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ALLUXIO (FORMERLY TACHYON): UNIFY DATA AT MEMORY SPEED
Global Big Data Conference, San Jose
March 2017
Gene Pang, Alluxio,...
HISTORY
• Started at UC Berkeley AMPLab In Summer 2012
• Originally named as Tachyon
• Rebranded to Alluxio in early 2016
...
ALLUXIO DEPLOYMENTS
© 2016 Alluxio Confidential 3
BIG DATA ECOSYSTEM TODAYBIG DATA ECOSYSTEM WITH ALLUXIO
…
…
FUSE Compatible File SystemHadoop Compatible File System Nativ...
5
6
7
• Fastest growing open-
source project in the
big data ecosystem
• 450+ contributors from
100+ organizations
• Running i...
8
Unification
New workflows across any
data in any storage system
Orders of magnitude
improvement in run time
Choice in co...
#1 – ACCELERATING REMOTE STORAGE I/O
9
• Scenario: Compute and Storage Separation
• Meet different compute and storage har...
I/O WITHOUT ALLUXIO
Spark
Storage
Low latency, memory
throughput
High latency, network
throughput
10
I/O WITH ALLUXIO
Spark
Storage
Alluxio
Keeping data in Alluxio
accelerates data access
11
CASE STUDY: BAIDU
12
The performance was amazing. With Spark
SQL alone, it took 100-150 seconds to finish a
query; using A...
#2 – SHARING DATA AT MEMORY-SPEED AMONG
APPLICATIONS
• Scenario: Data Sharing Architecture
• Pipelines: output of one job ...
SHARING WITHOUT ALLUXIO
Spark
Storage
MapReduce Spark
Network I/O
Disk I/O
I/O slows down
sharing
14
SHARING WITH ALLUXIO
Spark
Storage
MapReduce Spark
Memory-speed
sharing
Alluxio
15
CASE STUDY: BARCLAYS
Thanks to Alluxio, we now have the raw data
immediately available at every iteration and
we can skip ...
#3 – UNIFYING DATA ACCESS FROM DIFFERENT
STORAGE
• Scenario: Multiple Storage Systems
• Most enterprises have multiple sto...
ACCESSING DATA THROUGH ALLUXIO
Storage B
Alluxio
Spark MapReduce Flink
Storage A Storage C
Flexible,
simple
no application...
CASE STUDY: QUNAR
We’ve been running Alluxio in production for
over 9 months, Alluxio’s unified namespace
enable different...
ALLUXIO DEMO
SUMMARY
21
• Adopted by industry leaders
• Unified, memory-speed data access across
compute frameworks (Spark, Presto,
Map...
JOIN THE COMMUNITY
22
Contribute @ www.alluxio.org/contribute
Get started @ goo.gl/55ApFx
Contact: gene@alluxio.com
Twitter: @unityxx
Websites: www.alluxio.com and www.alluxio.org
Thank you!
We are hiring!
Demo V...
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Alluxio (Formerly Tachyon): Unify Data At Memory Speed at Global Big Data Conference San Jose 2017

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Gene Pang's Alluxio presentation at Global Big Data Conference San Jose 2017

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Alluxio (Formerly Tachyon): Unify Data At Memory Speed at Global Big Data Conference San Jose 2017

  1. 1. ALLUXIO (FORMERLY TACHYON): UNIFY DATA AT MEMORY SPEED Global Big Data Conference, San Jose March 2017 Gene Pang, Alluxio, Inc.
  2. 2. HISTORY • Started at UC Berkeley AMPLab In Summer 2012 • Originally named as Tachyon • Rebranded to Alluxio in early 2016 • Open Sourced in 2013 • Apache License 2.0 • Latest Stable Release: Alluxio 1.4.0 • Alluxio 1.5.0 Planned For Q2, 2017 2
  3. 3. ALLUXIO DEPLOYMENTS © 2016 Alluxio Confidential 3
  4. 4. BIG DATA ECOSYSTEM TODAYBIG DATA ECOSYSTEM WITH ALLUXIO … … FUSE Compatible File SystemHadoop Compatible File System Native Key-Value InterfaceNative File System Unifying Data at Memory Speed BIG DATA ECOSYSTEM ISSUES GlusterFS InterfaceAmazon S3 Interface Swift InterfaceHDFS Interface 4 BIG DATA ECOSYSTEM YESTERDAY
  5. 5. 5
  6. 6. 6
  7. 7. 7 • Fastest growing open- source project in the big data ecosystem • 450+ contributors from 100+ organizations • Running in large production clusters • Community members are welcome! FASTEST GROWING BIG DATA PROJECTS Popular Open Source Projects’ Growth Months NumberofContributors
  8. 8. 8 Unification New workflows across any data in any storage system Orders of magnitude improvement in run time Choice in compute and storage – grow each independently, buy only what is needed Performance Flexibility ALLUXIO BENEFITS
  9. 9. #1 – ACCELERATING REMOTE STORAGE I/O 9 • Scenario: Compute and Storage Separation • Meet different compute and storage hardware requirements • Scale compute and storage independently • Store data in traditional filers/SANs and object stores • Analyze existing data with Big Data compute frameworks • Limitation • Accessing data requires remote I/O
  10. 10. I/O WITHOUT ALLUXIO Spark Storage Low latency, memory throughput High latency, network throughput 10
  11. 11. I/O WITH ALLUXIO Spark Storage Alluxio Keeping data in Alluxio accelerates data access 11
  12. 12. CASE STUDY: BAIDU 12 The performance was amazing. With Spark SQL alone, it took 100-150 seconds to finish a query; using Alluxio, where data may hit local or remote Alluxio nodes, it took 10-15 seconds. - Baidu RESULTS • Data queries are now 30x faster with Alluxio • Alluxio cluster runs stably, providing over 50TB of RAM space • By using Alluxio, batch queries usually lasting over 15 minutes were transformed into an interactive query taking less than 30 seconds Baidu’s PMs and analysts run interactive queries to gain insights into their products and business • 200+ nodes deployment • 2+ petabytes of storage • Mix of memory + HDD ALLUXIO Baidu File System
  13. 13. #2 – SHARING DATA AT MEMORY-SPEED AMONG APPLICATIONS • Scenario: Data Sharing Architecture • Pipelines: output of one job is input of the next job • Applications, jobs, and contexts reading the same data • Limitation • Sharing data requires I/O 13
  14. 14. SHARING WITHOUT ALLUXIO Spark Storage MapReduce Spark Network I/O Disk I/O I/O slows down sharing 14
  15. 15. SHARING WITH ALLUXIO Spark Storage MapReduce Spark Memory-speed sharing Alluxio 15
  16. 16. CASE STUDY: BARCLAYS Thanks to Alluxio, we now have the raw data immediately available at every iteration and we can skip the costs of loading in terms of time waiting, network traffic, and RDBMS activity. - Barclays RESULTS • Barclays workflow iteration time decreased from hours to seconds • Alluxio enabled workflows that were impossible before • By keeping data only in memory, the I/O cost of loading and storing in Alluxio is now on the order of seconds Barclays uses query and machine learning to train models for risk management • 6 node deployment • 1TB of storage • Memory only ALLUXIO Relational Database 16
  17. 17. #3 – UNIFYING DATA ACCESS FROM DIFFERENT STORAGE • Scenario: Multiple Storage Systems • Most enterprises have multiple storage systems • New (better, faster, cheaper) storage systems arise • Limitation • Accessing data from different systems requires different APIs 17
  18. 18. ACCESSING DATA THROUGH ALLUXIO Storage B Alluxio Spark MapReduce Flink Storage A Storage C Flexible, simple no application changes, new mount point 18
  19. 19. CASE STUDY: QUNAR We’ve been running Alluxio in production for over 9 months, Alluxio’s unified namespace enable different applications and frameworks to easily interact with data from different storage systems. - Qunar RESULTS • Data sharing among Spark Streaming, Spark batch and Flink jobs provide efficient data sharing • Improved the performance of their system with 15x – 300x speedups • Tiered storage feature manages storage resources including memory and HDD • 200+ nodes deployment • 6 billion logs (4.5 TB) daily • Mix of Memory + HDD ALLUXIO Qunar uses real-time machine learning for their website ads. 19
  20. 20. ALLUXIO DEMO
  21. 21. SUMMARY 21 • Adopted by industry leaders • Unified, memory-speed data access across compute frameworks (Spark, Presto, MapReduce) and storage systems (S3, GCS, ECS, Ceph, HDFS, NFS, etc) • Rapidly growing OS community
  22. 22. JOIN THE COMMUNITY 22 Contribute @ www.alluxio.org/contribute Get started @ goo.gl/55ApFx
  23. 23. Contact: gene@alluxio.com Twitter: @unityxx Websites: www.alluxio.com and www.alluxio.org Thank you! We are hiring! Demo Videos: Spark + Alluxio + S3 Alluxio Unified Namespace 23

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