Flintrock is a command-line tool for launching Apache Spark clusters.
Flintrock is currently undergoing heavy development. Until we make a 1.0 release, you probably should not use Flintrock unless you are ready to keep up with frequent changes to how it works. Python hackers or heavy spark-ec2 users who are looking to experiment with something new are welcome to try Flintrock out and potentially even contribute.
Here's a quick way to launch a cluster on EC2, assuming you already have an AWS account set up.
flintrock launch test-cluster \ --num-slaves 1 \ --spark-version 2.0.2 \ --ec2-key-name key_name \ --ec2-identity-file /path/to/key.pem \ --ec2-ami ami-b73b63a0 \ --ec2-user ec2-user
If you persist these options to a file, you'll be able to do the same thing simply by typing:
flintrock launch test-cluster
Once you're done using a cluster, don't forget to destroy it with:
flintrock destroy test-cluster
Other things you can do with Flintrock include:
flintrock login test-cluster flintrock describe test-cluster flintrock add-slaves test-cluster --num-slaves 2 flintrock remove-slaves test-cluster --num-slaves 1 flintrock run-command test-cluster 'sudo yum install -y package' flintrock copy-file test-cluster /local/path /remote/path
To see what else Flintrock can do, or to see detailed help for a specific command, try:
flintrock --help flintrock <subcommand> --help
That's not all. Flintrock has a few more features that you may find interesting.
Flintrock requires Python 3.4 or newer, unless you are using one of our standalone packages. Flintrock has been thoroughly tested only on OS X, but it should run on all POSIX systems. A motivated contributor should be able to add Windows support without too much trouble, too.
To get the latest release of Flintrock, simply run pip:
pip3 install flintrock
This will install Flintrock and place it on your path. You should be good to go now!
You'll probably want to get started with the following two commands:
flintrock --help flintrock configure
Standalone version (Python not required!)
If you don't have a recent enough version of Python, or if you don't have Python installed at all, you can still use Flintrock. We publish standalone packages of Flintrock on GitHub with our releases.
Find the standalone package for your OS under our latest release, unzip it to a location of your choice, and run the
flintrock executable inside.
flintrock_version="0.7.0" curl --location --remote-name "https://github.com/nchammas/flintrock/releases/download/v$flintrock_version/Flintrock-$flintrock_version-standalone-OSX-x86_64.zip" unzip -q -d flintrock "Flintrock-$flintrock_version-standalone-OSX-x86_64.zip" cd flintrock/ # You're good to go! ./flintrock --help
You'll probably want to add the location of the Flintrock executable to your
PATH so that you can invoke it from any directory.
If you like living on the edge, install the development version of Flintrock:
pip3 install git+https://github.com/nchammas/flintrock
If you want to play around with Spark, develop a prototype application, run a one-off job, or otherwise just experiment, Flintrock is the fastest way to get you a working Spark cluster.
Flintrock exposes many options of its underlying providers (e.g. EBS-optimized volumes on EC2) which makes it easy to create a cluster with predictable performance for Spark performance testing.
Most people will use Flintrock interactively from the command line, but Flintrock is also designed to be used as part of an automated pipeline. Flintrock's exit codes are carefully chosen; it offers options to disable interactive prompts; and when appropriate it prints output in YAML, which is both human- and machine-friendly.
There are some things that Flintrock specifically does not support.
Managing permanent infrastructure
Flintrock is not for managing long-lived clusters, or any infrastructure that serves as a permanent part of some environment.
For starters, Flintrock provides no guarantee that clusters launched with one version of Flintrock can be managed by another version of Flintrock, and no considerations are made for any long-term use cases.
If you are looking for ways to manage permanent infrastructure, look at tools like Terraform, Ansible, SaltStack, or Ubuntu Juju. You might also find a service like Databricks useful if you're looking for someone else to host and manage Spark for you. Amazon also offers Spark on EMR.
Launching non-Spark-related services
Flintrock is meant for launching Spark clusters that include closely related services like HDFS, Mesos, and YARN.
Flintrock is not for launching external datasources (e.g. Cassandra), or other services that are not closely integrated with Spark (e.g. Tez).
If you are looking for an easy way to launch other services from the Hadoop ecosystem, look at the Apache Bigtop project.
Launching out-of-date services
Flintrock will always take advantage of new features of Spark and related services to make the process of launching a cluster faster, simpler, and easier to maintain. If that means dropping support for launching older versions of a service, then we will generally make that tradeoff.
Flintrock has a clean command-line interface.
flintrock --help flintrock describe flintrock destroy --help flintrock launch test-cluster --num-slaves 10
Configurable CLI Defaults
Flintrock lets you persist your desired configuration to a YAML file so that you don't have to keep typing out the same options over and over at the command line.
To setup and edit the default config file, run this:
You can also point Flintrock to a non-default config file by using the
provider: ec2 services: spark: version: 2.0.2 launch: num-slaves: 1 providers: ec2: key-name: key_name identity-file: /path/to/.ssh/key.pem instance-type: m3.medium region: us-east-1 ami: ami-b73b63a0 user: ec2-user
With a config file like that, you can now launch a cluster with just this:
flintrock launch test-cluster
And if you want, you can even override individual options in your config file at the command line:
flintrock launch test-cluster \ --num-slaves 10 \ --ec2-instance-type r3.xlarge
Flintrock is really fast. This is how quickly it can launch fully operational clusters on EC2 compared to spark-ec2.
- Provider: EC2
- Instance type:
- Launch time: Best of 6 tries
|Cluster Size||Flintrock Launch Time||spark-ec2 Launch Time|
|1 slave||2m 06s||8m 44s|
|50 slaves||2m 30s||37m 30s|
|100 slaves||2m 42s||1h 06m 05s|
The spark-ec2 launch times are sourced from SPARK-5189.
Note that AWS performance is highly variable, so you will not get these results consistently. They show the best case scenario for each tool, and not the typical case. For Flintrock, the typical launch time will be a minute or two longer.
Advanced Storage Setup
Flintrock automatically configures any available ephemeral storage on the cluster and makes it available to installed services like HDFS and Spark. This storage is fast and is perfect for use as a temporary store by those services.
Flintrock comes with a set of automated, end-to-end tests. These tests help us develop Flintrock with confidence and guarantee a certain level of quality.
Low-level Provider Options
Flintrock exposes low-level provider options (e.g. instance-initiated shutdown behavior) so you can control the details of how your cluster is setup if you want.
No Custom Machine Image Dependencies
Flintrock is built and tested against vanilla Amazon Linux and CentOS. You can easily launch Flintrock clusters using your own custom machine images built from either of those distributions.
Support for out-of-date versions of Python, EC2 APIs, etc.
Supporting multiple versions of anything is tough. There's more surface area to cover for testing, and over the long term the maintenance burden of supporting something non-current with bug fixes and workarounds really adds up.
There are projects that support stuff across a wide cut of language or API versions. For example, Spark supports Java 7 and 8, and Python 2.6+ and 3+. The people behind these projects are gods. They take on an immense maintenance burden for the benefit and convenience of their users.
We here at project Flintrock are much more modest in our abilities. We are best able to serve the project over the long term when we limit ourselves to supporting a small but widely applicable set of configurations.
Note: The explanation here is provided from the perspective of Flintrock's original author, Nicholas Chammas. spark-ec2 is still an active project, so the problems described below may no longer exist. However, they were all present at the time Flintrock was created.
I got started with Spark by using spark-ec2. It's one of the biggest reasons I found Spark so accessible. I didn't need to spend time upfront working through some setup guide before I could work on a "real" problem. Instead, with a simple spark-ec2 command I was able to launch a large, working cluster and get straight to business.
As I became a heavy user of spark-ec2, several limitations stood out and became an increasing pain. They provided me with the motivation for this project.
Among those limitations were:
- Slow launches: spark-ec2 cluster launch times increase linearly with the number of slaves being created. For example, it takes spark-ec2 over an hour to launch a cluster with 100 slaves. (SPARK-4325, SPARK-5189)
- No support for configuration files: spark-ec2 does not support reading options from a config file, so users are always forced to type them in at the command line. (SPARK-925)
- Un-resizable clusters: Adding or removing slaves from an existing spark-ec2 cluster is not possible. (SPARK-2008)
- Custom machine images: spark-ec2 uses custom machine images, and since the process of updating those machine images is not automated, they have not been updated in years. (SPARK-3821)
- Unexposed EC2 options: spark-ec2 does not expose all the EC2 options one would want to use as part of automated performance testing of Spark. (SPARK-6220)
- Poor support for programmatic use cases: spark-ec2 was not built with programmatic use in mind, so many flows are difficult or impossible to automate. (SPARK-5627, SPARK-5629)
- No standalone distribution: spark-ec2 comes bundled with Spark and has no independent releases or distribution. Instead of being a nimble tool that can progress independently and be installed separately, it is tied to Spark's release cycle and distributed with Spark, which clocks in at a few hundred megabytes.
Flintrock addresses all of these shortcomings.
Why didn't you build Flintrock on top of an orchestration tool?
People have asked me whether I considered building Flintrock on top of Ansible, Terraform, Docker, or something else. I looked into some of these things back when Flintrock was just an idea in my head and decided against using any of them for two basic reasons:
- Fun: I didn't have any experience with these tools, and it looked both simple enough and more fun to build something "from scratch".
- Focus: I wanted a single-purpose tool with a very limited focus, not a module or set of scripts that were part of a sprawling framework that did a lot of different things.
These are not necessarily the right reasons to build "from scratch", but they were my reasons. If you are already comfortable with any of the popular orchestration tools out there, you may find it more attractive to use them rather than add a new standalone tool to your toolchain.