## 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.
