Enable Fast Big Data Analytics on Ceph with Alluxio at Ceph Days 2017

1. ENABLE FAST BIG DATA ANALYTICS ON
CEPH WITH ALLUXIO
Adit Madan
March 2017

2. ABOUT ME
Adit Madan, Software Engineer @ Alluxio, Inc
Master’s @ Carnegie Mellon University
Bachelor’s @ Indian Institute of Technology, Delhi
Email: adit@alluxio.com
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3. ALLUXIO INTRODUCTION
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4. FASTEST-GROWING BIG DATA PROJECT
• Fastest growing
open-source
project in the big
data ecosystem
• 400+ contributors
from 100+
organizations
• Running world’s
largest production
clusters
• Welcome to join
the community!
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5. BIG DATA ECOSYSTEM TODAYBIG DATA ECOSYSTEM WITH ALLUXIOBIG DATA ECOSYSTEM YESTERDAY
…
…
FUSE Compatible File
System
Hadoop Compatible File
System
Native Key-Value
Interface
Native File System
Enabling Application to Access Data from any
Storage System at Memory-speed
BIG DATA ECOSYSTEM ISSUES
GlusterFS InterfaceAmazon S3 Interface Swift InterfaceHDFS Interface
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6. WHY ALLUXIO
Co-located with compute, provides memory-speed access to data
Virtualized across different storage systems under a unified global namespace
Distributed system, scale-out architecture
Software only, no change needed to existing application
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7. ALLUXIO BENEFITS
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
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8. USE CASE – ACCELERATE I/O TO/FROM
REMOTE STORAGE
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• Compute and Storage Separation
• Advantages
• Meet different compute and storage hardware
requirements efficiently
• Scale compute and storage independently
• Store data in Traditional filers/SANs and object
stores cost effectively
• Compute on data in existing storage via Big Data
Computational frameworks
• Disadvantage
• Accessing data requires remote I/O

9. USE CASE WITHOUT ALLUXIO
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Spark
Storage
Low latency, memory
throughput
High latency, network
throughput

10. USE CASE WITH ALLUXIO
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Spark
Storage
Alluxio
Keeping data in Alluxio
accelerates data access

11. ACCELERATE I/O TO/FROM REMOTE STORAGE
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
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12. ALLUXIO ON CEPH
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13. ALLUXIO ON CEPH
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Spark
Ceph Object
Storage
Alluxio
● Connect using RADOS Gateway
○ Swift Object Storage API

14. EC2 CONFIGURATION
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● 1 Compute Master
○ Spark and Alluxio Masters
● 3 Compute Workers
○ Spark and Alluxio Workers
● 1 Storage Manager
○ Ceph RadosGW and Monitor
● 2 Storage Devices
○ Ceph OSDs
● Instance type: r3.xlarge
● Availability Zone: us-­east-­1a

15. SOFTWARE VERSIONS
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● Ceph Version: 0.94.9
● Alluxio Version: 1.4.0
○ Custom JOSS library 0.9.13-­SNAPSHOT
● Spark Version 1.6.1

16. DEMO OF THE SOLUTION
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● Spark, Alluxio and Ceph Cluster pre-­deployed
● Ceph pre-­populated with a 60GB dataset
● Launch spark shell
a. First ‘count’
b. Second ‘count’
c. <Restart shell>
d. Third ‘count’
● Ad-­hoc queries w/ Alluxio
a. ‘wordcount’ w/ intermediate data

17. SPARK COUNT PERFORMANCE
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Count on 60 GB dataset
● 20x improvement for repeated access

18. FOR MORE INFORMATION ….
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Please take a look at our Whitepaper!
● Blog: https://alluxio.com/blog/accelerating-­data-­analytics-­on-­
ceph-­object-­storage-­with-­alluxio
● Whitepaper: https://alluxio.com/resources/accelerating-­data-­
analytics-­on-­ceph-­object-­storage-­with-­alluxio

19. Thank you!
Contact: adit@alluxio.com or info@alluxio.com
Twitter: @Alluxio
Websites: www.alluxio.com and www.alluxio.org
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