In-Memory Computing - Distributed Systems - Devoxx UK 2015
•
2 likes•898 views
Today’s amounts of collected data are showing a nearly exponential growth. More than 75% of all the data have been collected in the past 5 years. To store this data and process it in an appropriate time you need to partition the data and parallelize the processing of reports and analytics. This talk will demonstrate how to parallelize data processing using Hazelcast and it’s underlying distributed data structures. With a quick introduction into the different terms and some short live coding examples we will make the journey into the distributed computing.
Recommended
More Related Content
Similar to In-Memory Computing - Distributed Systems - Devoxx UK 2015
Similar to In-Memory Computing - Distributed Systems - Devoxx UK 2015 (20)
More from Christoph Engelbert
More from Christoph Engelbert (20)
Recently uploaded
Recently uploaded (20)
In-Memory Computing - Distributed Systems - Devoxx UK 2015
- 1. www.hazelcast.com /** * In-Memory Computing * Distributed Systems */
- 3. www.hazelcast.com Disclaimer This presentation may include certain forward-looking statements and projections provided by the Company. Any such 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 future events some of which are not within the control of the Company. Accordingly, actual results may vary from the projected results and such variations may be material. Statements contained herein describing documents and agreements are summaries only and such summaries are qualified in their entirety by reference to such documents and agreements.
- 4. www.hazelcast.com Who’s that dude? • Chris(toph) Engelbert • Twitter fanatic: @noctarius2k • Weird 9+ Java-Years • Performance, GC, Fairytales • Apache Committer • Gaming, Travel Management, …
- 5. www.hazelcast.com Who’s that dude? • Chris(toph) Engelbert • Twitter fanatic: @noctarius2k • Weird 9+ Java-Years • Performance, GC, Fairytales • Apache Committer • Gaming, Travel Management, … The www.hazelcast.ninja
- 7. www.hazelcast.com@noctarius2k Toaster Server 1 TV-Server 1 Power CoffeeMaker Server 1 Kettle Server 1 Stove Server 1 Power Grid Server 1, 2 Distributed Computing…
- 8. www.hazelcast.com@noctarius2k … but what means Distributed Computing?OOO ( )
- 10. www.hazelcast.com@noctarius2k ‘In-Memory’ - Don’t you mean ‘Wasting Memory’? 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
- 11. www.hazelcast.com@noctarius2k Evolution of: - Memory-Price - Memory-Size
- 12. www.hazelcast.com Data Distribution (Partitioning / Sharding)
- 13. www.hazelcast.com@noctarius2k Data Distribution String code = “devoxx.co.uk” Map<String, TShirtDelivery> shirts = Maps.create() shirts.put(code, new TShirtDelivery(“Your Address”))
- 14. www.hazelcast.com@noctarius2k Data Distribution String code = “devoxx.co.uk” int hashcode = hash(code) partitionId = hashcode % partitionCount cluster.send(partitionId, code, delivery)
- 15. www.hazelcast.com@noctarius2k 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
- 17. www.hazelcast.com@noctarius2k That was easy!But don’t tell anyone!
- 19. www.hazelcast.com@noctarius2k Parallelize Processing int[] values = [0..100000].map(randomInt()) int[][] blocks = split(values, 10000) int[] subresult = blocks.foreach(_.sum()) int result = subresult.sum()
- 20. www.hazelcast.com@noctarius2k 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)
- 25. www.hazelcast.com@noctarius2k …access fast when necessary!…access fast when necessary!
- 26. www.hazelcast.com@noctarius2k http://gadizmo.com/roll-your-own-with-the-ham-dogger.php Roll Your Own! Evolution Of Caching
- 27. www.hazelcast.com@noctarius2k 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); } }
- 31. www.hazelcast.com@noctarius2k What Is Wrong? O(n) 5 mio Entries
- 35. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution Of Caching Open Source
- 36. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution Of Caching Open Source Commercial
- 37. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution Of Caching Open Source Commercial Stupidly Expensive
- 40. www.hazelcast.com@noctarius2k Want to know more about #JCache? Gimme Caching The Distributed JCache (JSR107) Way 17:00 - 17:50 - Room Exec Centre David Brimley
- 41. www.hazelcast.com@noctarius2k Quick Shameless Plug :) Hazelcast!
- 42. www.hazelcast.com@noctarius2k Hazelcast in 5 Facts • Java Collections API • Java Concurrency API • Transparent Data Distribution • Drop-In Replacement • Disruptively Simple (c) Rod Johnson (@springrod)
- 43. www.hazelcast.com@noctarius2k Time to be Happy!
- 44. www.hazelcast.com@noctarius2k Time to be Happy! Apache License 2
- 45. www.hazelcast.com@noctarius2k Time to be Happy! www.hazelcast.org