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Tachyon: An Open Source Memory-Centric Distributed Storage System

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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.

Published in: Technology

Tachyon: An Open Source Memory-Centric Distributed Storage System

  1. 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. 2. Outline •  Open Source •  Introduction to Tachyon •  New Features •  Getting Involved 2
  3. 3. Outline •  Open Source •  Introduction to Tachyon •  New Features •  Getting Involved 3
  4. 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. 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. 6. Contributors Growth 6 > 150 Contributors (3x increment over the last Strata NYC) > 50 Organizations
  7. 7. Contributors Growth 7 One of the Fastest Growing Big Data Open Source Project
  8. 8. Thanks to Contributors and Users! 8
  9. 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. 10. Outline •  Open Source •  Introduction to Tachyon •  New Features •  Getting Involved 10
  11. 11. Tachyon is an Open Source
 Memory-centric
 Distributed Storage System 11
  12. 12. 12 Why Tachyon?
  13. 13. Performance Trend: 
 Memory is Fast •  RAM throughput 
 increasing exponentially •  Disk throughput increasing slowly 13 Memory-locality key to interactive response times
  14. 14. Price Trend: Memory is Cheaper source:  jcmit.com   14
  15. 15. Realized by many… 15
  16. 16. 16 Is the Problem Solved?
  17. 17. 17 Missing a Solution for the Storage Layer
  18. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 29. Previously Mentioned •  A memory-centric storage architecture •  Push lineage down to storage layer 29
  30. 30. Tachyon Memory-Centric Architecture 30
  31. 31. Tachyon Memory-Centric Architecture 31
  32. 32. Lineage in Tachyon 32
  33. 33. Outline •  Open Source •  Introduction to Tachyon •  New Features •  Getting Involved 33
  34. 34. 1) Eco-system: Enable new workload in any storage; Work with the framework of your choice; 34
  35. 35. 2) Tachyon running in production environment, both in the Cloud and on Premise. 35
  36. 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. 37. Use Case: a SAAS Company •  Framework: Impala •  Under Storage: S3 •  Storage Media: MEM + SSD •  15x Performance Improvement 37
  38. 38. Use Case: an Oil Company •  Framework: Spark •  Under Storage: GlusterFS •  Storage Media: MEM only •  Analyzing data in traditional storage 38
  39. 39. Use Case: a SAAS Company •  Framework: Spark •  Under Storage: S3 •  Storage Media: SSD only •  Elastic Tachyon deployment 39
  40. 40. 40 What if 
 data size exceeds 
 memory capacity?
  41. 41. 41 3) Tiered Storage:
 Tachyon Manages More Than DRAM MEM SSD HDD Faster Higher 
 Capacity
  42. 42. 42 Configurable Storage Tiers MEM only MEM + HHD SSD only
  43. 43. 43 4) Pluggable Data Management Policy Evict stale data to lower tier Promote hot data to upper tier
  44. 44. 44 Pin Data in Memory
  45. 45. 5) Transparent Naming 45
  46. 46. 6) Unified Namespace 46
  47. 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. 48. 12) More Under Storage Supports 48
  49. 49. Reported Tachyon Usage 49
  50. 50. Outline •  Open Source •  Introduction to Tachyon •  New Features •  Getting Involved 50
  51. 51. Memory-Centric Distributed Storage Welcome to try, contact, and collaborate! 51 JIRA New Contributor Tasks
  52. 52. •  Team consists of Tachyon creators, top contributors •  Series A ($7.5 million) from Andreessen Horowitz
 •  Committed to Tachyon Open Source
 52
  53. 53. 53
  54. 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. 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

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