Cloud Elephants and Witches: A Big Data Tale from Mendeley

Kris Jack, PhD

Data Mining Team Lead

➔ What's Mendeley?

➔ The curse that comes with success

➔ A framework for scaling up (Hadoop + MapReduce)

➔ Moving to the cloud (AWS)

➔ Conclusions

Overview

What's Mendeley?

...a large data technology startup company

...and it's on a mission to change the way that

research is done!

What is Mendeley?

works like this:

1) Install “Audioscrobbler”

2) Listen to music

3) Last.fm builds your music profile and recommends you music you also could like... and it’s the world‘s biggest open music database

Last.fmMendeley

research libraries

researchers

papers

disciplines

music libraries

artists

songs

genres

Last.fmMendeley

...organise their research

Mendeley provides tools to help users...

...organise their research

...organise their research

...collaborate with one another

Mendeley provides tools to help users...

...organise their research

...organise their research

...collaborate with one another

...discover new research

Mendeley provides tools to help users...

...organise their research

...organise their research

...collaborate with one another

...discover new research

Mendeley provides tools to help users...

...organise their research

The curse that comes with success

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

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

1.6 million+ users; the 20 largest userbases:

University of CambridgeStanford University

MITUniversity of Michigan

Harvard UniversityUniversity of OxfordSao Paulo University

Imperial College LondonUniversity of Edinburgh

Cornell UniversityUniversity of California at Berkeley

RWTH AachenColumbia University

Georgia TechUniversity of Wisconsin

UC San DiegoUniversity of California at LA

University of FloridaUniversity of North Carolina

Real-time data on 28m unique papers:

Thomson Reuters’ Web of Knowledge(dating from 1934)

Mendeley after 16 months:

50m

>150 million individual articles,

(>25TB)

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

A framework for scaling up (Hadoop and MapReduce)

What is Hadoop?

The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing

www.hadoop.apache.org

➔ 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

Hadoop

➔ 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)

HDFS

➔ 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

MapReduce

MapReduce Example:Article Readers by Country

doc_id1, reader_id1, usa, 2010, …doc_id2, reader_id2, austria, 2012, …doc_id1, reader_id3, china, 2010, …

.

.

.

HDFSLarge file (150M entries)Flattened dataStored across nodes

doc_id1, {usa, china, usa, uk, china, china...}doc_id2, {austria, austria, china, china, uk …}...

Map(pivot countries

by doc id)

Reduce(calc. document stats)

doc_id1, usa, 0.27doc_id1, china, 0.09doc_id1, uk, 0.09doc_id2, austria, 0.99

.

.

.

➔ HDFS for storing data➔ MapReduce for processing data

➔ Together, bring the program to the data

Hadoop

Hadoop's Users

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

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...

Moving to the cloud (AWS)

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

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

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)

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

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)

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

?, ?, ?...AWS elastic

load balancer

queries(100/s)

AWS Instance

AWS Instance

At 1AM, 100 queries/second

AWS Instance

At 1PM, 150 queries/second

queries(150/s)

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

Conclusions

Conclusions

➔ Mendeley has created the world's largest scientific database➔ 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

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

Conclusions

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

www.mendeley.com