Flintrock 0.2.0

A command-line tool for launching Apache Spark clusters.

Flintrock logo

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.

Usage

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 1.6.0 \
    --ec2-key-name key_name \
    --ec2-identity-file /path/to/key.pem \
    --ec2-ami ami-60b6c60a \
    --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 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.

Installation

Before using Flintrock, take a quick look at the copyright notice and license and make sure you’re OK with their terms.

Flintrock requires Python 3.4 or newer. It’s currently been tested only on OS X, but it should run on all POSIX systems. We have plans to add Windows support in the future, too.

Eventually, we also plan to release stand-alone executables so that you can install Flintrock without having to worry about having Python installed.

Release version

To get the latest release of Flintrock, simply run pip:

python3 -m pip 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

Development version

If you like living on the edge, or if you want to contribute, install the development version of Flintrock:

git clone https://github.com/nchammas/flintrock
cd flintrock

# Setup a virtual environment.
# Optional, but *strongly recommended*.
python3 -m venv venv
source venv/bin/activate

# Install Flintrock.
# If you want to contribute, install the developer requirements.
python3 -m pip install -r requirements/user.pip
python3 -m pip install -r requirements/developer.pip

When you git pull the latest changes, don’t forget to also rerun the pip install step so that Flintrock’s dependencies stay up-to-date.

Use Cases

Experimentation

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.

Performance testing

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.

Automated pipelines

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.

Anti-Use Cases

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.

Features

Polished CLI

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, call flintrock configure. You can also point Flintrock to a non-default config file by using the --config option.

Sample config.yaml

provider: ec2

services:
  spark:
    version: 1.6.0

launch:
  num-slaves: 1

ec2:
  key-name: key_name
  identity-file: /path/to/.ssh/key.pem
  instance-type: m3.medium
  region: us-east-1
  ami: ami-60b6c60a
  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

Fast Launches

Flintrock is really fast. This is how quickly it can launch fully operational clusters on EC2 compared to spark-ec2.

Setup

• Provider: EC2

• Instance type: m3.large

• AMI:

  • Flintrock: Default Amazon Linux AMI

  • spark-ec2: Custom spark-ec2 AMI

• Launch time: Best of 6 tries

Results

Cluster Size	Flintrock Launch Time	spark-ec2 Launch Time
1 slave	2m 06s	8m 44s
50 slaves	5m 12s	37m 30s
100 slaves	8m 46s	1h 06m 05s

The spark-ec2 launch times are sourced from SPARK-5189.

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.

Tests

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.

Anti-Features

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.

Motivation

Note: The explanation here is provided from the perspective of Flintrock’s original author, Nicholas Chammas.

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 are:

• 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 <https://issues.apache.org/jira/browse/SPARK-5189>`__ 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, or will address, all of these shortcomings.

About the Flintrock Logo

The Flintrock logo was created using Highbrow Cafetorium JNL and this icon. Licenses to use both the font and icon were purchased from their respective owners.