π Amplab Tachyon
π Summary
Amplab-Tachyon is a fault tolerant distributed file system enabling reliable file sharing at memory-speed across cluster frameworks.
π Owner
- Name: Timothy St. Clair
- Email: tstclair@redhat.com
- Release notes owner:
π Current status
- Targeted release: Fedora 21
- Last updated: 25 March 2014
- Tracker bug: <will be assigned by the Wrangler>
π Detailed Description
Amplab-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. Amplab-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.
π Benefit to Fedora
Amplab-Tachyon is an exciting project for Fedora as it enables in memory computation speeds for popular big-data frameworks that are part of fedora.
π Scope
- Proposal owners: Currently our Amplab-Tachyon package has been accepted into Fedora. It should feature all of the functionality available from the upstream release.
- Other developers: N/A (not a System Wide Change)
- Release engineering: N/A (not a System Wide Change)
- Policies and guidelines: N/A (not a System Wide Change)
π Upgrade/compatibility impact
N/A
π How To Test
Detailed instructions here
π User Experience
Users should be able to seamlessly deploy Amplab-Tachyon on an HDFS cluster to leverage in memory speeds using their exiting big-data frameworks.
π Dependencies
N/A (not a System Wide Change)
π Contingency Plan
- Contingency mechanism: N/A (not a System Wide Change)
- Contingency deadline: N/A (not a System Wide Change)
- Blocks release? N/A (not a System Wide Change)
- Blocks product? N/A
π Documentation
N/A (not a System Wide Change)
π Release Notes
Fedora 21 includes Amplab-Tachyon, a fault tolerant distributed file system enabling reliable file sharing at memory-speed across cluster frameworks.