• News
• What is Pachyderm?
• Key Features
• Is Pachyderm enterprise production ready?
• What is a commit-based file system?
• What are containerized analytics?
• Using Pachyderm
  • Prerequisites
  • Launch a Development Cluster
  • Launch a Production Cluster
  • Common Problems
• Environment Setup
  • Go Setup
  • Docker Setup
  • Vagrant
• Contributing

WE'RE HIRING! Love Docker, Go and distributed systems? Learn more about our team and email us at jobs@pachyderm.io.

Pachyderm is a complete data analytics solution that lets you efficiently store and analyze your data using containers. We offer the scalability and broad functionality of Hadoop, with the ease of use of Docker.

• Complete version control for your data
• Pipelines are containerized, so you can use any languages and tools you want
• Both batched and streaming analytics
• One-click deploy on AWS without data migration

No, Pachyderm is in beta, but can already solve some very meaningful data analytics problems. We'd love your help. :)

Pfs is implemented as a distributed layer on top of btrfs, the same copy-on-write file system that powers Docker. Btrfs already offers git-like semantics on a single machine; pfs scales these out to an entire cluster. This allows features such as:

• Commit-based history: File systems are generally single-state entities. Pfs, on the other hand, provides a rich history of every previous state of your cluster. You can always revert to a prior commit in the event of a disaster.
• Branching: Thanks to btrfs's copy-on-write semantics, branching is ridiculously cheap in pfs. Each user can experiment freely in their own branch without impacting anyone else or the underlying data. Branches can easily be merged back in the main cluster.
• Cloning: Btrfs's send/receive functionality allows pfs to efficiently copy an entire cluster's worth of data while still maintaining its commit history.

Rather than thinking in terms of map or reduce jobs, pps thinks in terms of pipelines expressed within a container. A pipeline is a generic way expressing computation over large datasets and it’s containerized to make it easily portable, isolated, and easy to monitor. In Pachyderm, all analysis runs in containers. You can write them in any language you want and include any libraries.

Requirements:

• Go 1.5
• Docker 1.9

More Info

To start a development cluster run:

make launch

This will compile the code on your local machine and launch it as a docker-compose service. A succesful launch looks like this:

docker-compose ps
        Name                       Command               State                                 Ports
-----------------------------------------------------------------------------------------------------------------------------------
pachyderm_btrfs_1       sh entrypoint.sh                 Up
pachyderm_etcd_1        /etcd -advertise-client-ur ...   Up      0.0.0.0:2379->2379/tcp, 2380/tcp, 4001/tcp, 7001/tcp
pachyderm_pfs-roler_1   /pfs-roler                       Up
pachyderm_pfsd_1        sh btrfs-mount.sh /pfsd          Up      0.0.0.0:1050->1050/tcp, 0.0.0.0:650->650/tcp, 0.0.0.0:750->750/tcp
pachyderm_ppsd_1        /ppsd                            Up      0.0.0.0:1051->1051/tcp, 0.0.0.0:651->651/tcp
pachyderm_rethink_1     rethinkdb --bind all             Up      28015/tcp, 29015/tcp, 8080/tcp

Pachyderm has a CLI called pach. To install it:

make install

pach should be able to access dev clusters without any additional setup.

Before you can launch a production cluster you'll need a working Kubernetes deployment. You can start one locally on Docker using:

etc/kube/start-kube-docker.sh

You can then deploy a Pachyderm cluster on Kubernetes with:

pachctl create-cluster test-cluster 1 1 0

With golang, it's generally easiest to have your fork match the import paths in the code. We recommend you do it like this:

# assuming your github username is alice
rm -rf ${GOPATH}/src/github.com/pachyderm/pachyderm
cd ${GOPATH}/src/github.com/pachyderm
git clone https://github.com/alice/pachyderm.git
git remote add upstream https://github.com/pachyderm/pachyderm.git # so you can run 'git fetch upstream' to get upstream changes

If you're on a Mac or Windows, easiest way to get up and running is the Docker toolbox. Linux users should follow this guide.

The Vagrantfile in this repository will set up a development environment for Pachyderm that has all dependencies installed.

The easiest way to install Vagrant on your mac is probably:

brew install caskroom/cask/brew-cask
brew cask install virtualbox vagrant

Basic usage:

mkdir -p pachyderm_vagrant
cd pachyderm_vagrant
curl https://raw.githubusercontent.com/pachyderm/pachyderm/master/etc/initdev/Vagrantfile > Vagrantfile
curl https://raw.githubusercontent.com/pachyderm/pachyderm/master/etc/initdev/init.sh > init.sh
vagrant up # starts the vagrant box
vagrant ssh # ssh into the vagrant box

Once in the vagrant box, set everything up and verify that it works:

go get github.com/pachyderm/pachyderm/...
cd ~/go/src/github.com/pachyderm/pachyderm
make test

Some other useful vagrant commands:

vagrant suspend # suspends the vagrant box, useful if you are not actively developing and want to free up resources
vagrant resume # resumes a suspended vagrant box
vagrant destroy # destroy the vagrant box, this will destroy everything on the box so be careful

See Vagrant's website for more details.

Problem: Nothing is running after launch.

• Check to make sure the docker daemon is running with ps -ef | grep docker.
• Check to see if the container exited with docker ps -a | grep IMAGE_NAME.
• Check the container logs with docker logs.

Problem: Docker commands are failing with permission denied

The bin scripts assume you have your user in the docker group as explained in the Docker Ubuntu installation docs. If this is set up properly, you do not need to use sudo to run docker. If you do not want this, and want to have to use sudo for docker development, wrap all commands like so:

sudo -E bash -c 'make test' # original command would have been `make test`

To get started, sign the Contributor License Agreement.

Send us PRs, we would love to see what you do!