spark-submit 和 spark-shell 后面可跟的参数
站在巨人的肩膀上：http://spark.apache.org/docs/latest/submitting-applications.html
Submitting Applications
The spark-submit script in Spark’s bin directory is used to launch applications on a cluster. It can use all of Spark’s supported cluster managersthrough a uniform interface so you don’t have to configure your application specially for each one.
Bundling Your Application’s Dependencies
If your code depends on other projects, you will need to package them alongside your application in order to distribute the code to a Spark cluster. To do this, create an assembly jar (or “uber” jar) containing your code and its dependencies. Both sbt and Maven have assembly plugins. When creating assembly jars, list Spark and Hadoop as provided dependencies; these need not be bundled since they are provided by the cluster manager at runtime. Once you have an assembled jar you can call the bin/spark-submit script as shown here while passing your jar.
For Python, you can use the --py-files argument of spark-submit to add .py, .zip or .egg files to be distributed with your application. If you depend on multiple Python files we recommend packaging them into a .zip or .egg.
Launching Applications with spark-submit
Once a user application is bundled, it can be launched using the bin/spark-submit script. This script takes care of setting up the classpath with Spark and its dependencies, and can support different cluster managers and deploy modes that Spark supports:
./bin/spark-submit \
--class
--master \
--deploy-mode \
--conf = \
... # other options
\
[application-arguments] Some of the commonly used options are:
--class: The entry point for your application (e.g.org.apache.spark.examples.SparkPi)--master: The master URL for the cluster (e.g.spark://23.195.26.187:7077)--deploy-mode: Whether to deploy your driver on the worker nodes (cluster) or locally as an external client (client) (default:client) †--conf: Arbitrary Spark configuration property in key=value format. For values that contain spaces wrap “key=value” in quotes (as shown).application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, anhdfs://path or afile://path that is present on all nodes.application-arguments: Arguments passed to the main method of your main class, if any
† A common deployment strategy is to submit your application from a gateway machine that is physically co-located with your worker machines (e.g. Master node in a standalone EC2 cluster). In this setup, client mode is appropriate. In client mode, the driver is launched directly within the spark-submit process which acts as a client to the cluster. The input and output of the application is attached to the console. Thus, this mode is especially suitable for applications that involve the REPL (e.g. Spark shell).
Alternatively, if your application is submitted from a machine far from the worker machines (e.g. locally on your laptop), it is common to usecluster mode to minimize network latency between the drivers and the executors. Note that cluster mode is currently not supported for Mesos clusters. Currently only YARN supports cluster mode for Python applications.
For Python applications, simply pass a .py file in the place of .zip, .egg or .py files to the search path with --py-files.
There are a few options available that are specific to the cluster manager that is being used. For example, with a Spark standalone cluster withcluster deploy mode, you can also specify --supervise to make sure that the driver is automatically restarted if it fails with non-zero exit code. To enumerate all such options available to spark-submit, run it with --help. Here are a few examples of common options:
# Run application locally on 8 cores
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master local[8] \
/path/to/examples.jar \
100
# Run on a Spark standalone cluster in client deploy mode
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://207.184.161.138:7077 \
--executor-memory 20G \
--total-executor-cores 100 \
/path/to/examples.jar \
1000
# Run on a Spark standalone cluster in cluster deploy mode with supervise
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master spark://207.184.161.138:7077 \
--deploy-mode cluster
--supervise
--executor-memory 20G \
--total-executor-cores 100 \
/path/to/examples.jar \
1000
# Run on a YARN cluster
export HADOOP_CONF_DIR=XXX
./bin/spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn-cluster \ # can also be `yarn-client` for client mode
--executor-memory 20G \
--num-executors 50 \
/path/to/examples.jar \
1000
# Run a Python application on a Spark standalone cluster
./bin/spark-submit \
--master spark://207.184.161.138:7077 \
examples/src/main/python/pi.py \
1000Master URLs
The master URL passed to Spark can be in one of the following formats:
| Master URL | Meaning |
|---|---|
| local | Run Spark locally with one worker thread (i.e. no parallelism at all). |
| local[K] | Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). |
| local[*] | Run Spark locally with as many worker threads as logical cores on your machine. |
| spark://HOST:PORT | Connect to the given Spark standalone cluster master. The port must be whichever one your master is configured to use, which is 7077 by default. |
| mesos://HOST:PORT | Connect to the given Mesos cluster. The port must be whichever one your is configured to use, which is 5050 by default. Or, for a Mesos cluster using ZooKeeper, use mesos://zk://.... |
| yarn-client | Connect to a YARN cluster in client mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable. |
| yarn-cluster | Connect to a YARN cluster in cluster mode. The cluster location will be found based on the HADOOP_CONF_DIR or YARN_CONF_DIR variable. |
Loading Configuration from a File
The spark-submit script can load default Spark configuration values from a properties file and pass them on to your application. By default it will read options from conf/spark-defaults.conf in the Spark directory. For more detail, see the section on loading default configurations.
Loading default Spark configurations this way can obviate the need for certain flags to spark-submit. For instance, if the spark.master property is set, you can safely omit the --master flag from spark-submit. In general, configuration values explicitly set on a SparkConf take the highest precedence, then flags passed to spark-submit, then values in the defaults file.
If you are ever unclear where configuration options are coming from, you can print out fine-grained debugging information by running spark-submit with the --verbose option.
Advanced Dependency Management
When using spark-submit, the application jar along with any jars included with the --jars option will be automatically transferred to the cluster. Spark uses the following URL scheme to allow different strategies for disseminating jars:
- file: - Absolute paths and
file:/URIs are served by the driver’s HTTP file server, and every executor pulls the file from the driver HTTP server. - hdfs:, http:, https:, ftp: - these pull down files and JARs from the URI as expected
- local: - a URI starting with local:/ is expected to exist as a local file on each worker node. This means that no network IO will be incurred, and works well for large files/JARs that are pushed to each worker, or shared via NFS, GlusterFS, etc.
Note that JARs and files are copied to the working directory for each SparkContext on the executor nodes. This can use up a significant amount of space over time and will need to be cleaned up. With YARN, cleanup is handled automatically, and with Spark standalone, automatic cleanup can be configured with the spark.worker.cleanup.appDataTtl property.
Users may also include any other dependencies by supplying a comma-delimited list of maven coordinates with --packages. All transitive dependencies will be handled when using this command. Additional repositories (or resolvers in SBT) can be added in a comma-delimited fashion with the flag --repositories. These commands can be used with pyspark, spark-shell, and spark-submit to include Spark Packages.
For Python, the equivalent --py-files option can be used to distribute .egg, .zip and .py libraries to executors.
bin/spark-submit -help
Options:
--master MASTER_URL spark://host:port, mesos://host:port, yarn, or local.
--deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or
on one of the worker machines inside the cluster ("cluster")
(Default: client).
--class CLASS_NAME Your application's main class (for Java / Scala apps).
--name NAME A name of your application.
--jars JARS Comma-separated list of local jars to include on the driver
and executor classpaths.
--packages Comma-separated list of maven coordinates of jars to include
on the driver and executor classpaths. Will search the local
maven repo, then maven central and any additional remote
repositories given by --repositories. The format for the
coordinates should be groupId:artifactId:version.
--exclude-packages Comma-separated list of groupId:artifactId, to exclude while
resolving the dependencies provided in --packages to avoid
dependency conflicts.
--repositories Comma-separated list of additional remote repositories to
search for the maven coordinates given with --packages.
--py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place
on the PYTHONPATH for Python apps.
--files FILES Comma-separated list of files to be placed in the working
directory of each executor.
--conf PROP=VALUE Arbitrary Spark configuration property.
--properties-file FILE Path to a file from which to load extra properties. If not
specified, this will look for conf/spark-defaults.conf.
--driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M).
--driver-java-options Extra Java options to pass to the driver.
--driver-library-path Extra library path entries to pass to the driver.
--driver-class-path Extra class path entries to pass to the driver. Note that
jars added with --jars are automatically included in the
classpath.
--executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G).
--proxy-user NAME User to impersonate when submitting the application.
--help, -h Show this help message and exit
--verbose, -v Print additional debug output
--version, Print the version of current Spark
Spark standalone with cluster deploy mode only:
--driver-cores NUM Cores for driver (Default: 1).
Spark standalone or Mesos with cluster deploy mode only:
--supervise If given, restarts the driver on failure.
--kill SUBMISSION_ID If given, kills the driver specified.
--status SUBMISSION_ID If given, requests the status of the driver specified.
Spark standalone and Mesos only:
--total-executor-cores NUM Total cores for all executors.
Spark standalone and YARN only:
--executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode,
or all available cores on the worker in standalone mode)
YARN-only:
--driver-cores NUM Number of cores used by the driver, only in cluster mode
(Default: 1).
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
--num-executors NUM Number of executors to launch (Default: 2).
--archives ARCHIVES Comma separated list of archives to be extracted into the
working directory of each executor.
--principal PRINCIPAL Principal to be used to login to KDC, while running on
secure HDFS.
--keytab KEYTAB The full path to the file that contains the keytab for the
principal specified above. This keytab will be copied to
the node running the Application Master via the Secure
Distributed Cache, for renewing the login tickets and the
delegation tokens periodically.