Tachyon: An Open Source Memory-Centric Distributed Storage System Presentation at AMPCamp 6, November 2015. It describes Tachyon's history, open source status, brief review of Tachyon project before 2015, exciting deployments and new features in 2016, and how to get involved with the Tachyon open source community.
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Tachyon Presentation at AMPCamp 6 (November, 2015)
1. Haoyuan Li, Tachyon Nexus & UC Berkeley
November 19, 2015 @ AMPCamp 6
An Open Source Memory-Centric
Distributed Storage System
2. Outline
• Open Source
• Introduction to Tachyon (Before 2015)
• Deployments and New Features
• Getting Involved
2
3. Background
• Started at UC Berkeley AMPLab
– From summer 2012
• Open sourced
– April 2013 (two and half years ago)
– Apache License 2.0
– Latest Release: Version 0.8.2 (November 2015)
• Deployed at > 100 companies
3
12. Performance Trend:
Memory is Fast
• RAM throughput
increasing exponentially
• Disk throughput
increasing slowly
12
Memory-locality key to interactive response times
17. A Use Case Example with -
• Fast, in-memory data processing framework
– Keep one in-memory copy inside JVM
– Track lineage of operations used to derive data
– Upon failure, use lineage to recompute data
map
filter
map
join
reduce
Lineage Tracking
17
18. Issue 1
18
Data Sharing is the bottleneck in
analytics pipeline:
Slow writes to disk
Spark Job1
Spark mem
block manager
block 1
block 3
Spark Job2
Spark mem
block manager
block 3
block 1
HDFS / Amazon S3
block 1
block 3
block 2
block 4
storage engine &
execution engine
same process
(slow writes)
19. Issue 1
19
Spark Job
Spark mem
block manager
block 1
block 3
Hadoop MR Job
YARN
HDFS / Amazon S3
block 1
block 3
block 2
block 4
Data Sharing is the bottleneck in
analytics pipeline:
Slow writes to disk
storage engine &
execution engine
same process
(slow writes)
20. Issue 1 resolved with Tachyon
20
Memory-speed data sharing
among jobs in different
frameworks
execution engine &
storage engine
same process
(fast writes)
Spark Job
Spark mem
Hadoop MR Job
YARN
HDFS / Amazon S3
block 1
block 3
block 2
block 4
HDFS
disk
block 1
block 3
block 2
block 4
Tachyon!
in-memory
block 1
block 3
block 4
21. Issue 2
21
Spark Task
Spark memory
block manager
block 1
block 3
HDFS / Amazon S3
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
Cache loss when process
crashes
22. Issue 2
22
crash
Spark memory
block manager
block 1
block 3
HDFS / Amazon S3
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
Cache loss when process
crashes
23. HDFS / Amazon S3
Issue 2
23
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
crash
Cache loss when process
crashes
24. HDFS / Amazon S3
block 1
block 3
block 2
block 4
Tachyon!
in-memory
block 1
block 3
block 4
Issue 2 resolved with Tachyon
24
Spark Task
Spark memory
block manager
execution engine &
storage engine
same process
Keep in-memory data safe,
even when a job crashes.
25. Issue 2 resolved with Tachyon
25
HDFS
disk
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
Tachyon!
in-memory
block 1
block 3
block 4
crash
HDFS / Amazon S3
block 1
block 3
block 2
block 4
Keep in-memory data safe,
even when a job crashes.
49. More Features
• 7) Remote Write Support
• 8) Easy deployment with Mesos and Yarn
• 9) Initial Security Support
• 10) One Command Cluster Deployment
• 11) Metrics Reporting for Clients, Workers,
and Master
49
52. • Team consists of Tachyon creators, top contributors
• Series A ($7.5 million) from Andreessen Horowitz
• Committed to Tachyon Open Source
• http://www.tachyonnexus.com
52
53. Outline
• Open Source
• Introduction to Tachyon (Before 2015)
• Deployments and New Features
• Getting Involved
53