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spark-ec2

spark-ec2 allows you to launch, manage and shut down Spark clusters on Amazon EC2. It automatically sets 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 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.

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, 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 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.

    For example:

    export AWS_SECRET_ACCESS_KEY=AaBbCcDdEeFGgHhIiJjKkLlMmNnOoPpQqRrSsTtU

export 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 sees all the slaves by going to its web UI, which will be printed at the end of the script (typically 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. The <version> can be a version number (e.g. "0.7.3") or a specific git hash. By default, a recent version will be used.
  • --spark-git-repo=<repository url> will let you run a custom version of Spark that is built from the given git repository. By default, the Apache Github mirror will be used. When using a custom Spark version, --spark-version must be set to git commit hash, such as 317e114, instead of a version number.
  • 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.

Launching a Cluster in a VPC

  • 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 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), <vpc-id> is the name of your VPC, <subnet-id> is the name of your subnet, and <cluster-name> is the name to give to your cluster.

    For example:

    export AWS_SECRET_ACCESS_KEY=AaBbCcDdEeFGgHhIiJjKkLlMmNnOoPpQqRrSsTtU

export 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 ```

Running Applications

  • 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 ~/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 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/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.
  • Finally, if you get errors while running your application, look at the slave's logs for that application inside of the scheduler work directory (/root/spark/work). You can also view the status of the cluster using the web UI: http://<master-hostname>:8080.

Configuration

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.

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 --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 previous section.

Limitations

  • 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.

If you have a patch or suggestion for one of these limitations, feel free to contribute it!

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://<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.

This repository contains the set of scripts used to setup a Spark cluster on EC2. These scripts are intended to be used by the default Spark AMI and is not expected to work on other AMIs. If you wish to start a cluster using Spark, please refer to http://spark-project.org/docs/latest/ec2-scripts.html

spark-ec2 Internals

The Spark cluster setup is guided by the values set in ec2-variables.sh.setup.sh first performs basic operations like enabling ssh across machines, mounting ephemeral drives and also creates files named /root/spark-ec2/masters, and /root/spark-ec2/slaves. Following that every module listed in MODULES is initialized.

To add a new module, you will need to do the following:

  1. Create a directory with the module's name.

  2. Optionally add a file named init.sh. This is called before templates are configured and can be used to install any pre-requisites.

  3. Add any files that need to be configured based on the cluster setup to templates/. The path of the file determines where the configured file will be copied to. Right now the set of variables that can be used in a template are:

    {{master_list}}
{{active_master}}
{{slave_list}}
{{zoo_list}}
{{cluster_url}}
{{hdfs_data_dirs}}
{{mapred_local_dirs}}
{{spark_local_dirs}}
{{spark_worker_mem}}
{{spark_worker_instances}}
{{spark_worker_cores}}
{{spark_master_opts}}

You can add new variables by modifying deploy_templates.py.

  1. Add a file named setup.sh to launch any services on the master/slaves. This is called after the templates have been configured. You can use the environment variables $SLAVES to get a list of slave hostnames and /root/spark-ec2/copy-dir to sync a directory across machines.

  2. Modify spark_ec2.py to add your module to the list of enabled modules.