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Diffstat (limited to 'docs')
-rwxr-xr-x | docs/_layouts/global.html | 2 | ||||
-rw-r--r-- | docs/cluster-overview.md | 2 | ||||
-rw-r--r-- | docs/ec2-scripts.md | 192 | ||||
-rw-r--r-- | docs/index.md | 5 |
4 files changed, 2 insertions, 199 deletions
diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index 62d75eff71..d493f62f0e 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -98,8 +98,6 @@ <li><a href="spark-standalone.html">Spark Standalone</a></li> <li><a href="running-on-mesos.html">Mesos</a></li> <li><a href="running-on-yarn.html">YARN</a></li> - <li class="divider"></li> - <li><a href="ec2-scripts.html">Amazon EC2</a></li> </ul> </li> diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index faaf154d24..2810112f52 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -53,8 +53,6 @@ The system currently supports three cluster managers: and service applications. * [Hadoop YARN](running-on-yarn.html) -- the resource manager in Hadoop 2. -In addition, Spark's [EC2 launch scripts](ec2-scripts.html) make it easy to launch a standalone -cluster on Amazon EC2. # Submitting Applications diff --git a/docs/ec2-scripts.md b/docs/ec2-scripts.md deleted file mode 100644 index 7f60f82b96..0000000000 --- a/docs/ec2-scripts.md +++ /dev/null @@ -1,192 +0,0 @@ ---- -layout: global -title: Running Spark on EC2 ---- - -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 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](http://aws.amazon.com/). - -`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](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. - - For example: - - ```bash - 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](http://aws.amazon.com/ec2/instance-types) and [EC2 -pricing](http://aws.amazon.com/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](http://aws.amazon.com/ec2/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](https://github.com/apache/spark) 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: - - ```bash - 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](configuration.html) 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](contributing-to-spark.html) 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](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. diff --git a/docs/index.md b/docs/index.md index ae26f97c86..9dfc52a2bd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -64,7 +64,7 @@ To run Spark interactively in a R interpreter, use `bin/sparkR`: ./bin/sparkR --master local[2] Example applications are also provided in R. For example, - + ./bin/spark-submit examples/src/main/r/dataframe.R # Launching on a Cluster @@ -73,7 +73,6 @@ The Spark [cluster mode overview](cluster-overview.html) explains the key concep Spark can run both by itself, or over several existing cluster managers. It currently provides several options for deployment: -* [Amazon EC2](ec2-scripts.html): our EC2 scripts let you launch a cluster in about 5 minutes * [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster * [Apache Mesos](running-on-mesos.html) * [Hadoop YARN](running-on-yarn.html) @@ -103,7 +102,7 @@ options for deployment: * [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster * [Submitting Applications](submitting-applications.html): packaging and deploying applications * Deployment modes: - * [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes + * [Amazon EC2](https://github.com/amplab/spark-ec2): scripts that let you launch a cluster on EC2 in about 5 minutes * [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager * [Mesos](running-on-mesos.html): deploy a private cluster using [Apache Mesos](http://mesos.apache.org) |