Running Spark on EC2 - Spark 1.6.2 Documentation (2024)

The spark-ec2 script, located in Spark’s ec2 directory, allows youto launch, manage and shut down Spark clusters on Amazon EC2. It automaticallysets up Spark 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 canlaunch a new cluster (telling the script its size and giving it a name),shutdown an existing cluster, or log into a cluster. Each cluster isidentified by placing its machines into EC2 security groups whose namesare derived from the name of the cluster. For example, a cluster namedtest will contain a master node in a security group calledtest-master, and a number of slave nodes in a security group calledtest-slaves. The spark-ec2 script will create these security groupsfor you based on the cluster name you request. You can also use them toidentify machines belonging to each cluster in the Amazon EC2 Console.

• Create an Amazon EC2 key pair for yourself. This can be done bylogging into your Amazon Web Services account through the AWSconsole, clicking Key Pairs on theleft sidebar, and creating and downloading a key. Make sure that youset the permissions for the private key file to 600 (i.e. only youcan read and write it) so that ssh will work.
• Whenever you want to use the spark-ec2 script, set the environmentvariables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY to yourAmazon EC2 access key ID and secret access key. These can beobtained from the AWS homepage by clickingAccount > Security Credentials > Access Credentials.

• 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 itwhen you created it), <key-file> is the private key file for yourkey pair, <num-slaves> is the number of slave nodes to launch (try1 at first), and <cluster-name> is the name to give to yourcluster.

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  For example:

  bashexport AWS_SECRET_ACCESS_KEY=AaBbCcDdEeFGgHhIiJjKkLlMmNnOoPpQqRrSsTtUexport AWS_ACCESS_KEY_ID=ABCDEFG1234567890123./spark-ec2 --key-pair=awskey --identity-file=awskey.pem --region=us-west-1 --zone=us-west-1a launch my-spark-cluster

• After everything launches, check that the cluster scheduler is up and seesall the slaves by going to its web UI, which will be printed at the end ofthe script (typically http://<master-hostname>:8080).

You can also run ./spark-ec2 --help to see more usage options. Thefollowing options are worth pointing out:

• --instance-type=<instance-type> can be used to specify an EC2instance type to use. For now, the script only supports 64-bit instancetypes, and the default type is m1.large (which has 2 cores and 7.5 GBRAM). Refer to the Amazon pages about EC2 instancetypes and EC2pricing for information about otherinstance types.
• --region=<ec2-region> specifies an EC2 region in which to launchinstances. The default region is us-east-1.
• --zone=<ec2-zone> can be used to specify an EC2 availability zoneto launch instances in. Sometimes, you will get an error because thereis not enough capacity in one zone, and you should try to launch inanother.
• --ebs-vol-size=<GB> will attach an EBS volume with a given amountof space to each node so that you can have a persistent HDFS clusteron your nodes across cluster restarts (see below).
• --spot-price=<price> will launch the worker nodes asSpot Instances,bidding for the given maximum price (in dollars).
• --spark-version=<version> will pre-load the cluster with thespecified version of Spark. The <version> can be a version number(e.g. “0.7.3”) or a specific git hash. By default, a recentversion will be used.
• --spark-git-repo=<repository url> will let you run a custom version ofSpark that is built from the given git repository. By default, theApache Github mirror will be used.When using a custom Spark version, --spark-version must be set to gitcommit hash, such as 317e114, instead of a version number.
• If one of your launches fails due to e.g. not having the rightpermissions on your private key file, you can run launch with the--resume option to restart the setup process on an existing cluster.

• Run./spark-ec2 -k <keypair> -i <key-file> -s <num-slaves> --vpc-id=<vpc-id> --subnet-id=<subnet-id> launch <cluster-name>,where <keypair> is the name of your EC2 key pair (that you gave itwhen you created it), <key-file> is the private key file for yourkey pair, <num-slaves> is the number of slave nodes to launch (try1 at first), <vpc-id> is the name of your VPC, <subnet-id> is thename of your subnet, and <cluster-name> is the name to give to yourcluster.

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  For example:

  bashexport AWS_SECRET_ACCESS_KEY=AaBbCcDdEeFGgHhIiJjKkLlMmNnOoPpQqRrSsTtUexport AWS_ACCESS_KEY_ID=ABCDEFG1234567890123./spark-ec2 --key-pair=awskey --identity-file=awskey.pem --region=us-west-1 --zone=us-west-1a --vpc-id=vpc-a28d24c7 --subnet-id=subnet-4eb27b39 --spark-version=1.1.0 launch my-spark-cluster

• Go into the ec2 directory in the release of Spark you downloaded.
• Run ./spark-ec2 -k <keypair> -i <key-file> login <cluster-name> toSSH into the cluster, where <keypair> and <key-file> are asabove. (This is just for convenience; you could also usethe EC2 console.)
• To deploy code or data within your cluster, you can log in and use theprovided script ~/spark-ec2/copy-dir, which,given a directory path, RSYNCs it to the same location on all the slaves.
• If your application needs to access large datasets, the fastest way to dothat is to load them from Amazon S3 or an Amazon EBS device into aninstance of the Hadoop Distributed File System (HDFS) on your nodes.The spark-ec2 script already sets up a HDFS instance for you. It’sinstalled in /root/ephemeral-hdfs, and can be accessed using thebin/hadoop script in that directory. Note that the data in thisHDFS goes away when you stop and restart a machine.
• There is also a persistent HDFS instance in/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 tospark-ec2 to attach a persistent EBS volume to each node forstoring the persistent HDFS.
• Finally, if you get errors while running your application, look at the slave’s logsfor that application inside of the scheduler work directory (/root/spark/work). You canalso view the status of the cluster using the web UI: http://<master-hostname>:8080.

You can edit /root/spark/conf/spark-env.sh on each machine to set Spark configuration options, suchas JVM options. This file needs to be copied to every machine to reflect the change. The easiest way todo 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 shuttingthem down! Make sure you have copied everything important off the nodesbefore stopping them.

• Go into the ec2 directory in the release of Spark you downloaded.
• Run ./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 theylose all data on ephemeral disks but keep the data in theirroot partitions and their persistent-hdfs. Stopped machines will notcost you any EC2 cycles, but will continue to cost money for EBSstorage.

• To stop one of your clusters, go into the ec2 directory and run./spark-ec2 --region=<ec2-region> stop <cluster-name>.
• To restart it later, run./spark-ec2 -i <key-file> --region=<ec2-region> start <cluster-name>.
• To ultimately destroy the cluster and stop consuming EBS space, run./spark-ec2 --region=<ec2-region> destroy <cluster-name> as described in the previoussection.

• Support for “cluster compute” nodes is limited – there’s no way to specify alocality 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.

If you have a patch or suggestion for one of these limitations, feel free tocontribute 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. To provide AWS credentials for S3 access, launch the Spark cluster with the option --copy-aws-credentials. 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.