## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
The updates to the regression metrics were based on (and updated with new changes based on comments):
https://issues.apache.org/jira/browse/SPARK-11520
("RegressionMetrics should support instance weights")
but the pull request was closed as the changes were never checked in.
## How was this patch tested?
I added tests to the metrics class.
Closes#17085 from imatiach-msft/ilmat/regression-evaluate.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Follow up style fixes to PIC in R; see #23072
## How was this patch tested?
Existing tests.
Closes#23292 from srowen/SPARK-19827.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Follow up pr for #23207, include following changes:
- Rename `SQLShuffleMetricsReporter` to `SQLShuffleReadMetricsReporter` to make it match with write side naming.
- Display text changes for read side for naming consistent.
- Rename function in `ShuffleWriteProcessor`.
- Delete `private[spark]` in execution package.
## How was this patch tested?
Existing tests.
Closes#23286 from xuanyuanking/SPARK-26193-follow.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This proposes an alternative way to load secret keys into a Spark application that is running on Kubernetes. Instead of automatically generating the secret, the secret key can reside in a file that is shared between both the driver and executor containers.
Unit tests.
Closes#23252 from mccheah/auth-secret-with-file.
Authored-by: mcheah <mcheah@palantir.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
If `checkForContinuous` is called ( `checkForStreaming` is called in `checkForContinuous` ), the `checkForStreaming` mothod will be called twice in `createQuery` , this is not necessary, and the `checkForStreaming` method has a lot of statements, so it's better to remove one of them.
## How was this patch tested?
Existing unit tests in `StreamingQueryManagerSuite` and `ContinuousAggregationSuite`
Closes#23251 from 10110346/isUnsupportedOperationCheckEnabled.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
In `BytesToBytesMap.MapIterator.advanceToNextPage`, We will first lock this `MapIterator` and then `TaskMemoryManager` when going to free a memory page by calling `freePage`. At the same time, it is possibly that another memory consumer first locks `TaskMemoryManager` and then this `MapIterator` when it acquires memory and causes spilling on this `MapIterator`.
So it ends with the `MapIterator` object holds lock to the `MapIterator` object and waits for lock on `TaskMemoryManager`, and the other consumer holds lock to `TaskMemoryManager` and waits for lock on the `MapIterator` object.
To avoid deadlock here, this patch proposes to keep reference to the page to free and free it after releasing the lock of `MapIterator`.
## How was this patch tested?
Added test and manually test by running the test 100 times to make sure there is no deadlock.
Closes#23272 from viirya/SPARK-26265.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to return partial results from JSON datasource and JSON functions in the PERMISSIVE mode if some of JSON fields are parsed and converted to desired types successfully. The changes are made only for `StructType`. Whole bad JSON records are placed into the corrupt column specified by the `columnNameOfCorruptRecord` option or SQL config.
Partial results are not returned for malformed JSON input.
## How was this patch tested?
Added new UT which checks converting JSON strings with one invalid and one valid field at the end of the string.
Closes#23253 from MaxGekk/json-bad-record.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a regression introduced by https://github.com/apache/spark/pull/22104 at Spark 2.4.0.
When we have Python UDF in subquery, we will hit an exception
```
Caused by: java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.AttributeReference cannot be cast to org.apache.spark.sql.catalyst.expressions.PythonUDF
at scala.collection.immutable.Stream.map(Stream.scala:414)
at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:98)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:815)
...
```
https://github.com/apache/spark/pull/22104 turned `ExtractPythonUDFs` from a physical rule to optimizer rule. However, there is a difference between a physical rule and optimizer rule. A physical rule always runs once, an optimizer rule may be applied twice on a query tree even the rule is located in a batch that only runs once.
For a subquery, the `OptimizeSubqueries` rule will execute the entire optimizer on the query plan inside subquery. Later on subquery will be turned to joins, and the optimizer rules will be applied to it again.
Unfortunately, the `ExtractPythonUDFs` rule is not idempotent. When it's applied twice on a query plan inside subquery, it will produce a malformed plan. It extracts Python UDF from Python exec plans.
This PR proposes 2 changes to be double safe:
1. `ExtractPythonUDFs` should skip python exec plans, to make the rule idempotent
2. `ExtractPythonUDFs` should skip subquery
## How was this patch tested?
a new test.
Closes#23248 from cloud-fan/python.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`RDDConversions` would get disproportionately slower as the number of columns in the query increased,
for the type of `converters` before is `scala.collection.immutable.::` which is a subtype of list.
This PR removing `RDDConversions` and using `RowEncoder` to convert the Row to InternalRow.
The test of `PrunedScanSuite` for 2000 columns and 20k rows takes 409 seconds before this PR, and 361 seconds after.
## How was this patch tested?
Test case of `PrunedScanSuite`
Closes#23262 from eatoncys/toarray.
Authored-by: 10129659 <chen.yanshan@zte.com.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
SBT 0.13.14 ~ 1.1.1 has a bug on accessing `java.util.Base64.getDecoder` with JDK9+. It's fixed at 1.1.2 and backported to [0.13.18 (released on Nov 28th)](https://github.com/sbt/sbt/releases/tag/v0.13.18). This PR aims to update SBT.
## How was this patch tested?
Pass the Jenkins with the building and existing tests.
Closes#23270 from dongjoon-hyun/SPARK-26317.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
This adds the entire memory used by spark’s executor (as measured by procfs) to the executor metrics. The memory usage is collected from the entire process tree under the executor. The metrics are subdivided into memory used by java, by python, and by other processes, to aid users in diagnosing the source of high memory usage.
The additional metrics are sent to the driver in heartbeats, using the mechanism introduced by SPARK-23429. This also slightly extends that approach to allow one ExecutorMetricType to collect multiple metrics.
Added unit tests and also tested on a live cluster.
Closes#22612 from rezasafi/ptreememory2.
Authored-by: Reza Safi <rezasafi@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
`1. The shuffle dependency specifies no aggregation or output ordering.`
If the shuffle dependency specifies aggregation, but it only aggregates at the reduce-side, serialized shuffle can still be used.
`3. The shuffle produces fewer than 16777216 output partitions.`
If the number of output partitions is 16777216 , we can use serialized shuffle.
We can see this mothod: `canUseSerializedShuffle`
## How was this patch tested?
N/A
Closes#23228 from 10110346/SerializedShuffle_doc.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Add MAXIMUM_PAGE_SIZE_BYTES Exception test
## How was this patch tested?
Existing tests
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23226 from wangjiaochun/BytesToBytesMapSuite.
Authored-by: 10087686 <wang.jiaochun@zte.com.cn>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Add appendReadColumns Unit Test for HiveShimSuite.
## How was this patch tested?
```
$ build/sbt
> project hive
> testOnly *HiveShimSuite
```
Closes#23268 from sadhen/refactor/hiveshim.
Authored-by: Darcy Shen <sadhen@zoho.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This is a Spark 2.3 regression introduced in https://github.com/apache/spark/pull/20521. We should add the partition info for InsertIntoHiveTable in CreateHiveTableAsSelectCommand. Otherwise, we will hit the following error by running the newly added test case:
```
[info] - CTAS: INSERT a partitioned table using Hive serde *** FAILED *** (829 milliseconds)
[info] org.apache.spark.SparkException: Requested partitioning does not match the tab1 table:
[info] Requested partitions:
[info] Table partitions: part
[info] at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.processInsert(InsertIntoHiveTable.scala:179)
[info] at org.apache.spark.sql.hive.execution.InsertIntoHiveTable.run(InsertIntoHiveTable.scala:107)
```
## How was this patch tested?
Added a test case.
Closes#23255 from gatorsmile/fixCTAS.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
If there are no records in memory, then we don't need to create an empty temp spill file.
## How was this patch tested?
Existing tests
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23225 from wangjiaochun/ShufflSorter.
Authored-by: 10087686 <wang.jiaochun@zte.com.cn>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Root cause: Prior to Spark2.4, When we enable zst for eventLog compression, for inprogress application, It always throws exception in the Application UI, when we open from the history server. But after 2.4 it will display the UI information based on the completed frames in the zstd compressed eventLog. But doesn't read incomplete frames for inprogress application.
In this PR, we have added 'setContinous(true)' for reading input stream from eventLog, so that it can read from open frames also. (By default 'isContinous=false' for zstd inputStream and when we try to read an open frame, it throws truncated error)
## How was this patch tested?
Test steps:
1) Add the configurations in the spark-defaults.conf
(i) spark.eventLog.compress true
(ii) spark.io.compression.codec zstd
2) Restart history server
3) bin/spark-shell
4) sc.parallelize(1 to 1000, 1000).count
5) Open app UI from the history server UI
**Before fix**
**After fix:**
Closes#23241 from shahidki31/zstdEventLog.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
1. Implement `SQLShuffleWriteMetricsReporter` on the SQL side as the customized `ShuffleWriteMetricsReporter`.
2. Add shuffle write metrics to `ShuffleExchangeExec`, and use these metrics to create corresponding `SQLShuffleWriteMetricsReporter` in shuffle dependency.
3. Rework on `ShuffleMapTask` to add new class named `ShuffleWriteProcessor` which control shuffle write process, we use sql shuffle write metrics by customizing a ShuffleWriteProcessor on SQL side.
## How was this patch tested?
Add UT in SQLMetricsSuite.
Manually test locally, update screen shot to document attached in JIRA.
Closes#23207 from xuanyuanking/SPARK-26193.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
#22148 introduces a behavior change. According to discussion at #22184, this PR updates migration guide when upgrade from Spark 2.3 to 2.4.
## How was this patch tested?
N/A
Closes#23238 from seancxmao/SPARK-25132-doc-2.4.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
A followup of https://github.com/apache/spark/pull/23043
There are 4 places we need to deal with NaN and -0.0:
1. comparison expressions. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
2. Join keys. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
3. grouping keys. `-0.0` and `0.0` should be assigned to the same group. Different NaNs should be assigned to the same group.
4. window partition keys. `-0.0` and `0.0` should be treated as same. Different NaNs should be treated as same.
The case 1 is OK. Our comparison already handles NaN and -0.0, and for struct/array/map, we will recursively compare the fields/elements.
Case 2, 3 and 4 are problematic, as they compare `UnsafeRow` binary directly, and different NaNs have different binary representation, and the same thing happens for -0.0 and 0.0.
To fix it, a simple solution is: normalize float/double when building unsafe data (`UnsafeRow`, `UnsafeArrayData`, `UnsafeMapData`). Then we don't need to worry about it anymore.
Following this direction, this PR moves the handling of NaN and -0.0 from `Platform` to `UnsafeWriter`, so that places like `UnsafeRow.setFloat` will not handle them, which reduces the perf overhead. It's also easier to add comments explaining why we do it in `UnsafeWriter`.
## How was this patch tested?
existing tests
Closes#23239 from cloud-fan/minor.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
follow-up PR for SPARK-24207 to fix code style problems
Closes#23256 from huaxingao/spark-24207-cnt.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Update to Scala 2.12.8
## How was this patch tested?
Existing tests.
Closes#23218 from srowen/SPARK-26266.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
spark.kafka.sasl.kerberos.service.name is an optional parameter but most of the time value `kafka` has to be set. As I've written in the jira the following reasoning is behind:
* Kafka's configuration guide suggest the same value: https://kafka.apache.org/documentation/#security_sasl_kerberos_brokerconfig
* It would be easier for spark users by providing less configuration
* Other streaming engines are doing the same
In this PR I've changed the parameter from optional to `WithDefault` and set `kafka` as default value.
## How was this patch tested?
Available unit tests + on cluster.
Closes#23254 from gaborgsomogyi/SPARK-26304.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Parquet files appear to have nullability info when being written, not being read.
## How was this patch tested?
Some test code: (running spark 2.3, but the relevant code in DataSource looks identical on master)
case class NullTest(bo: Boolean, opbol: Option[Boolean])
val testDf = spark.createDataFrame(Seq(NullTest(true, Some(false))))
defined class NullTest
testDf: org.apache.spark.sql.DataFrame = [bo: boolean, opbol: boolean]
testDf.write.parquet("s3://asana-stats/tmp_dima/parquet_check_schema")
spark.read.parquet("s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet4").printSchema()
root
|-- bo: boolean (nullable = true)
|-- opbol: boolean (nullable = true)
Meanwhile, the parquet file formed does have nullable info:
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 ls s3://asana-stats/tmp_dima/parquet_check_schema/
2018-10-17 21:03:52 0 _SUCCESS
2018-10-17 21:03:50 504 part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ aws s3 cp s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet .
download: s3://asana-stats/tmp_dima/parquet_check_schema/part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet to ./part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
[]batchprod-report000:/tmp/dimakamalov-batch$ java -jar parquet-tools-1.8.2.jar schema part-00000-b1bf4a19-d9fe-4ece-a2b4-9bbceb490857-c000.snappy.parquet
message spark_schema {
required boolean bo;
optional boolean opbol;
}
Closes#22759 from dima-asana/dima-asana-nullable-parquet-doc.
Authored-by: dima-asana <42555784+dima-asana@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Delete unnecessary If statement, because it Impossible execution when
records less than or equal to zero.it is only execution when records begin zero.
...................
if (inMemSorter == null || inMemSorter.numRecords() <= 0) {
return 0L;
}
....................
if (inMemSorter.numRecords() > 0) {
.....................
}
## How was this patch tested?
Existing tests
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23247 from wangjiaochun/inMemSorter.
Authored-by: 10087686 <wang.jiaochun@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Adds a new method to SparkAppHandle called getError which returns
the exception (if present) that caused the underlying Spark app to
fail.
New tests added to SparkLauncherSuite for the new method.
Closes#21849Closes#23221 from vanzin/SPARK-24243.
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
This is a follow-up of #23031 to rename the config name to `spark.sql.legacy.setCommandRejectsSparkCoreConfs`.
## How was this patch tested?
Existing tests.
Closes#23245 from ueshin/issues/SPARK-26060/rename_config.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This PR aims to upgrade Janino compiler to the latest version 3.0.11. The followings are the changes from the [release note](http://janino-compiler.github.io/janino/changelog.html).
- Script with many "helper" variables.
- Java 9+ compatibility
- Compilation Error Messages Generated by JDK.
- Added experimental support for the "StackMapFrame" attribute; not active yet.
- Make Unparser more flexible.
- Fixed NPEs in various "toString()" methods.
- Optimize static method invocation with rvalue target expression.
- Added all missing "ClassFile.getConstant*Info()" methods, removing the necessity for many type casts.
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23250 from dongjoon-hyun/SPARK-26298.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Currently if user provides data schema, partition column values are converted as per it. But if the conversion failed, e.g. converting string to int, the column value is null.
This PR proposes to throw exception in such case, instead of converting into null value silently:
1. These null partition column values doesn't make sense to users in most cases. It is better to show the conversion failure, and then users can adjust the schema or ETL jobs to fix it.
2. There are always exceptions on such conversion failure for non-partition data columns. Partition columns should have the same behavior.
We can reproduce the case above as following:
```
/tmp/testDir
├── p=bar
└── p=foo
```
If we run:
```
val schema = StructType(Seq(StructField("p", IntegerType, false)))
spark.read.schema(schema).csv("/tmp/testDir/").show()
```
We will get:
```
+----+
| p|
+----+
|null|
|null|
+----+
```
## How was this patch tested?
Unit test
Closes#23215 from gengliangwang/SPARK-26263.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
`enablePerfMetrics `was originally designed in `BytesToBytesMap `to control `getNumHashCollisions getTimeSpentResizingNs getAverageProbesPerLookup`.
However, as the Spark version gradual progress. this parameter is only used for `getAverageProbesPerLookup ` and always given to true when using `BytesToBytesMap`.
it is also dangerous to determine whether `getAverageProbesPerLookup `opens and throws an `IllegalStateException `exception.
So this pr will be remove `enablePerfMetrics `parameter from `BytesToBytesMap`. thanks.
## How was this patch tested?
the existed test cases.
Closes#23244 from heary-cao/enablePerfMetrics.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This change modifies the logic in the SecurityManager to do two
things:
- generate unique app secrets also when k8s is being used
- only store the secret in the user's UGI on YARN
The latter is needed so that k8s won't unnecessarily create
k8s secrets for the UGI credentials when only the auth token
is stored there.
On the k8s side, the secret is propagated to executors using
an environment variable instead. This ensures it works in both
client and cluster mode.
Security doc was updated to mention the feature and clarify that
proper access control in k8s should be enabled for it to be secure.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#23174 from vanzin/SPARK-26194.
## What changes were proposed in this pull request?
Kafka delegation token support implemented in [PR#22598](https://github.com/apache/spark/pull/22598) but that didn't contain documentation because of rapid changes. Because it has been merged in this PR I've documented it.
## How was this patch tested?
jekyll build + manual html check
Closes#23195 from gaborgsomogyi/SPARK-26236.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When executing `toPandas` with Arrow enabled, partitions that arrive in the JVM out-of-order must be buffered before they can be send to Python. This causes an excess of memory to be used in the driver JVM and increases the time it takes to complete because data must sit in the JVM waiting for preceding partitions to come in.
This change sends un-ordered partitions to Python as soon as they arrive in the JVM, followed by a list of partition indices so that Python can assemble the data in the correct order. This way, data is not buffered at the JVM and there is no waiting on particular partitions so performance will be increased.
Followup to #21546
## How was this patch tested?
Added new test with a large number of batches per partition, and test that forces a small delay in the first partition. These test that partitions are collected out-of-order and then are are put in the correct order in Python.
## Performance Tests - toPandas
Tests run on a 4 node standalone cluster with 32 cores total, 14.04.1-Ubuntu and OpenJDK 8
measured wall clock time to execute `toPandas()` and took the average best time of 5 runs/5 loops each.
Test code
```python
df = spark.range(1 << 25, numPartitions=32).toDF("id").withColumn("x1", rand()).withColumn("x2", rand()).withColumn("x3", rand()).withColumn("x4", rand())
for i in range(5):
start = time.time()
_ = df.toPandas()
elapsed = time.time() - start
```
Spark config
```
spark.driver.memory 5g
spark.executor.memory 5g
spark.driver.maxResultSize 2g
spark.sql.execution.arrow.enabled true
```
Current Master w/ Arrow stream | This PR
---------------------|------------
5.16207 | 4.342533
5.133671 | 4.399408
5.147513 | 4.468471
5.105243 | 4.36524
5.018685 | 4.373791
Avg Master | Avg This PR
------------------|--------------
5.1134364 | 4.3898886
Speedup of **1.164821449**
Closes#22275 from BryanCutler/arrow-toPandas-oo-batches-SPARK-25274.
Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
Looks this test is flaky
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99704/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99569/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99644/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99548/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99454/consolehttps://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/99609/console
```
======================================================================
FAIL: test_training_and_prediction (pyspark.mllib.tests.test_streaming_algorithms.StreamingLogisticRegressionWithSGDTests)
Test that the model improves on toy data with no. of batches
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 367, in test_training_and_prediction
self._eventually(condition)
File "/home/jenkins/workspace/SparkPullRequestBuilder/python/pyspark/mllib/tests/test_streaming_algorithms.py", line 78, in _eventually
% (timeout, lastValue))
AssertionError: Test failed due to timeout after 30 sec, with last condition returning: Latest errors: 0.67, 0.71, 0.78, 0.7, 0.75, 0.74, 0.73, 0.69, 0.62, 0.71, 0.69, 0.75, 0.72, 0.77, 0.71, 0.74
----------------------------------------------------------------------
Ran 13 tests in 185.051s
FAILED (failures=1, skipped=1)
```
This looks happening after increasing the parallelism in Jenkins to speed up at https://github.com/apache/spark/pull/23111. I am able to reproduce this manually when the resource usage is heavy (with manual decrease of timeout).
## How was this patch tested?
Manually tested by
```
cd python
./run-tests --testnames 'pyspark.mllib.tests.test_streaming_algorithms StreamingLogisticRegressionWithSGDTests.test_training_and_prediction' --python-executables=python
```
Closes#23236 from HyukjinKwon/SPARK-26275.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Updated SQL migration guide according to changes in https://github.com/apache/spark/pull/23120Closes#23235 from MaxGekk/failuresafe-partial-result-followup.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
this code come from PR: https://github.com/apache/spark/pull/11190,
but this code has never been used, only since PR: https://github.com/apache/spark/pull/14548,
Let's continue fix it. thanks.
## How was this patch tested?
N / A
Closes#23227 from heary-cao/unuseSparkPlan.
Authored-by: caoxuewen <cao.xuewen@zte.com.cn>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This fixes doc of renamed OneHotEncoder in PySpark.
## How was this patch tested?
N/A
Closes#23230 from viirya/remove_one_hot_encoder_followup.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This PR proposes add a developer option, `--testnames`, to our testing script to allow run specific set of unittests and doctests.
**1. Run unittests in the class**
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests']
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests (14s)
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests (14s) ... 22 tests were skipped
Tests passed in 14 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_disabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_enabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped
...
```
**2. Run single unittest in the class.**
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion']
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (0s) ... 1 tests were skipped
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (8s)
Tests passed in 8 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion with pypy:
test_null_conversion (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
**3. Run doctests in single PySpark module.**
```bash
./run-tests --testnames pyspark.sql.dataframe
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.dataframe']
Starting test(pypy): pyspark.sql.dataframe
Starting test(python2.7): pyspark.sql.dataframe
Finished test(python2.7): pyspark.sql.dataframe (47s)
Finished test(pypy): pyspark.sql.dataframe (48s)
Tests passed in 48 seconds
```
Of course, you can mix them:
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests,pyspark.sql.dataframe'
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python2.7', 'pypy']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests', 'pyspark.sql.dataframe']
Starting test(pypy): pyspark.sql.dataframe
Starting test(pypy): pyspark.sql.tests.test_arrow ArrowTests
Starting test(python2.7): pyspark.sql.dataframe
Starting test(python2.7): pyspark.sql.tests.test_arrow ArrowTests
Finished test(pypy): pyspark.sql.tests.test_arrow ArrowTests (0s) ... 22 tests were skipped
Finished test(python2.7): pyspark.sql.tests.test_arrow ArrowTests (18s)
Finished test(python2.7): pyspark.sql.dataframe (50s)
Finished test(pypy): pyspark.sql.dataframe (52s)
Tests passed in 52 seconds
Skipped tests in pyspark.sql.tests.test_arrow ArrowTests with pypy:
test_createDataFrame_column_name_encoding (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_does_not_modify_input (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
test_createDataFrame_fallback_disabled (pyspark.sql.tests.test_arrow.ArrowTests) ... skipped 'Pandas >= 0.19.2 must be installed; however, it was not found.'
```
and also you can use all other options (except `--modules`, which will be ignored)
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion' --python-executables=python
```
```
Running PySpark tests. Output is in /.../spark/python/unit-tests.log
Will test against the following Python executables: ['python']
Will test the following Python tests: ['pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion']
Starting test(python): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion
Finished test(python): pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion (12s)
Tests passed in 12 seconds
```
See help below:
```bash
./run-tests --help
```
```
Usage: run-tests [options]
Options:
...
Developer Options:
--testnames=TESTNAMES
A comma-separated list of specific modules, classes
and functions of doctest or unittest to test. For
example, 'pyspark.sql.foo' to run the module as
unittests or doctests, 'pyspark.sql.tests FooTests' to
run the specific class of unittests,
'pyspark.sql.tests FooTests.test_foo' to run the
specific unittest in the class. '--modules' option is
ignored if they are given.
```
I intentionally grouped it as a developer option to be more conservative.
## How was this patch tested?
Manually tested. Negative tests were also done.
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowTests.test_null_conversion1' --python-executables=python
```
```
...
AttributeError: type object 'ArrowTests' has no attribute 'test_null_conversion1'
...
```
```bash
./run-tests --testnames 'pyspark.sql.tests.test_arrow ArrowT' --python-executables=python
```
```
...
AttributeError: 'module' object has no attribute 'ArrowT'
...
```
```bash
./run-tests --testnames 'pyspark.sql.tests.test_ar' --python-executables=python
```
```
...
/.../python2.7: No module named pyspark.sql.tests.test_ar
```
Closes#23203 from HyukjinKwon/SPARK-26252.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
The PR starts from the [comment](https://github.com/apache/spark/pull/23124#discussion_r236112390) in the main one and it aims at:
- simplifying the code for `MapConcat`;
- be more precise in checking the limit size.
## How was this patch tested?
existing tests
Closes#23217 from mgaido91/SPARK-25829_followup.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
We explicitly avoid files with hdfs erasure coding for the streaming WAL
and for event logs, as hdfs EC does not support all relevant apis.
However, the new builder api used has different semantics -- it does not
create parent dirs, and it does not resolve relative paths. This
updates createNonEcFile to have similar semantics to the old api.
## How was this patch tested?
Ran tests with the WAL pointed at a non-existent dir, which failed before this change. Manually tested the new function with a relative path as well.
Unit tests via jenkins.
Closes#23092 from squito/SPARK-26094.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
When we encode a Decimal from external source we don't check for overflow. That method is useful not only in order to enforce that we can represent the correct value in the specified range, but it also changes the underlying data to the right precision/scale. Since in our code generation we assume that a decimal has exactly the same precision and scale of its data type, missing to enforce it can lead to corrupted output/results when there are subsequent transformations.
## How was this patch tested?
added UT
Closes#23210 from mgaido91/SPARK-26233.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
