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![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()](https://image.slidesharecdn.com/distributedcomputing-devoxxuk-150618173544-lva1-app6892/85/In-Memory-Computing-Distributed-Systems-Devoxx-UK-2015-19-320.jpg)
![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)](https://image.slidesharecdn.com/distributedcomputing-devoxxuk-150618173544-lva1-app6892/85/In-Memory-Computing-Distributed-Systems-Devoxx-UK-2015-20-320.jpg)
Uploaded byChristoph Engelbert
In-Memory Computing - Distributed Systems - Devoxx UK 2015
The document presents an overview of distributed computing and in-memory computing, focusing on data distribution, caching, and the use of consistent hashing for efficient processing. It introduces Chris Engelbert, emphasizes the evolution and significance of caching technologies, and mentions Hazelcast as a powerful tool for managing distributed data. The presentation includes code examples and highlights the role of Java collections and concurrency APIs in simplifying data distribution.
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In-Memory Computing - Distributed Systems - Devoxx UK 2015
- 1. www.hazelcast.com /** * In-Memory Computing *Distributed Systems */
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- 3. www.hazelcast.com Disclaimer This presentation mayinclude 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
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- 7. www.hazelcast.com@noctarius2k Toaster Server 1 TV-Server 1 Power CoffeeMaker Server1 Kettle Server 1 Stove Server 1 Power Grid Server 1, 2 Distributed Computing…
- 8. www.hazelcast.com@noctarius2k … but whatmeans Distributed Computing?OOO ( )
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- 10. www.hazelcast.com@noctarius2k ‘In-Memory’ - Don’t youmean ‘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
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- 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
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- 17. www.hazelcast.com@noctarius2k That was easy!Butdon’t tell anyone!
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- 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)
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- 25. www.hazelcast.com@noctarius2k …access fast whennecessary!…access fast when necessary!
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- 27. www.hazelcast.com@noctarius2k Caches Are Simple! publicclass 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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- 35. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution OfCaching Open Source
- 36. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution OfCaching Open Source Commercial
- 37. www.hazelcast.com@noctarius2k Proprietary Cache! Evolution OfCaching Open Source Commercial Stupidly Expensive
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- 40. www.hazelcast.com@noctarius2k Want to knowmore 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 5Facts • Java Collections API • Java Concurrency API • Transparent Data Distribution • Drop-In Replacement • Disruptively Simple (c) Rod Johnson (@springrod)
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- 44. www.hazelcast.com@noctarius2k Time to beHappy! Apache License 2
- 45. www.hazelcast.com@noctarius2k Time to beHappy! www.hazelcast.org
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