Alluxio: Unify Data at Memory Speed at Strata and Hadoop World San Jose 2017

1. ALLUXIO (FORMERLY TACHYON): UNIFY DATA AT MEMORY SPEED
Strata and Hadoop World San Jose
March 2017
Haoyuan Li, Alluxio, Inc.
Calvin Jia, Alluxio, Inc.

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
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3. © 2016 Alluxio Confidential
ALLUXIO DEPLOYMENTS
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BIG DATA ECOSYSTEM YESTERDAY

5. BIG DATA ECOSYSTEM TODAY
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BIG DATA ECOSYSTEM ISSUES
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7. BIG DATA ECOSYSTEM WITH ALLUXIO
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FUSE Compatible File SystemHadoop Compatible File System Native Key-Value InterfaceNative File System
GlusterFS InterfaceAmazon S3 Interface Swift InterfaceHDFS Interface
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8. BIG DATA ECOSYSTEM WITH ALLUXIO
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FUSE Compatible File SystemHadoop Compatible File System Native Key-Value InterfaceNative File System
Unifying Data at Memory Speed
GlusterFS InterfaceAmazon S3 Interface Swift InterfaceHDFS Interface
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11. 7
• Fastest growing open-
source project in the big
data ecosystem
• 500+ 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

12. WHY ALLUXIO
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Co-located compute and data with memory-speed access to data
Virtualized across different storage systems under a unified namespace
Scale-out architecture
File system API, software only

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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
BENEFITS

14. #1 – ACCELERATING REMOTE STORAGE I/O
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• 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

15. I/O WITHOUT ALLUXIO
Spark
Storage
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16. I/O WITHOUT ALLUXIO
Spark
Storage
Low latency, memory
throughput
High latency, network
throughput
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17. I/O WITH ALLUXIO
Spark
Storage
Alluxio
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18. I/O WITH ALLUXIO
Spark
Storage
Alluxio
Keeping data in Alluxio
accelerates data access
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19. CASE STUDY: BAIDU
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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

20. #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
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21. SHARING WITHOUT ALLUXIO
Spark
Storage
MapReduce Spark
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22. SHARING WITHOUT ALLUXIO
Spark
Storage
MapReduce Spark
Network I/O
Disk I/O
I/O slows down
sharing
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23. SHARING WITH ALLUXIO
Spark
Storage
MapReduce Spark
Alluxio
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24. SHARING WITH ALLUXIO
Spark
Storage
MapReduce Spark
Memory-speed
sharing
Alluxio
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25. 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
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26. #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
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27. ACCESSING DATA THROUGH ALLUXIO
Storage B
Alluxio
Spark MapReduce Spark
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28. ACCESSING DATA THROUGH ALLUXIO
Storage B
Alluxio
Spark MapReduce Spark
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29. ACCESSING DATA THROUGH ALLUXIO
Storage B
Alluxio
Spark MapReduce Spark
Storage A Storage C
Flexible,
simple
no application
changes,
new mount
point
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30. 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.
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31. DEMO AND MORE CASES

32. SUMMARY
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• 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

33. JOIN THE COMMUNITY
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Contribute @ www.alluxio.org/contribute
Get started @ goo.gl/55ApFx

34. Contact: {haoyuan, calvin}@alluxio.com
Twitter: @haoyuan
Websites: www.alluxio.com and www.alluxio.org
Thank you!
We are hiring!
Demo:
Spark + Alluxio + S3
Alluxio Unified Namespace
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