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---
layout: global
title: Using the Spark EC2 Scripts
---
This guide describes how to get Spark running on an EC2 cluster, including how to launch clusters, how to run jobs on them, and how to shut them down. It assumes you have already signed up for Amazon EC2 account on the [Amazon Web Services site](http://aws.amazon.com/).
The `spark-ec2` script, located in Spark's `ec2` directory, allows you
to launch, manage and shut down Spark clusters on Amazon EC2. It builds
on the [Mesos EC2 script](https://github.com/mesos/mesos/wiki/EC2-Scripts)
in Apache Mesos.
`spark-ec2` is designed to manage multiple named clusters. You can
launch a new cluster (telling the script its size and giving it a name),
shutdown an existing cluster, or log into a cluster. Each cluster is
identified by placing its machines into EC2 security groups whose names
are derived from the name of the cluster. For example, a cluster named
`test` will contain a master node in a security group called
`test-master`, and a number of slave nodes in a security group called
`test-slaves`. The `spark-ec2` script will create these security groups
for you based on the cluster name you request. You can also use them to
identify machines belonging to each cluster in the EC2 Console or
ElasticFox.
# Before You Start
- Create an Amazon EC2 key pair for yourself. This can be done by
logging into your Amazon Web Services account through the [AWS
console](http://aws.amazon.com/console/), clicking Key Pairs on the
left sidebar, and creating and downloading a key. Make sure that you
set the permissions for the private key file to `600` (i.e. only you
can read and write it) so that `ssh` will work.
- Whenever you want to use the `spark-ec2` script, set the environment
variables `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY` to your
Amazon EC2 access key ID and secret access key. These can be
obtained from the [AWS homepage](http://aws.amazon.com/) by clicking
Account \> Security Credentials \> Access Credentials.
# Launching a Cluster
- Go into the `ec2` directory in the release of Spark you downloaded.
- Run
`./spark-ec2 -k <keypair> -i <key-file> -s <num-slaves> launch <cluster-name>`,
where `<keypair>` is the name of your EC2 key pair (that you gave it
when you created it), `<key-file>` is the private key file for your
key pair, `<num-slaves>` is the number of slave nodes to launch (try
1 at first), and `<cluster-name>` is the name to give to your
cluster.
- After everything launches, check that Mesos is up and sees all the
slaves by going to the Mesos Web UI link printed at the end of the
script (`http://<master-hostname>:8080`).
You can also run `./spark-ec2 --help` to see more usage options. The
following options are worth pointing out:
- `--instance-type=<INSTANCE_TYPE>` can be used to specify an EC2
instance type to use. For now, the script only supports 64-bit instance
types, and the default type is `m1.large` (which has 2 cores and 7.5 GB
RAM). Refer to the Amazon pages about [EC2 instance
types](http://aws.amazon.com/ec2/instance-types) and [EC2
pricing](http://aws.amazon.com/ec2/#pricing) for information about other
instance types.
- `--zone=<EC2_ZONE>` can be used to specify an EC2 availability zone
to launch instances in. Sometimes, you will get an error because there
is not enough capacity in one zone, and you should try to launch in
another. This happens mostly with the `m1.large` instance types;
extra-large (both `m1.xlarge` and `c1.xlarge`) instances tend to be more
available.
- `--ebs-vol-size=GB` will attach an EBS volume with a given amount
of space to each node so that you can have a persistent HDFS cluster
on your nodes across cluster restarts (see below).
- If one of your launches fails due to e.g. not having the right
permissions on your private key file, you can run `launch` with the
`--resume` option to restart the setup process on an existing cluster.
# Running Jobs
- Go into the `ec2` directory in the release of Spark you downloaded.
- Run `./spark-ec2 -k <keypair> -i <key-file> login <cluster-name>` to
SSH into the cluster, where `<keypair>` and `<key-file>` are as
above. (This is just for convenience; you could also use
the EC2 console.)
- To deploy code or data within your cluster, you can log in and use the
provided script `~/mesos-ec2/copy-dir`, which,
given a directory path, RSYNCs it to the same location on all the slaves.
- If your job needs to access large datasets, the fastest way to do
that is to load them from Amazon S3 or an Amazon EBS device into an
instance of the Hadoop Distributed File System (HDFS) on your nodes.
The `spark-ec2` script already sets up a HDFS instance for you. It's
installed in `/root/ephemeral-hdfs`, and can be accessed using the
`bin/hadoop` script in that directory. Note that the data in this
HDFS goes away when you stop and restart a machine.
- There is also a *persistent HDFS* instance in
`/root/presistent-hdfs` that will keep data across cluster restarts.
Typically each node has relatively little space of persistent data
(about 3 GB), but you can use the `--ebs-vol-size` option to
`spark-ec2` to attach a persistent EBS volume to each node for
storing the persistent HDFS.
- Finally, if you get errors while running your jobs, look at the slave's logs
for that job using the Mesos web UI (`http://<master-hostname>:8080`).
# Terminating a Cluster
***Note that there is no way to recover data on EC2 nodes after shutting
them down! Make sure you have copied everything important off the nodes
before stopping them.***
- Go into the `ec2` directory in the release of Spark you downloaded.
- Run `./spark-ec2 destroy <cluster-name>`.
# Pausing and Restarting Clusters
The `spark-ec2` script also supports pausing a cluster. In this case,
the VMs are stopped but not terminated, so they
***lose all data on ephemeral disks*** but keep the data in their
root partitions and their `persistent-hdfs`. Stopped machines will not
cost you any EC2 cycles, but ***will*** continue to cost money for EBS
storage.
- To stop one of your clusters, go into the `ec2` directory and run
`./spark-ec2 stop <cluster-name>`.
- To restart it later, run
`./spark-ec2 -i <key-file> start <cluster-name>`.
- To ultimately destroy the cluster and stop consuming EBS space, run
`./spark-ec2 destroy <cluster-name>` as described in the previous
section.
# Limitations
- `spark-ec2` currently only launches machines in the US-East region of EC2.
It should not be hard to make it launch VMs in other zones, but you will need
to create your own AMIs in them.
- Support for "cluster compute" nodes is limited -- there's no way to specify a
locality group. However, you can launch slave nodes in your
`<clusterName>-slaves` group manually and then use `spark-ec2 launch
--resume` to start a cluster with them.
- Support for spot instances is limited.
If you have a patch or suggestion for one of these limitations, feel free to
[contribute]({{HOME_PATH}}contributing-to-spark.html) it!
# Using a Newer Spark Version
The Spark EC2 machine images may not come with the latest version of Spark. To use a newer version, you can run `git pull` to pull in `/root/spark` to pull in the latest version of Spark from `git`, and build it using `sbt/sbt compile`. You will also need to copy it to all the other nodes in the cluster using `~/mesos-ec2/copy-dir /root/spark`.
# Accessing Data in S3
Spark's file interface allows it to process data in Amazon S3 using the same URI formats that are supported for Hadoop. You can specify a path in S3 as input through a URI of the form `s3n://<id>:<secret>@<bucket>/path`, where `<id>` is your Amazon access key ID and `<secret>` is your Amazon secret access key. Note that you should escape any `/` characters in the secret key as `%2F`. Full instructions can be found on the [Hadoop S3 page](http://wiki.apache.org/hadoop/AmazonS3).
In addition to using a single input file, you can also use a directory of files as input by simply giving the path to the directory.
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