The document discusses distributed computing and in-memory computing using Hazelcast. It covers how Hazelcast partitions and distributes data across nodes, allows parallel processing of distributed data, and provides distributed caching capabilities with features like TTL and auto-cleanup. Examples of using Hazelcast for distributed maps, parallel sums, and caching are shown.

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/**
* Distributed Computing
* with Hazelcast
*/

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Interactive!

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Disclaimer
# A fatal error has been detected by the Java Runtime Environment:
#
# SIGSEGV (0xb) at pc=0x00002aaaaaf6d9c3, pid=2185, tid=1086892352
#
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# Java VM: Java HotSpot(TM) 64-Bit Server VM (17.0-b16 mixed mode linux-amd64 )
#
# An error report file with more information is saved as:
# /path/to/hs_err_pid2185.log
#
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# statements and projections reflect various estimates and assumptions by the Company concerning anticipated
# results. These statements involve risks, uncertainties and assumptions and are based on the current estimates and
# assumptions of the management of the Company as of the date of this presentation and are subject to uncertainty and
# changes. No representations or warranties are made by the Company as to the accuracy of any such statements or
# projections. Whether or not any such forward-looking statements or projections are in fact achieved will depend upon
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Who’s that dude?
• Chris(toph) Engelbert
• Twitter fanatic: @noctarius2k
• Weird 9+ Java-Years
• Performance, GC, Fairytales
• Apache Committer
• Gaming, Travel Management, …

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• Chris(toph) Engelbert
• Twitter fanatic: @noctarius2k
• Weird 9+ Java-Years
• Performance, GC, Fairytales
• Apache Committer
• Gaming, Travel Management, …
The www.hazelcast.ninja
Who’s that dude?

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Distributed
Computing…

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Toaster
Server 1
TV-Server 1
Power
CoffeeMaker
Server 1
Kettle
Server 1
Stove
Server 1
Power Grid
Server 1, 2
Distributed
Computing…

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… but what means
Distributed Computing?OOO
( )

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“In-Memory Computing”

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‘In-Memory Computing’
L1 Cache Reference
L2 Cache Reference
Main Memory Reference
1K send over 1 Gbps Network
4K read from SSD
Seek on Spinning Disk
Send Packet CA->AMS->CA
0.5 ns
7 ns
100 ns
10,000 ns
150,000 ns
10,000,000 ns
150,000,000 ns
0.01 ms
0.15 ms
10 ms
150 ms

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Evolution of:
- Memory-Price
- Memory-Size

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Data Distribution
(Partitioning / Sharding)

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Data Distribution
String code = “JCP-TOUR-2015”
Map<String, TShirtDelivery> shirts = Maps.create()
shirts.put(code, new TShirtDelivery(“Your Address”))

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Data Distribution
String code = “JCP-TOUR-2015”
int hashcode = hash(code)
partitionId = hashcode % partitionCount
cluster.send(partitionId, code, delivery)

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Data Distribution
int hashcode = hash(code)
partitionId = hashcode % partitionCount
cluster.send(partitionId, code, delivery)
Must be a consistent hash function
Partition count must be constant
Can be calculated, smart routing possible

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Demo Time

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That was easy!But don’t tell anyone!

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Parallelize Processing

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Parallelize Processing
int[] values = [0..100000].map(randomInt())
int[][] blocks = split(values, 10000)
int[] subresult = blocks.foreach(_.sum())
int result = subresult.sum()

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Parallelize Processing
int[][] blocks = split(values, 10000)
int[] subresult = blocks.foreach(_.sum())
int result = subresult.sum()
No mutation, only new values
Independently computable
Might split further down (fork-join, work-stealing)

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Demo Time
2

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I told you:
Easy Peasy!

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Distributed Caching!

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Collect, store …

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…access fast when necessary!…access fast when necessary!

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Caches

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Evolution Of Caching
Custom

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http://gadizmo.com/roll-your-own-with-the-ham-dogger.php
Roll Your Own!
Evolution Of Caching

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Caches Are Simple!
public class Cache<K, V> {
private final Map<K, V> cache = new CHM<>();
public void put(K key, V value) {
cache.put(key, value);
}
public V get(K key) {
return cache.get(key);
}
}

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Even With TTL!

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And Auto-Cleanup

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What Is Wrong?

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What Is Wrong?
O(n)
5 mio
Entries

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It was a trap!

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Evolution Of Caching
Custom

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Proprietary
Evolution Of Caching
Custom

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Proprietary
Evolution Of Caching
Open Source
Custom

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Proprietary
Evolution Of Caching
Open Source
Commercial
Custom

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Proprietary
Evolution Of Caching
Open Source
Commercial
Stupidly Expensive
Custom

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http://technoodling.net/mili-power-crystal-battery-pack-review/
Terracotta /
EHcache
Oracle
Coherence
Infinispan
Java
Collections
API
Hazelcast
JCache
Custom

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Success!
Feels like

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Quick Shameless Plug :)
Hazelcast!

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Hazelcast in 5 Facts
• Java Collections API
• Java Concurrency API
• Transparent Data Distribution
• Drop-In Replacement
• Disruptively Simple
(c) Rod Johnson (@springrod)

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Time to be Happy!

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Time to be Happy!
Apache License 2

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Time to be Happy!
www.hazelcast.org

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You can do it!

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Thank You!
Any Questions?
@noctarius2k
http://www.sourceprojects.org
http://github.com/noctarius
@hazelcast
http://www.hazelcast.com
http://www.hazelcast.org
http://github.com/hazelcast

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Thank You!
Any Questions?
@noctarius2k
http://www.sourceprojects.org
http://github.com/noctarius
@hazelcast
http://www.hazelcast.com
http://www.hazelcast.org
http://github.com/hazelcast
Free Hazelcast T-Shirt
http://bit.ly/tshirtcast

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Thank You!
Any Questions?
@noctarius2k
http://www.sourceprojects.org
http://github.com/noctarius
@hazelcast
http://www.hazelcast.com
http://www.hazelcast.org
http://github.com/hazelcast
Free Hazelcast T-Shirt
http://bit.ly/tshirtcast
We’re hiring!
www.hazelcast.com/careers