www.hazelcast.com

1. © 2014 Hazelcast Inc.
How to speed up your Database.
DAVID BRIMLEY
SENIOR SOLUTIONS ARCHITECT

2. © 2014 Hazelcast Inc.
About me
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Senior Solutions Architect for Hazelcast.
Oracle Coherence @ HSBC 2007-2010
Gemfire @ Nomura 2010 - 2013
Worked for Major Investment Banks in the City of
London.
Java Programmer since 1998.
David Brimley
@dbrimley

3. © 2014 Hazelcast Inc.
Agenda - 40 Minutes + Q&A time.
• Introduction to Hazelcast.
• The Slow Database problem.
• Adding Hazelcast for faster queries.
• And doing the same for inserts and updates.
• Q&A
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Introduction to Hazelcast
• Hazelcast the Open Source Project
www.hazelcast.org"
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• Hazelcast the Company
www.hazelcast.com
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The Slow Database Problem
• Traditional Databases are hard to scale.
• They cost a lot in time and effort.
• Common solution is a Cache."
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• IMDG like Hazelcast give horizontal scalability.
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The Hazelcast Approach
• Cluster of JVM that all contribute memory.
• Client API can work against Maps.
• Query using Predicates or SQL like strings."
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• Faster Reads using the Cache.
• Faster Update/Inserts using write-behind.
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The Test Suite
• Postgres 9.3.4 installed on a Macbook Pro Dual
Core with 16gb RAM and 256gb SSD.
• Dataset : Stackoverflow Public Dump (47gb)
• Hazelcast 3.2
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The Simple Postgres Java DAO
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Step 1 : Configure the Hazelcast Distributed Map."
A) MapStore for connecting Hazelcast to the Database."
B) Configure Indexes for faster In Memory Query of Map."
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Integrating Hazelcast in 4 steps
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<map name="user">
<map-store enabled="true">
<class-name>com.craftedbytes.hazelcast.dao.UserMapStore</class-name>
<write-delay-seconds>30</write-delay-seconds>
</map-store>
<indexes>
<index ordered="true">location</index>
</indexes>
</map>
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10. © 2014 Hazelcast Inc.
Integrating Hazelcast in 4 steps
Step 2 : Code your MapStore implementation"
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public class UserMapStore implements MapStore<Integer, User> {
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private UserDAO userDao = new UserDAO();
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private UserMapStore() {}
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@Override
public void store(Integer userKey, User user) {
userDao.insertUser(user);
}
@Override
public User load(Integer userKey) {
return userDao.getUser(userKey);
}
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@Override
public Map<Integer, User> loadAll(Collection<Integer> userKeys) {
return userDao.loadAll(userKeys);
}
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11. © 2014 Hazelcast Inc.
Integrating Hazelcast in 4 steps
Step 3 : Query against a Java Map !"
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@Override
public Collection<User> getUserByLocation(String location){
return userMap.values(Predicates.equal("location", location));
}
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@Override
public User getUser(Integer userKey) {
return userMap.get(userKey);
}
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@Override
public void removeUser(Integer userKey) {
userMap.evict(userKey);
}
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@Override
public void addUsers(Map<Integer,User> users) {
userMap.putAll(users);
}
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12. © 2014 Hazelcast Inc.
Integrating Hazelcast in 4 steps
Step 4 : Start your cluster !
Hazelcast will prime from Database using the loadAllKeys method from your
MapStore."
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No changes were required to the Database.
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Hazelcast for faster reads
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Hazelcast will read through to Database
• If you only load a subset of data from your whole
set.
• Hazelcast will use the MapStore to read to the
Database to cache a Map Entry on a Cache Miss.
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Hazelcast read-through to DB
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Hazelcast can make updates faster as well
• A Map put can be async to the Database.
• Write behind stores records and writes to DB
after delay.
• The User doesn’t have to wait for the DB to
complete, once entry hits cache the transaction is
complete.
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Hazelcast write-behind to DB
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Q&A
ANY QUESTIONS?
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