Tachyon Overview

Tachyon is a fault tolerant distributed file system enabling reliable file sharing at memory-speed across cluster frameworks, such as Spark and MapReduce. It achieves high performance by leveraging lineage information and using memory aggressively. Tachyon caches working set files in memory thereby avoiding going to disk to load datasets that are frequently read. This enables different jobs/queries and frameworks to access cached files at memory speed.

Github Repository | Releases and Downloads | User Documentation | Developer Documentation | Acknowledgement

Current Features

User Documentation

Running Tachyon Locally: Get Tachyon up and running on a single node for a quick spin in ~ 5 minutes.

Running Tachyon on a Cluster: Get Tachyon up and running on your own cluster.

Fault Tolerant Tachyon Cluster: Make your cluster fault tolerant.

Running Spark on Tachyon: Get Spark running on Tachyon

Running Shark on Tachyon: Get Shark running on Tachyon

Running Hadoop MapReduce on Tachyon: Get Hadoop MapReduce running on Tachyon

Configuration Settings: How to configure Tachyon.

Command-Line Interface: Interact with Tachyon through the command line.

Syncing the Underlying Filesystem: Make Tachyon understand an existing underlayer filesystem.

Tachyon Presentation at Strata and Hadoop World 2013 (October, 2013)

Developer Documentation

Startup Tasks for New Contributors

Building Tachyon Master Branch

External resources

Tachyon Mini Courses at Strata 2014

Hot Rod Hadoop With Tachyon on Fedora 21

Support or Contact

You are welcome to join our mailing list to discuss questions and make suggestions. We use JIRA to track development and issues. If you are interested in trying out Tachyon in your cluster, please contact Haoyuan.

Acknowledgement

Tachyon is an open source project started in the UC Berkeley AMP Lab. This research and development is supported in part by NSF CISE Expeditions award CCF-1139158 and DARPA XData Award FA8750-12-2-0331, and gifts from Amazon Web Services, Google, SAP, Apple, Inc., Cisco, Clearstory Data, Cloudera, Ericsson, Facebook, GameOnTalis, General Electric, Hortonworks, Huawei, Intel, Microsoft, NetApp, Oracle, Samsung, Splunk, VMware, WANdisco and Yahoo!.

We would also like to thank to our project contributors.

Related Projects

Berkeley Data Analysis Stack (BDAS) from AMPLab at Berkeley