pachyderm

README

Pachyderm: A Containerized Data Lake

Pachyderm is Git for Data Science. We offer complete version control for data and give data scientists the same first-class development tools as software developers. Pachyderm is ideal for building machine learning pipelines and ETL workflows because we version and track track every model/output directly to the raw input datasets that created it (aka: Provenance).

Pachyderm is built on Docker and Kubernetes. Since everything in Pachyderm is a container, data scientists can use any languages or libraries they want (e.g. R, Python, OpenCV, etc) without any additional infrastructure overhead.

Getting Started

Install Pachyderm locally or deploy on AWS/GCE in about 5 minutes.

You can also refer to our complete developer docs to see tutorials, check out example projects, and learn about advanced features of Pachyderm.

If you'd like to see some examples and learn about core use cases for Pachyderm:

• Examples
• Use Cases
• Case Studies: Learn how General Fusion uses Pachyderm to power commercial fusion research.

What is Pachyderm?

Pachyderm is a software platform the supports the storage and processing of large data sets. Pachyderm is inspired by the Hadoop ecosystem but shares no code with it. Instead, we leverage the container ecosystem to provide the broad functionality of Hadoop with the ease of use of Docker.

Pachyderm was designed to enable everything from "weekend data science" projects to large-scale data collaboration, just like Git does for code.

What's new about Pachyderm? (How is it different from Hadoop?)

There are two bold new ideas in Pachyderm:

• Containers as the core processing primitive
• Version Control for data

These ideas lead directly to a system that's much more powerful, flexible and easy to use.

To process data, you simply create a containerized program which reads and writes to the local filesystem. You can use any tools you want because it's all just going in a container! Pachyderm will take your container and inject data into it by way of a FUSE volume. We'll then automatically replicate your container, showing each copy a different chunk of data. With this technique, Pachyderm can scale any code you write to process up to petabytes of data (Example: distributed grep).

Pachyderm also version controls all data using a commit-based distributed filesystem (PFS), similar to what git does with code. Version control for data has far reaching consequences in a distributed filesystem. You get the full history of your data, can track changes and diffs, collaborate with teammates, and if anything goes wrong you can revert the entire cluster with one click!

Version control is also very synergistic with our containerized processing engine. Pachyderm understands how your data changes and thus, as new data is ingested, can run your workload on the diff of the data rather than the whole thing. This means that there's no difference between a batched job and a streaming job, the same code will work for both!

Community

Keep up to date and get Pachyderm support via:

• Twitter
• Join our community Slack Channel to get help from the Pachyderm team and other users.

Contributing

To get started, sign the Contributor License Agreement.

Send us PRs, we would love to see what you do! You can also check our GH issues for things labeled "noob-friendly" as a good place to start. We're sometimes bad about keeping that label up-to-date, so if you don't see any, just let us know.

Join Us

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

Usage Metrics

Pachyderm automatically reports anonymized usage metrics. These metrics help us understand how people are using Pachyderm and make it better. They can be disabled by setting the env variable METRICS to false in the pachd container.

Documentation

Index

• func Asset(name string) ([]byte, error)
• func AssetDir(name string) ([]string, error)
• func AssetInfo(name string) (os.FileInfo, error)
• func AssetNames() []string
• func MustAsset(name string) []byte
• func RestoreAsset(dir, name string) error
• func RestoreAssets(dir, name string) error

Functions

func Asset

func Asset(name string) ([]byte, error)

Asset loads and returns the asset for the given name. It returns an error if the asset could not be found or could not be loaded.

func AssetDir

func AssetDir(name string) ([]string, error)

AssetDir returns the file names below a certain directory embedded in the file by go-bindata. For example if you run go-bindata on data/... and data contains the following hierarchy:

data/
  foo.txt
  img/
    a.png
    b.png

then AssetDir("data") would return []string{"foo.txt", "img"} AssetDir("data/img") would return []string{"a.png", "b.png"} AssetDir("foo.txt") and AssetDir("notexist") would return an error AssetDir("") will return []string{"data"}.

func AssetInfo

func AssetInfo(name string) (os.FileInfo, error)

AssetInfo loads and returns the asset info for the given name. It returns an error if the asset could not be found or could not be loaded.

func AssetNames

func AssetNames() []string

AssetNames returns the names of the assets.

func MustAsset

func MustAsset(name string) []byte

MustAsset is like Asset but panics when Asset would return an error. It simplifies safe initialization of global variables.

func RestoreAsset

func RestoreAsset(dir, name string) error

RestoreAsset restores an asset under the given directory

func RestoreAssets

func RestoreAssets(dir, name string) error

RestoreAssets restores an asset under the given directory recursively