Apache Spark - A unified analytics engine for large-scale data processing
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gwang3 64fe82b519 [SPARK-29189][SQL] Add an option to ignore block locations when listing file
### What changes were proposed in this pull request?
In our PROD env, we have a pure Spark cluster, I think this is also pretty common, where computation is separated from storage layer. In such deploy mode, data locality is never reachable.
And there are some configurations in Spark scheduler to reduce waiting time for data locality(e.g. "spark.locality.wait"). While, problem is that, in listing file phase, the location informations of all the files, with all the blocks inside each file, are all fetched from the distributed file system. Actually, in a PROD environment, a table can be so huge that even fetching all these location informations need take tens of seconds.
To improve such scenario, Spark need provide an option, where data locality can be totally ignored, all we need in the listing file phase are the files locations, without any block location informations.

### Why are the changes needed?
And we made a benchmark in our PROD env, after ignore the block locations, we got a pretty huge improvement.

Table Size | Total File Number | Total Block Number | List File Duration(With Block Location) | List File Duration(Without Block Location)
-- | -- | -- | -- | --
22.6T | 30000 | 120000 | 16.841s | 1.730s
28.8 T | 42001 | 148964 | 10.099s | 2.858s
3.4 T | 20000 | 20000 | 5.833s | 4.881s

### Does this PR introduce any user-facing change?
No.

### How was this patch tested?
Via ut.

Closes #25869 from wangshisan/SPARK-29189.

Authored-by: gwang3 <gwang3@ebay.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
2019-10-07 14:52:55 -05:00
.github [SPARK-29199][INFRA] Add linters and license/dependency checkers to GitHub Action 2019-09-21 08:13:00 -07:00
assembly [SPARK-27300][GRAPH] Add Spark Graph modules and dependencies 2019-06-09 00:26:26 -07:00
bin [SPARK-28525][DEPLOY] Allow Launcher to be applied Java options 2019-07-30 12:45:32 -07:00
build [SPARK-29159][BUILD] Increase ReservedCodeCacheSize to 1G 2019-09-19 00:24:15 -07:00
common [SPARK-29342][SQL] Make casting of string values to intervals case insensitive 2019-10-07 09:33:01 -07:00
conf [SPARK-29032][CORE] Add PrometheusServlet to monitor Master/Worker/Driver 2019-09-13 21:31:21 +00:00
core [SPARK-29263][SCHEDULER][FOLLOWUP][TEST] Update FakeTask.createTaskSet() method 2019-10-05 14:44:58 -07:00
data [SPARK-22666][ML][SQL] Spark datasource for image format 2018-09-05 11:59:00 -07:00
dev [SPARK-29332][BUILD] Update zstd-jni to 1.4.3-1 2019-10-02 11:37:02 -07:00
docs [SPARK-28816][DOC][SQL] Document ADD JAR statement in SQL Reference 2019-10-07 13:39:03 -05:00
examples [SPARK-29291][CORE][SQL][STREAMING][MLLIB] Change procedure-like declaration to function + Unit for 2.13 2019-09-30 10:03:23 -07:00
external [SPARK-29054][SS] Invalidate Kafka consumer when new delegation token available 2019-10-03 09:34:31 -07:00
graph [SPARK-27300][GRAPH] Add Spark Graph modules and dependencies 2019-06-09 00:26:26 -07:00
graphx [SPARK-29291][CORE][SQL][STREAMING][MLLIB] Change procedure-like declaration to function + Unit for 2.13 2019-09-30 10:03:23 -07:00
hadoop-cloud [SPARK-28903][STREAMING][PYSPARK][TESTS] Fix AWS JDK version conflict that breaks Pyspark Kinesis tests 2019-08-31 10:29:46 -05:00
launcher [SPARK-29070][CORE] Make SparkLauncher log full spark-submit command line 2019-09-27 11:32:22 -07:00
licenses [SPARK-27557][DOC] Add copy button to Python API docs for easier copying of code-blocks 2019-05-01 11:26:18 -05:00
licenses-binary [SPARK-29305][BUILD] Update LICENSE and NOTICE for Hadoop 3.2 2019-10-03 01:02:41 -05:00
mllib [SPARK-29363][MLLIB] Make o.a.s.regression.Regressor public 2019-10-05 18:16:28 -07:00
mllib-local [SPARK-29307][BUILD][TESTS] Remove scalatest deprecation warnings 2019-09-30 21:00:11 -07:00
project [SPARK-29282][TESTS] Use the same VM configurations for test/benchmark 2019-09-29 15:11:46 -07:00
python [SPARK-29143][PYTHON][ML] Pyspark feature models support column setters/getters 2019-10-07 10:55:48 -05:00
R [SPARK-29339][R] Support Arrow 0.14 in vectoried dapply and gapply (test it in AppVeyor build) 2019-10-04 08:56:45 +09:00
repl [SPARK-29307][BUILD][TESTS] Remove scalatest deprecation warnings 2019-09-30 21:00:11 -07:00
resource-managers [SPARK-28938][K8S] Move to supported OpenJDK docker image for Kubernetes 2019-10-07 08:52:35 -07:00
sbin [SPARK-28164] Fix usage description of start-slave.sh 2019-06-26 12:42:33 -05:00
sql [SPARK-29189][SQL] Add an option to ignore block locations when listing file 2019-10-07 14:52:55 -05:00
streaming [SPARK-29296][BUILD][CORE] Remove use of .par to make 2.13 support easier; add scala-2.13 profile to enable pulling in par collections library separately, for the future 2019-10-03 08:56:08 -05:00
tools [SPARK-29291][CORE][SQL][STREAMING][MLLIB] Change procedure-like declaration to function + Unit for 2.13 2019-09-30 10:03:23 -07:00
.gitattributes [SPARK-3870] EOL character enforcement 2014-10-31 12:39:52 -07:00
.gitignore [SPARK-27371][CORE] Support GPU-aware resources scheduling in Standalone 2019-08-09 07:49:03 -05:00
appveyor.yml [SPARK-29339][R] Support Arrow 0.14 in vectoried dapply and gapply (test it in AppVeyor build) 2019-10-04 08:56:45 +09:00
CONTRIBUTING.md [MINOR][DOCS] Tighten up some key links to the project and download pages to use HTTPS 2019-05-21 10:56:42 -07:00
LICENSE [SPARK-27557][DOC] Add copy button to Python API docs for easier copying of code-blocks 2019-05-01 11:26:18 -05:00
LICENSE-binary [SPARK-29305][BUILD] Update LICENSE and NOTICE for Hadoop 3.2 2019-10-03 01:02:41 -05:00
NOTICE [SPARK-23654][BUILD] remove jets3t as a dependency of spark 2018-08-16 12:34:23 -07:00
NOTICE-binary [SPARK-29305][BUILD] Update LICENSE and NOTICE for Hadoop 3.2 2019-10-03 01:02:41 -05:00
pom.xml [SPARK-29296][BUILD][CORE] Remove use of .par to make 2.13 support easier; add scala-2.13 profile to enable pulling in par collections library separately, for the future 2019-10-03 08:56:08 -05:00
README.md [SPARK-28473][DOC] Stylistic consistency of build command in README 2019-07-23 16:29:46 -07:00
scalastyle-config.xml [SPARK-25986][BUILD] Add rules to ban throw Errors in application code 2018-11-14 13:05:18 -08:00

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

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Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

./build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1,000,000,000:

scala> spark.range(1000 * 1000 * 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1,000,000,000:

>>> spark.range(1000 * 1000 * 1000).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.