This commit modifies the Mesos submission client to allow the principal
and secret to be provided indirectly via files. The path to these files
can be specified either via Spark configuration or via environment
variable.
Assuming these files are appropriately protected by FS/OS permissions
this means we don't ever leak the actual values in process info like ps
Environment variable specification is useful because it allows you to
interpolate the location of this file when using per-user Mesos
credentials.
For some background as to why we have taken this approach I will briefly describe our set up. On our systems we provide each authorised user account with their own Mesos credentials to provide certain security and audit guarantees to our customers. These credentials are managed by a central Secret management service. In our `spark-env.sh` we determine the appropriate secret and principal files to use depending on the user who is invoking Spark hence the need to inject these via environment variables as well as by configuration properties. So we set these environment variables appropriately and our Spark read in the contents of those files to authenticate itself with Mesos.
This is functionality we have been using it in production across multiple customer sites for some time. This has been in the field for around 18 months with no reported issues. These changes have been sufficient to meet our customer security and audit requirements.
We have been building and deploying custom builds of Apache Spark with various minor tweaks like this which we are now looking to contribute back into the community in order that we can rely upon stock Apache Spark builds and stop maintaining our own internal fork.
Author: Rob Vesse <rvesse@dotnetrdf.org>
Closes#20167 from rvesse/SPARK-16501.
## What changes were proposed in this pull request?
This PR proposes to disallow default value None when 'to_replace' is not a dictionary.
It seems weird we set the default value of `value` to `None` and we ended up allowing the case as below:
```python
>>> df.show()
```
```
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
...
```
```python
>>> df.na.replace('Alice').show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
**After**
This PR targets to disallow the case above:
```python
>>> df.na.replace('Alice').show()
```
```
...
TypeError: value is required when to_replace is not a dictionary.
```
while we still allow when `to_replace` is a dictionary:
```python
>>> df.na.replace({'Alice': None}).show()
```
```
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
...
```
## How was this patch tested?
Manually tested, tests were added in `python/pyspark/sql/tests.py` and doctests were fixed.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#20499 from HyukjinKwon/SPARK-19454-followup.
## What changes were proposed in this pull request?
Further clarification of caveats in using stream-stream outer joins.
## How was this patch tested?
N/A
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#20494 from tdas/SPARK-23064-2.
Add breaking changes, as well as update behavior changes, to `2.3` ML migration guide.
## How was this patch tested?
Doc only
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#20421 from MLnick/SPARK-23112-ml-guide.
## 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.
## What changes were proposed in this pull request?
Update the url of reference paper.
## How was this patch tested?
It is comments, so nothing tested.
Author: bomeng <bmeng@us.ibm.com>
Closes#19614 from bomeng/22399.
Often times we want to impute custom values other than 'NaN'. My addition helps people locate this function without reading the API.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(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.
Author: tengpeng <tengpeng@users.noreply.github.com>
Closes#19600 from tengpeng/patch-5.
## Background
In #18837 , ArtRand added Mesos secrets support to the dispatcher. **This PR is to add the same secrets support to the drivers.** This means if the secret configs are set, the driver will launch executors that have access to either env or file-based secrets.
One use case for this is to support TLS in the driver <=> executor communication.
## What changes were proposed in this pull request?
Most of the changes are a refactor of the dispatcher secrets support (#18837) - moving it to a common place that can be used by both the dispatcher and drivers. The same goes for the unit tests.
## How was this patch tested?
There are four config combinations: [env or file-based] x [value or reference secret]. For each combination:
- Added a unit test.
- Tested in DC/OS.
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#19437 from susanxhuynh/sh-mesos-driver-secret.
## What changes were proposed in this pull request?
Event Log Server has a total of five configuration parameters, and now the description of the other two configuration parameters on the doc, user-friendly access and use.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: guoxiaolong <guo.xiaolong1@zte.com.cn>
Closes#19242 from guoxiaolongzte/addEventLogConf.
## What changes were proposed in this pull request?
The HTTP Strict-Transport-Security response header (often abbreviated as HSTS) is a security feature that lets a web site tell browsers that it should only be communicated with using HTTPS, instead of using HTTP.
Note: The Strict-Transport-Security header is ignored by the browser when your site is accessed using HTTP; this is because an attacker may intercept HTTP connections and inject the header or remove it. When your site is accessed over HTTPS with no certificate errors, the browser knows your site is HTTPS capable and will honor the Strict-Transport-Security header.
The HTTP X-XSS-Protection response header is a feature of Internet Explorer, Chrome and Safari that stops pages from loading when they detect reflected cross-site scripting (XSS) attacks.
The HTTP X-Content-Type-Options response header is used to protect against MIME sniffing vulnerabilities.
## How was this patch tested?
Checked on my system locally.
<img width="750" alt="screen shot 2017-10-03 at 6 49 20 pm" src="https://user-images.githubusercontent.com/6433184/31127234-eadf7c0c-a86b-11e7-8e5d-f6ea3f97b210.png">
Author: krishna-pandey <krish.pandey21@gmail.com>
Author: Krishna Pandey <krish.pandey21@gmail.com>
Closes#19419 from krishna-pandey/SPARK-22188.
Hive delegation tokens are only needed when the Spark driver has no access
to the kerberos TGT. That happens only in two situations:
- when using a proxy user
- when using cluster mode without a keytab
This change modifies the Hive provider so that it only generates delegation
tokens in those situations, and tweaks the YARN AM so that it makes the proper
user visible to the Hive code when running with keytabs, so that the TGT
can be used instead of a delegation token.
The effect of this change is that now it's possible to initialize multiple,
non-concurrent SparkContext instances in the same JVM. Before, the second
invocation would fail to fetch a new Hive delegation token, which then could
make the second (or third or...) application fail once the token expired.
With this change, the TGT will be used to authenticate to the HMS instead.
This change also avoids polluting the current logged in user's credentials
when launching applications. The credentials are copied only when running
applications as a proxy user. This makes it possible to implement SPARK-11035
later, where multiple threads might be launching applications, and each app
should have its own set of credentials.
Tested by verifying HDFS and Hive access in following scenarios:
- client and cluster mode
- client and cluster mode with proxy user
- client and cluster mode with principal / keytab
- long-running cluster app with principal / keytab
- pyspark app that creates (and stops) multiple SparkContext instances
through its lifetime
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#19509 from vanzin/SPARK-22290.
## What changes were proposed in this pull request?
I see that block updates are not logged to the event log.
This makes sense as a default for performance reasons.
However, I find it helpful when trying to get a better understanding of caching for a job to be able to log these updates.
This PR adds a configuration setting `spark.eventLog.blockUpdates` (defaulting to false) which allows block updates to be recorded in the log.
This contribution is original work which is licensed to the Apache Spark project.
## How was this patch tested?
Current and additional unit tests.
Author: Michael Mior <mmior@uwaterloo.ca>
Closes#19263 from michaelmior/log-block-updates.
## What changes were proposed in this pull request?
In the current BlockManager's `getRemoteBytes`, it will call `BlockTransferService#fetchBlockSync` to get remote block. In the `fetchBlockSync`, Spark will allocate a temporary `ByteBuffer` to store the whole fetched block. This will potentially lead to OOM if block size is too big or several blocks are fetched simultaneously in this executor.
So here leveraging the idea of shuffle fetch, to spill the large block to local disk before consumed by upstream code. The behavior is controlled by newly added configuration, if block size is smaller than the threshold, then this block will be persisted in memory; otherwise it will first spill to disk, and then read from disk file.
To achieve this feature, what I did is:
1. Rename `TempShuffleFileManager` to `TempFileManager`, since now it is not only used by shuffle.
2. Add a new `TempFileManager` to manage the files of fetched remote blocks, the files are tracked by weak reference, will be deleted when no use at all.
## How was this patch tested?
This was tested by adding UT, also manual verification in local test to perform GC to clean the files.
Author: jerryshao <sshao@hortonworks.com>
Closes#19476 from jerryshao/SPARK-22062.
## What changes were proposed in this pull request?
Adds links to the fork that provides integration with Nomad, in the same places the k8s integration is linked to.
## How was this patch tested?
I clicked on the links to make sure they're correct ;)
Author: Ben Barnard <barnardb@gmail.com>
Closes#19354 from barnardb/link-to-nomad-integration.
## What changes were proposed in this pull request?
Added documentation for loading csv files into Dataframes
## How was this patch tested?
/dev/run-tests
Author: Jorge Machado <jorge.w.machado@hotmail.com>
Closes#19429 from jomach/master.
## What changes were proposed in this pull request?
The number of cores assigned to each executor is configurable. When this is not explicitly set, multiple executors from the same application may be launched on the same worker too.
## How was this patch tested?
N/A
Author: liuxian <liu.xian3@zte.com.cn>
Closes#18711 from 10110346/executorcores.
## What changes were proposed in this pull request?
Move flume behind a profile, take 2. See https://github.com/apache/spark/pull/19365 for most of the back-story.
This change should fix the problem by removing the examples module dependency and moving Flume examples to the module itself. It also adds deprecation messages, per a discussion on dev about deprecating for 2.3.0.
## How was this patch tested?
Existing tests, which still enable flume integration.
Author: Sean Owen <sowen@cloudera.com>
Closes#19412 from srowen/SPARK-22142.2.
## What changes were proposed in this pull request?
Add 'flume' profile to enable Flume-related integration modules
## How was this patch tested?
Existing tests; no functional change
Author: Sean Owen <sowen@cloudera.com>
Closes#19365 from srowen/SPARK-22142.
The application listing is still generated from event logs, but is now stored
in a KVStore instance. By default an in-memory store is used, but a new config
allows setting a local disk path to store the data, in which case a LevelDB
store will be created.
The provider stores things internally using the public REST API types; I believe
this is better going forward since it will make it easier to get rid of the
internal history server API which is mostly redundant at this point.
I also added a finalizer to LevelDBIterator, to make sure that resources are
eventually released. This helps when code iterates but does not exhaust the
iterator, thus not triggering the auto-close code.
HistoryServerSuite was modified to not re-start the history server unnecessarily;
this makes the json validation tests run more quickly.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#18887 from vanzin/SPARK-20642.
## What changes were proposed in this pull request?
The `percentile_approx` function previously accepted numeric type input and output double type results.
But since all numeric types, date and timestamp types are represented as numerics internally, `percentile_approx` can support them easily.
After this PR, it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.
This change is also required when we generate equi-height histograms for these types.
## How was this patch tested?
Added a new test and modified some existing tests.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#19321 from wzhfy/approx_percentile_support_types.
## What changes were proposed in this pull request?
Updated docs so that a line of python in the quick start guide executes. Closes#19283
## How was this patch tested?
Existing tests.
Author: John O'Leary <jgoleary@gmail.com>
Closes#19326 from jgoleary/issues/22107.
Change-Id: I88c272444ca734dc2cbc2592607c11287b90a383
## What changes were proposed in this pull request?
The documentation on File DStreams is enhanced to
1. Detail the exact timestamp logic for examining directories and files.
1. Detail how object stores different from filesystems, and so how using them as a source of data should be treated with caution, possibly publishing data to the store differently (direct PUTs as opposed to stage + rename)
## How was this patch tested?
n/a
Author: Steve Loughran <stevel@hortonworks.com>
Closes#17743 from steveloughran/cloud/SPARK-20448-document-dstream-blobstore.
## What changes were proposed in this pull request?
In the current Spark, when submitting application on YARN with remote resources `./bin/spark-shell --jars http://central.maven.org/maven2/com/github/swagger-akka-http/swagger-akka-http_2.11/0.10.1/swagger-akka-http_2.11-0.10.1.jar --master yarn-client -v`, Spark will be failed with:
```
java.io.IOException: No FileSystem for scheme: http
at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2586)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2593)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:91)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2632)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2614)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:370)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
at org.apache.spark.deploy.yarn.Client.copyFileToRemote(Client.scala:354)
at org.apache.spark.deploy.yarn.Client.org$apache$spark$deploy$yarn$Client$$distribute$1(Client.scala:478)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$11$$anonfun$apply$6.apply(Client.scala:600)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$11$$anonfun$apply$6.apply(Client.scala:599)
at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:74)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$11.apply(Client.scala:599)
at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$11.apply(Client.scala:598)
at scala.collection.immutable.List.foreach(List.scala:381)
at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:598)
at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:848)
at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:173)
```
This is because `YARN#client` assumes resources are on the Hadoop compatible FS. To fix this problem, here propose to download remote http(s) resources to local and add this local downloaded resources to dist cache. This solution has one downside: remote resources are downloaded and uploaded again, but it only restricted to only remote http(s) resources, also the overhead is not so big. The advantages of this solution is that it is simple and the code changes restricts to only `SparkSubmit`.
## How was this patch tested?
Unit test added, also verified in local cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#19130 from jerryshao/SPARK-21917.
## What changes were proposed in this pull request?
https://github.com/apache/spark/pull/18266 add a new feature to support read JDBC table use custom schema, but we must specify all the fields. For simplicity, this PR support specify partial fields.
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#19231 from wangyum/SPARK-22002.
## What changes were proposed in this pull request?
Auto generated Oracle schema some times not we expect:
- `number(1)` auto mapped to BooleanType, some times it's not we expect, per [SPARK-20921](https://issues.apache.org/jira/browse/SPARK-20921).
- `number` auto mapped to Decimal(38,10), It can't read big data, per [SPARK-20427](https://issues.apache.org/jira/browse/SPARK-20427).
This PR fix this issue by custom schema as follows:
```scala
val props = new Properties()
props.put("customSchema", "ID decimal(38, 0), N1 int, N2 boolean")
val dfRead = spark.read.schema(schema).jdbc(jdbcUrl, "tableWithCustomSchema", props)
dfRead.show()
```
or
```sql
CREATE TEMPORARY VIEW tableWithCustomSchema
USING org.apache.spark.sql.jdbc
OPTIONS (url '$jdbcUrl', dbTable 'tableWithCustomSchema', customSchema'ID decimal(38, 0), N1 int, N2 boolean')
```
## How was this patch tested?
unit tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18266 from wangyum/SPARK-20427.
## What changes were proposed in this pull request?
Put Kafka 0.8 support behind a kafka-0-8 profile.
## How was this patch tested?
Existing tests, but, until PR builder and Jenkins configs are updated the effect here is to not build or test Kafka 0.8 support at all.
Author: Sean Owen <sowen@cloudera.com>
Closes#19134 from srowen/SPARK-21893.
# What changes were proposed in this pull request?
Added tunable parallelism to the pyspark implementation of one vs. rest classification. Added a parallelism parameter to the Scala implementation of one vs. rest along with functionality for using the parameter to tune the level of parallelism.
I take this PR #18281 over because the original author is busy but we need merge this PR soon.
After this been merged, we can close#18281 .
## How was this patch tested?
Test suite added.
Author: Ajay Saini <ajays725@gmail.com>
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19110 from WeichenXu123/spark-21027.
## What changes were proposed in this pull request?
Recently, I found two unreachable links in the document and fixed them.
Because of small changes related to the document, I don't file this issue in JIRA but please suggest I should do it if you think it's needed.
## How was this patch tested?
Tested manually.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#19195 from sarutak/fix-unreachable-link.
## What changes were proposed in this pull request?
Fixed wrong documentation for Mean Absolute Error.
Even though the code is correct for the MAE:
```scala
Since("1.2.0")
def meanAbsoluteError: Double = {
summary.normL1(1) / summary.count
}
```
In the documentation the division by N is missing.
## How was this patch tested?
All of spark tests were run.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: FavioVazquez <favio.vazquezp@gmail.com>
Author: faviovazquez <favio.vazquezp@gmail.com>
Author: Favio André Vázquez <favio.vazquezp@gmail.com>
Closes#19190 from FavioVazquez/mae-fix.
## What changes were proposed in this pull request?
```
echo '{"field": 1}
{"field": 2}
{"field": "3"}' >/tmp/sample.json
```
```scala
import org.apache.spark.sql.types._
val schema = new StructType()
.add("field", ByteType)
.add("_corrupt_record", StringType)
val file = "/tmp/sample.json"
val dfFromFile = spark.read.schema(schema).json(file)
scala> dfFromFile.show(false)
+-----+---------------+
|field|_corrupt_record|
+-----+---------------+
|1 |null |
|2 |null |
|null |{"field": "3"} |
+-----+---------------+
scala> dfFromFile.filter($"_corrupt_record".isNotNull).count()
res1: Long = 0
scala> dfFromFile.filter($"_corrupt_record".isNull).count()
res2: Long = 3
```
When the `requiredSchema` only contains `_corrupt_record`, the derived `actualSchema` is empty and the `_corrupt_record` are all null for all rows. This PR captures above situation and raise an exception with a reasonable workaround messag so that users can know what happened and how to fix the query.
## How was this patch tested?
Added test case.
Author: Jen-Ming Chung <jenmingisme@gmail.com>
Closes#18865 from jmchung/SPARK-21610.
## What changes were proposed in this pull request?
Since [SPARK-15639](https://github.com/apache/spark/pull/13701), `spark.sql.parquet.cacheMetadata` and `PARQUET_CACHE_METADATA` is not used. This PR removes from SQLConf and docs.
## How was this patch tested?
Pass the existing Jenkins.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19129 from dongjoon-hyun/SPARK-13656.
## What changes were proposed in this pull request?
Modified `CrossValidator` and `TrainValidationSplit` to be able to evaluate models in parallel for a given parameter grid. The level of parallelism is controlled by a parameter `numParallelEval` used to schedule a number of models to be trained/evaluated so that the jobs can be run concurrently. This is a naive approach that does not check the cluster for needed resources, so care must be taken by the user to tune the parameter appropriately. The default value is `1` which will train/evaluate in serial.
## How was this patch tested?
Added unit tests for CrossValidator and TrainValidationSplit to verify that model selection is the same when run in serial vs parallel. Manual testing to verify tasks run in parallel when param is > 1. Added parameter usage to relevant examples.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16774 from BryanCutler/parallel-model-eval-SPARK-19357.
## What changes were proposed in this pull request?
Update the line "For example, the data (12:09, cat) is out of order and late, and it falls in windows 12:05 - 12:15 and 12:10 - 12:20." as follow "For example, the data (12:09, cat) is out of order and late, and it falls in windows 12:00 - 12:10 and 12:05 - 12:15." under the programming structured streaming programming guide.
Author: Riccardo Corbella <r.corbella@reply.it>
Closes#19137 from riccardocorbella/bugfix.
## What changes were proposed in this pull request?
All built-in data sources support `Partition Discovery`. We had better update the document to give the users more benefit clearly.
**AFTER**
<img width="906" alt="1" src="https://user-images.githubusercontent.com/9700541/30083628-14278908-9244-11e7-98dc-9ad45fe233a9.png">
## How was this patch tested?
```
SKIP_API=1 jekyll serve --watch
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#19139 from dongjoon-hyun/partitiondiscovery.
Mesos has secrets primitives for environment and file-based secrets, this PR adds that functionality to the Spark dispatcher and the appropriate configuration flags.
Unit tested and manually tested against a DC/OS cluster with Mesos 1.4.
Author: ArtRand <arand@soe.ucsc.edu>
Closes#18837 from ArtRand/spark-20812-dispatcher-secrets-and-labels.
This patch adds statsd sink to the current metrics system in spark core.
Author: Xiaofeng Lin <xlin@twilio.com>
Closes#9518 from xflin/statsd.
Change-Id: Ib8720e86223d4a650df53f51ceb963cd95b49a44
## What changes were proposed in this pull request?
This PR adds ML examples for the FeatureHasher transform in Scala, Java, Python.
## How was this patch tested?
Manually ran examples and verified that output is consistent for different APIs
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#19024 from BryanCutler/ml-examples-FeatureHasher-SPARK-21810.
## What changes were proposed in this pull request?
Fair Scheduler can be built via one of the following options:
- By setting a `spark.scheduler.allocation.file` property,
- By setting `fairscheduler.xml` into classpath.
These options are checked **in order** and fair-scheduler is built via first found option. If invalid path is found, `FileNotFoundException` will be expected.
This PR aims unit test coverage of these use cases and a minor documentation change has been added for second option(`fairscheduler.xml` into classpath) to inform the users.
Also, this PR was related with #16813 and has been created separately to keep patch content as isolated and to help the reviewers.
## How was this patch tested?
Added new Unit Tests.
Author: erenavsarogullari <erenavsarogullari@gmail.com>
Closes#16992 from erenavsarogullari/SPARK-19662.
History Server Launch uses SparkClassCommandBuilder for launching the server. It is observed that SPARK_CLASSPATH has been removed and deprecated. For spark-submit this takes a different route and spark.driver.extraClasspath takes care of specifying additional jars in the classpath that were previously specified in the SPARK_CLASSPATH. Right now the only way specify the additional jars for launching daemons such as history server is using SPARK_DIST_CLASSPATH (https://spark.apache.org/docs/latest/hadoop-provided.html) but this I presume is a distribution classpath. It would be nice to have a similar config like spark.driver.extraClasspath for launching daemons similar to history server.
Added new environment variable SPARK_DAEMON_CLASSPATH to set classpath for launching daemons. Tested and verified for History Server and Standalone Mode.
## How was this patch tested?
Initially, history server start script would fail for the reason being that it could not find the required jars for launching the server in the java classpath. Same was true for running Master and Worker in standalone mode. By adding the environment variable SPARK_DAEMON_CLASSPATH to the java classpath, both the daemons(History Server, Standalone daemons) are starting up and running.
Author: pgandhi <pgandhi@yahoo-inc.com>
Author: pgandhi999 <parthkgandhi9@gmail.com>
Closes#19047 from pgandhi999/master.
## What changes were proposed in this pull request?
This PR proposes both:
- Add information about Javadoc, SQL docs and few more information in `docs/README.md` and a comment in `docs/_plugins/copy_api_dirs.rb` related with Javadoc.
- Adds some commands so that the script always runs the SQL docs build under `./sql` directory (for directly running `./sql/create-docs.sh` in the root directory).
## How was this patch tested?
Manual tests with `jekyll build` and `./sql/create-docs.sh` in the root directory.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#19019 from HyukjinKwon/minor-doc-build.
JIRA ticket: https://issues.apache.org/jira/browse/SPARK-21694
## What changes were proposed in this pull request?
Spark already supports launching containers attached to a given CNI network by specifying it via the config `spark.mesos.network.name`.
This PR adds support to pass in network labels to CNI plugins via a new config option `spark.mesos.network.labels`. These network labels are key-value pairs that are set in the `NetworkInfo` of both the driver and executor tasks. More details in the related Mesos documentation: http://mesos.apache.org/documentation/latest/cni/#mesos-meta-data-to-cni-plugins
## How was this patch tested?
Unit tests, for both driver and executor tasks.
Manual integration test to submit a job with the `spark.mesos.network.labels` option, hit the mesos/state.json endpoint, and check that the labels are set in the driver and executor tasks.
ArtRand skonto
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#18910 from susanxhuynh/sh-mesos-cni-labels.
Right now the spark shuffle service has a cache for index files. It is based on a # of files cached (spark.shuffle.service.index.cache.entries). This can cause issues if people have a lot of reducers because the size of each entry can fluctuate based on the # of reducers.
We saw an issues with a job that had 170000 reducers and it caused NM with spark shuffle service to use 700-800MB or memory in NM by itself.
We should change this cache to be memory based and only allow a certain memory size used. When I say memory based I mean the cache should have a limit of say 100MB.
https://issues.apache.org/jira/browse/SPARK-21501
Manual Testing with 170000 reducers has been performed with cache loaded up to max 100MB default limit, with each shuffle index file of size 1.3MB. Eviction takes place as soon as the total cache size reaches the 100MB limit and the objects will be ready for garbage collection there by avoiding NM to crash. No notable difference in runtime has been observed.
Author: Sanket Chintapalli <schintap@yahoo-inc.com>
Closes#18940 from redsanket/SPARK-21501.
## What changes were proposed in this pull request?
This PR proposes to install `mkdocs` by `pip install` if missing in the path. Mainly to fix Jenkins's documentation build failure in `spark-master-docs`. See https://amplab.cs.berkeley.edu/jenkins/job/spark-master-docs/3580/console.
It also adds `mkdocs` as requirements in `docs/README.md`.
## How was this patch tested?
I manually ran `jekyll build` under `docs` directory after manually removing `mkdocs` via `pip uninstall mkdocs`.
Also, tested this in the same way but on CentOS Linux release 7.3.1611 (Core) where I built Spark few times but never built documentation before and `mkdocs` is not installed.
```
...
Moving back into docs dir.
Moving to SQL directory and building docs.
Missing mkdocs in your path, trying to install mkdocs for SQL documentation generation.
Collecting mkdocs
Downloading mkdocs-0.16.3-py2.py3-none-any.whl (1.2MB)
100% |████████████████████████████████| 1.2MB 574kB/s
Requirement already satisfied: PyYAML>=3.10 in /usr/lib64/python2.7/site-packages (from mkdocs)
Collecting livereload>=2.5.1 (from mkdocs)
Downloading livereload-2.5.1-py2-none-any.whl
Collecting tornado>=4.1 (from mkdocs)
Downloading tornado-4.5.1.tar.gz (483kB)
100% |████████████████████████████████| 491kB 1.4MB/s
Collecting Markdown>=2.3.1 (from mkdocs)
Downloading Markdown-2.6.9.tar.gz (271kB)
100% |████████████████████████████████| 276kB 2.4MB/s
Collecting click>=3.3 (from mkdocs)
Downloading click-6.7-py2.py3-none-any.whl (71kB)
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Requirement already satisfied: Jinja2>=2.7.1 in /usr/lib/python2.7/site-packages (from mkdocs)
Requirement already satisfied: six in /usr/lib/python2.7/site-packages (from livereload>=2.5.1->mkdocs)
Requirement already satisfied: backports.ssl_match_hostname in /usr/lib/python2.7/site-packages (from tornado>=4.1->mkdocs)
Collecting singledispatch (from tornado>=4.1->mkdocs)
Downloading singledispatch-3.4.0.3-py2.py3-none-any.whl
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Downloading backports_abc-0.5-py2.py3-none-any.whl
Requirement already satisfied: MarkupSafe>=0.23 in /usr/lib/python2.7/site-packages (from Jinja2>=2.7.1->mkdocs)
Building wheels for collected packages: tornado, Markdown
Running setup.py bdist_wheel for tornado ... done
Stored in directory: /root/.cache/pip/wheels/84/83/cd/6a04602633457269d161344755e6766d24307189b7a67ff4b7
Running setup.py bdist_wheel for Markdown ... done
Stored in directory: /root/.cache/pip/wheels/bf/46/10/c93e17ae86ae3b3a919c7b39dad3b5ccf09aeb066419e5c1e5
Successfully built tornado Markdown
Installing collected packages: singledispatch, certifi, backports-abc, tornado, livereload, Markdown, click, mkdocs
Successfully installed Markdown-2.6.9 backports-abc-0.5 certifi-2017.7.27.1 click-6.7 livereload-2.5.1 mkdocs-0.16.3 singledispatch-3.4.0.3 tornado-4.5.1
Generating markdown files for SQL documentation.
Generating HTML files for SQL documentation.
INFO - Cleaning site directory
INFO - Building documentation to directory: .../spark/sql/site
Moving back into docs dir.
Making directory api/sql
cp -r ../sql/site/. api/sql
Source: .../spark/docs
Destination: .../spark/docs/_site
Generating...
done.
Auto-regeneration: disabled. Use --watch to enable.
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18984 from HyukjinKwon/sql-doc-mkdocs.
Add an option to the JDBC data source to initialize the environment of the remote database session
## What changes were proposed in this pull request?
This proposes an option to the JDBC datasource, tentatively called " sessionInitStatement" to implement the functionality of session initialization present for example in the Sqoop connector for Oracle (see https://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html#_oraoop_oracle_session_initialization_statements ) . After each database session is opened to the remote DB, and before starting to read data, this option executes a custom SQL statement (or a PL/SQL block in the case of Oracle).
See also https://issues.apache.org/jira/browse/SPARK-21519
## How was this patch tested?
Manually tested using Spark SQL data source and Oracle JDBC
Author: LucaCanali <luca.canali@cern.ch>
Closes#18724 from LucaCanali/JDBC_datasource_sessionInitStatement.
## What changes were proposed in this pull request?
This commit adds a new argument for IllegalArgumentException message. This recent commit added the argument:
[dcac1d57f0)
## How was this patch tested?
Unit test have been passed
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Marcos P. Sanchez <mpenate@stratio.com>
Closes#18862 from mpenate/feature/exception-errorifexists.
## What changes were proposed in this pull request?
Adds a sandbox link per driver in the dispatcher ui with minimal changes after a bug was fixed here:
https://issues.apache.org/jira/browse/MESOS-4992
The sandbox uri has the following format:
http://<proxy_uri>/#/slaves/\<agent-id\>/ frameworks/ \<scheduler-id\>/executors/\<driver-id\>/browse
For dc/os the proxy uri is <dc/os uri>/mesos. For the dc/os deployment scenario and to make things easier I introduced a new config property named `spark.mesos.proxy.baseURL` which should be passed to the dispatcher when launched using --conf. If no such configuration is detected then no sandbox uri is depicted, and there is an empty column with a header (this can be changed so nothing is shown).
Within dc/os the base url must be a property for the dispatcher that we should add in the future here:
9e7c909c3b/repo/packages/S/spark/26/config.json
It is not easy to detect in different environments what is that uri so user should pass it.
## How was this patch tested?
Tested with the mesos test suite here: https://github.com/typesafehub/mesos-spark-integration-tests.
Attached image shows the ui modification where the sandbox header is added.
Tested the uri redirection the way it was suggested here:
https://issues.apache.org/jira/browse/MESOS-4992
Built mesos 1.4 from the master branch and started the mesos dispatcher with the command:
`./sbin/start-mesos-dispatcher.sh --conf spark.mesos.proxy.baseURL=http://localhost:5050 -m mesos://127.0.0.1:5050`
Run a spark example:
`./bin/spark-submit --class org.apache.spark.examples.SparkPi --master mesos://10.10.1.79:7078 --deploy-mode cluster --executor-memory 2G --total-executor-cores 2 http://<path>/spark-examples_2.11-2.1.1.jar 10`
Sandbox uri is shown at the bottom of the page:
Redirection works as expected:
Author: Stavros Kontopoulos <st.kontopoulos@gmail.com>
Closes#18528 from skonto/adds_the_sandbox_uri.
## What changes were proposed in this pull request?
When we use `bin/spark-sql` command configuring `--conf spark.hadoop.foo=bar`, the `SparkSQLCliDriver` initializes an instance of hiveconf, it does not add `foo->bar` to it.
this pr gets `spark.hadoop.*` properties from sysProps to this hiveconf
## How was this patch tested?
UT
Author: hzyaoqin <hzyaoqin@corp.netease.com>
Author: Kent Yao <yaooqinn@hotmail.com>
Closes#18668 from yaooqinn/SPARK-21451.
## What changes were proposed in this pull request?
Add missing import and missing parentheses to invoke `SparkSession::text()`.
## How was this patch tested?
Built and the code for this application, ran jekyll locally per docs/README.md.
Author: Christiam Camacho <camacho@ncbi.nlm.nih.gov>
Closes#18795 from christiam/master.
## What changes were proposed in this pull request?
Fix 2 rendering errors on configuration doc page, due to SPARK-21243 and SPARK-15355.
## How was this patch tested?
Manually built and viewed docs with jekyll
Author: Sean Owen <sowen@cloudera.com>
Closes#18793 from srowen/SPARK-21593.
## What changes were proposed in this pull request?
This pr added documents about unsupported functions in Hive UDF/UDTF/UDAF.
This pr relates to #18768 and #18527.
## How was this patch tested?
N/A
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#18792 from maropu/HOTFIX-20170731.
In programming guide, `numTasks` is used in several places as arguments of Transformations. However, in code, `numPartitions` is used. In this fix, I replace `numTasks` with `numPartitions` in programming guide for consistency.
Author: Cheng Wang <chengwang0511@gmail.com>
Closes#18774 from polarke/replace-numtasks-with-numpartitions-in-doc.
## What changes were proposed in this pull request?
Update the description of `spark.shuffle.maxChunksBeingTransferred` to include that the new coming connections will be closed when the max is hit and client should have retry mechanism.
Author: jinxing <jinxing6042@126.com>
Closes#18735 from jinxing64/SPARK-21530.
## What changes were proposed in this pull request?
This generates a documentation for Spark SQL built-in functions.
One drawback is, this requires a proper build to generate built-in function list.
Once it is built, it only takes few seconds by `sql/create-docs.sh`.
Please see https://spark-test.github.io/sparksqldoc/ that I hosted to show the output documentation.
There are few more works to be done in order to make the documentation pretty, for example, separating `Arguments:` and `Examples:` but I guess this should be done within `ExpressionDescription` and `ExpressionInfo` rather than manually parsing it. I will fix these in a follow up.
This requires `pip install mkdocs` to generate HTMLs from markdown files.
## How was this patch tested?
Manually tested:
```
cd docs
jekyll build
```
,
```
cd docs
jekyll serve
```
and
```
cd sql
create-docs.sh
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18702 from HyukjinKwon/SPARK-21485.
## What changes were proposed in this pull request?
A shuffle service can serves blocks from multiple apps/tasks. Thus the shuffle service can suffers high memory usage when lots of shuffle-reads happen at the same time. In my cluster, OOM always happens on shuffle service. Analyzing heap dump, memory cost by Netty(ChannelOutboundBufferEntry) can be up to 2~3G. It might make sense to reject "open blocks" request when memory usage is high on shuffle service.
93dd0c518d and 85c6ce6193 tried to alleviate the memory pressure on shuffle service but cannot solve the root cause. This pr proposes to control currency of shuffle read.
## How was this patch tested?
Added unit test.
Author: jinxing <jinxing6042@126.com>
Closes#18388 from jinxing64/SPARK-21175.
I find a bug about 'quick start',and created a new issues,Sean Owen let
me to make a pull request, and I do
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
## How was this patch tested?
(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.
Author: Trueman <lizhaoch@users.noreply.github.com>
Author: lizhaoch <lizhaoc@163.com>
Closes#18722 from lizhaoch/master.
## What changes were proposed in this pull request?
The examples and docs for Spark-Kinesis integrations use the deprecated KinesisUtils. We should update the docs to use the KinesisInputDStream builder to create DStreams.
## How was this patch tested?
The patch primarily updates the documents. The patch will also need to make changes to the Spark-Kinesis examples. The examples need to be tested.
Author: Yash Sharma <ysharma@atlassian.com>
Closes#18071 from yssharma/ysharma/kinesis_docs.
## What changes were proposed in this pull request?
Update the Quickstart and RDD programming guides to mention pip.
## How was this patch tested?
Built docs locally.
Author: Holden Karau <holden@us.ibm.com>
Closes#18698 from holdenk/SPARK-21434-add-pyspark-pip-documentation.
## What changes were proposed in this pull request?
Minor change to kafka integration document for structured streaming.
## How was this patch tested?
N/A, doc change only.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#18550 from viirya/minor-ss-kafka-doc.
## What changes were proposed in this pull request?
After SPARK-12661, I guess we officially dropped Python 2.6 support. It looks there are few places missing this notes.
I grepped "Python 2.6" and "python 2.6" and the results were below:
```
./core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala: // Unpickle array.array generated by Python 2.6
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
./python/pyspark/ml/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/mllib/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/serializers.py: # On Python 2.6, we can't write bytearrays to streams, so we need to convert them
./python/pyspark/sql/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/streaming/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
./python/pyspark/tests.py: # NOTE: dict is used instead of collections.Counter for Python 2.6
```
This PR only proposes to change visible changes as below:
```
./docs/rdd-programming-guide.md:Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
./docs/rdd-programming-guide.md:Note that support for Python 2.6 is deprecated as of Spark 2.0.0, and may be removed in Spark 2.2.0.
./python/pyspark/context.py: warnings.warn("Support for Python 2.6 is deprecated as of Spark 2.0.0")
```
This one is already correct:
```
./docs/index.md:Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.
```
## How was this patch tested?
```bash
grep -r "Python 2.6" .
grep -r "python 2.6" .
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#18682 from HyukjinKwon/minor-python.26.
## What changes were proposed in this pull request?
Current behavior: in Mesos cluster mode, the driver failover_timeout is set to zero. If the driver temporarily loses connectivity with the Mesos master, the framework will be torn down and all executors killed.
Proposed change: make the failover_timeout configurable via a new option, spark.mesos.driver.failoverTimeout. The default value is still zero.
Note: with non-zero failover_timeout, an explicit teardown is needed in some cases. This is captured in https://issues.apache.org/jira/browse/SPARK-21458
## How was this patch tested?
Added a unit test to make sure the config option is set while creating the scheduler driver.
Ran an integration test with mesosphere/spark showing that with a non-zero failover_timeout the Spark job finishes after a driver is disconnected from the master.
Author: Susan X. Huynh <xhuynh@mesosphere.com>
Closes#18674 from susanxhuynh/sh-mesos-failover-timeout.
## What changes were proposed in this pull request?
For configurations with external shuffle enabled, we have observed that if a very large no. of blocks are being fetched from a remote host, it puts the NM under extra pressure and can crash it. This change introduces a configuration `spark.reducer.maxBlocksInFlightPerAddress` , to limit the no. of map outputs being fetched from a given remote address. The changes applied here are applicable for both the scenarios - when external shuffle is enabled as well as disabled.
## How was this patch tested?
Ran the job with the default configuration which does not change the existing behavior and ran it with few configurations of lower values -10,20,50,100. The job ran fine and there is no change in the output. (I will update the metrics related to NM in some time.)
Author: Dhruve Ashar <dhruveashar@gmail.com>
Closes#18487 from dhruve/impr/SPARK-21243.
## What changes were proposed in this pull request?
Update internal references from programming-guide to rdd-programming-guide
See 5ddf243fd8 and https://github.com/apache/spark/pull/18485#issuecomment-314789751
Let's keep the redirector even if it's problematic to build, but not rely on it internally.
## How was this patch tested?
(Doc build)
Author: Sean Owen <sowen@cloudera.com>
Closes#18625 from srowen/SPARK-21267.2.
## What changes were proposed in this pull request?
- Remove Scala 2.10 build profiles and support
- Replace some 2.10 support in scripts with commented placeholders for 2.12 later
- Remove deprecated API calls from 2.10 support
- Remove usages of deprecated context bounds where possible
- Remove Scala 2.10 workarounds like ScalaReflectionLock
- Other minor Scala warning fixes
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17150 from srowen/SPARK-19810.
## What changes were proposed in this pull request?
Since this document became obsolete, we had better remove this for Apache Spark 2.3.0. The original document is removed via SPARK-12735 on January 2016, and currently it's just redirection page. The only reference in Apache Spark website will go directly to the destination in https://github.com/apache/spark-website/pull/54.
## How was this patch tested?
N/A. This is a removal of documentation.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#18578 from dongjoon-hyun/SPARK-REMOVE-EC2.
## What changes were proposed in this pull request?
Spark provides several ways to set configurations, either from configuration file, or from `spark-submit` command line options, or programmatically through `SparkConf` class. It may confuses beginners why some configurations set through `SparkConf` cannot take affect. So here add some docs to address this problems and let beginners know how to correctly set configurations.
## How was this patch tested?
N/A
Author: jerryshao <sshao@hortonworks.com>
Closes#18552 from jerryshao/improve-doc.
## What changes were proposed in this pull request?
In current code, reducer can break the old shuffle service when `spark.reducer.maxReqSizeShuffleToMem` is enabled. Let's refine document.
Author: jinxing <jinxing6042@126.com>
Closes#18566 from jinxing64/SPARK-21343.
## What changes were proposed in this pull request?
Some link fixes for the documentation [Running Spark on Mesos](https://spark.apache.org/docs/latest/running-on-mesos.html):
* Updated Link to Mesos Frameworks (Projects built on top of Mesos)
* Update Link to Mesos binaries from Mesosphere (former link was redirected to dcos install page)
## How was this patch tested?
Documentation was built and changed page manually/visually inspected.
No code was changed, hence no dev tests.
Since these changes are rather trivial I did not open a new JIRA ticket.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Joachim Hereth <joachim.hereth@numberfour.eu>
Closes#18564 from daten-kieker/mesos_doc_fixes.
## What changes were proposed in this pull request?
SPARK-20979 added a new structured streaming source: Rate source. This patch adds the corresponding documentation to programming guide.
## How was this patch tested?
Tested by running jekyll locally.
Author: Prashant Sharma <prashant@apache.org>
Author: Prashant Sharma <prashsh1@in.ibm.com>
Closes#18562 from ScrapCodes/spark-21069/rate-source-docs.
## What changes were proposed in this pull request?
Few changes to the Structured Streaming documentation
- Clarify that the entire stream input table is not materialized
- Add information for Ganglia
- Add Kafka Sink to the main docs
- Removed a couple of leftover experimental tags
- Added more associated reading material and talk videos.
In addition, https://github.com/apache/spark/pull/16856 broke the link to the RDD programming guide in several places while renaming the page. This PR fixes those sameeragarwal cloud-fan.
- Added a redirection to avoid breaking internal and possible external links.
- Removed unnecessary redirection pages that were there since the separate scala, java, and python programming guides were merged together in 2013 or 2014.
## How was this patch tested?
(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.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#18485 from tdas/SPARK-21267.
Current "--jars (spark.jars)", "--files (spark.files)", "--py-files (spark.submit.pyFiles)" and "--archives (spark.yarn.dist.archives)" only support non-glob path. This is OK for most of the cases, but when user requires to add more jars, files into Spark, it is too verbose to list one by one. So here propose to add glob path support for resources.
Also improving the code of downloading resources.
## How was this patch tested?
UT added, also verified manually in local cluster.
Author: jerryshao <sshao@hortonworks.com>
Closes#18235 from jerryshao/SPARK-21012.
## What changes were proposed in this pull request?
This change adds a new configuration option `spark.scheduler.listenerbus.eventqueue.size` to the configuration docs to specify the capacity of the spark listener bus event queue. Default value is 10000.
This is doc PR for [SPARK-15703](https://issues.apache.org/jira/browse/SPARK-15703).
I added option to the `Scheduling` section, however it might be more related to `Spark UI` section.
## How was this patch tested?
Manually verified correct rendering of configuration option.
Author: sadikovi <ivan.sadikov@lincolnuni.ac.nz>
Author: Ivan Sadikov <ivan.sadikov@team.telstra.com>
Closes#18476 from sadikovi/SPARK-20858.
## What changes were proposed in this pull request?
Disable spark.reducer.maxReqSizeShuffleToMem because it breaks the old shuffle service.
Credits to wangyum
Closes#18466
## How was this patch tested?
Jenkins
Author: Shixiong Zhu <shixiong@databricks.com>
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18467 from zsxwing/SPARK-21253.
## What changes were proposed in this pull request?
Currently we are running into an issue with Yarn work preserving enabled + external shuffle service.
In the work preserving enabled scenario, the failure of NM will not lead to the exit of executors, so executors can still accept and run the tasks. The problem here is when NM is failed, external shuffle service is actually inaccessible, so reduce tasks will always complain about the “Fetch failure”, and the failure of reduce stage will make the parent stage (map stage) rerun. The tricky thing here is Spark scheduler is not aware of the unavailability of external shuffle service, and will reschedule the map tasks on the executor where NM is failed, and again reduce stage will be failed with “Fetch failure”, and after 4 retries, the job is failed. This could also apply to other cluster manager with external shuffle service.
So here the main problem is that we should avoid assigning tasks to those bad executors (where shuffle service is unavailable). Current Spark's blacklist mechanism could blacklist executors/nodes by failure tasks, but it doesn't handle this specific fetch failure scenario. So here propose to improve the current application blacklist mechanism to handle fetch failure issue (especially with external shuffle service unavailable issue), to blacklist the executors/nodes where shuffle fetch is unavailable.
## How was this patch tested?
Unit test and small cluster verification.
Author: jerryshao <sshao@hortonworks.com>
Closes#17113 from jerryshao/SPARK-13669.
## What changes were proposed in this pull request?
Add lost `<tr>` tag for `configuration.md`.
## How was this patch tested?
N/A
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18372 from wangyum/docs-missing-tr.
## What changes were proposed in this pull request?
Currently the shuffle service registration timeout and retry has been hardcoded. This works well for small workloads but under heavy workload when the shuffle service is busy transferring large amount of data we see significant delay in responding to the registration request, as a result we often see the executors fail to register with the shuffle service, eventually failing the job. We need to make these two parameters configurable.
## How was this patch tested?
* Updated `BlockManagerSuite` to test registration timeout and max attempts configuration actually works.
cc sitalkedia
Author: Li Yichao <lyc@zhihu.com>
Closes#18092 from liyichao/SPARK-20640.
## What changes were proposed in this pull request?
The description for several options of File Source for structured streaming appeared in the File Sink description instead.
This pull request has two commits: The first includes changes to the version as it appeared in spark 2.1 and the second handled an additional option added for spark 2.2
## How was this patch tested?
Built the documentation by SKIP_API=1 jekyll build and visually inspected the structured streaming programming guide.
The original documentation was written by tdas and lw-lin
Author: assafmendelson <assaf.mendelson@gmail.com>
Closes#18342 from assafmendelson/spark-21123.
## What changes were proposed in this pull request?
Update Running R Tests dependence packages to:
```bash
R -e "install.packages(c('knitr', 'rmarkdown', 'testthat', 'e1071', 'survival'), repos='http://cran.us.r-project.org')"
```
## How was this patch tested?
manual tests
Author: Yuming Wang <wgyumg@gmail.com>
Closes#18271 from wangyum/building-spark.
## What changes were proposed in this pull request?
Move Hadoop delegation token code from `spark-yarn` to `spark-core`, so that other schedulers (such as Mesos), may use it. In order to avoid exposing Hadoop interfaces in spark-core, the new Hadoop delegation token classes are kept private. In order to provider backward compatiblity, and to allow YARN users to continue to load their own delegation token providers via Java service loading, the old YARN interfaces, as well as the client code that uses them, have been retained.
Summary:
- Move registered `yarn.security.ServiceCredentialProvider` classes from `spark-yarn` to `spark-core`. Moved them into a new, private hierarchy under `HadoopDelegationTokenProvider`. Client code in `HadoopDelegationTokenManager` now loads credentials from a whitelist of three providers (`HadoopFSDelegationTokenProvider`, `HiveDelegationTokenProvider`, `HBaseDelegationTokenProvider`), instead of service loading, which means that users are not able to implement their own delegation token providers, as they are in the `spark-yarn` module.
- The `yarn.security.ServiceCredentialProvider` interface has been kept for backwards compatibility, and to continue to allow YARN users to implement their own delegation token provider implementations. Client code in YARN now fetches tokens via the new `YARNHadoopDelegationTokenManager` class, which fetches tokens from the core providers through `HadoopDelegationTokenManager`, as well as service loads them from `yarn.security.ServiceCredentialProvider`.
Old Hierarchy:
```
yarn.security.ServiceCredentialProvider (service loaded)
HadoopFSCredentialProvider
HiveCredentialProvider
HBaseCredentialProvider
yarn.security.ConfigurableCredentialManager
```
New Hierarchy:
```
HadoopDelegationTokenManager
HadoopDelegationTokenProvider (not service loaded)
HadoopFSDelegationTokenProvider
HiveDelegationTokenProvider
HBaseDelegationTokenProvider
yarn.security.ServiceCredentialProvider (service loaded)
yarn.security.YARNHadoopDelegationTokenManager
```
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Author: Dr. Stefan Schimanski <sttts@mesosphere.io>
Closes#17723 from mgummelt/SPARK-20434-refactor-kerberos.
## What changes were proposed in this pull request?
doc only change
## How was this patch tested?
manually
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#18312 from felixcheung/sqljsonwholefiledoc.
## What changes were proposed in this pull request?
`df.groupBy.count()` should be `df.groupBy().count()` , otherwise there is an error :
ambiguous reference to overloaded definition, both method groupBy in class Dataset of type (col1: String, cols: String*) and method groupBy in class Dataset of type (cols: org.apache.spark.sql.Column*)
## How was this patch tested?
```scala
val df = spark.readStream.schema(...).json(...)
val dfCounts = df.groupBy().count()
```
Author: Ziyue Huang <zyhuang94@gmail.com>
Closes#18272 from ZiyueHuang/master.
## What changes were proposed in this pull request?
Add Mesos labels support to the Spark Dispatcher
## How was this patch tested?
unit tests
Author: Michael Gummelt <mgummelt@mesosphere.io>
Closes#18220 from mgummelt/SPARK-21000-dispatcher-labels.
## What changes were proposed in this pull request?
Add a new property `spark.streaming.kafka.consumer.cache.enabled` that allows users to enable or disable the cache for Kafka consumers. This property can be especially handy in cases where issues like SPARK-19185 get hit, for which there isn't a solution committed yet. By default, the cache is still on, so this change doesn't change any out-of-box behavior.
## How was this patch tested?
Running unit tests
Author: Mark Grover <mark@apache.org>
Author: Mark Grover <grover.markgrover@gmail.com>
Closes#18234 from markgrover/spark-19185.
## What changes were proposed in this pull request?
In our use case of launching Spark applications via REST APIs (Livy), there's no way for user to specify command line arguments, all Spark configurations are set through configurations map. For "--repositories" because there's no equivalent Spark configuration, so we cannot specify the custom repository through configuration.
So here propose to add "--repositories" equivalent configuration in Spark.
## How was this patch tested?
New UT added.
Author: jerryshao <sshao@hortonworks.com>
Closes#18201 from jerryshao/SPARK-20981.
## What changes were proposed in this pull request?
- Add Scala, Python and Java examples for `partitionBy`, `sortBy` and `bucketBy`.
- Add _Bucketing, Sorting and Partitioning_ section to SQL Programming Guide
- Remove bucketing from Unsupported Hive Functionalities.
## How was this patch tested?
Manual tests, docs build.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17938 from zero323/DOCS-BUCKETING-AND-PARTITIONING.
Now that Structured Streaming has been out for several Spark release and has large production use cases, the `Experimental` label is no longer appropriate. I've left `InterfaceStability.Evolving` however, as I think we may make a few changes to the pluggable Source & Sink API in Spark 2.3.
Author: Michael Armbrust <michael@databricks.com>
Closes#18065 from marmbrus/streamingGA.
## What changes were proposed in this pull request?
1, add an example for sparkr `decisionTree`
2, document it in user guide
## How was this patch tested?
local submit
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#18067 from zhengruifeng/dt_example.
(Link to Jira: https://issues.apache.org/jira/browse/SPARK-20888)
## What changes were proposed in this pull request?
Document change of default setting of spark.sql.hive.caseSensitiveInferenceMode configuration key from NEVER_INFO to INFER_AND_SAVE in the Spark SQL 2.1 to 2.2 migration notes.
Author: Michael Allman <michael@videoamp.com>
Closes#18112 from mallman/spark-20888-document_infer_and_save.
## What changes were proposed in this pull request?
Currently the whole block is fetched into memory(off heap by default) when shuffle-read. A block is defined by (shuffleId, mapId, reduceId). Thus it can be large when skew situations. If OOM happens during shuffle read, job will be killed and users will be notified to "Consider boosting spark.yarn.executor.memoryOverhead". Adjusting parameter and allocating more memory can resolve the OOM. However the approach is not perfectly suitable for production environment, especially for data warehouse.
Using Spark SQL as data engine in warehouse, users hope to have a unified parameter(e.g. memory) but less resource wasted(resource is allocated but not used). The hope is strong especially when migrating data engine to Spark from another one(e.g. Hive). Tuning the parameter for thousands of SQLs one by one is very time consuming.
It's not always easy to predict skew situations, when happen, it make sense to fetch remote blocks to disk for shuffle-read, rather than kill the job because of OOM.
In this pr, I propose to fetch big blocks to disk(which is also mentioned in SPARK-3019):
1. Track average size and also the outliers(which are larger than 2*avgSize) in MapStatus;
2. Request memory from `MemoryManager` before fetch blocks and release the memory to `MemoryManager` when `ManagedBuffer` is released.
3. Fetch remote blocks to disk when failing acquiring memory from `MemoryManager`, otherwise fetch to memory.
This is an improvement for memory control when shuffle blocks and help to avoid OOM in scenarios like below:
1. Single huge block;
2. Sizes of many blocks are underestimated in `MapStatus` and the actual footprint of blocks is much larger than the estimated.
## How was this patch tested?
Added unit test in `MapStatusSuite` and `ShuffleBlockFetcherIteratorSuite`.
Author: jinxing <jinxing6042@126.com>
Closes#16989 from jinxing64/SPARK-19659.
## What changes were proposed in this pull request?
Currently, when number of reduces is above 2000, HighlyCompressedMapStatus is used to store size of blocks. in HighlyCompressedMapStatus, only average size is stored for non empty blocks. Which is not good for memory control when we shuffle blocks. It makes sense to store the accurate size of block when it's above threshold.
## How was this patch tested?
Added test in MapStatusSuite.
Author: jinxing <jinxing6042@126.com>
Closes#18031 from jinxing64/SPARK-20801.
Quick follow up to #17996 - forgot to add the HTML links to the relevant sections of the guide in the highlights list.
## How was this patch tested?
Built docs locally and tested links.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18043 from MLnick/SPARK-20506-2.2-migration-guide-2.
Update ML guide for migration `2.1` -> `2.2` and the previous version migration guide section.
## How was this patch tested?
Build doc locally.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17996 from MLnick/SPARK-20506-2.2-migration-guide.
