Updated existing tests.
Closes#29437 from imback82/path_bug.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This is a follow-up PR of #29192 that adds integration tests for character arrays in `PostgresIntegrationSuite`.
### Why are the changes needed?
For better test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add tests.
Closes#29397 from maropu/SPARK-32576-FOLLOWUP.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR intends to add tests to check if all the character types in PostgreSQL supported.
The document for character types in PostgreSQL: https://www.postgresql.org/docs/current/datatype-character.htmlCloses#29192.
### Why are the changes needed?
For better test coverage.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add tests.
Closes#29394 from maropu/pr29192.
Lead-authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Co-authored-by: kujon <jakub.korzeniowski@vortexa.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to update Kafka client library to 2.6.0 for Apache Spark 3.1.0.
### Why are the changes needed?
This will bring client-side bug fixes like KAFKA-10134 and KAFKA-10223.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the existing tests.
Closes#29386 from dongjoon-hyun/SPARK-32568.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch is a follow-up to fill the gap in #29272 which missed to also provide `default.api.timeout.ms` as well. #29272 unintentionally changed the behavior on Kafka side timeout which is incompatible with the test timeout. (`default.api.timeout.ms` gets default value which is 60 seconds, longer than test timeout.)
### Why are the changes needed?
We realized the PR for SPARK-32468 (#29272) doesn't work as we expect. See https://github.com/apache/spark/pull/29272#issuecomment-668333483 for more details.
### Does this PR introduce _any_ user-facing change?
No, as it only touches the tests.
### How was this patch tested?
Will trigger builds from Jenkins or Github Action multiple time and confirm.
Closes#29343 from HeartSaVioR/SPARK-32468-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
While I'm implementing SPARK-32032 I've found a bug in Kafka: https://issues.apache.org/jira/browse/KAFKA-10318. This will cause issues only later when it's fixed but it would be good to fix it now because SPARK-32032 would like to bring in `AdminClient` where the code blows up with the mentioned `ConfigException`. This would reduce the code changes in the mentioned jira. In this PR I've changed `default.api.timeout.ms` to `request.timeout.ms` which fulfils this condition.
### Why are the changes needed?
Solve later problems and reduce SPARK-32032 PR size.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#29272 from gaborgsomogyi/SPARK-32468.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
Structured Streaming Kafka connector tests are now using a deprecated `poll(long)` API which could cause infinite wait. In this PR I've eliminated these calls and replaced them with `AdminClient`.
### Why are the changes needed?
Deprecated `poll(long)` API calls.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#29289 from gaborgsomogyi/SPARK-32482.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
### What changes were proposed in this pull request?
When `spark.sql.caseSensitive` is `false` (by default), check that there are not duplicate column names on the same level (top level or nested levels) in reading from in-built datasources Parquet, ORC, Avro and JSON. If such duplicate columns exist, throw the exception:
```
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema:
```
### Why are the changes needed?
To make handling of duplicate nested columns is similar to handling of duplicate top-level columns i. e. output the same error when `spark.sql.caseSensitive` is `false`:
```Scala
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema: `camelcase`
```
Checking of top-level duplicates was introduced by https://github.com/apache/spark/pull/17758.
### Does this PR introduce _any_ user-facing change?
Yes. For the example from SPARK-32431:
ORC:
```scala
java.io.IOException: Error reading file: file:/private/var/folders/p3/dfs6mf655d7fnjrsjvldh0tc0000gn/T/spark-c02c2f9a-0cdc-4859-94fc-b9c809ca58b1/part-00001-63e8c3f0-7131-4ec9-be02-30b3fdd276f4-c000.snappy.orc
at org.apache.orc.impl.RecordReaderImpl.nextBatch(RecordReaderImpl.java:1329)
at org.apache.orc.mapreduce.OrcMapreduceRecordReader.ensureBatch(OrcMapreduceRecordReader.java:78)
...
Caused by: java.io.EOFException: Read past end of RLE integer from compressed stream Stream for column 3 kind DATA position: 6 length: 6 range: 0 offset: 12 limit: 12 range 0 = 0 to 6 uncompressed: 3 to 3
at org.apache.orc.impl.RunLengthIntegerReaderV2.readValues(RunLengthIntegerReaderV2.java:61)
at org.apache.orc.impl.RunLengthIntegerReaderV2.next(RunLengthIntegerReaderV2.java:323)
```
JSON:
```scala
+------------+
|StructColumn|
+------------+
| [,,]|
+------------+
```
Parquet:
```scala
+------------+
|StructColumn|
+------------+
| [0,, 1]|
+------------+
```
Avro:
```scala
+------------+
|StructColumn|
+------------+
| [,,]|
+------------+
```
After the changes, Parquet, ORC, JSON and Avro output the same error:
```scala
Found duplicate column(s) in the data schema: `camelcase`;
org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data schema: `camelcase`;
at org.apache.spark.sql.util.SchemaUtils$.checkColumnNameDuplication(SchemaUtils.scala:112)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtils.scala:51)
at org.apache.spark.sql.util.SchemaUtils$.checkSchemaColumnNameDuplication(SchemaUtils.scala:67)
```
### How was this patch tested?
Run modified test suites:
```
$ build/sbt "sql/test:testOnly org.apache.spark.sql.FileBasedDataSourceSuite"
$ build/sbt "avro/test:testOnly org.apache.spark.sql.avro.*"
```
and added new UT to `SchemaUtilsSuite`.
Closes#29234 from MaxGekk/nested-case-insensitive-column.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to support pushed down filters in Avro datasource V1 and V2.
1. Added new SQL config `spark.sql.avro.filterPushdown.enabled` to control filters pushdown to Avro datasource. It is on by default.
2. Renamed `CSVFilters` to `OrderedFilters`.
3. `OrderedFilters` is used in `AvroFileFormat` (DSv1) and in `AvroPartitionReaderFactory` (DSv2)
4. Modified `AvroDeserializer` to return None from the `deserialize` method when pushdown filters return `false`.
### Why are the changes needed?
The changes improve performance on synthetic benchmarks up to **2** times on JDK 11:
```
OpenJDK 64-Bit Server VM 11.0.7+10-post-Ubuntu-2ubuntu218.04 on Linux 4.15.0-1063-aws
Intel(R) Xeon(R) CPU E5-2670 v2 2.50GHz
Filters pushdown: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
w/o filters 9614 9669 54 0.1 9614.1 1.0X
pushdown disabled 10077 10141 66 0.1 10077.2 1.0X
w/ filters 4681 4713 29 0.2 4681.5 2.1X
```
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
- Added UT to `AvroCatalystDataConversionSuite` and `AvroSuite`
- Re-running `AvroReadBenchmark` using Amazon EC2:
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge (spot instance) |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/11 installed by`sudo add-apt-repository ppa:openjdk-r/ppa` & `sudo apt install openjdk-11-jdk`|
and `./dev/run-benchmarks`:
```python
#!/usr/bin/env python3
import os
from sparktestsupport.shellutils import run_cmd
benchmarks = [
['avro/test', 'org.apache.spark.sql.execution.benchmark.AvroReadBenchmark']
]
print('Set SPARK_GENERATE_BENCHMARK_FILES=1')
os.environ['SPARK_GENERATE_BENCHMARK_FILES'] = '1'
for b in benchmarks:
print("Run benchmark: %s" % b[1])
run_cmd(['build/sbt', '%s:runMain %s' % (b[0], b[1])])
```
Closes#29145 from MaxGekk/avro-filters-pushdown.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
`UninterruptibleThread` running functionality is baked into `KafkaOffsetReader` which can be extracted into a class. The main intention is to simplify `KafkaOffsetReader` in order to make easier to solve SPARK-32032. In this PR I've made this extraction without functionality change.
### Why are the changes needed?
`UninterruptibleThread` running functionality is baked into `KafkaOffsetReader`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing + additional unit tests.
Closes#29187 from gaborgsomogyi/SPARK-32387.
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?
Updates to scalatest 3.2.0. Though it looks large, it is 99% changes to the new location of scalatest classes.
### Why are the changes needed?
3.2.0+ has a fix that is required for Scala 2.13.3+ compatibility.
### Does this PR introduce _any_ user-facing change?
No, only affects tests.
### How was this patch tested?
Existing tests.
Closes#29196 from srowen/SPARK-32398.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
See again the related PRs like https://github.com/apache/spark/pull/28971
This completes fixing compilation for 2.13 for all but `repl`, which is a separate task.
### Why are the changes needed?
Eventually, we need to support a Scala 2.13 build, perhaps in Spark 3.1.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests. (2.13 was not tested; this is about getting it to compile without breaking 2.12)
Closes#29147 from srowen/SPARK-29292.4.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to update the docker/spark-test and clean up unused stuff.
### Why are the changes needed?
Since Spark 3.0.0, Java 11 is supported. We had better use the latest Java and OS.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually do the following as described in https://github.com/apache/spark/blob/master/external/docker/spark-test/README.md .
```
docker run -v $SPARK_HOME:/opt/spark spark-test-master
docker run -v $SPARK_HOME:/opt/spark spark-test-worker spark://<master_ip>:7077
```
Closes#29150 from williamhyun/docker.
Authored-by: William Hyun <williamhyun3@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
Same as https://github.com/apache/spark/pull/29078 and https://github.com/apache/spark/pull/28971 . This makes the rest of the default modules (i.e. those you get without specifying `-Pyarn` etc) compile under Scala 2.13. It does not close the JIRA, as a result. this also of course does not demonstrate that tests pass yet in 2.13.
Note, this does not fix the `repl` module; that's separate.
### Why are the changes needed?
Eventually, we need to support a Scala 2.13 build, perhaps in Spark 3.1.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Existing tests. (2.13 was not tested; this is about getting it to compile without breaking 2.12)
Closes#29111 from srowen/SPARK-29292.3.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR will remove references to these "blacklist" and "whitelist" terms besides the blacklisting feature as a whole, which can be handled in a separate JIRA/PR.
This touches quite a few files, but the changes are straightforward (variable/method/etc. name changes) and most quite self-contained.
### Why are the changes needed?
As per discussion on the Spark dev list, it will be beneficial to remove references to problematic language that can alienate potential community members. One such reference is "blacklist" and "whitelist". While it seems to me that there is some valid debate as to whether these terms have racist origins, the cultural connotations are inescapable in today's world.
### Does this PR introduce _any_ user-facing change?
In the test file `HiveQueryFileTest`, a developer has the ability to specify the system property `spark.hive.whitelist` to specify a list of Hive query files that should be tested. This system property has been renamed to `spark.hive.includelist`. The old property has been kept for compatibility, but will log a warning if used. I am open to feedback from others on whether keeping a deprecated property here is unnecessary given that this is just for developers running tests.
### How was this patch tested?
Existing tests should be suitable since no behavior changes are expected as a result of this PR.
Closes#28874 from xkrogen/xkrogen-SPARK-32036-rename-blacklists.
Authored-by: Erik Krogen <ekrogen@linkedin.com>
Signed-off-by: Thomas Graves <tgraves@apache.org>
### What changes were proposed in this pull request?
Continuation of https://github.com/apache/spark/pull/28971 which lets streaming, catalyst and sql compile for 2.13. Same idea.
### Why are the changes needed?
Eventually, we need to support a Scala 2.13 build, perhaps in Spark 3.1.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests. (2.13 was not tested; this is about getting it to compile without breaking 2.12)
Closes#29078 from srowen/SPARK-29292.2.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
This PR aims to drop Python 2.7, 3.4 and 3.5.
Roughly speaking, it removes all the widely known Python 2 compatibility workarounds such as `sys.version` comparison, `__future__`. Also, it removes the Python 2 dedicated codes such as `ArrayConstructor` in Spark.
### Why are the changes needed?
1. Unsupport EOL Python versions
2. Reduce maintenance overhead and remove a bit of legacy codes and hacks for Python 2.
3. PyPy2 has a critical bug that causes a flaky test, SPARK-28358 given my testing and investigation.
4. Users can use Python type hints with Pandas UDFs without thinking about Python version
5. Users can leverage one latest cloudpickle, https://github.com/apache/spark/pull/28950. With Python 3.8+ it can also leverage C pickle.
### Does this PR introduce _any_ user-facing change?
Yes, users cannot use Python 2.7, 3.4 and 3.5 in the upcoming Spark version.
### How was this patch tested?
Manually tested and also tested in Jenkins.
Closes#28957 from HyukjinKwon/SPARK-32138.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
I resolved some of the inconsistencies of AWS env variables. They're fixed in the documentation as well as in the examples. I grep-ed through the repo to try & find any more instances but nothing popped up.
### Why are the changes needed?
As previously mentioned, there is a JIRA request, SPARK-32035, which encapsulates all the issues. But, in summary, the naming of items was inconsistent.
### Does this PR introduce _any_ user-facing change?
Correct names:
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_SESSION_TOKEN
These are the same that AWS uses in their libraries.
However, looking through the Spark documentation and comments, I see that these are not denoted correctly across the board:
docs/cloud-integration.md
106:1. `spark-submit` reads the `AWS_ACCESS_KEY`, `AWS_SECRET_KEY` <-- both different
107:and `AWS_SESSION_TOKEN` environment variables and sets the associated authentication options
docs/streaming-kinesis-integration.md
232:- Set up the environment variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_KEY` with your AWS credentials. <-- secret key different
external/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py
34: $ export AWS_ACCESS_KEY_ID=<your-access-key>
35: $ export AWS_SECRET_KEY=<your-secret-key> <-- different
48: Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
438: val keyId = System.getenv("AWS_ACCESS_KEY_ID")
439: val accessKey = System.getenv("AWS_SECRET_ACCESS_KEY")
448: val sessionToken = System.getenv("AWS_SESSION_TOKEN")
external/kinesis-asl/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCountASL.scala
53: * $ export AWS_ACCESS_KEY_ID=<your-access-key>
54: * $ export AWS_SECRET_KEY=<your-secret-key> <-- different
65: * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
external/kinesis-asl/src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java
59: * $ export AWS_ACCESS_KEY_ID=[your-access-key]
60: * $ export AWS_SECRET_KEY=<your-secret-key> <-- different
71: * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY <-- secret key different
These were all fixed to match names listed under the "correct names" heading.
### How was this patch tested?
I built the documentation using jekyll and verified that the changes were present & accurate.
Closes#29058 from Moovlin/SPARK-32035.
Authored-by: moovlin <richjoerger@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
`MariaDBKrbIntegrationSuite` fails because the docker image contains MariaDB version `1:10.4.12+maria~bionic` but `1:10.4.13+maria~bionic` came out and `mariadb-plugin-gssapi-server` installation triggered unwanted database upgrade inside the docker image. The main problem is that the docker image scripts are prepared to handle `1:10.4.12+maria~bionic` version and not any future development.
### Why are the changes needed?
Failing test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Executed `MariaDBKrbIntegrationSuite` manually.
Closes#29025 from gaborgsomogyi/SPARK-32211.
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?
This is a followup of https://github.com/apache/spark/pull/28760 to fix the remaining issues:
1. should consider data source options when refreshing cache by path at the end of `InsertIntoHadoopFsRelationCommand`
2. should consider data source options when inferring schema for file source
3. should consider data source options when getting the qualified path in file source v2.
### Why are the changes needed?
We didn't catch these issues in https://github.com/apache/spark/pull/28760, because the test case is to check error when initializing the file system. If we initialize the file system multiple times during a simple read/write action, the test case actually only test the first time.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
rewrite the test to make sure the entire data source read/write action can succeed.
Closes#28948 from cloud-fan/fix.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Gengliang Wang <gengliang.wang@databricks.com>
### What changes were proposed in this pull request?
`OracleIntegrationSuite` is not using the latest oracle JDBC driver. In this PR I've upgraded the driver to the latest which supports JDK8, JDK9, and JDK11.
### Why are the changes needed?
Old JDBC driver.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Existing integration tests (especially `OracleIntegrationSuite`)
Closes#28893 from gaborgsomogyi/SPARK-32049.
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?
Spark uses an old and deprecated API named `KafkaConsumer.poll(long)` which never returns and stays in live lock if metadata is not updated (for instance when broker disappears at consumer creation). Please see [Kafka documentation](https://kafka.apache.org/25/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-long-) and [standalone test application](https://github.com/gaborgsomogyi/kafka-get-assignment) for further details.
In this PR I've applied the new `KafkaConsumer.poll(Duration)` API on executor side. Please note driver side still uses the old API which will be fixed in SPARK-32032.
### Why are the changes needed?
Infinite wait in `KafkaConsumer.poll(long)`.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#28871 from gaborgsomogyi/SPARK-32033.
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?
When loading DataFrames from JDBC datasource with Kerberos authentication, remote executors (yarn-client/cluster etc. modes) fail to establish a connection due to lack of Kerberos ticket or ability to generate it.
This is a real issue when trying to ingest data from kerberized data sources (SQL Server, Oracle) in enterprise environment where exposing simple authentication access is not an option due to IT policy issues.
In this PR I've added MS SQL support.
What this PR contains:
* Added `MSSQLConnectionProvider`
* Added `MSSQLConnectionProviderSuite`
* Changed MS SQL JDBC driver to use the latest (test scope only)
* Changed `MsSqlServerIntegrationSuite` docker image to use the latest
* Added a version comment to `MariaDBConnectionProvider` to increase trackability
### Why are the changes needed?
Missing JDBC kerberos support.
### Does this PR introduce _any_ user-facing change?
Yes, now user is able to connect to MS SQL using kerberos.
### How was this patch tested?
* Additional + existing unit tests
* Existing integration tests
* Test on cluster manually
Closes#28635 from gaborgsomogyi/SPARK-31337.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
### What changes were proposed in this pull request?
1. Add the following parquet files to the resource folder `external/avro/src/test/resources`:
- Files saved by Spark 2.4.5 (cee4ecbb16) without meta info `org.apache.spark.version`
- `before_1582_date_v2_4_5.avro` with a date column: `avro.schema {"type":"record","name":"topLevelRecord","fields":[{"name":"dt","type":[{"type":"int","logicalType":"date"},"null"]}]}`
- `before_1582_timestamp_millis_v2_4_5.avro` with a timestamp column: `avro.schema {"type":"record","name":"test","namespace":"logical","fields":[{"name":"dt","type":["null",{"type":"long","logicalType":"timestamp-millis"}],"default":null}]}`
- `before_1582_timestamp_micros_v2_4_5.avro` with a timestamp column: `avro.schema {"type":"record","name":"topLevelRecord","fields":[{"name":"dt","type":[{"type":"long","logicalType":"timestamp-micros"},"null"]}]}`
- Files saved by Spark 2.4.6-rc3 (570848da7c) with the meta info `org.apache.spark.version 2.4.6`:
- `before_1582_date_v2_4_6.avro` is similar to `before_1582_date_v2_4_5.avro` except Spark version in parquet meta info.
- `before_1582_timestamp_micros_v2_4_6.avro` is similar to `before_1582_timestamp_micros_v2_4_5.avro` except meta info.
- `before_1582_timestamp_millis_v2_4_6.avro` is similar to `before_1582_timestamp_millis_v2_4_5.avro` except meta info.
2. Removed a few avro files becaused they are replaced by Avro files generated by Spark 2.4.5 above.
3. Add new test "generate test files for checking compatibility with Spark 2.4" to `AvroSuite` (marked as ignored). The parquet files above were generated by this test.
4. Modified "SPARK-31159: compatibility with Spark 2.4 in reading dates/timestamps" in `AvroSuite` to use new parquet files.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By `AvroV1Suite` and `AvroV2Suite`.
Closes#28664 from MaxGekk/avro-update-resource-files.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR adds a private `WriteBuilder` mixin trait: `SupportsStreamingUpdate`, so that the builtin v2 streaming sinks can still support the update mode.
Note: it's private because we don't have a proper design yet. I didn't take the proposal in https://github.com/apache/spark/pull/23702#discussion_r258593059 because we may want something more general, like updating by an expression `key1 = key2 + 10`.
### Why are the changes needed?
In Spark 2.4, all builtin v2 streaming sinks support all streaming output modes, and v2 sinks are enabled by default, see https://issues.apache.org/jira/browse/SPARK-22911
It's too risky for 3.0 to go back to v1 sinks, so I propose to add a private trait to fix builtin v2 sinks, to keep backward compatibility.
### Does this PR introduce _any_ user-facing change?
Yes, now all the builtin v2 streaming sinks support all streaming output modes, which is the same as 2.4
### How was this patch tested?
existing tests.
Closes#28523 from cloud-fan/update.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR is kind of a follow up of SPARK-31631. In some cases, it only attempts once for ~35 seconds. Seems 10 seconds are not enough to try multiple times - took a quick look and seems difficult to manipulate the socket configuration as well.
It simply proposes to increase the time limit for now. It affects master and branch-3.0.
```
sbt.ForkMain$ForkError: org.scalatest.exceptions.TestFailedDueToTimeoutException: The code passed to eventually never returned normally. Attempted 1 times over 34.294744142999996 seconds. Last failure message: Address already in use.
at org.scalatest.concurrent.Eventually.tryTryAgain$1(Eventually.scala:432)
at org.scalatest.concurrent.Eventually.eventually(Eventually.scala:439)
at org.scalatest.concurrent.Eventually.eventually$(Eventually.scala:391)
at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:479)
at org.scalatest.concurrent.Eventually.eventually(Eventually.scala:308)
at org.scalatest.concurrent.Eventually.eventually$(Eventually.scala:307)
at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:479)
at org.apache.spark.deploy.security.HadoopDelegationTokenManagerSuite.$anonfun$new$4(HadoopDelegationTokenManagerSuite.scala:106)
at org.scalatest.OutcomeOf.outcomeOf(OutcomeOf.scala:85)
at org.scalatest.OutcomeOf.outcomeOf$(OutcomeOf.scala:83)
at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
at org.scalatest.Transformer.apply(Transformer.scala:22)
at org.scalatest.Transformer.apply(Transformer.scala:20)
at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:186)
at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:157)
at org.scalatest.FunSuiteLike.invokeWithFixture$1(FunSuiteLike.scala:184)
at org.scalatest.FunSuiteLike.$anonfun$runTest$1(FunSuiteLike.scala:196)
at org.scalatest.SuperEngine.runTestImpl(Engine.scala:286)
at org.scalatest.FunSuiteLike.runTest(FunSuiteLike.scala:196)
at org.scalatest.FunSuiteLike.runTest$(FunSuiteLike.scala:178)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterEach$$super$runTest(SparkFunSuite.scala:59)
at org.scalatest.BeforeAndAfterEach.runTest(BeforeAndAfterEach.scala:221)
at org.scalatest.BeforeAndAfterEach.runTest$(BeforeAndAfterEach.scala:214)
at org.apache.spark.SparkFunSuite.runTest(SparkFunSuite.scala:59)
at org.scalatest.FunSuiteLike.$anonfun$runTests$1(FunSuiteLike.scala:229)
at org.scalatest.SuperEngine.$anonfun$runTestsInBranch$1(Engine.scala:393)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:381)
at org.scalatest.SuperEngine.runTestsInBranch(Engine.scala:376)
at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:458)
at org.scalatest.FunSuiteLike.runTests(FunSuiteLike.scala:229)
at org.scalatest.FunSuiteLike.runTests$(FunSuiteLike.scala:228)
at org.scalatest.FunSuite.runTests(FunSuite.scala:1560)
at org.scalatest.Suite.run(Suite.scala:1124)
at org.scalatest.Suite.run$(Suite.scala:1106)
at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1560)
at org.scalatest.FunSuiteLike.$anonfun$run$1(FunSuiteLike.scala:233)
at org.scalatest.SuperEngine.runImpl(Engine.scala:518)
at org.scalatest.FunSuiteLike.run(FunSuiteLike.scala:233)
at org.scalatest.FunSuiteLike.run$(FunSuiteLike.scala:232)
at org.apache.spark.SparkFunSuite.org$scalatest$BeforeAndAfterAll$$super$run(SparkFunSuite.scala:59)
at org.scalatest.BeforeAndAfterAll.liftedTree1$1(BeforeAndAfterAll.scala:213)
at org.scalatest.BeforeAndAfterAll.run(BeforeAndAfterAll.scala:210)
at org.scalatest.BeforeAndAfterAll.run$(BeforeAndAfterAll.scala:208)
at org.apache.spark.SparkFunSuite.run(SparkFunSuite.scala:59)
at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:317)
at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:510)
at sbt.ForkMain$Run$2.call(ForkMain.java:296)
at sbt.ForkMain$Run$2.call(ForkMain.java:286)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: sbt.ForkMain$ForkError: java.net.BindException: Address already in use
at sun.nio.ch.Net.bind0(Native Method)
at sun.nio.ch.Net.bind(Net.java:433)
at sun.nio.ch.Net.bind(Net.java:425)
at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:223)
at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:198)
at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:51)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor.registerHandles(AbstractPollingIoAcceptor.java:547)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor.access$400(AbstractPollingIoAcceptor.java:68)
at org.apache.mina.core.polling.AbstractPollingIoAcceptor$Acceptor.run(AbstractPollingIoAcceptor.java:422)
at org.apache.mina.util.NamePreservingRunnable.run(NamePreservingRunnable.java:64)
... 3 more
```
### Why are the changes needed?
To fix flakiness in the tests.
### Does this PR introduce _any_ user-facing change?
No, dev-only.
### How was this patch tested?
Jenkins will test it out.
Closes#28562 from HyukjinKwon/SPARK-31742.
Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
It's quite annoying to be blocked by flaky tests in several PRs. This PR disables them. The tests come from 3 PRs I'm recently watching:
https://github.com/apache/spark/pull/28526https://github.com/apache/spark/pull/28463https://github.com/apache/spark/pull/28517
### Why are the changes needed?
To make PR builder more stable
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
N/A
Closes#28547 from cloud-fan/test.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
### What changes were proposed in this pull request?
When reading/writing datetime values that before the rebase switch day, from/to Avro/Parquet files, fail by default and ask users to set a config to explicitly do rebase or not.
### Why are the changes needed?
Rebase or not rebase have different behaviors and we should let users decide it explicitly. In most cases, users won't hit this exception as it only affects ancient datetime values.
### Does this PR introduce _any_ user-facing change?
Yes, now users will see an error when reading/writing dates before 1582-10-15 or timestamps before 1900-01-01 from/to Parquet/Avro files, with an error message to ask setting a config.
### How was this patch tested?
updated tests
Closes#28477 from cloud-fan/rebase.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
The `Kafka*Suite`s are flaky because of the Hadoop MiniKdc issue - https://issues.apache.org/jira/browse/HADOOP-12656
> Looking at MiniKdc implementation, if port is 0, the constructor use ServerSocket to find an unused port, assign the port number to the member variable port and close the ServerSocket object; later, in initKDCServer(), instantiate a TcpTransport object and bind at that port.
> It appears that the port may be used in between, and then throw the exception.
Related test failures are suspected, such as https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/122225/testReport/org.apache.spark.sql.kafka010/KafkaDelegationTokenSuite/_It_is_not_a_test_it_is_a_sbt_testing_SuiteSelector_/
```scala
[info] org.apache.spark.sql.kafka010.KafkaDelegationTokenSuite *** ABORTED *** (15 seconds, 426 milliseconds)
[info] java.net.BindException: Address already in use
[info] at sun.nio.ch.Net.bind0(Native Method)
[info] at sun.nio.ch.Net.bind(Net.java:433)
[info] at sun.nio.ch.Net.bind(Net.java:425)
[info] at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:223)
[info] at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
[info] at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:198)
[info] at org.apache.mina.transport.socket.nio.NioSocketAcceptor.open(NioSocketAcceptor.java:51)
[info] at org.apache.mina.core.polling.AbstractPollingIoAcceptor.registerHandles(AbstractPollingIoAcceptor.java:547)
[info] at org.apache.mina.core.polling.AbstractPollingIoAcceptor.access$400(AbstractPollingIoAcceptor.java:68)
[info] at org.apache.mina.core.polling.AbstractPollingIoAcceptor$Acceptor.run(AbstractPollingIoAcceptor.java:422)
[info] at org.apache.mina.util.NamePreservingRunnable.run(NamePreservingRunnable.java:64)
[info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
[info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
[info] at java.lang.Thread.run(Thread.java:748)
```
After comparing the error stack trace with similar issues reported in different projects, such as
https://issues.apache.org/jira/browse/KAFKA-3453https://issues.apache.org/jira/browse/HBASE-14734
We can be sure that they are caused by the same problem issued in HADOOP-12656.
In the PR, We apply the approach from HBASE first before we finally drop Hadoop 2.7.x
### Why are the changes needed?
fix test flakiness
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
the test itself passing Jenkins
Closes#28442 from yaooqinn/SPARK-31631.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This is a followup PR discussed [here](https://github.com/apache/spark/pull/28215#discussion_r410748547).
### Why are the changes needed?
It would be good to re-enable `DB2IntegrationSuite` and upgrade the docker image inside to use the latest.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing docker integration tests.
Closes#28325 from gaborgsomogyi/SPARK-31533.
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?
When loading DataFrames from JDBC datasource with Kerberos authentication, remote executors (yarn-client/cluster etc. modes) fail to establish a connection due to lack of Kerberos ticket or ability to generate it.
This is a real issue when trying to ingest data from kerberized data sources (SQL Server, Oracle) in enterprise environment where exposing simple authentication access is not an option due to IT policy issues.
In this PR I've added DB2 support (other supported databases will come in later PRs).
What this PR contains:
* Added `DB2ConnectionProvider`
* Added `DB2ConnectionProviderSuite`
* Added `DB2KrbIntegrationSuite` docker integration test
* Changed DB2 JDBC driver to use the latest (test scope only)
* Changed test table data type to a type which is supported by all the databases
* Removed double connection creation on test side
* Increased connection timeout in docker tests because DB2 docker takes quite a time to start
### Why are the changes needed?
Missing JDBC kerberos support.
### Does this PR introduce any user-facing change?
Yes, now user is able to connect to DB2 using kerberos.
### How was this patch tested?
* Additional + existing unit tests
* Additional + existing integration tests
* Test on cluster manually
Closes#28215 from gaborgsomogyi/SPARK-31272.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@apache.org>
