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…d Java.
## What changes were proposed in this pull request?
Implemented **`isInCollection `** in DataFrame API for both Scala and Java, so users can do
```scala
val profileDF = Seq(
Some(1), Some(2), Some(3), Some(4),
Some(5), Some(6), Some(7), None
).toDF("profileID")
val validUsers: Seq[Any] = Seq(6, 7.toShort, 8L, "3")
val result = profileDF.withColumn("isValid", $"profileID". isInCollection(validUsers))
result.show(10)
"""
+---------+-------+
|profileID|isValid|
+---------+-------+
| 1| false|
| 2| false|
| 3| true|
| 4| false|
| 5| false|
| 6| true|
| 7| true|
| null| null|
+---------+-------+
""".stripMargin
```
## How was this patch tested?
Several unit tests are added.
Author: DB Tsai <[email protected]>
Closes apache#21416 from dbtsai/optimize-set.
## What changes were proposed in this pull request? Add Data Source write benchmark. So that it would be easier to measure the writer performance. Author: Gengliang Wang <[email protected]> Closes apache#21409 from gengliangwang/parquetWriteBenchmark.
|
@attilapiros I opened this just to give me a place to put comments ... though I don't have any yet. Most of the files that you touch seem like cosmetic changes, so maybe this isn't so bad. I need to understand it better, though. I'd like to understand why StreamResponse has a String streamId while ChunkFetchSuccess has a (long, int) streamChunkId ... if it weren't for that, seems we could simplify it further. |
…ntries to SparkR
## What changes were proposed in this pull request?
The PR adds functions `arrays_overlap`, `array_repeat`, `map_entries` to SparkR.
## How was this patch tested?
Tests added into R/pkg/tests/fulltests/test_sparkSQL.R
## Examples
### arrays_overlap
```
df <- createDataFrame(list(list(list(1L, 2L), list(3L, 1L)),
list(list(1L, 2L), list(3L, 4L)),
list(list(1L, NA), list(3L, 4L))))
collect(select(df, arrays_overlap(df[[1]], df[[2]])))
```
```
arrays_overlap(_1, _2)
1 TRUE
2 FALSE
3 NA
```
### array_repeat
```
df <- createDataFrame(list(list("a", 3L), list("b", 2L)))
collect(select(df, array_repeat(df[[1]], df[[2]])))
```
```
array_repeat(_1, _2)
1 a, a, a
2 b, b
```
```
collect(select(df, array_repeat(df[[1]], 2L)))
```
```
array_repeat(_1, 2)
1 a, a
2 b, b
```
### map_entries
```
df <- createDataFrame(list(list(map = as.environment(list(x = 1, y = 2)))))
collect(select(df, map_entries(df$map)))
```
```
map_entries(map)
1 x, 1, y, 2
```
Author: Marek Novotny <[email protected]>
Closes apache#21434 from mn-mikke/SPARK-24331.
## What changes were proposed in this pull request? Make sure that `StopIteration`s raised in users' code do not silently interrupt processing by spark, but are raised as exceptions to the users. The users' functions are wrapped in `safe_iter` (in `shuffle.py`), which re-raises `StopIteration`s as `RuntimeError`s ## How was this patch tested? Unit tests, making sure that the exceptions are indeed raised. I am not sure how to check whether a `Py4JJavaError` contains my exception, so I simply looked for the exception message in the java exception's `toString`. Can you propose a better way? ## License This is my original work, licensed in the same way as spark Author: e-dorigatti <[email protected]> Author: edorigatti <[email protected]> Closes apache#21383 from e-dorigatti/fix_spark_23754.
## What changes were proposed in this pull request? Upgrade SBT to 0.13.17 with Scala 2.10.7 for JDK9+ ## How was this patch tested? Existing tests Author: DB Tsai <[email protected]> Closes apache#21458 from dbtsai/sbt.
squito
commented
May 30, 2018
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I'd advise you against making cosmetic changes like this in your upstream change. You are changing enough parts of the really core part of spark that will already make reviewers a little skeptical, its better to keep the diff small.
Refactoring and cleanup is good, of course, I'd just keep this change small. If you feel strongly about it, you can open up a separate change for that. (though to be honest, some of these don't really seem worth bothering with IMHO.)
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It is made to avoid using hardcoded values in a very different place in TransportFrameDecoder. Where I have Message.Type object before decoding the type but the static member can be accessed.
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there is a pretty simple mapping between StreamChunkId used by ChunkFetchSuccess and the String id used by StreamResponse -- I think if you just used that you could eliminate a lot of these changes
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Before the request it is decided whether the use stream or chunk fetch. See https://github.com/apache/spark/blob/master/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java#L75
and
https://github.com/apache/spark/blob/master/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java#L93
If we just Decode StreamResponse instead of ChunkFetchSuccess it wont work out of the box.
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I think you could actually decode a StreamResponse here with the stream Id conversion ... that might make it a lot simpler.
…s having the same argument set
## What changes were proposed in this pull request?
This pr fixed an issue when having multiple distinct aggregations having the same argument set, e.g.,
```
scala>: paste
val df = sql(
s"""SELECT corr(DISTINCT x, y), corr(DISTINCT y, x), count(*)
| FROM (VALUES (1, 1), (2, 2), (2, 2)) t(x, y)
""".stripMargin)
java.lang.RuntimeException
You hit a query analyzer bug. Please report your query to Spark user mailing list.
```
The root cause is that `RewriteDistinctAggregates` can't detect multiple distinct aggregations if they have the same argument set. This pr modified code so that `RewriteDistinctAggregates` could count the number of aggregate expressions with `isDistinct=true`.
## How was this patch tested?
Added tests in `DataFrameAggregateSuite`.
Author: Takeshi Yamamuro <[email protected]>
Closes apache#21443 from maropu/SPARK-24369.
…onRunner in submit with client mode in spark-submit
## What changes were proposed in this pull request?
In client side before context initialization specifically, .py file doesn't work in client side before context initialization when the application is a Python file. See below:
```
$ cat /home/spark/tmp.py
def testtest():
return 1
```
This works:
```
$ cat app.py
import pyspark
pyspark.sql.SparkSession.builder.getOrCreate()
import tmp
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
...
************************1
```
but this doesn't:
```
$ cat app.py
import pyspark
import tmp
pyspark.sql.SparkSession.builder.getOrCreate()
print("************************%s" % tmp.testtest())
$ ./bin/spark-submit --master yarn --deploy-mode client --py-files /home/spark/tmp.py app.py
Traceback (most recent call last):
File "/home/spark/spark/app.py", line 2, in <module>
import tmp
ImportError: No module named tmp
```
### How did it happen?
In client mode specifically, the paths are being added into PythonRunner as are:
https://github.com/apache/spark/blob/628c7b517969c4a7ccb26ea67ab3dd61266073ca/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala#L430
https://github.com/apache/spark/blob/628c7b517969c4a7ccb26ea67ab3dd61266073ca/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala#L49-L88
The problem here is, .py file shouldn't be added as are since `PYTHONPATH` expects a directory or an archive like zip or egg.
### How does this PR fix?
We shouldn't simply just add its parent directory because other files in the parent directory could also be added into the `PYTHONPATH` in client mode before context initialization.
Therefore, we copy .py files into a temp directory for .py files and add it to `PYTHONPATH`.
## How was this patch tested?
Unit tests are added and manually tested in both standalond and yarn client modes with submit.
Author: hyukjinkwon <[email protected]>
Closes apache#21426 from HyukjinKwon/SPARK-24384.
## What changes were proposed in this pull request? Add featureSubsetStrategy in GBTClassifier and GBTRegressor. Also make GBTClassificationModel inherit from JavaClassificationModel instead of prediction model so it will have numClasses. ## How was this patch tested? Add tests in doctest Author: Huaxin Gao <[email protected]> Closes apache#21413 from huaxingao/spark-23161.
## What changes were proposed in this pull request? The PR adds the masking function as they are described in Hive's documentation: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF#LanguageManualUDF-DataMaskingFunctions. This means that only `string`s are accepted as parameter for the masking functions. ## How was this patch tested? added UTs Author: Marco Gaido <[email protected]> Closes apache#21246 from mgaido91/SPARK-23901.
… equality ## What changes were proposed in this pull request? When two `In` operators are created with the same list of values, but different order, we are considering them as semantically different. This is wrong, since they have the same semantic meaning. The PR adds a canonicalization rule which orders the literals in the `In` operator so the semantic equality works properly. ## How was this patch tested? added UT Author: Marco Gaido <[email protected]> Closes apache#21331 from mgaido91/SPARK-24276.
## What changes were proposed in this pull request? Improve the exception messages when retrieving Spark conf values to include the key name when the value is invalid. ## How was this patch tested? Unit tests for all get* operations in SparkConf that require a specific value format Author: William Sheu <[email protected]> Closes apache#21454 from PenguinToast/SPARK-24337-spark-config-errors.
…ing - PrefixSpan: Python API ## What changes were proposed in this pull request? spark.ml parity for sequential pattern mining - PrefixSpan: Python API ## How was this patch tested? doctests Author: WeichenXu <[email protected]> Closes apache#21265 from WeichenXu123/prefix_span_py.
As discussed separately, this avoids the possibility of XSS on certain request param keys. CC vanzin Author: Sean Owen <[email protected]> Closes apache#21464 from srowen/XSS2.
This change takes into account all non-pending tasks when calculating the number of tasks to be shown. This also means that when the stage is pending, the task table (or, in fact, most of the data in the stage page) will not be rendered. I also fixed the label when the known number of tasks is larger than the recorded number of tasks (it was inverted). Author: Marcelo Vanzin <[email protected]> Closes apache#21457 from vanzin/SPARK-24414.
## What changes were proposed in this pull request? This adds a new API `TaskContext.getLocalProperty(key)` to the Python TaskContext. It mirrors the Java TaskContext API of returning a string value if the key exists, or None if the key does not exist. ## How was this patch tested? New test added. Author: Tathagata Das <[email protected]> Closes apache#21437 from tdas/SPARK-24397.
…ment ## What changes were proposed in this pull request? `format_number` support user specifed format as argument. For example: ```sql spark-sql> SELECT format_number(12332.123456, '##################.###'); 12332.123 ``` ## How was this patch tested? unit test Author: Yuming Wang <[email protected]> Closes apache#21010 from wangyum/SPARK-23900.
## What changes were proposed in this pull request?
* Allows to refer a secret as an env var.
* Introduces new config properties in the form: spark.kubernetes{driver,executor}.secretKeyRef.ENV_NAME=name:key
ENV_NAME is case sensitive.
* Updates docs.
* Adds required unit tests.
## How was this patch tested?
Manually tested and confirmed that the secrets exist in driver's and executor's container env.
Also job finished successfully.
First created a secret with the following yaml:
```
apiVersion: v1
kind: Secret
metadata:
name: test-secret
data:
username: c3RhdnJvcwo=
password: Mzk1MjgkdmRnN0pi
-------
$ echo -n 'stavros' | base64
c3RhdnJvcw==
$ echo -n '39528$vdg7Jb' | base64
MWYyZDFlMmU2N2Rm
```
Run a job as follows:
```./bin/spark-submit \
--master k8s://http://localhost:9000 \
--deploy-mode cluster \
--name spark-pi \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.instances=1 \
--conf spark.kubernetes.container.image=skonto/spark:k8envs3 \
--conf spark.kubernetes.driver.secretKeyRef.MY_USERNAME=test-secret:username \
--conf spark.kubernetes.driver.secretKeyRef.My_password=test-secret:password \
--conf spark.kubernetes.executor.secretKeyRef.MY_USERNAME=test-secret:username \
--conf spark.kubernetes.executor.secretKeyRef.My_password=test-secret:password \
local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0-SNAPSHOT.jar 10000
```
Secret loaded correctly at the driver container:
Also if I log into the exec container:
kubectl exec -it spark-pi-1526555613156-exec-1 bash
bash-4.4# env
> SPARK_EXECUTOR_MEMORY=1g
> SPARK_EXECUTOR_CORES=1
> LANG=C.UTF-8
> HOSTNAME=spark-pi-1526555613156-exec-1
> SPARK_APPLICATION_ID=spark-application-1526555618626
> **MY_USERNAME=stavros**
>
> JAVA_HOME=/usr/lib/jvm/java-1.8-openjdk
> KUBERNETES_PORT_443_TCP_PROTO=tcp
> KUBERNETES_PORT_443_TCP_ADDR=10.100.0.1
> JAVA_VERSION=8u151
> KUBERNETES_PORT=tcp://10.100.0.1:443
> PWD=/opt/spark/work-dir
> HOME=/root
> SPARK_LOCAL_DIRS=/var/data/spark-b569b0ae-b7ef-4f91-bcd5-0f55535d3564
> KUBERNETES_SERVICE_PORT_HTTPS=443
> KUBERNETES_PORT_443_TCP_PORT=443
> SPARK_HOME=/opt/spark
> SPARK_DRIVER_URL=spark://CoarseGrainedSchedulerspark-pi-1526555613156-driver-svc.default.svc:7078
> KUBERNETES_PORT_443_TCP=tcp://10.100.0.1:443
> SPARK_EXECUTOR_POD_IP=9.0.9.77
> TERM=xterm
> SPARK_EXECUTOR_ID=1
> SHLVL=1
> KUBERNETES_SERVICE_PORT=443
> SPARK_CONF_DIR=/opt/spark/conf
> PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/lib/jvm/java-1.8-openjdk/jre/bin:/usr/lib/jvm/java-1.8-openjdk/bin
> JAVA_ALPINE_VERSION=8.151.12-r0
> KUBERNETES_SERVICE_HOST=10.100.0.1
> **My_password=39528$vdg7Jb**
> _=/usr/bin/env
>
Author: Stavros Kontopoulos <[email protected]>
Closes apache#21317 from skonto/k8s-fix-env-secrets.
… message This PR adds a debugging log for YARN-specific credential providers which is loaded by service loader mechanism. It took me a while to debug if it's actually loaded or not. I had to explicitly set the deprecated configuration and check if that's actually being loaded. The change scope is manually tested. Logs are like: ``` Using the following builtin delegation token providers: hadoopfs, hive, hbase. Using the following YARN-specific credential providers: yarn-test. ``` Author: hyukjinkwon <[email protected]> Closes apache#21466 from HyukjinKwon/minor-log. Change-Id: I18e2fb8eeb3289b148f24c47bb3130a560a881cf
…ng files ## What changes were proposed in this pull request? 1. Refactor ExecuteWriteTask in FileFormatWriter to reduce common logic and improve readability. After the change, callers only need to call `commit()` or `abort` at the end of task. Also there is less code in `SingleDirectoryWriteTask` and `DynamicPartitionWriteTask`. Definitions of related classes are moved to a new file, and `ExecuteWriteTask` is renamed to `FileFormatDataWriter`. 2. As per code style guide: https://github.com/databricks/scala-style-guide#traversal-and-zipwithindex , we avoid using `for` for looping in [FileFormatWriter](https://github.com/apache/spark/pull/21381/files#diff-3b69eb0963b68c65cfe8075f8a42e850L536) , or `foreach` in [WriteToDataSourceV2Exec](https://github.com/apache/spark/pull/21381/files#diff-6fbe10db766049a395bae2e785e9d56eL119). In such critical code path, using `while` is good for performance. ## How was this patch tested? Existing unit test. I tried the microbenchmark in apache#21409 | Workload | Before changes(Best/Avg Time(ms)) | After changes(Best/Avg Time(ms)) | | --- | --- | -- | |Output Single Int Column| 2018 / 2043 | 2096 / 2236 | |Output Single Double Column| 1978 / 2043 | 2013 / 2018 | |Output Int and String Column| 6332 / 6706 | 6162 / 6298 | |Output Partitions| 4458 / 5094 | 3792 / 4008 | |Output Buckets| 5695 / 6102 | 5120 / 5154 | Also a microbenchmark on my laptop for general comparison among while/foreach/for : ``` class Writer { var sum = 0L def write(l: Long): Unit = sum += l } def testWhile(iterator: Iterator[Long]): Long = { val w = new Writer while (iterator.hasNext) { w.write(iterator.next()) } w.sum } def testForeach(iterator: Iterator[Long]): Long = { val w = new Writer iterator.foreach(w.write) w.sum } def testFor(iterator: Iterator[Long]): Long = { val w = new Writer for (x <- iterator) { w.write(x) } w.sum } val data = 0L to 100000000L val start = System.nanoTime (0 to 10).foreach(_ => testWhile(data.iterator)) println("benchmark while: " + (System.nanoTime - start)/1000000) val start2 = System.nanoTime (0 to 10).foreach(_ => testForeach(data.iterator)) println("benchmark foreach: " + (System.nanoTime - start2)/1000000) val start3 = System.nanoTime (0 to 10).foreach(_ => testForeach(data.iterator)) println("benchmark for: " + (System.nanoTime - start3)/1000000) ``` Benchmark result: `while`: 15401 ms `foreach`: 43034 ms `for`: 41279 ms Author: Gengliang Wang <[email protected]> Closes apache#21381 from gengliangwang/refactorExecuteWriteTask.
… assignment ## What changes were proposed in this pull request? Added sections to pandas_udf docs, in the grouped map section, to indicate columns are assigned by position. ## How was this patch tested? NA Author: Bryan Cutler <[email protected]> Closes apache#21471 from BryanCutler/arrow-doc-pandas_udf-column_by_pos-SPARK-21427.
## What changes were proposed in this pull request? * Adds support for local:// scheme like in k8s case for image based deployments where the jar is already in the image. Affects cluster mode and the mesos dispatcher. Covers also file:// scheme. Keeps the default case where jar resolution happens on the host. ## How was this patch tested? Dispatcher image with the patch, use it to start DC/OS Spark service: skonto/spark-local-disp:test Test image with my application jar located at the root folder: skonto/spark-local:test Dockerfile for that image. From mesosphere/spark:2.3.0-2.2.1-2-hadoop-2.6 COPY spark-examples_2.11-2.2.1.jar / WORKDIR /opt/spark/dist Tests: The following work as expected: * local normal example ``` dcos spark run --submit-args="--conf spark.mesos.appJar.local.resolution.mode=container --conf spark.executor.memory=1g --conf spark.mesos.executor.docker.image=skonto/spark-local:test --conf spark.executor.cores=2 --conf spark.cores.max=8 --class org.apache.spark.examples.SparkPi local:///spark-examples_2.11-2.2.1.jar" ``` * make sure the flag does not affect other uris ``` dcos spark run --submit-args="--conf spark.mesos.appJar.local.resolution.mode=container --conf spark.executor.memory=1g --conf spark.executor.cores=2 --conf spark.cores.max=8 --class org.apache.spark.examples.SparkPi https://s3-eu-west-1.amazonaws.com/fdp-stavros-test/spark-examples_2.11-2.1.1.jar" ``` * normal example no local ``` dcos spark run --submit-args="--conf spark.executor.memory=1g --conf spark.executor.cores=2 --conf spark.cores.max=8 --class org.apache.spark.examples.SparkPi https://s3-eu-west-1.amazonaws.com/fdp-stavros-test/spark-examples_2.11-2.1.1.jar" ``` The following fails * uses local with no setting, default is host. ``` dcos spark run --submit-args="--conf spark.executor.memory=1g --conf spark.mesos.executor.docker.image=skonto/spark-local:test --conf spark.executor.cores=2 --conf spark.cores.max=8 --class org.apache.spark.examples.SparkPi local:///spark-examples_2.11-2.2.1.jar" ```  Author: Stavros Kontopoulos <[email protected]> Closes apache#21378 from skonto/local-upstream.
… element from array ## What changes were proposed in this pull request? add array_remove to remove all elements that equal element from array ## How was this patch tested? add unit tests Author: Huaxin Gao <[email protected]> Closes apache#21069 from huaxingao/spark-23920.
…e computed with current committed epoch but not currentBatchId in CP mode ## What changes were proposed in this pull request? Compute the thresholdBatchId to purge metadata based on current committed epoch instead of currentBatchId in CP mode to avoid cleaning all the committed metadata in some case as described in the jira [SPARK-24351](https://issues.apache.org/jira/browse/SPARK-24351). ## How was this patch tested? Add new unit test. Author: Huang Tengfei <[email protected]> Closes apache#21400 from ivoson/branch-cp-meta.
…regations having the same argument set" This reverts commit 1e46f92.
…lowing executor exits on a Standalone cluster ## What changes were proposed in this pull request? Currently we only clean up the local directories on application removed. However, when executors die and restart repeatedly, many temp files are left untouched in the local directories, which is undesired behavior and could cause disk space used up gradually. We can detect executor death in the Worker, and clean up the non-shuffle files (files not ended with ".index" or ".data") in the local directories, we should not touch the shuffle files since they are expected to be used by the external shuffle service. Scope of this PR is limited to only implement the cleanup logic on a Standalone cluster, we defer to experts familiar with other cluster managers(YARN/Mesos/K8s) to determine whether it's worth to add similar support. ## How was this patch tested? Add new test suite to cover. Author: Xingbo Jiang <[email protected]> Closes apache#21390 from jiangxb1987/cleanupNonshuffleFiles.
…etes.md ## What changes were proposed in this pull request? PR apache#20811 introduced a new Spark configuration property `spark.kubernetes.container.image.pullSecrets` for specifying image pull secrets. However, the documentation wasn't updated accordingly. This PR adds the property introduced into running-on-kubernetes.md. ## How was this patch tested? N/A. foxish mccheah please help merge this. Thanks! Author: Yinan Li <[email protected]> Closes apache#21480 from liyinan926/master.
…mentManagedBuffer This patch eliminates duplicate strings that come from the 'path' field of java.io.File objects created by FileSegmentManagedBuffer. That is, we want to avoid the situation when multiple File instances for the same pathname "foo/bar" are created, each with a separate copy of the "foo/bar" String instance. In some scenarios such duplicate strings may waste a lot of memory (~ 10% of the heap). To avoid that, we intern the pathname with String.intern(), and before that we make sure that it's in a normalized form (contains no "//", "///" etc.) Otherwise, the code in java.io.File would normalize it later, creating a new "foo/bar" String copy. Unfortunately, the normalization code that java.io.File uses internally is in the package-private class java.io.FileSystem, so we cannot call it here directly. ## What changes were proposed in this pull request? Added code to ExternalShuffleBlockResolver.getFile(), that normalizes and then interns the pathname string before passing it to the File() constructor. ## How was this patch tested? Added unit test Author: Misha Dmitriev <[email protected]> Closes apache#21456 from countmdm/misha/spark-24356.
change runTasks to submitTasks in the TaskSchedulerImpl.scala 's comment Author: xueyu <[email protected]> Author: Xue Yu <[email protected]> Closes apache#21485 from xueyumusic/fixtypo1.
…s having the same argument set ## What changes were proposed in this pull request? bring back apache#21443 This is a different approach: just change the check to count distinct columns with `toSet` ## How was this patch tested? a new test to verify the planner behavior. Author: Wenchen Fan <[email protected]> Author: Takeshi Yamamuro <[email protected]> Closes apache#21487 from cloud-fan/back.
## What changes were proposed in this pull request? Currently column names of headers in CSV files are not checked against provided schema of CSV data. It could cause errors like showed in the [SPARK-23786](https://issues.apache.org/jira/browse/SPARK-23786) and apache#20894 (comment). I introduced new CSV option - `enforceSchema`. If it is enabled (by default `true`), Spark forcibly applies provided or inferred schema to CSV files. In that case, CSV headers are ignored and not checked against the schema. If `enforceSchema` is set to `false`, additional checks can be performed. For example, if column in CSV header and in the schema have different ordering, the following exception is thrown: ``` java.lang.IllegalArgumentException: CSV file header does not contain the expected fields Header: depth, temperature Schema: temperature, depth CSV file: marina.csv ``` ## How was this patch tested? The changes were tested by existing tests of CSVSuite and by 2 new tests. Author: Maxim Gekk <[email protected]> Author: Maxim Gekk <[email protected]> Closes apache#20894 from MaxGekk/check-column-names.
## What changes were proposed in this pull request? Add support for date `extract` function: ```sql spark-sql> SELECT EXTRACT(YEAR FROM TIMESTAMP '2000-12-16 12:21:13'); 2000 ``` Supported field same as [Hive](https://github.com/apache/hive/blob/rel/release-2.3.3/ql/src/java/org/apache/hadoop/hive/ql/parse/IdentifiersParser.g#L308-L316): `YEAR`, `QUARTER`, `MONTH`, `WEEK`, `DAY`, `DAYOFWEEK`, `HOUR`, `MINUTE`, `SECOND`. ## How was this patch tested? unit tests Author: Yuming Wang <[email protected]> Closes apache#21479 from wangyum/SPARK-23903.
…e aggregate functions ## What changes were proposed in this pull request? This PR explicitly prohibits window functions inside aggregates. Currently, this will cause StackOverflow during analysis. See PR apache#19193 for previous discussion. ## How was this patch tested? This PR comes with a dedicated unit test. Author: aokolnychyi <[email protected]> Closes apache#21473 from aokolnychyi/fix-stackoverflow-window-funcs.
…NamedValue ## What changes were proposed in this pull request? Extend instrumentation.logNamedValue to support Array input change the logging for "clusterSizes" to new method ## How was this patch tested? N/A Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Lu WANG <[email protected]> Closes apache#21347 from ludatabricks/SPARK-24290.
…enerateLDAData ## What changes were proposed in this pull request? Using different RNG in all different partitions. ## How was this patch tested? manually Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Lu WANG <[email protected]> Closes apache#21492 from ludatabricks/SPARK-24300.
## What changes were proposed in this pull request? Implement `_repr_html_` for PySpark while in notebook and add config named "spark.sql.repl.eagerEval.enabled" to control this. The dev list thread for context: http://apache-spark-developers-list.1001551.n3.nabble.com/eager-execution-and-debuggability-td23928.html ## How was this patch tested? New ut in DataFrameSuite and manual test in jupyter. Some screenshot below. **After:**  **Before:**  Author: Yuanjian Li <[email protected]> Closes apache#21370 from xuanyuanking/SPARK-24215.
Currently, in spark-shell, if the session fails to start, the
user sees a bunch of unrelated errors which are caused by code
in the shell initialization that references the "spark" variable,
which does not exist in that case. Things like:
```
<console>:14: error: not found: value spark
import spark.sql
```
The user is also left with a non-working shell (unless they want
to just write non-Spark Scala or Python code, that is).
This change fails the whole shell session at the point where the
failure occurs, so that the last error message is the one with
the actual information about the failure.
For the python error handling, I moved the session initialization code
to session.py, so that traceback.print_exc() only shows the last error.
Otherwise, the printed exception would contain all previous exceptions
with a message "During handling of the above exception, another
exception occurred", making the actual error kinda hard to parse.
Tested with spark-shell, pyspark (with 2.7 and 3.5), by forcing an
error during SparkContext initialization.
Author: Marcelo Vanzin <[email protected]>
Closes apache#21368 from vanzin/SPARK-16451.
## What changes were proposed in this pull request? According to the discussion on JIRA. I rewrite the Power Iteration Clustering API in `spark.ml`. ## How was this patch tested? Unit test. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: WeichenXu <[email protected]> Closes apache#21493 from WeichenXu123/pic_api.
…batch ## What changes were proposed in this pull request? The error occurs when we are recovering from a failure in a no-data batch (say X) that has been planned (i.e. written to offset log) but not executed (i.e. not written to commit log). Upon recovery the following sequence of events happen. 1. `MicroBatchExecution.populateStartOffsets` sets `currentBatchId` to X. Since there was no data in the batch, the `availableOffsets` is same as `committedOffsets`, so `isNewDataAvailable` is `false`. 2. When `MicroBatchExecution.constructNextBatch` is called, ideally it should immediately return true because the next batch has already been constructed. However, the check of whether the batch has been constructed was `if (isNewDataAvailable) return true`. Since the planned batch is a no-data batch, it escaped this check and proceeded to plan the same batch X *once again*. The solution is to have an explicit flag that signifies whether a batch has already been constructed or not. `populateStartOffsets` is going to set the flag appropriately. ## How was this patch tested? new unit test Author: Tathagata Das <[email protected]> Closes apache#21491 from tdas/SPARK-24453.
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@squito. The SparkSaslSuite.testFileRegionEncryption test is failing and I decided to find out the reason why. The reason is: My plan is to pass a new param to NettyUtils.createFrameDecoder() which indicates whether parsed frames or simple ByteBuf is passed to the above layer. |
… in Cast ## What changes were proposed in this pull request? Sql below will get all partitions from metastore, which put much burden on metastore; ``` CREATE TABLE `partition_test`(`col` int) PARTITIONED BY (`pt` byte) SELECT * FROM partition_test WHERE CAST(pt AS INT)=1 ``` The reason is that the the analyzed attribute `dt` is wrapped in `Cast` and `HiveShim` fails to generate a proper partition filter. This pr proposes to take `Cast` into consideration when generate partition filter. ## How was this patch tested? Test added. This pr proposes to use analyzed expressions in `HiveClientSuite` Author: jinxing <[email protected]> Closes apache#19602 from jinxing64/SPARK-22384.
## What changes were proposed in this pull request? This PR adds array_join function to SparkR ## How was this patch tested? Add unit test in test_sparkSQL.R Author: Huaxin Gao <[email protected]> Closes apache#21313 from huaxingao/spark-24187.
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I have merged with master which introduced a lot of change commits and diffs, so I have created a new PR. |
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## What changes were proposed in this pull request? This PR aims at improving the way physical plans are explained in spark. Currently, the explain output for physical plan may look very cluttered and each operator's string representation can be very wide and wraps around in the display making it little hard to follow. This especially happens when explaining a query 1) Operating on wide tables 2) Has complex expressions etc. This PR attempts to split the output into two sections. In the header section, we display the basic operator tree with a number associated with each operator. In this section, we strictly control what we output for each operator. In the footer section, each operator is verbosely displayed. Based on the feedback from Maryann, the uncorrelated subqueries (SubqueryExecs) are not included in the main plan. They are printed separately after the main plan and can be correlated by the originating expression id from its parent plan. To illustrate, here is a simple plan displayed in old vs new way. Example query1 : ``` EXPLAIN SELECT key, Max(val) FROM explain_temp1 WHERE key > 0 GROUP BY key HAVING max(val) > 0 ``` Old : ``` *(2) Project [key#2, max(val)apache#15] +- *(2) Filter (isnotnull(max(val#3)apache#18) AND (max(val#3)apache#18 > 0)) +- *(2) HashAggregate(keys=[key#2], functions=[max(val#3)], output=[key#2, max(val)apache#15, max(val#3)apache#18]) +- Exchange hashpartitioning(key#2, 200) +- *(1) HashAggregate(keys=[key#2], functions=[partial_max(val#3)], output=[key#2, max#21]) +- *(1) Project [key#2, val#3] +- *(1) Filter (isnotnull(key#2) AND (key#2 > 0)) +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), (key#2 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), GreaterThan(key,0)], ReadSchema: struct<key:int,val:int> ``` New : ``` Project (8) +- Filter (7) +- HashAggregate (6) +- Exchange (5) +- HashAggregate (4) +- Project (3) +- Filter (2) +- Scan parquet default.explain_temp1 (1) (1) Scan parquet default.explain_temp1 [codegen id : 1] Output: [key#2, val#3] (2) Filter [codegen id : 1] Input : [key#2, val#3] Condition : (isnotnull(key#2) AND (key#2 > 0)) (3) Project [codegen id : 1] Output : [key#2, val#3] Input : [key#2, val#3] (4) HashAggregate [codegen id : 1] Input: [key#2, val#3] (5) Exchange Input: [key#2, max#11] (6) HashAggregate [codegen id : 2] Input: [key#2, max#11] (7) Filter [codegen id : 2] Input : [key#2, max(val)#5, max(val#3)#8] Condition : (isnotnull(max(val#3)#8) AND (max(val#3)#8 > 0)) (8) Project [codegen id : 2] Output : [key#2, max(val)#5] Input : [key#2, max(val)#5, max(val#3)#8] ``` Example Query2 (subquery): ``` SELECT * FROM explain_temp1 WHERE KEY = (SELECT Max(KEY) FROM explain_temp2 WHERE KEY = (SELECT Max(KEY) FROM explain_temp3 WHERE val > 0) AND val = 2) AND val > 3 ``` Old: ``` *(1) Project [key#2, val#3] +- *(1) Filter (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#39)) AND (val#3 > 3)) : +- Subquery scalar-subquery#39 : +- *(2) HashAggregate(keys=[], functions=[max(KEY#26)], output=[max(KEY)apache#45]) : +- Exchange SinglePartition : +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#26)], output=[max#47]) : +- *(1) Project [key#26] : +- *(1) Filter (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#38)) AND (val#27 = 2)) : : +- Subquery scalar-subquery#38 : : +- *(2) HashAggregate(keys=[], functions=[max(KEY#28)], output=[max(KEY)apache#43]) : : +- Exchange SinglePartition : : +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#28)], output=[max#49]) : : +- *(1) Project [key#28] : : +- *(1) Filter (isnotnull(val#29) AND (val#29 > 0)) : : +- *(1) FileScan parquet default.explain_temp3[key#28,val#29] Batched: true, DataFilters: [isnotnull(val#29), (val#29 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp3], PartitionFilters: [], PushedFilters: [IsNotNull(val), GreaterThan(val,0)], ReadSchema: struct<key:int,val:int> : +- *(1) FileScan parquet default.explain_temp2[key#26,val#27] Batched: true, DataFilters: [isnotnull(key#26), isnotnull(val#27), (val#27 = 2)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp2], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), EqualTo(val,2)], ReadSchema: struct<key:int,val:int> +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), isnotnull(val#3), (val#3 > 3)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), GreaterThan(val,3)], ReadSchema: struct<key:int,val:int> ``` New: ``` Project (3) +- Filter (2) +- Scan parquet default.explain_temp1 (1) (1) Scan parquet default.explain_temp1 [codegen id : 1] Output: [key#2, val#3] (2) Filter [codegen id : 1] Input : [key#2, val#3] Condition : (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#23)) AND (val#3 > 3)) (3) Project [codegen id : 1] Output : [key#2, val#3] Input : [key#2, val#3] ===== Subqueries ===== Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#23 HashAggregate (9) +- Exchange (8) +- HashAggregate (7) +- Project (6) +- Filter (5) +- Scan parquet default.explain_temp2 (4) (4) Scan parquet default.explain_temp2 [codegen id : 1] Output: [key#26, val#27] (5) Filter [codegen id : 1] Input : [key#26, val#27] Condition : (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#22)) AND (val#27 = 2)) (6) Project [codegen id : 1] Output : [key#26] Input : [key#26, val#27] (7) HashAggregate [codegen id : 1] Input: [key#26] (8) Exchange Input: [max#35] (9) HashAggregate [codegen id : 2] Input: [max#35] Subquery:2 Hosting operator id = 5 Hosting Expression = Subquery scalar-subquery#22 HashAggregate (15) +- Exchange (14) +- HashAggregate (13) +- Project (12) +- Filter (11) +- Scan parquet default.explain_temp3 (10) (10) Scan parquet default.explain_temp3 [codegen id : 1] Output: [key#28, val#29] (11) Filter [codegen id : 1] Input : [key#28, val#29] Condition : (isnotnull(val#29) AND (val#29 > 0)) (12) Project [codegen id : 1] Output : [key#28] Input : [key#28, val#29] (13) HashAggregate [codegen id : 1] Input: [key#28] (14) Exchange Input: [max#37] (15) HashAggregate [codegen id : 2] Input: [max#37] ``` Note: I opened this PR as a WIP to start getting feedback. I will be on vacation starting tomorrow would not be able to immediately incorporate the feedback. I will start to work on them as soon as i can. Also, currently this PR provides a basic infrastructure for explain enhancement. The details about individual operators will be implemented in follow-up prs ## How was this patch tested? Added a new test `explain.sql` that tests basic scenarios. Need to add more tests. Closes apache#24759 from dilipbiswal/explain_feature. Authored-by: Dilip Biswal <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
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Please review http://spark.apache.org/contributing.html before opening a pull request.