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diff --git a/docs/running-on-amazon-ec2.md b/docs/running-on-amazon-ec2.md deleted file mode 100644 index 4e1c191bda..0000000000 --- a/docs/running-on-amazon-ec2.md +++ /dev/null @@ -1,29 +0,0 @@ ---- -layout: global -title: Running Spark on Amazon EC2 ---- -This guide describes how to get Spark running on an EC2 cluster. It assumes you have already signed up for Amazon EC2 account on the [Amazon Web Services site](http://aws.amazon.com/). - -# For Spark 0.5 - -Spark now includes some [EC2 Scripts]({{HOME_PATH}}ec2-scripts.html) for launching and managing clusters on EC2. You can typically launch a cluster in about five minutes. Follow the instructions at this link for details. - -# For older versions of Spark - -Older versions of Spark use the EC2 launch scripts included in Mesos. You can use them as follows: -- Download Mesos using the instructions on the [Mesos wiki](http://github.com/mesos/mesos/wiki). There's no need to compile it. -- Launch a Mesos EC2 cluster following the [EC2 guide on the Mesos wiki](http://github.com/mesos/mesos/wiki/EC2-Scripts). (Essentially, this involves setting some environment variables and running a Python script.) -- Log into your EC2 cluster's master node using `mesos-ec2 -k <keypair> -i <key-file> login cluster-name`. -- Go into the `spark` directory in `root`'s home directory. -- Run either `spark-shell` or another Spark program, setting the Mesos master to use to `master@<ec2-master-node>:5050`. You can also find this master URL in the file `~/mesos-ec2/cluster-url` in newer versions of Mesos. -- Use the Mesos web UI at `http://<ec2-master-node>:8080` to view the status of your job. - -# Using a Newer Spark Version - -The Spark EC2 machines 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. |