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title: Spark Standalone Mode
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In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided [launch scripts](#cluster-launch-scripts). It is also possible to run these daemons on a single machine for testing.
# Installing Spark Standalone to a Cluster
To install Spark Standalone mode, you simply place a compiled version of Spark on each node on the cluster. You can obtain pre-built versions of Spark with each release or [build it yourself](index.html#building).
# Starting a Cluster Manually
You can start a standalone master server by executing:
./sbin/start-master.sh
Once started, the master will print out a `spark://HOST:PORT` URL for itself, which you can use to connect workers to it,
or pass as the "master" argument to `SparkContext`. You can also find this URL on
the master's web UI, which is [http://localhost:8080](http://localhost:8080) by default.
Similarly, you can start one or more workers and connect them to the master via:
./bin/spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default).
You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).
Finally, the following configuration options can be passed to the master and worker:
Argument | Meaning |
-i IP , --ip IP |
IP address or DNS name to listen on |
-p PORT , --port PORT |
Port for service to listen on (default: 7077 for master, random for worker) |
--webui-port PORT |
Port for web UI (default: 8080 for master, 8081 for worker) |
-c CORES , --cores CORES |
Total CPU cores to allow Spark applications to use on the machine (default: all available); only on worker |
-m MEM , --memory MEM |
Total amount of memory to allow Spark applications to use on the machine, in a format like 1000M or 2G (default: your machine's total RAM minus 1 GB); only on worker |
-d DIR , --work-dir DIR |
Directory to use for scratch space and job output logs (default: SPARK_HOME/work); only on worker |
# Cluster Launch Scripts
To launch a Spark standalone cluster with the launch scripts, you should create a file called conf/slaves in your Spark directory,
which must contain the hostnames of all the machines where you intend to start Spark workers, one per line.
If conf/slaves does not exist, the launch scripts defaults to a single machine (localhost), which is useful for testing.
Note, the master machine accesses each of the worker machines via ssh. By default, ssh is run in parallel and requires password-less (using a private key) access to be setup.
If you do not have a password-less setup, you can set the environment variable SPARK_SSH_FOREGROUND and serially provide a password for each worker.
Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`:
- `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on.
- `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
- `sbin/start-all.sh` - Starts both a master and a number of slaves as described above.
- `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
- `sbin/stop-slaves.sh` - Stops all slave instances on the machines specified in the `conf/slaves` file.
- `sbin/stop-all.sh` - Stops both the master and the slaves as described above.
Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.
You can optionally configure the cluster further by setting environment variables in `conf/spark-env.sh`. Create this file by starting with the `conf/spark-env.sh.template`, and _copy it to all your worker machines_ for the settings to take effect. The following settings are available:
Environment Variable | Meaning |
SPARK_MASTER_IP |
Bind the master to a specific IP address, for example a public one. |
SPARK_MASTER_PORT |
Start the master on a different port (default: 7077). |
SPARK_MASTER_WEBUI_PORT |
Port for the master web UI (default: 8080). |
SPARK_MASTER_OPTS |
Configuration properties that apply only to the master in the form "-Dx=y" (default: none). See below for a list of possible options. |
SPARK_LOCAL_DIRS |
Directory to use for "scratch" space in Spark, including map output files and RDDs that get
stored on disk. This should be on a fast, local disk in your system. It can also be a
comma-separated list of multiple directories on different disks.
|
SPARK_WORKER_CORES |
Total number of cores to allow Spark applications to use on the machine (default: all available cores). |
SPARK_WORKER_MEMORY |
Total amount of memory to allow Spark applications to use on the machine, e.g. 1000m , 2g (default: total memory minus 1 GB); note that each application's individual memory is configured using its spark.executor.memory property. |
SPARK_WORKER_PORT |
Start the Spark worker on a specific port (default: random). |
SPARK_WORKER_WEBUI_PORT |
Port for the worker web UI (default: 8081). |
SPARK_WORKER_INSTANCES |
Number of worker instances to run on each machine (default: 1). You can make this more than 1 if
you have have very large machines and would like multiple Spark worker processes. If you do set
this, make sure to also set SPARK_WORKER_CORES explicitly to limit the cores per worker,
or else each worker will try to use all the cores.
|
SPARK_WORKER_DIR |
Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work). |
SPARK_WORKER_OPTS |
Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). See below for a list of possible options. |
SPARK_DAEMON_MEMORY |
Memory to allocate to the Spark master and worker daemons themselves (default: 512m). |
SPARK_DAEMON_JAVA_OPTS |
JVM options for the Spark master and worker daemons themselves in the form "-Dx=y" (default: none). |
SPARK_PUBLIC_DNS |
The public DNS name of the Spark master and workers (default: none). |
**Note:** The launch scripts do not currently support Windows. To run a Spark cluster on Windows, start the master and workers by hand.
SPARK_MASTER_OPTS supports the following system properties:
Property Name | Default | Meaning |
spark.deploy.retainedApplications |
200 |
The maximum number of completed applications to display. Older applications will be dropped from the UI to maintain this limit.
|
spark.deploy.retainedDrivers |
200 |
The maximum number of completed drivers to display. Older drivers will be dropped from the UI to maintain this limit.
|
spark.deploy.spreadOut |
true |
Whether the standalone cluster manager should spread applications out across nodes or try
to consolidate them onto as few nodes as possible. Spreading out is usually better for
data locality in HDFS, but consolidating is more efficient for compute-intensive workloads.
|
spark.deploy.defaultCores |
(infinite) |
Default number of cores to give to applications in Spark's standalone mode if they don't
set spark.cores.max . If not set, applications always get all available
cores unless they configure spark.cores.max themselves.
Set this lower on a shared cluster to prevent users from grabbing
the whole cluster by default.
|
spark.worker.timeout |
60 |
Number of seconds after which the standalone deploy master considers a worker lost if it
receives no heartbeats.
|
SPARK_WORKER_OPTS supports the following system properties:
Property Name | Default | Meaning |
spark.worker.cleanup.enabled |
false |
Enable periodic cleanup of worker / application directories. Note that this only affects standalone
mode, as YARN works differently. Applications directories are cleaned up regardless of whether
the application is still running.
|
spark.worker.cleanup.interval |
1800 (30 minutes) |
Controls the interval, in seconds, at which the worker cleans up old application work dirs
on the local machine.
|
spark.worker.cleanup.appDataTtl |
7 * 24 * 3600 (7 days) |
The number of seconds to retain application work directories on each worker. This is a Time To Live
and should depend on the amount of available disk space you have. Application logs and jars are
downloaded to each application work dir. Over time, the work dirs can quickly fill up disk space,
especially if you run jobs very frequently.
|
# Connecting an Application to the Cluster
To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext`
constructor](programming-guide.html#initializing-spark).
To run an interactive Spark shell against the cluster, run the following command:
./bin/spark-shell --master spark://IP:PORT
You can also pass an option `--total-executor-cores