Tachyon talk at Strata and Hadoop World 2015 at New York City, given by Haoyuan Li, Founder & CEO of Tachyon Nexus. If you are interested, please do not hesitate to contact us at info@tachyonnexus.com . You are welcome to visit our website ( www.tachyonnexus.com ) as well.

1. Haoyuan Li, Tachyon Nexus
haoyuan@tachyonnexus.com
September 30, 2015 @ Strata and Hadoop World NYC 2015
An Open Source Memory-Centric
Distributed Storage System

2. Outline
• Open Source
• Introduction to Tachyon
• New Features
• Getting Involved
2

3. Outline
• Open Source
• Introduction to Tachyon
• New Features
• Getting Involved
3

4. History
• Started at UC Berkeley AMPLab
– From summer 2012
– Same lab produced Apache Spark and Apache Mesos
• Open sourced
– April 2013
– Apache License 2.0
– Latest Release: Version 0.7.1 (August 2015)
• Deployed at > 100 companies
4

5. Contributors Growth
5
v0.4!
Feb ‘14
v0.3!
Oct ‘13
v0.2
Apr ‘13
v0.1
Dec ‘12
v0.6!
Mar ‘15
v0.5!
Jul ‘14
v0.7!
Jul ‘15
1
3
15
30
46
70
111

6. Contributors Growth
6
> 150 Contributors
(3x increment over the last Strata NYC)
> 50 Organizations

7. Contributors Growth
7
One of the Fastest
Growing Big Data
Open Source
Project

8. Thanks to Contributors and Users!
8

9. One Tachyon Production
Deployment Example
• Baidu (Dominant Search Engine in China,
~ 50 Billion USD Market Cap)
• Framework: SparkSQL
• Under Storage: Baidu’s File System
• Storage Media: MEM + HDD
• 100+ nodes deployment
• 1PB+ managed space
• 30x Performance Improvement
9

10. Outline
• Open Source
• Introduction to Tachyon
• New Features
• Getting Involved
10

11. Tachyon is an
Open Source
Memory-centric
Distributed
Storage System
11

12. 12
Why Tachyon?

13. Performance Trend:
Memory is Fast
• RAM throughput
increasing exponentially
• Disk throughput
increasing slowly
13
Memory-locality key to interactive response times

14. Price Trend: Memory is Cheaper
source:
jcmit.com
14

15. Realized by many…
15

16. 16
Is the
Problem Solved?

17. 17
Missing a Solution
for the Storage Layer

18. 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
18

19. Issue 1
19
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)

20. Issue 1
20
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)

21. Issue 1 resolved with Tachyon
21
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

22. Issue 2
22
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

23. Issue 2
23
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

24. HDFS / Amazon S3
Issue 2
24
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
crash
Cache loss when process
crashes

25. 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
25
Spark Task
Spark memory
block manager
execution engine &
storage engine
same process
Keep in-memory data safe,
even when a job crashes.

26. Issue 2 resolved with Tachyon
26
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.

27. HDFS / Amazon S3
Issue 3
27
In-memory Data Duplication &
Java Garbage Collection
Spark Job1
Spark mem
block manager
block 1
block 3
Spark Job2
Spark mem
block manager
block 3
block 1
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
(duplication & GC)

28. Issue 3 resolved with Tachyon
28
No in-memory data duplication,
much less GC
Spark Job1
Spark mem
Spark Job2
Spark mem
HDFS / Amazon S3
block 1
block 3
block 2
block 4
execution engine &
storage engine
same process
(no duplication & GC)
HDFS
disk
block
1
block
3
block
2
block
4
Tachyon!
in-memory
block 1
block 3
block 4

29. Previously Mentioned
• A memory-centric storage architecture
• Push lineage down to storage layer
29

30. Tachyon Memory-Centric Architecture
30

31. Tachyon Memory-Centric Architecture
31

32. Lineage in Tachyon
32

33. Outline
• Open Source
• Introduction to Tachyon
• New Features
• Getting Involved
33

34. 1) Eco-system:
Enable new workload in any storage;
Work with the framework of your choice;
34

35. 2) Tachyon running in
production environment,
both
in the Cloud and on Premise.
35

36. Use Case: Baidu
• Framework: SparkSQL
• Under Storage: Baidu’s File System
• Storage Media: MEM + HDD
• 100+ nodes deployment
• 1PB+ managed space
• 30x Performance Improvement
36

37. Use Case: a SAAS Company
• Framework: Impala
• Under Storage: S3
• Storage Media: MEM + SSD
• 15x Performance Improvement
37

38. Use Case: an Oil Company
• Framework: Spark
• Under Storage: GlusterFS
• Storage Media: MEM only
• Analyzing data in traditional storage
38

39. Use Case: a SAAS Company
• Framework: Spark
• Under Storage: S3
• Storage Media: SSD only
• Elastic Tachyon deployment
39

40. 40
What if
data size exceeds
memory capacity?

41. 41
3) Tiered Storage:
Tachyon Manages More Than DRAM
MEM
SSD
HDD
Faster
Higher
Capacity

42. 42
Configurable Storage Tiers
MEM only
MEM + HHD
SSD only

43. 43
4) Pluggable Data Management Policy
Evict stale data to
lower tier
Promote hot data to
upper tier

44. 44
Pin Data in Memory

45. 5) Transparent Naming
45

46. 6) Unified Namespace
46

47. 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
47

48. 12) More Under Storage Supports
48

49. Reported Tachyon Usage
49

50. Outline
• Open Source
• Introduction to Tachyon
• New Features
• Getting Involved
50

51. Memory-Centric Distributed Storage
Welcome to try, contact, and collaborate!
51
JIRA New Contributor Tasks

52. • Team consists of Tachyon creators, top contributors
• Series A ($7.5 million) from Andreessen Horowitz
• Committed to Tachyon Open Source
52

53. 53

54. Strata NYC 2015
• Welcome to visit us at our booth #P18.
• Check out other Tachyon related talks.
– First-ever scalable, distributed deep learning architecture
using Spark and Tachyon
• Christopher Nguyen (Adatao, Inc.), Vu Pham (Adatao, Inc)
• 2:05pm–2:45pm Thursday, 10/01/2015
– Faster time to insight using Spark, Tachyon, and Zeppelin
• Nirmal Ranganathan (Rackspace Hosting)
• 2:05pm–2:45pm Thursday, 10/01/2015
54

55. • Try Tachyon: http://tachyon-project.org
• Develop Tachyon: https://github.com/amplab/tachyon
• Meet Friends: http://www.meetup.com/Tachyon
• Get News: http://goo.gl/mwB2sX
• Tachyon Nexus: http://www.tachyonnexus.com
• Contact us: haoyuan@tachyonnexus.com
55