spark-instrumented-optimizer/docs/spark-standalone.md

layout	title
global	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. It is also possible to run these daemons on a single machine for testing.

Installing Spark Standalone to a Cluster

To install Spark Standlone 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.

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 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 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 need to create a file called conf/slaves in your Spark directory, which should contain the hostnames of all the machines where you would like to start Spark workers, one per line. The master machine must be able to access each of the slave machines via password-less ssh (using a private key). For testing, you can just put localhost in this file.

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 the slave instances that were started via bin/start-slaves.sh.
• 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_WORKER_PORT	Start the Spark worker on a specific port (default: random).
SPARK_WORKER_DIR	Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work).
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_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_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 (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.

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.

To run an interactive Spark shell against the cluster, run the following command:

MASTER=spark://IP:PORT ./bin/spark-shell

Note that if you are running spark-shell from one of the spark cluster machines, the bin/spark-shell script will automatically set MASTER from the SPARK_MASTER_IP and SPARK_MASTER_PORT variables in conf/spark-env.sh.

You can also pass an option -c <numCores> to control the number of cores that spark-shell uses on the cluster.

Launching Applications Inside the Cluster

You may also run your application entirely inside of the cluster by submitting your application driver using the submission client. The syntax for submitting applications is as follows:

./bin/spark-class org.apache.spark.deploy.Client launch
   [client-options] \
   <cluster-url> <application-jar-url> <main-class> \
   [application-options]

cluster-url: The URL of the master node.
application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. 
main-class: The entry point for your application.

Client Options:
  --memory <count> (amount of memory, in MB, allocated for your driver program)
  --cores <count> (number of cores allocated for your driver program)
  --supervise (whether to automatically restart your driver on application or node failure)
  --verbose (prints increased logging output)

Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways:

• Environment variables: These will be captured from the environment in which you launch the client and applied when launching the driver program.
• Java options: You can add java options by setting SPARK_JAVA_OPTS in the environment in which you launch the submission client.
• Dependencies: You'll still need to call sc.addJar inside of your program to make your bundled application jar visible on all worker nodes.

Once you submit a driver program, it will appear in the cluster management UI at port 8080 and be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using the same client:

./bin/spark-class org.apache.spark.deploy.Client kill <driverId>

Resource Scheduling

The standalone cluster mode currently only supports a simple FIFO scheduler across applications. However, to allow multiple concurrent users, you can control the maximum number of resources each application will use. By default, it will acquire all cores in the cluster, which only makes sense if you just run one application at a time. You can cap the number of cores by setting spark.cores.max in your SparkConf. For example:

{% highlight scala %} val conf = new SparkConf() .setMaster(...) .setAppName(...) .set("spark.cores.max", "10") val sc = new SparkContext(conf) {% endhighlight %}

In addition, you can configure spark.deploy.defaultCores on the cluster master process to change the default for applications that don't set spark.cores.max to something less than infinite. Do this by adding the following to conf/spark-env.sh:

{% highlight bash %} export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=" {% endhighlight %}

This is useful on shared clusters where users might not have configured a maximum number of cores individually.

Monitoring and Logging

Spark's standalone mode offers a web-based user interface to monitor the cluster. The master and each worker has its own web UI that shows cluster and job statistics. By default you can access the web UI for the master at port 8080. The port can be changed either in the configuration file or via command-line options.

In addition, detailed log output for each job is also written to the work directory of each slave node (SPARK_HOME/work by default). You will see two files for each job, stdout and stderr, with all output it wrote to its console.

Running Alongside Hadoop

You can run Spark alongside your existing Hadoop cluster by just launching it as a separate service on the same machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically hdfs://<namenode>:9000/path, but you can find the right URL on your Hadoop Namenode's web UI). Alternatively, you can set up a separate cluster for Spark, and still have it access HDFS over the network; this will be slower than disk-local access, but may not be a concern if you are still running in the same local area network (e.g. you place a few Spark machines on each rack that you have Hadoop on).

High Availability

By default, standalone scheduling clusters are resilient to Worker failures (insofar as Spark itself is resilient to losing work by moving it to other workers). However, the scheduler uses a Master to make scheduling decisions, and this (by default) creates a single point of failure: if the Master crashes, no new applications can be created. In order to circumvent this, we have two high availability schemes, detailed below.

Standby Masters with ZooKeeper

Overview

Utilizing ZooKeeper to provide leader election and some state storage, you can launch multiple Masters in your cluster connected to the same ZooKeeper instance. One will be elected "leader" and the others will remain in standby mode. If the current leader dies, another Master will be elected, recover the old Master's state, and then resume scheduling. The entire recovery process (from the time the the first leader goes down) should take between 1 and 2 minutes. Note that this delay only affects scheduling new applications -- applications that were already running during Master failover are unaffected.

Learn more about getting started with ZooKeeper here.

Configuration

In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:

System property	Meaning
spark.deploy.recoveryMode	Set to ZOOKEEPER to enable standby Master recovery mode (default: NONE).
spark.deploy.zookeeper.url	The ZooKeeper cluster url (e.g., 192.168.1.100:2181,192.168.1.101:2181).
spark.deploy.zookeeper.dir	The directory in ZooKeeper to store recovery state (default: /spark).

Possible gotcha: If you have multiple Masters in your cluster but fail to correctly configure the Masters to use ZooKeeper, the Masters will fail to discover each other and think they're all leaders. This will not lead to a healthy cluster state (as all Masters will schedule independently).

Details

After you have a ZooKeeper cluster set up, enabling high availability is straightforward. Simply start multiple Master processes on different nodes with the same ZooKeeper configuration (ZooKeeper URL and directory). Masters can be added and removed at any time.

In order to schedule new applications or add Workers to the cluster, they need to know the IP address of the current leader. This can be accomplished by simply passing in a list of Masters where you used to pass in a single one. For example, you might start your SparkContext pointing to spark://host1:port1,host2:port2. This would cause your SparkContext to try registering with both Masters -- if host1 goes down, this configuration would still be correct as we'd find the new leader, host2.

There's an important distinction to be made between "registering with a Master" and normal operation. When starting up, an application or Worker needs to be able to find and register with the current lead Master. Once it successfully registers, though, it is "in the system" (i.e., stored in ZooKeeper). If failover occurs, the new leader will contact all previously registered applications and Workers to inform them of the change in leadership, so they need not even have known of the existence of the new Master at startup.

Due to this property, new Masters can be created at any time, and the only thing you need to worry about is that new applications and Workers can find it to register with in case it becomes the leader. Once registered, you're taken care of.

Single-Node Recovery with Local File System

Overview

ZooKeeper is the best way to go for production-level high availability, but if you just want to be able to restart the Master if it goes down, FILESYSTEM mode can take care of it. When applications and Workers register, they have enough state written to the provided directory so that they can be recovered upon a restart of the Master process.

Configuration

In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env using this configuration:

System property	Meaning
spark.deploy.recoveryMode	Set to FILESYSTEM to enable single-node recovery mode (default: NONE).
spark.deploy.recoveryDirectory	The directory in which Spark will store recovery state, accessible from the Master's perspective.

Details

• This solution can be used in tandem with a process monitor/manager like monit, or just to enable manual recovery via restart.
• While filesystem recovery seems straightforwardly better than not doing any recovery at all, this mode may be suboptimal for certain development or experimental purposes. In particular, killing a master via stop-master.sh does not clean up its recovery state, so whenever you start a new Master, it will enter recovery mode. This could increase the startup time by up to 1 minute if it needs to wait for all previously-registered Workers/clients to timeout.
• While it's not officially supported, you could mount an NFS directory as the recovery directory. If the original Master node dies completely, you could then start a Master on a different node, which would correctly recover all previously registered Workers/applications (equivalent to ZooKeeper recovery). Future applications will have to be able to find the new Master, however, in order to register.