I gave this talk at the first DataScience Graz Meetup (http://datascience.at/) on 2012/02/09. It was made with the aim of presenting a selection of Big Data experiences that Mendeley has had and lessons that we have learned along the way. The event had around 15-20 participants who were all computer professionals interested in solutions to real-world Big Data problems.

1. Cloud Elephants and
Witches: A Big Data
Tale from Mendeley
Kris Jack, PhD
Data Mining Team Lead

2. Overview
➔
What's Mendeley?
➔
The curse that comes with success
➔
A framework for scaling up (Hadoop + MapReduce)
➔
Moving to the cloud (AWS)
➔
Conclusions

3. What's Mendeley?

4. What is Mendeley?
...a large data technology
startup company
...and it's on a mission to
change the way that
research is done!

5. Mendeley Last.fm
3) Last.fm builds your music
works like this: profile and recommends you
music you also could like... and
1) Install “Audioscrobbler” it’s the world‘s biggest open
music database
2) Listen to music

6. Mendeley Last.fm
music libraries research libraries
artists researchers
songs papers
genres disciplines

7. Mendeley provides tools to help users...
...organise
their research

8. Mendeley provides tools to help users...
...collaborate with
one another
...organise
their research

9. Mendeley provides tools to help users...
...collaborate with
one another
...organise ...discover new
their research research

11. Mendeley provides tools to help users...
...collaborate with
one another
...organise ...discover new
their research research

12. The curse that comes
with success

13. In the beginning, there was...
➔
MySQL:
➔
Normalised tables for storing and serving:
➔
User data
➔
Article data
➔
The system was happy
➔
With this, we launched
the article catalogue
➔
Lots of number crunching
➔
Many joins for basic stats

14. Here's where the curse of success comes
➔
More articles came
➔
More users came
➔
The system became unhappy
➔
Keeping data fresh was a burden
➔
Algorithms relied on global counts
➔
Iterating over tables was slow
➔
Needed to shard tables to grow catalogue
➔
In short, our system didn't scale

15. 1.6 million+ users; the 20 largest userbases:
University of Cambridge
Stanford University
MIT
University of Michigan
Harvard University
University of Oxford
Sao Paulo University
Imperial College London
University of Edinburgh
Cornell University
University of California at Berkeley
RWTH Aachen
Columbia University
Georgia Tech
University of Wisconsin
UC San Diego
University of California at LA
University of Florida
University of North Carolina

16. 50m
Real-time data on 28m unique papers:
Thomson Reuters’
Web of Knowledge
(dating from 1934)
Mendeley after
16 months:
>150 million
individual articles,
(>25TB)

17. We had serious needs
➔
Scale up to the millions (billions for some items)
➔
Keep data fresh
➔
Support newly planned services
➔
Search
➔
Recommendations
➔
Business context
➔
Agile development (rapid prototyping)
➔
Cost effective
➔
Going viral

18. A framework for scaling up
(Hadoop and MapReduce)

19. What is Hadoop?
The Apache Hadoop project develops open-source
software for reliable, scalable, distributed
computing
www.hadoop.apache.org

20. Hadoop
➔
Designed to operate on a cluster of computers
➔
1...thousands
➔
Commodity hardware (low cost units)
➔
Each node offers local computation and storage
➔
Provides framework for working with petabytes of data
➔
When learning about Hadoop, you need to learn about:
➔
HDFS
➔
MapReduce

21. HDFS
➔
Hadoop Distributed File System
➔
Based on Google File System
➔
Replicates data storage (reliability, x3, across racks)
➔
Designed to handle very large files (e.g. 64MB)
➔
Provides high-throughput
➔
File access through Java and Thrift APIs, CL and Wepapp
➔
Name node is a single point of failure (availability issue)

22. MapReduce
➔
MapReduce is a programming model
➔
Allows distributed processing of large data sets
➔
Based on Google's MapReduce
➔
Inspired by functional programming
➔
Take the program to the data, not the data to the program

23. MapReduce Example:
Article Readers by Country
doc_id1, reader_id1, usa, 2010, … HDFS
doc_id2, reader_id2, austria, 2012, … Large file (150M entries)
doc_id1, reader_id3, china, 2010, … Flattened data
.
Stored across nodes
.
.
Map
(pivot countries doc_id1, {usa, china, usa, uk, china, china...}
by doc id) doc_id2, {austria, austria, china, china, uk …}
...
doc_id1, usa, 0.27 Reduce
doc_id1, china, 0.09 (calc. document stats)
doc_id1, uk, 0.09
doc_id2, austria, 0.99
.
.
.

24. Hadoop
➔
HDFS for storing data
➔
MapReduce for processing data
➔
Together, bring the program to the data

25. Hadoop's Users

26. We make a lot of use of HDFS and MapReduce
➔
Catalogue Stats
➔
Recommendations (Mahout)
➔
Log Analysis (business analytics)
➔
Top Articles
➔
… and more
➔
Quick, reliable and scalable

27. Beware that these benefits have costs
➔
Migrating to a new system (data consistency)
➔
Setup costs
➔
Learn black magic to configure
➔
Hardware for cluster
➔
Administrative costs
➔
High learning curve to administrate Hadoop
➔
Still an immature technology
➔
You may need to debug the source code
➔
Tips
➔
Get involved in the community (e.g. meetups, forums)
➔
Use good commodity hardware
➔
Consider moving to the cloud...

28. Moving to the cloud
(AWS)

29. What is AWS?
Amazon Web Services (AWS) delivers a set of
services that together form a reliable, scalable,
and inexpensive computing platform “in the
cloud”
www.aws.amazon.com

30. Why move to AWS?
➔
The cost of running your own cluster can be high
➔
Monetary (e.g. hardware)
➔
Time (e.g. training, setup, administration)
➔
AWS takes on these problems, renting their
services to you based on your usage

31. Article Recommendations
➔
Aim: help researchers to find interest articles
➔
Combat information deluge
➔
Keep up-to-date with recent movements
➔
1.6M users
➔
50M articles
➔
Batch process for generating regular
recommendations (using Mahout)

32. Article Recommendations in EMR
➔
Use Amazon's Elastic Map Reduce (EMR)
➔
Upload input data (user libraries)
➔
Upload Mahout jar
➔
Spin up cluster
➔
Run the job
➔
You decide the number of nodes (cost vs time)
➔
You decide the spec of the nodes (cost vs quality)
➔
Retrieve the output

33. Catalogue Search
➔
50 million articles
➔
50GB index in Solr
➔
Variable load (over 24 hours)
➔
1AM is quieter (100 q/s), 1PM is busier (150 q/s)

34. At 1AM, 150 queries/second
1PM, 100 queries/second
AWS Instance
?, ?, ?...
queries
(100/s)
(150/s) AWS elastic
load balancer AWS Instance
AWS Instance
Catalogue Search in Context of Variable Load
➔
Amazon's Elastic Load Balancer
➔
Only pay for nodes when you need them
➔
Spin up when load is high
➔
Tear down load is low
➔
Cost effective and scalable

35. Problems we've faced
➔
Lack of control can be an issue
➔
Trade-off administration and control
➔
Orchestration issues
➔
We have many services to coordinate
➔
Cloud formation & Elastic Beanstalk
➔
Migrating live services is hard work

36. Conclusions

37. Conclusions
➔
Mendeley has created one of the world's largest
scientific databases
➔
Storing and processing this data is a large scale
challenge
➔
Hadoop, through HDFS and MapReduce, provides a
framework for large scale data processing
➔
Be aware of administration costs when doing this in
house

38. Conclusions
➔
AWS can make scaling up efficient and cost
effective
➔
Tap into the rich big data community out there
➔
We plan to have make no more substantial
hardware purchases, instead use AWS
➔
Scaling up isn't a trivial problem, to save pain,
plan for it from the outset

39. Conclusions
➔
Magic elephants that live in clouds can lift the
curses of evil witches

40. www.mendeley.com