## What changes were proposed in this pull request?
Rename the public APIs and names of pandas udfs.
- `PANDAS SCALAR UDF` -> `SCALAR PANDAS UDF`
- `PANDAS GROUP MAP UDF` -> `GROUPED MAP PANDAS UDF`
- `PANDAS GROUP AGG UDF` -> `GROUPED AGG PANDAS UDF`
## How was this patch tested?
The existing tests
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20428 from gatorsmile/renamePandasUDFs.
## What changes were proposed in this pull request?
User guide and examples are updated to reflect multiclass logistic regression summary which was added in [SPARK-17139](https://issues.apache.org/jira/browse/SPARK-17139).
I did not make a separate summary example, but added the summary code to the multiclass example that already existed. I don't see the need for a separate example for the summary.
## How was this patch tested?
Docs and examples only. Ran all examples locally using spark-submit.
Author: sethah <shendrickson@cloudera.com>
Closes#20332 from sethah/multiclass_summary_example.
## What changes were proposed in this pull request?
Adding user facing documentation for working with Arrow in Spark
Author: Bryan Cutler <cutlerb@gmail.com>
Author: Li Jin <ice.xelloss@gmail.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19575 from BryanCutler/arrow-user-docs-SPARK-2221.
Update ML user guide with highlights and migration guide for `2.3`.
## How was this patch tested?
Doc only.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#20363 from MLnick/SPARK-23112-ml-guide.
## What changes were proposed in this pull request?
Added documentation for new transformer.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20285 from MrBago/sizeHintDocs.
## What changes were proposed in this pull request?
The example jar file is now in ./examples/jars directory of Spark distribution.
Author: Arseniy Tashoyan <tashoyan@users.noreply.github.com>
Closes#20349 from tashoyan/patch-1.
## What changes were proposed in this pull request?
streaming programming guide changes
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20340 from felixcheung/rstreamdoc.
## What changes were proposed in this pull request?
Fix spelling in quick-start doc.
## How was this patch tested?
Doc only.
Author: Shashwat Anand <me@shashwat.me>
Closes#20336 from ashashwat/SPARK-23165.
## What changes were proposed in this pull request?
Docs changes:
- Adding a warning that the backend is experimental.
- Removing a defunct internal-only option from documentation
- Clarifying that node selectors can be used right away, and other minor cosmetic changes
## How was this patch tested?
Docs only change
Author: foxish <ramanathana@google.com>
Closes#20314 from foxish/ambiguous-docs.
## What changes were proposed in this pull request?
We have `OneHotEncoderEstimator` now and `OneHotEncoder` will be deprecated since 2.3.0. We should add `OneHotEncoderEstimator` into mllib document.
We also need to provide corresponding examples for `OneHotEncoderEstimator` which are used in the document too.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20257 from viirya/SPARK-23048.
Update user guide entry for `FeatureHasher` to match the Scala / Python doc, to describe the `categoricalCols` parameter.
## How was this patch tested?
Doc only
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#20293 from MLnick/SPARK-23127-catCol-userguide.
## What changes were proposed in this pull request?
In latest structured-streaming-kafka-integration document, Java code example for Kafka integration is using `DataFrame<Row>`, shouldn't it be changed to `DataSet<Row>`?
## How was this patch tested?
manual test has been performed to test the updated example Java code in Spark 2.2.1 with Kafka 1.0
Author: brandonJY <brandonJY@users.noreply.github.com>
Closes#20312 from brandonJY/patch-2.
## What changes were proposed in this pull request?
In the Kubernetes mode, fails fast in the submission process if any submission client local dependencies are used as the use case is not supported yet.
## How was this patch tested?
Unit tests, integration tests, and manual tests.
vanzin foxish
Author: Yinan Li <liyinan926@gmail.com>
Closes#20320 from liyinan926/master.
## What changes were proposed in this pull request?
This PR completes the docs, specifying the default units assumed in configuration entries of type size.
This is crucial since unit-less values are accepted and the user might assume the base unit is bytes, which in most cases it is not, leading to hard-to-debug problems.
## How was this patch tested?
This patch updates only documentation only.
Author: Fernando Pereira <fernando.pereira@epfl.ch>
Closes#20269 from ferdonline/docs_units.
## What changes were proposed in this pull request?
When there is an operation between Decimals and the result is a number which is not representable exactly with the result's precision and scale, Spark is returning `NULL`. This was done to reflect Hive's behavior, but it is against SQL ANSI 2011, which states that "If the result cannot be represented exactly in the result type, then whether it is rounded or truncated is implementation-defined". Moreover, Hive now changed its behavior in order to respect the standard, thanks to HIVE-15331.
Therefore, the PR propose to:
- update the rules to determine the result precision and scale according to the new Hive's ones introduces in HIVE-15331;
- round the result of the operations, when it is not representable exactly with the result's precision and scale, instead of returning `NULL`
- introduce a new config `spark.sql.decimalOperations.allowPrecisionLoss` which default to `true` (ie. the new behavior) in order to allow users to switch back to the previous one.
Hive behavior reflects SQLServer's one. The only difference is that the precision and scale are adjusted for all the arithmetic operations in Hive, while SQL Server is said to do so only for multiplications and divisions in the documentation. This PR follows Hive's behavior.
A more detailed explanation is available here: https://mail-archives.apache.org/mod_mbox/spark-dev/201712.mbox/%3CCAEorWNAJ4TxJR9NBcgSFMD_VxTg8qVxusjP%2BAJP-x%2BJV9zH-yA%40mail.gmail.com%3E.
## How was this patch tested?
modified and added UTs. Comparisons with results of Hive and SQLServer.
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20023 from mgaido91/SPARK-22036.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
Small typing correction - double word
## How was this patch tested?
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Matthias Beaupère <matthias.beaupere@gmail.com>
Closes#20212 from matthiasbe/patch-1.
This change allows a user to submit a Spark application on kubernetes
having to provide a single image, instead of one image for each type
of container. The image's entry point now takes an extra argument that
identifies the process that is being started.
The configuration still allows the user to provide different images
for each container type if they so desire.
On top of that, the entry point was simplified a bit to share more
code; mainly, the same env variable is used to propagate the user-defined
classpath to the different containers.
Aside from being modified to match the new behavior, the
'build-push-docker-images.sh' script was renamed to 'docker-image-tool.sh'
to more closely match its purpose; the old name was a little awkward
and now also not entirely correct, since there is a single image. It
was also moved to 'bin' since it's not necessarily an admin tool.
Docs have been updated to match the new behavior.
Tested locally with minikube.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20192 from vanzin/SPARK-22994.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18164 introduces the behavior changes. We need to document it.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20234 from gatorsmile/docBehaviorChange.
## What changes were proposed in this pull request?
doc update
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20198 from felixcheung/rrefreshdoc.
[SPARK-21786][SQL] When acquiring 'compressionCodecClassName' in 'ParquetOptions', `parquet.compression` needs to be considered.
## What changes were proposed in this pull request?
Since Hive 1.1, Hive allows users to set parquet compression codec via table-level properties parquet.compression. See the JIRA: https://issues.apache.org/jira/browse/HIVE-7858 . We do support orc.compression for ORC. Thus, for external users, it is more straightforward to support both. See the stackflow question: https://stackoverflow.com/questions/36941122/spark-sql-ignores-parquet-compression-propertie-specified-in-tblproperties
In Spark side, our table-level compression conf compression was added by #11464 since Spark 2.0.
We need to support both table-level conf. Users might also use session-level conf spark.sql.parquet.compression.codec. The priority rule will be like
If other compression codec configuration was found through hive or parquet, the precedence would be compression, parquet.compression, spark.sql.parquet.compression.codec. Acceptable values include: none, uncompressed, snappy, gzip, lzo.
The rule for Parquet is consistent with the ORC after the change.
Changes:
1.Increased acquiring 'compressionCodecClassName' from `parquet.compression`,and the precedence order is `compression`,`parquet.compression`,`spark.sql.parquet.compression.codec`, just like what we do in `OrcOptions`.
2.Change `spark.sql.parquet.compression.codec` to support "none".Actually in `ParquetOptions`,we do support "none" as equivalent to "uncompressed", but it does not allowed to configured to "none".
3.Change `compressionCode` to `compressionCodecClassName`.
## How was this patch tested?
Add test.
Author: fjh100456 <fu.jinhua6@zte.com.cn>
Closes#20076 from fjh100456/ParquetOptionIssue.
## What changes were proposed in this pull request?
This pr modified `elt` to output binary for binary inputs.
`elt` in the current master always output data as a string. But, in some databases (e.g., MySQL), if all inputs are binary, `elt` also outputs binary (Also, this might be a small surprise).
This pr is related to #19977.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#20135 from maropu/SPARK-22937.
- Make it possible to build images from a git clone.
- Make it easy to use minikube to test things.
Also fixed what seemed like a bug: the base image wasn't getting the tag
provided in the command line. Adding the tag allows users to use multiple
Spark builds in the same kubernetes cluster.
Tested by deploying images on minikube and running spark-submit from a dev
environment; also by building the images with different tags and verifying
"docker images" in minikube.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#20154 from vanzin/SPARK-22960.
## What changes were proposed in this pull request?
update R migration guide and vignettes
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#20106 from felixcheung/rreleasenote23.
## What changes were proposed in this pull request?
Fixing three small typos in the docs, in particular:
It take a `RDD` -> It takes an `RDD` (twice)
It take an `JavaRDD` -> It takes a `JavaRDD`
I didn't create any Jira issue for this minor thing, I hope it's ok.
## How was this patch tested?
visually by clicking on 'preview'
Author: Jirka Kremser <jkremser@redhat.com>
Closes#20108 from Jiri-Kremser/docs-typo.
## What changes were proposed in this pull request?
Currently, we do not guarantee an order evaluation of conjuncts in either Filter or Join operator. This is also true to the mainstream RDBMS vendors like DB2 and MS SQL Server. Thus, we should also push down the deterministic predicates that are after the first non-deterministic, if possible.
## How was this patch tested?
Updated the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20069 from gatorsmile/morePushDown.
## What changes were proposed in this pull request?
This pr modified `concat` to concat binary inputs into a single binary output.
`concat` in the current master always output data as a string. But, in some databases (e.g., PostgreSQL), if all inputs are binary, `concat` also outputs binary.
## How was this patch tested?
Added tests in `SQLQueryTestSuite` and `TypeCoercionSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#19977 from maropu/SPARK-22771.
## What changes were proposed in this pull request?
This PR updates the Kubernetes documentation corresponding to the following features/changes in #19954.
* Ability to use remote dependencies through the init-container.
* Ability to mount user-specified secrets into the driver and executor pods.
vanzin jiangxb1987 foxish
Author: Yinan Li <liyinan926@gmail.com>
Closes#20059 from liyinan926/doc-update.
What changes were proposed in this pull request?
This PR contains documentation on the usage of Kubernetes scheduler in Spark 2.3, and a shell script to make it easier to build docker images required to use the integration. The changes detailed here are covered by https://github.com/apache/spark/pull/19717 and https://github.com/apache/spark/pull/19468 which have merged already.
How was this patch tested?
The script has been in use for releases on our fork. Rest is documentation.
cc rxin mateiz (shepherd)
k8s-big-data SIG members & contributors: foxish ash211 mccheah liyinan926 erikerlandson ssuchter varunkatta kimoonkim tnachen ifilonenko
reviewers: vanzin felixcheung jiangxb1987 mridulm
TODO:
- [x] Add dockerfiles directory to built distribution. (https://github.com/apache/spark/pull/20007)
- [x] Change references to docker to instead say "container" (https://github.com/apache/spark/pull/19995)
- [x] Update configuration table.
- [x] Modify spark.kubernetes.allocation.batch.delay to take time instead of int (#20032)
Author: foxish <ramanathana@google.com>
Closes#19946 from foxish/update-k8s-docs.
## What changes were proposed in this pull request?
Like `Parquet`, users can use `ORC` with Apache Spark structured streaming. This PR adds `orc()` to `DataStreamReader`(Scala/Python) in order to support creating streaming dataset with ORC file format more easily like the other file formats. Also, this adds a test coverage for ORC data source and updates the document.
**BEFORE**
```scala
scala> spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
<console>:24: error: value orc is not a member of org.apache.spark.sql.streaming.DataStreamReader
spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
```
**AFTER**
```scala
scala> spark.readStream.schema("a int").orc("/tmp/orc_ss").writeStream.format("console").start()
res0: org.apache.spark.sql.streaming.StreamingQuery = org.apache.spark.sql.execution.streaming.StreamingQueryWrapper678b3746
scala>
-------------------------------------------
Batch: 0
-------------------------------------------
+---+
| a|
+---+
| 1|
+---+
```
## How was this patch tested?
Pass the newly added test cases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19975 from dongjoon-hyun/SPARK-22781.
## What changes were proposed in this pull request?
Easy fix in the link.
## How was this patch tested?
Tested manually
Author: Mahmut CAVDAR <mahmutcvdr@gmail.com>
Closes#19996 from mcavdar/master.
This PR contains implementation of the basic submission client for the cluster mode of Spark on Kubernetes. It's step 2 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) vote which passed on Aug 31.
This PR and #19468 together form a MVP of Spark on Kubernetes that allows users to run Spark applications that use resources locally within the driver and executor containers on Kubernetes 1.6 and up. Some changes on pom and build/test setup are copied over from #19468 to make this PR self contained and testable.
The submission client is mainly responsible for creating the Kubernetes pod that runs the Spark driver. It follows a step-based approach to construct the driver pod, as the code under the `submit.steps` package shows. The steps are orchestrated by `DriverConfigurationStepsOrchestrator`. `Client` creates the driver pod and waits for the application to complete if it's configured to do so, which is the case by default.
This PR also contains Dockerfiles of the driver and executor images. They are included because some of the environment variables set in the code would not make sense without referring to the Dockerfiles.
* The patch contains unit tests which are passing.
* Manual testing: ./build/mvn -Pkubernetes clean package succeeded.
* It is a subset of the entire changelist hosted at http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
* There is integration testing enabled in the fork currently hosted by PepperData which is being moved over to RiseLAB CI.
* Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah foxish ash211 ssuchter varunkatta kimoonkim erikerlandson tnachen ifilonenko liyinan926
Author: Yinan Li <liyinan926@gmail.com>
Closes#19717 from liyinan926/spark-kubernetes-4.
## What changes were proposed in this pull request?
Update broadcast behavior changes in migration section.
## How was this patch tested?
N/A
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19858 from wangyum/SPARK-22489-migration.
## What changes were proposed in this pull request?
The spark properties for configuring the ContextCleaner are not documented in the official documentation at https://spark.apache.org/docs/latest/configuration.html#available-properties.
This PR adds the doc.
## How was this patch tested?
Manual.
```
cd docs
jekyll build
open _site/configuration.html
```
Author: gaborgsomogyi <gabor.g.somogyi@gmail.com>
Closes#19826 from gaborgsomogyi/SPARK-22428.
## What changes were proposed in this pull request?
How to reproduce:
```scala
import org.apache.spark.sql.execution.joins.BroadcastHashJoinExec
spark.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value").createTempView("table1")
spark.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value").createTempView("table2")
val bl = sql("SELECT /*+ MAPJOIN(t1) */ * FROM table1 t1 JOIN table2 t2 ON t1.key = t2.key").queryExecution.executedPlan
println(bl.children.head.asInstanceOf[BroadcastHashJoinExec].buildSide)
```
The result is `BuildRight`, but should be `BuildLeft`. This PR fix this issue.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19714 from wangyum/SPARK-22489.
## What changes were proposed in this pull request?
This is a stripped down version of the `KubernetesClusterSchedulerBackend` for Spark with the following components:
- Static Allocation of Executors
- Executor Pod Factory
- Executor Recovery Semantics
It's step 1 from the step-wise plan documented [here](https://github.com/apache-spark-on-k8s/spark/issues/441#issuecomment-330802935).
This addition is covered by the [SPIP vote](http://apache-spark-developers-list.1001551.n3.nabble.com/SPIP-Spark-on-Kubernetes-td22147.html) which passed on Aug 31 .
## How was this patch tested?
- The patch contains unit tests which are passing.
- Manual testing: `./build/mvn -Pkubernetes clean package` succeeded.
- It is a **subset** of the entire changelist hosted in http://github.com/apache-spark-on-k8s/spark which is in active use in several organizations.
- There is integration testing enabled in the fork currently [hosted by PepperData](spark-k8s-jenkins.pepperdata.org:8080) which is being moved over to RiseLAB CI.
- Detailed documentation on trying out the patch in its entirety is in: https://apache-spark-on-k8s.github.io/userdocs/running-on-kubernetes.html
cc rxin felixcheung mateiz (shepherd)
k8s-big-data SIG members & contributors: mccheah ash211 ssuchter varunkatta kimoonkim erikerlandson liyinan926 tnachen ifilonenko
Author: Yinan Li <liyinan926@gmail.com>
Author: foxish <ramanathana@google.com>
Author: mcheah <mcheah@palantir.com>
Closes#19468 from foxish/spark-kubernetes-3.
## What changes were proposed in this pull request?
When converting Pandas DataFrame/Series from/to Spark DataFrame using `toPandas()` or pandas udfs, timestamp values behave to respect Python system timezone instead of session timezone.
For example, let's say we use `"America/Los_Angeles"` as session timezone and have a timestamp value `"1970-01-01 00:00:01"` in the timezone. Btw, I'm in Japan so Python timezone would be `"Asia/Tokyo"`.
The timestamp value from current `toPandas()` will be the following:
```
>>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
>>> df = spark.createDataFrame([28801], "long").selectExpr("timestamp(value) as ts")
>>> df.show()
+-------------------+
| ts|
+-------------------+
|1970-01-01 00:00:01|
+-------------------+
>>> df.toPandas()
ts
0 1970-01-01 17:00:01
```
As you can see, the value becomes `"1970-01-01 17:00:01"` because it respects Python timezone.
As we discussed in #18664, we consider this behavior is a bug and the value should be `"1970-01-01 00:00:01"`.
## How was this patch tested?
Added tests and existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#19607 from ueshin/issues/SPARK-22395.
## What changes were proposed in this pull request?
A discussed in SPARK-19606, the addition of a new config property named "spark.mesos.constraints.driver" for constraining drivers running on a Mesos cluster
## How was this patch tested?
Corresponding unit test added also tested locally on a Mesos cluster
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Paul Mackles <pmackles@adobe.com>
Closes#19543 from pmackles/SPARK-19606.
## What changes were proposed in this pull request?
Documentation about Mesos Reject Offer Configurations
## Related PR
https://github.com/apache/spark/pull/19510 for `spark.mem.max`
Author: Li, YanKit | Wilson | RIT <yankit.li@rakuten.com>
Closes#19555 from windkit/spark_22133.
## What changes were proposed in this pull request?
Add incompatible Hive UDF describe to DOC.
## How was this patch tested?
N/A
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18833 from wangyum/SPARK-21625.
## What changes were proposed in this pull request?
Easy fix in the documentation, which is reporting that only numeric types and string are supported in type inference for partition columns, while Date and Timestamp are supported too since 2.1.0, thanks to SPARK-17388.
## How was this patch tested?
n/a
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19628 from mgaido91/SPARK-22398.
## What changes were proposed in this pull request?
Using zstd compression for Spark jobs spilling 100s of TBs of data, we could reduce the amount of data written to disk by as much as 50%. This translates to significant latency gain because of reduced disk io operations. There is a degradation CPU time by 2 - 5% because of zstd compression overhead, but for jobs which are bottlenecked by disk IO, this hit can be taken.
## Benchmark
Please note that this benchmark is using real world compute heavy production workload spilling TBs of data to disk
| | zstd performance as compred to LZ4 |
| ------------- | -----:|
| spill/shuffle bytes | -48% |
| cpu time | + 3% |
| cpu reservation time | -40%|
| latency | -40% |
## How was this patch tested?
Tested by running few jobs spilling large amount of data on the cluster and amount of intermediate data written to disk reduced by as much as 50%.
Author: Sital Kedia <skedia@fb.com>
Closes#18805 from sitalkedia/skedia/upstream_zstd.
