The spark-ec2
script, located in Spark‘s ec2
directory, allows you to launch, manage and shut down Spark clusters on Amazon EC2. It automatically sets up Spark, Shark and HDFS on the cluster for you. This guide describes how to use spark-ec2
to launch clusters, how to run jobs on them, and how to shut them down. It assumes you’ve already signed up for an EC2 account on the Amazon Web Services site.
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 Amazon EC2 Console.
600
(i.e. only you can read and write it) so that ssh
will work.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 by clicking Account > Security Credentials > Access Credentials.ec2
directory in the release of Spark you downloaded../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.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 and EC2 pricing for information about other instance types.--region=<EC2_REGION>
specifies an EC2 region in which to launch instances. The default region is us-east-1
.--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.--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).--spot-price=PRICE
will launch the worker nodes as Spot Instances, bidding for the given maximum price (in dollars).--spark-version=VERSION
will pre-load the cluster with the specified version of Spark. VERSION can be a version number (e.g. “0.7.3”) or a specific git hash. By default, a recent version will be used.launch
with the --resume
option to restart the setup process on an existing cluster.ec2
directory in the release of Spark you downloaded../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.)~/spark-ec2/copy-dir
, which, given a directory path, RSYNCs it to the same location on all the slaves.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./root/persistent-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.http://<master-hostname>:8080
.You can edit /root/spark/conf/spark-env.sh
on each machine to set Spark configuration options, such as JVM options. This file needs to be copied to every machine to reflect the change. The easiest way to do this is to use a script we provide called copy-dir
. First edit your spark-env.sh
file on the master, then run ~/spark-ec2/copy-dir /root/spark/conf
to RSYNC it to all the workers.
The configuration guide describes the available configuration options.
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.
ec2
directory in the release of Spark you downloaded../spark-ec2 destroy <cluster-name>
.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.
ec2
directory and run ./spark-ec2 stop <cluster-name>
../spark-ec2 -i <key-file> start <cluster-name>
../spark-ec2 destroy <cluster-name>
as described in the previous section.<clusterName>-slaves
group manually and then use spark-ec2 launch --resume
to start a cluster with them.If you have a patch or suggestion for one of these limitations, feel free to contribute it!
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://<bucket>/path
. You will also need to set your Amazon security credentials, either by setting the environment variables AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
before your program or through SparkContext.hadoopConfiguration
. Full instructions on S3 access using the Hadoop input libraries can be found on the Hadoop S3 page.
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.