Fixes https://issues.apache.org/jira/browse/SPARK-23823
Keep origin for all the methods using transformExpression
## What changes were proposed in this pull request?
Keep origin in transformExpression
## How was this patch tested?
Manually tested that this fixes https://issues.apache.org/jira/browse/SPARK-23823 and columns have correct origins after Analyzer.analyze
Author: JiahuiJiang <jjiang@palantir.com>
Author: Jiahui Jiang <jjiang@palantir.com>
Closes#20961 from JiahuiJiang/jj/keep-origin.
## What changes were proposed in this pull request?
`handleInvalid` Param was forwarded to the VectorAssembler used by RFormula.
## How was this patch tested?
added a test and ran all tests for RFormula and VectorAssembler
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Closes#20970 from yogeshg/spark_23562.
## What changes were proposed in this pull request?
This PR allows us to use one of several types of `MemoryBlock`, such as byte array, int array, long array, or `java.nio.DirectByteBuffer`. To use `java.nio.DirectByteBuffer` allows to have off heap memory which is automatically deallocated by JVM. `MemoryBlock` class has primitive accessors like `Platform.getInt()`, `Platform.putint()`, or `Platform.copyMemory()`.
This PR uses `MemoryBlock` for `OffHeapColumnVector`, `UTF8String`, and other places. This PR can improve performance of operations involving memory accesses (e.g. `UTF8String.trim`) by 1.8x.
For now, this PR does not use `MemoryBlock` for `BufferHolder` based on cloud-fan's [suggestion](https://github.com/apache/spark/pull/11494#issuecomment-309694290).
Since this PR is a successor of #11494, close#11494. Many codes were ported from #11494. Many efforts were put here. **I think this PR should credit to yzotov.**
This PR can achieve **1.1-1.4x performance improvements** for operations in `UTF8String` or `Murmur3_x86_32`. Other operations are almost comparable performances.
Without this PR
```
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Hash byte arrays with length 268435487: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Murmur3_x86_32 526 / 536 0.0 131399881.5 1.0X
UTF8String benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hashCode 525 / 552 1022.6 1.0 1.0X
substring 414 / 423 1298.0 0.8 1.3X
```
With this PR
```
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Hash byte arrays with length 268435487: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Murmur3_x86_32 474 / 488 0.0 118552232.0 1.0X
UTF8String benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hashCode 476 / 480 1127.3 0.9 1.0X
substring 287 / 291 1869.9 0.5 1.7X
```
Benchmark program
```
test("benchmark Murmur3_x86_32") {
val length = 8192 * 32768 + 31
val seed = 42L
val iters = 1 << 2
val random = new Random(seed)
val arrays = Array.fill[MemoryBlock](numArrays) {
val bytes = new Array[Byte](length)
random.nextBytes(bytes)
new ByteArrayMemoryBlock(bytes, Platform.BYTE_ARRAY_OFFSET, length)
}
val benchmark = new Benchmark("Hash byte arrays with length " + length,
iters * numArrays, minNumIters = 20)
benchmark.addCase("HiveHasher") { _: Int =>
var sum = 0L
for (_ <- 0L until iters) {
sum += HiveHasher.hashUnsafeBytesBlock(
arrays(i), Platform.BYTE_ARRAY_OFFSET, length)
}
}
benchmark.run()
}
test("benchmark UTF8String") {
val N = 512 * 1024 * 1024
val iters = 2
val benchmark = new Benchmark("UTF8String benchmark", N, minNumIters = 20)
val str0 = new java.io.StringWriter() { { for (i <- 0 until N) { write(" ") } } }.toString
val s0 = UTF8String.fromString(str0)
benchmark.addCase("hashCode") { _: Int =>
var h: Int = 0
for (_ <- 0L until iters) { h += s0.hashCode }
}
benchmark.addCase("substring") { _: Int =>
var s: UTF8String = null
for (_ <- 0L until iters) { s = s0.substring(N / 2 - 5, N / 2 + 5) }
}
benchmark.run()
}
```
I run [this benchmark program](https://gist.github.com/kiszk/94f75b506c93a663bbbc372ffe8f05de) using [the commit](ee5a79861c). I got the following results:
```
OpenJDK 64-Bit Server VM 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12 on Linux 4.4.0-66-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Memory access benchmarks: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ByteArrayMemoryBlock get/putInt() 220 / 221 609.3 1.6 1.0X
Platform get/putInt(byte[]) 220 / 236 610.9 1.6 1.0X
Platform get/putInt(Object) 492 / 494 272.8 3.7 0.4X
OnHeapMemoryBlock get/putLong() 322 / 323 416.5 2.4 0.7X
long[] 221 / 221 608.0 1.6 1.0X
Platform get/putLong(long[]) 321 / 321 418.7 2.4 0.7X
Platform get/putLong(Object) 561 / 563 239.2 4.2 0.4X
```
I also run [this benchmark program](https://gist.github.com/kiszk/5fdb4e03733a5d110421177e289d1fb5) for comparing performance of `Platform.copyMemory()`.
```
OpenJDK 64-Bit Server VM 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12 on Linux 4.4.0-66-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Platform copyMemory: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Object to Object 1961 / 1967 8.6 116.9 1.0X
System.arraycopy Object to Object 1917 / 1921 8.8 114.3 1.0X
byte array to byte array 1961 / 1968 8.6 116.9 1.0X
System.arraycopy byte array to byte array 1909 / 1937 8.8 113.8 1.0X
int array to int array 1921 / 1990 8.7 114.5 1.0X
double array to double array 1918 / 1923 8.7 114.3 1.0X
Object to byte array 1961 / 1967 8.6 116.9 1.0X
Object to short array 1965 / 1972 8.5 117.1 1.0X
Object to int array 1910 / 1915 8.8 113.9 1.0X
Object to float array 1971 / 1978 8.5 117.5 1.0X
Object to double array 1919 / 1944 8.7 114.4 1.0X
byte array to Object 1959 / 1967 8.6 116.8 1.0X
int array to Object 1961 / 1970 8.6 116.9 1.0X
double array to Object 1917 / 1924 8.8 114.3 1.0X
```
These results show three facts:
1. According to the second/third or sixth/seventh results in the first experiment, if we use `Platform.get/putInt(Object)`, we achieve more than 2x worse performance than `Platform.get/putInt(byte[])` with concrete type (i.e. `byte[]`).
2. According to the second/third or fourth/fifth/sixth results in the first experiment, the fastest way to access an array element on Java heap is `array[]`. **Cons of `array[]` is that it is not possible to support unaligned-8byte access.**
3. According to the first/second/third or fourth/sixth/seventh results in the first experiment, `getInt()/putInt() or getLong()/putLong()` in subclasses of `MemoryBlock` can achieve comparable performance to `Platform.get/putInt()` or `Platform.get/putLong()` with concrete type (second or sixth result). There is no overhead regarding virtual call.
4. According to results in the second experiment, for `Platform.copy()`, to pass `Object` can achieve the same performance as to pass any type of primitive array as source or destination.
5. According to second/fourth results in the second experiment, `Platform.copy()` can achieve the same performance as `System.arrayCopy`. **It would be good to use `Platform.copy()` since `Platform.copy()` can take any types for src and dst.**
We are incrementally replace `Platform.get/putXXX` with `MemoryBlock.get/putXXX`. This is because we have two advantages.
1) Achieve better performance due to having a concrete type for an array.
2) Use simple OO design instead of passing `Object`
It is easy to use `MemoryBlock` in `InternalRow`, `BufferHolder`, `TaskMemoryManager`, and others that are already abstracted. It is not easy to use `MemoryBlock` in utility classes related to hashing or others.
Other candidates are
- UnsafeRow, UnsafeArrayData, UnsafeMapData, SpecificUnsafeRowJoiner
- UTF8StringBuffer
- BufferHolder
- TaskMemoryManager
- OnHeapColumnVector
- BytesToBytesMap
- CachedBatch
- classes for hash
- others.
## How was this patch tested?
Added `UnsafeMemoryAllocator`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19222 from kiszk/SPARK-10399.
## What changes were proposed in this pull request?
Add interpreted execution for `InitializeJavaBean` expression.
## How was this patch tested?
Added unit test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20985 from viirya/SPARK-23593-2.
## What changes were proposed in this pull request?
This pr added interpreted execution for `StaticInvoke`.
## How was this patch tested?
Added tests in `ObjectExpressionsSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20753 from kiszk/SPARK-23582.
## What changes were proposed in this pull request?
Add interpreted execution for `InitializeJavaBean` expression.
## How was this patch tested?
Added unit test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20756 from viirya/SPARK-23593.
## What changes were proposed in this pull request?
`YarnAllocator` uses `numExecutorsRunning` to track the number of running executor. `numExecutorsRunning` is used to check if there're executors missing and need to allocate more.
In current code, `numExecutorsRunning` can be negative when driver asks to kill a same idle executor multiple times.
## How was this patch tested?
UT added
Author: jinxing <jinxing6042@126.com>
Closes#20781 from jinxing64/SPARK-23637.
## What changes were proposed in this pull request?
Pass through the `imagePullSecrets` option to the k8s pod in order to allow user to access private image registries.
See https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
## How was this patch tested?
Unit tests + manual testing.
Manual testing procedure:
1. Have private image registry.
2. Spark-submit application with no `spark.kubernetes.imagePullSecret` set. Do `kubectl describe pod ...`. See the error message:
```
Error syncing pod, skipping: failed to "StartContainer" for "spark-kubernetes-driver" with ErrImagePull: "rpc error: code = 2 desc = Error: Status 400 trying to pull repository ...: \"{\\n \\\"errors\\\" : [ {\\n \\\"status\\\" : 400,\\n \\\"message\\\" : \\\"Unsupported docker v1 repository request for '...'\\\"\\n } ]\\n}\""
```
3. Create secret `kubectl create secret docker-registry ...`
4. Spark-submit with `spark.kubernetes.imagePullSecret` set to the new secret. See that deployment was successful.
Author: Andrew Korzhuev <andrew.korzhuev@klarna.com>
Author: Andrew Korzhuev <korzhuev@andrusha.me>
Closes#20811 from andrusha/spark-23668-image-pull-secrets.
## What changes were proposed in this pull request?
This pr added interpreted execution for `Invoke`.
## How was this patch tested?
Added tests in `ObjectExpressionsSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20797 from kiszk/SPARK-28583.
## What changes were proposed in this pull request?
In TestHive, the base spark session does this in getOrCreate(), we emulate that behavior for tests.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20969 from gatorsmile/setDefault.
## What changes were proposed in this pull request?
Add cast to nulls introduced by PropagateEmptyRelation so in cases they're part of coalesce they will not break its type checking rules
## How was this patch tested?
Added unit test
Author: Robert Kruszewski <robertk@palantir.com>
Closes#20914 from robert3005/rk/propagate-empty-fix.
## What changes were proposed in this pull request?
Currently, the active spark session is set inconsistently (e.g., in createDataFrame, prior to query execution). Many places in spark also incorrectly query active session when they should be calling activeSession.getOrElse(defaultSession) and so might get None even if a Spark session exists.
The semantics here can be cleaned up if we also set the active session when the default session is set.
Related: https://github.com/apache/spark/pull/20926/files
## How was this patch tested?
Unit test, existing test. Note that if https://github.com/apache/spark/pull/20926 merges first we should also update the tests there.
Author: Eric Liang <ekl@databricks.com>
Closes#20927 from ericl/active-session-cleanup.
## What changes were proposed in this pull request?
Add interpreted execution for `MapObjects` expression.
## How was this patch tested?
Added unit test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20771 from viirya/SPARK-23587.
## What changes were proposed in this pull request?
Migrate foreach sink to DataSourceV2.
Since the previous attempt at this PR #20552, we've changed and strictly defined the lifecycle of writer components. This means we no longer need the complicated lifecycle shim from that PR; it just naturally works.
## How was this patch tested?
existing tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#20951 from jose-torres/foreach.
## What changes were proposed in this pull request?
Address https://github.com/apache/spark/pull/20924#discussion_r177987175, show block manager id when remove RDD/Broadcast fails.
## How was this patch tested?
N/A
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20960 from jiangxb1987/bmid.
## What changes were proposed in this pull request?
Easy fix in the markdown.
## How was this patch tested?
jekyII build test manually.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: lemonjing <932191671@qq.com>
Closes#20897 from Lemonjing/master.
These tests can fail with a timeout if the remote repos are not responding,
or slow. The tests don't need anything from those repos, so use an empty
ivy config file to avoid setting up the defaults.
The tests are passing reliably for me locally now, and failing more often
than not today without this change since http://dl.bintray.com/spark-packages/maven
doesn't seem to be loading from my machine.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20916 from vanzin/SPARK-19964.
## What changes were proposed in this pull request?
Introduce `handleInvalid` parameter in `VectorAssembler` that can take in `"keep", "skip", "error"` options. "error" throws an error on seeing a row containing a `null`, "skip" filters out all such rows, and "keep" adds relevant number of NaN. "keep" figures out an example to find out what this number of NaN s should be added and throws an error when no such number could be found.
## How was this patch tested?
Unit tests are added to check the behavior of `assemble` on specific rows and the transformer is called on `DataFrame`s of different configurations to test different corner cases.
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Author: Bago Amirbekian <bago@databricks.com>
Author: Yogesh Garg <1059168+yogeshg@users.noreply.github.com>
Closes#20829 from yogeshg/rformula_handleinvalid.
It was possible that the disconnect() was called on the handle before the
server had received the handshake messages, so no connection was yet
attached to the handle. The fix waits until we're sure the handle has been
mapped to a client connection.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20950 from vanzin/SPARK-23834.
## What changes were proposed in this pull request?
This PR implemented the following cleanups related to `UnsafeWriter` class:
- Remove code duplication between `UnsafeRowWriter` and `UnsafeArrayWriter`
- Make `BufferHolder` class internal by delegating its accessor methods to `UnsafeWriter`
- Replace `UnsafeRow.setTotalSize(...)` with `UnsafeRowWriter.setTotalSize()`
## How was this patch tested?
Tested by existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20850 from kiszk/SPARK-23713.
## What changes were proposed in this pull request?
As mentioned in SPARK-23285, this PR introduces a new configuration property `spark.kubernetes.executor.cores` for specifying the physical CPU cores requested for each executor pod. This is to avoid changing the semantics of `spark.executor.cores` and `spark.task.cpus` and their role in task scheduling, task parallelism, dynamic resource allocation, etc. The new configuration property only determines the physical CPU cores available to an executor. An executor can still run multiple tasks simultaneously by using appropriate values for `spark.executor.cores` and `spark.task.cpus`.
## How was this patch tested?
Unit tests.
felixcheung srowen jiangxb1987 jerryshao mccheah foxish
Author: Yinan Li <ynli@google.com>
Author: Yinan Li <liyinan926@gmail.com>
Closes#20553 from liyinan926/master.
## What changes were proposed in this pull request?
Kubernetes driver and executor pods should request `memory + memoryOverhead` as their resources instead of just `memory`, see https://issues.apache.org/jira/browse/SPARK-23825
## How was this patch tested?
Existing unit tests were adapted.
Author: David Vogelbacher <dvogelbacher@palantir.com>
Closes#20943 from dvogelbacher/spark-23825.
## What changes were proposed in this pull request?
Adding test for default params for `CountVectorizerModel` constructed from vocabulary. This required that the param `maxDF` be added, which was done in SPARK-23615.
## How was this patch tested?
Added an explicit test for CountVectorizerModel in DefaultValuesTests.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20942 from BryanCutler/pyspark-CountVectorizerModel-default-param-test-SPARK-15009.
## What changes were proposed in this pull request?
Address https://github.com/apache/spark/pull/20449#discussion_r172414393, If `resultIter` is already a `InterruptibleIterator`, don't double wrap it.
## How was this patch tested?
Existing tests.
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20920 from jiangxb1987/SPARK-23040.
## What changes were proposed in this pull request?
Currently, the requiredChildDistribution does not specify the partitions. This can cause the weird corner cases where the child's distribution is `SinglePartition` which satisfies the required distribution of `ClusterDistribution(no-num-partition-requirement)`, thus eliminating the shuffle needed to repartition input data into the required number of partitions (i.e. same as state stores). That can lead to "file not found" errors on the state store delta files as the micro-batch-with-no-shuffle will not run certain tasks and therefore not generate the expected state store delta files.
This PR adds the required constraint on the number of partitions.
## How was this patch tested?
Modified test harness to always check that ANY stateful operator should have a constraint on the number of partitions. As part of that, the existing opt-in checks on child output partitioning were removed, as they are redundant.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20941 from tdas/SPARK-23827.
## What changes were proposed in this pull request?
It may be get `spark.shuffle.service.port` from 9745ec3a61/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala (L459)
Therefore, the client configuration `spark.shuffle.service.port` does not working unless the configuration is `spark.hadoop.spark.shuffle.service.port`.
- This configuration is not working:
```
bin/spark-sql --master yarn --conf spark.shuffle.service.port=7338
```
- This configuration works:
```
bin/spark-sql --master yarn --conf spark.hadoop.spark.shuffle.service.port=7338
```
This PR fix this issue.
## How was this patch tested?
It's difficult to carry out unit testing. But I've tested it manually.
Author: Yuming Wang <yumwang@ebay.com>
Closes#20785 from wangyum/SPARK-23640.
## What changes were proposed in this pull request?
This PR is to improve the test coverage of the original PR https://github.com/apache/spark/pull/20687
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20911 from gatorsmile/addTests.
## What changes were proposed in this pull request?
Roll forward c68ec4e (#20688).
There are two minor test changes required:
* An error which used to be TreeNodeException[ArithmeticException] is no longer wrapped and is now just ArithmeticException.
* The test framework simply does not set the active Spark session. (Or rather, it doesn't do so early enough - I think it only happens when a query is analyzed.) I've added the required logic to SQLTestUtils.
## How was this patch tested?
existing tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Author: jerryshao <sshao@hortonworks.com>
Closes#20922 from jose-torres/ratefix.
## What changes were proposed in this pull request?
This PR supports for pushing down filters for DateType in parquet
## How was this patch tested?
Added UT and tested in local.
Author: yucai <yyu1@ebay.com>
Closes#20851 from yucai/SPARK-23727.
## What changes were proposed in this pull request?
isSharedClass returns if some classes can/should be shared or not. It checks if the classes names have some keywords or start with some names. Following the logic, it can occur unintended behaviors when a custom package has `slf4j` inside the package or class name. As I guess, the first intention seems to figure out the class containing `org.slf4j`. It would be better to change the comparison logic to `name.startsWith("org.slf4j")`
## How was this patch tested?
This patch should pass all of the current tests and keep all of the current behaviors. In my case, I'm using ProtobufDeserializer to get a table schema from hive tables. Thus some Protobuf packages and names have `slf4j` inside. Without this patch, it cannot be resolved because of ClassCastException from different classloaders.
Author: Jongyoul Lee <jongyoul@gmail.com>
Closes#20860 from jongyoul/SPARK-23743.
## What changes were proposed in this pull request?
Set default Spark session in the TestSparkSession and TestHiveSparkSession constructors.
## How was this patch tested?
new unit tests
Author: Jose Torres <torres.joseph.f+github@gmail.com>
Closes#20926 from jose-torres/test3.
## What changes were proposed in this pull request?
In SparkSQLCLI, SessionState generates before SparkContext instantiating. When we use --proxy-user to impersonate, it's unable to initializing a metastore client to talk to the secured metastore for no kerberos ticket.
This PR use real user ugi to obtain token for owner before talking to kerberized metastore.
## How was this patch tested?
Manually verified with kerberized hive metasotre / hdfs.
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#20784 from yaooqinn/SPARK-23639.
## What changes were proposed in this pull request?
Changed `LauncherBackend` `set` method so that it checks if the connection is open or not before writing to it (uses `isConnected`).
## How was this patch tested?
None
Author: Sahil Takiar <stakiar@cloudera.com>
Closes#20893 from sahilTakiar/master.
… with dynamic allocation
## What changes were proposed in this pull request?
ignore errors when you are waiting for a broadcast.unpersist. This is handling it the same way as doing rdd.unpersist in https://issues.apache.org/jira/browse/SPARK-22618
## How was this patch tested?
Patch was tested manually against a couple jobs that exhibit this behavior, with the change the application no longer dies due to this and just prints the warning.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Thomas Graves <tgraves@unharmedunarmed.corp.ne1.yahoo.com>
Closes#20924 from tgravescs/SPARK-23806.
## What changes were proposed in this pull request?
This PR proposes to add lineSep option for a configurable line separator in text datasource.
It supports this option by using `LineRecordReader`'s functionality with passing it to the constructor.
The approach is similar with https://github.com/apache/spark/pull/20727; however, one main difference is, it uses text datasource's `lineSep` option to parse line by line in JSON's schema inference.
## How was this patch tested?
Manually tested and unit tests were added.
Author: hyukjinkwon <gurwls223@apache.org>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20877 from HyukjinKwon/linesep-json.
## What changes were proposed in this pull request?
When using Arrow for createDataFrame or toPandas and an error is encountered with fallback disabled, this will raise the same type of error instead of a RuntimeError. This change also allows for the traceback of the error to be retained and prevents the accidental chaining of exceptions with Python 3.
## How was this patch tested?
Updated existing tests to verify error type.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20839 from BryanCutler/arrow-raise-same-error-SPARK-23699.
## What changes were proposed in this pull request?
This PR migrate micro batch rate source to V2 API and rewrite UTs to suite V2 test.
## How was this patch tested?
UTs.
Author: jerryshao <sshao@hortonworks.com>
Closes#20688 from jerryshao/SPARK-23096.
## What changes were proposed in this pull request?
The UUID() expression is stateful and should implement the `Stateful` trait instead of the `Nondeterministic` trait.
## How was this patch tested?
Added test.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20912 from viirya/SPARK-23794.
## What changes were proposed in this pull request?
Adding r2adj in LinearRegressionSummary for Python API.
## How was this patch tested?
Added unit tests to exercise the api calls for the summary classes in tests.py.
Author: Kevin Yu <qyu@us.ibm.com>
Closes#20842 from kevinyu98/spark-23162.
This change basically rewrites the security documentation so that it's
up to date with new features, more correct, and more complete.
Because security is such an important feature, I chose to move all the
relevant configuration documentation to the security page, instead of
having them peppered all over the place in the configuration page. This
allows an almost one-stop shop for security configuration in Spark. The
only exceptions are some YARN-specific minor features which I left in
the YARN page.
I also re-organized the page's topics, since they didn't make a lot of
sense. You had kerberos features described inside paragraphs talking
about UI access control, and other oddities. It should be easier now
to find information about specific Spark security features. I also
enabled TOCs for both the Security and YARN pages, since that makes it
easier to see what is covered.
I removed most of the comments from the SecurityManager javadoc since
they just replicated information in the security doc, with different
levels of out-of-dateness.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20742 from vanzin/SPARK-23572.
This particular test assumed that Hadoop libraries did not support
http as a file system. Hadoop 2.9 does, so the test failed. The test
now forces a non-existent implementation for the http fs, which
forces the expected error.
There were also a couple of other issues in the same test: SparkSubmit
arguments in the wrong order, and the wrong check later when asserting,
which was being masked by the previous issues.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20895 from vanzin/SPARK-23787.
## What changes were proposed in this pull request?
Add documentation about the limitations of `pandas_udf` with keyword arguments and related concepts, like `functools.partial` fn objects.
NOTE: intermediate commits on this PR show some of the steps that can be taken to fix some (but not all) of these pain points.
### Survey of problems we face today:
(Initialize) Note: python 3.6 and spark 2.4snapshot.
```
from pyspark.sql import SparkSession
import inspect, functools
from pyspark.sql.functions import pandas_udf, PandasUDFType, col, lit, udf
spark = SparkSession.builder.getOrCreate()
print(spark.version)
df = spark.range(1,6).withColumn('b', col('id') * 2)
def ok(a,b): return a+b
```
Using a keyword argument at the call site `b=...` (and yes, *full* stack trace below, haha):
```
---> 14 df.withColumn('ok', pandas_udf(f=ok, returnType='bigint')('id', b='id')).show() # no kwargs
TypeError: wrapper() got an unexpected keyword argument 'b'
```
Using partial with a keyword argument where the kw-arg is the first argument of the fn:
*(Aside: kind of interesting that lines 15,16 work great and then 17 explodes)*
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-e9f31b8799c1> in <module>()
15 df.withColumn('ok', pandas_udf(f=functools.partial(ok, 7), returnType='bigint')('id')).show()
16 df.withColumn('ok', pandas_udf(f=functools.partial(ok, b=7), returnType='bigint')('id')).show()
---> 17 df.withColumn('ok', pandas_udf(f=functools.partial(ok, a=7), returnType='bigint')('id')).show()
/Users/stu/ZZ/spark/python/pyspark/sql/functions.py in pandas_udf(f, returnType, functionType)
2378 return functools.partial(_create_udf, returnType=return_type, evalType=eval_type)
2379 else:
-> 2380 return _create_udf(f=f, returnType=return_type, evalType=eval_type)
2381
2382
/Users/stu/ZZ/spark/python/pyspark/sql/udf.py in _create_udf(f, returnType, evalType)
54 argspec.varargs is None:
55 raise ValueError(
---> 56 "Invalid function: 0-arg pandas_udfs are not supported. "
57 "Instead, create a 1-arg pandas_udf and ignore the arg in your function."
58 )
ValueError: Invalid function: 0-arg pandas_udfs are not supported. Instead, create a 1-arg pandas_udf and ignore the arg in your function.
```
Author: Michael (Stu) Stewart <mstewart141@gmail.com>
Closes#20900 from mstewart141/udfkw2.
## What changes were proposed in this pull request?
This cleans up unused imports, mainly from pyspark.sql module. Added a note in function.py that imports `UserDefinedFunction` only to maintain backwards compatibility for using `from pyspark.sql.function import UserDefinedFunction`.
## How was this patch tested?
Existing tests and built docs.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20892 from BryanCutler/pyspark-cleanup-imports-SPARK-23700.
## What changes were proposed in this pull request?
This PR fixes an incorrect comparison in SQL between timestamp and date. This is because both of them are casted to `string` and then are compared lexicographically. This implementation shows `false` regarding this query `spark.sql("select cast('2017-03-01 00:00:00' as timestamp) between cast('2017-02-28' as date) and cast('2017-03-01' as date)").show`.
This PR shows `true` for this query by casting `date("2017-03-01")` to `timestamp("2017-03-01 00:00:00")`.
(Please fill in changes proposed in this fix)
## How was this patch tested?
Added new UTs to `TypeCoercionSuite`.
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#20774 from kiszk/SPARK-23549.