In this short guide, we’ll walk through how to run modest Spark jobs on a cluster. We’ll do this in 2 parts:
spark-shell, Spark’s interactive REPL.
spark-submitto submit our job and its dependencies to the cluster, without invoking the spark-shell.
We’ll begin with the simple task of spinning up a Spark cluster on Amazon EC2, and running a very simple Spark program from the spark-shell (Spark’s REPL).
To do this, we’ll need Flintrock, a Python-based command-line tool designed to quickly and easily spin up Spark clusters on Amazon Web Services (AWS).
Flintrock is a command-line tool that makes it easy to start/pause/tear down Spark clusters, and to otherwise seamlessly interact with your cluster from the command line.
At the time of writing, the most up-to-date version of Flintrock is 0.9.0.
To get the latest release of Flintrock, simply install using pip:
$ pip3 install flintrock
If you would like to use an option other than pip to get Flintrock, see Flintrock’s installation instructions.
I assume at this point that you’ve created an AWS account.
Take note of the availability zone that your account is set for (top right corner of your AWS console). You will need to ensure that all of your Flintrock configuration is for the same availability zone
In this guide, I am using
We must now create a user that we can use with Flintrock to start and manage a Spark cluster. For the sake of simplicity and reproducability, we’re going to create an almost-root user, which has the rights to do most anything except AWS account administration such as changing billing details.
(In a production scenario, you should obviously further restrict access to individual users.)
To create a new user, simply:
On the page that appears (add user step 1):
On the Permissions page (add user step 2):
Select “Add user to group” and click “Create group”. Create a group with the following policy selected:
Add your user to this newly created permissions group, and click the Next: Review button.
Verify that the user details and permissions summary look correct, and click Create user.
Important next step: save the key ID, secret access key, and password associated with your newly-created user! Either download as a CSV file, or copy/paste the Access Key ID, Secret Access Key, and Password to a safe place.
In order for Flintrock to use your newly-created user’s Access Key ID and Secret Access Key, you’ll need to set the following environment variables so Flintrock can use this user later.
$ export AWS_ACCESS_KEY_ID=xxxxxxxxxxxxxxxxxxxx $ export AWS_SECRET_ACCESS_KEY=yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy
In order for you to remotely access your EC2 instances, you will need to generate a key pair in order to authenticate.
To generate a key pair for you to use to authenticate, simply:
spark_cluster, and click Create.
$ chmod 400 spark_cluster.pem
Now, all we have to do is provide our desired cluster configuration to Flintrock before we can start up our Spark cluster.
To do this, at the command line, just do:
This will open up your system’s configured text editor with a default
YAML configuration file, called
For my job, I have set the following options:
services: spark: version: 2.2.0 hdfs: version: 2.7.5 provider: ec2 providers: ec2: key-name: spark_cluster identity-file: /path/to/spark_cluster.pem instance-type: m4.large region: us-east-1 ami: ami-97785bed # Amazon Linux, us-east-1 user: ec2-user tenancy: default # default | dedicated ebs-optimized: no # yes | no instance-initiated-shutdown-behavior: terminate # terminate | stop launch: num-slaves: 1 install-hdfs: True install-spark: True debug: false
To explain this selection of options:
identity-fileare simply the .pem file that we generated above.
instance-typeis the EC2 instance type you’d like to run. The full pricing list for all instance types per region is available here.
regionis the AWS region that we’ve chosen in our account. In my case, I’m using
amiis the Amazon Machine Image (AMI) that we want to use in our cluster. For my region,
us-east-1, I’ve simply chosen the latest Linux AMI for my region, which happens to be
ami-97785bedat the time of writing.
useris the username of the non-root user that’s created on each VM
num-slavesis the number of non-master Spark nodes in the cluster. So in selecting 1 slave, that means I’ll have 2 nodes in total; one master, and one worker/slave.
install-hdfsshould be set to true if you want to access data in S3.
We now have everything we need setup to spin up a Spark cluster!
To start our cluster, we just have to choose a name for our cluster, e.g.,
my-spark-cluster and do:
$ flintrock launch my-spark-cluster
It should take ~2 minutes to complete. When finished, you should see the following:
$ flintrock launch my-spark-cluster Launching 2 instances... [18.104.22.168] SSH online. [22.214.171.124] Configuring ephemeral storage... [126.96.36.199] Installing Java 1.8... [188.8.131.52] SSH online. [184.108.40.206] Configuring ephemeral storage... [220.127.116.11] Installing Java 1.8... [18.104.22.168] Installing HDFS... [22.214.171.124] Installing HDFS... [126.96.36.199] Installing Spark... [188.8.131.52] Installing Spark... [184.108.40.206] Configuring HDFS master... [220.127.116.11] Configuring Spark master... HDFS online. Spark online. launch finished in 0:02:53. Cluster master: ec2-107-21-87-63.compute-1.amazonaws.com Login with: flintrock login my-spark-cluster
Note: if you get an error saying that Flintrock could not find your AWS
credentials, remember to set your
AWS_SECRET_ACCESS_KEY environment variables as specified above.
Now we can login to the master node of our cluster! To do that, just do:
$ flintrock login my-spark-cluster
And you should see something to the effect of:
__| __|_ ) _| ( / Amazon Linux AMI ___|\___|___| https://aws.amazon.com/amazon-linux-ami/2017.09-release-notes/ [ec2-user@ip]$
On the command line of the master node of our cluster (i.e., in the EC2 shell shown above), to launch the spark-shell, just do:
After some start-up output, you should eventually see:
Spark context Web UI available at http://ec2-xxxxxxxxxx.compute-1.amazonaws.com:4040 Spark context available as 'sc' (master = local[*], app id = local-1522613551031). Spark session available as 'spark'. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 2.2.0 /_/ Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_161) Type in expressions to have them evaluated. Type :help for more information. scala>
To run a simple word count example on our Spark cluster, just enter:
val text = List("Hadoop MapReduce, a disk-based big data processing engine, is being replaced by a new generation of memory-based processing frameworks, the most popular of which is Spark.", "Spark supports Scala, Java, Python, and R.") val rdd = sc.parallelize(text) val counts = rdd.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.collect()
The result should look like:
scala> counts.collect() res0: Array[(String, Int)] = Array((is,2), (Java,,1), (big,1), (data,1), (new,1), (generation,1), (engine,,1), (Scala,,1), (MapReduce,,1), (Python,,1), (R.,1), (supports,1), (replaced,1), (Spark,1), (frameworks,,1), (memory-based,1), (most,1), (a,2), (popular,1), (disk-based,1), (processing,2), (Spark.,1), (which,1), (of,2), (by,1), (and,1), (being,1), (the,1), (Hadoop,1))
And voilà! You’ve just run a simple Spark job on your own Spark cluster!
To shut down your cluster, just run:
$ flintrock destroy my-spark-cluster