high_scalability high_scalability-2012 high_scalability-2012-1221 knowledge-graph by maker-knowledge-mining
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Introduction: As it is said in the recent article "Google: Taming the Long Latency Tail - When More Machines Equals Worse Results" , latency variability has greater impact in larger scale clusters where a typical request is composed of multiple distributed/parallel requests. The overall response time dramatically decreases if latency of each request is not consistent and low. In dynamically scalable partitioned storage systems, whether it is a NoSQL database, filesystem or in-memory data grid, changes in the cluster (adding or removing a node) can lead to big data moves in the network to re-balance the cluster. Re-balancing will be needed for both primary and backup data on those nodes. If a node crashes for example, dead node’s data has to be re-owned (become primary) by other node(s) and also its backup has to be taken immediately to be fail-safe again. Shuffling MBs of data around has a negative effect in the cluster as it consumes your valuable resources such as network, CPU and RAM. It mig
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1 As it is said in the recent article "Google: Taming the Long Latency Tail - When More Machines Equals Worse Results" , latency variability has greater impact in larger scale clusters where a typical request is composed of multiple distributed/parallel requests. [sent-1, score-0.229]
2 In dynamically scalable partitioned storage systems, whether it is a NoSQL database, filesystem or in-memory data grid, changes in the cluster (adding or removing a node) can lead to big data moves in the network to re-balance the cluster. [sent-3, score-0.401]
3 Re-balancing will be needed for both primary and backup data on those nodes. [sent-4, score-0.311]
4 If a node crashes for example, dead node’s data has to be re-owned (become primary) by other node(s) and also its backup has to be taken immediately to be fail-safe again. [sent-5, score-0.546]
5 Shuffling MBs of data around has a negative effect in the cluster as it consumes your valuable resources such as network, CPU and RAM. [sent-6, score-0.412]
6 It might also lead to higher latency of your operations during that period. [sent-7, score-0.232]
7 0 release, Hazelcast , an open source clustering and highly scalable data distribution platform written in Java, focuses on latency and makes it easier to cache/share/operate TB's of data in-memory. [sent-9, score-0.389]
8 Storing terabytes of data in-memory is not a problem but avoiding GC to achieve predictable, low latency and being resilient to crashes are big challenges. [sent-10, score-0.526]
9 By default, Hazelcast stores your distributed data (map entries, queue items) into Java heap which is subject to garbage collection. [sent-11, score-0.475]
10 As your heap gets bigger, garbage collection might cause your application to pause tens of seconds, badly effecting your application performance and response times. [sent-12, score-0.541]
11 Even if you have terabytes of cache in-memory with lots of updates, GC will have almost no effect; resulting in more predictable latency and throughput. [sent-14, score-0.361]
12 Here is how things work: User defines the number of GB storage to have off the heap per JVM, let’s say it is 40GB. [sent-16, score-0.312]
13 If you have, say 100 nodes, then you have total of 4TB off-heap storage capacity. [sent-18, score-0.209]
14 Each buffer is divided into configurable chunks (blocks) (default chunk-size is 1KB). [sent-19, score-0.246]
15 When the value is removed, these blocks are returned back into the available blocks queue so that they can be reused to store another value. [sent-22, score-0.574]
16 With new backup implementation, data owned by a node is divided into chunks and evenly backed up by all the other nodes. [sent-23, score-0.802]
17 In other words, every node takes equal responsibility to backup every other node. [sent-24, score-0.337]
18 This leads to better memory usage and less influence in the cluster when you add/remove nodes. [sent-25, score-0.258]
19 Initially the application will load the grid with total of 500M entries, each with 4KB value size. [sent-30, score-0.306]
20 Later on, we'll terminate an instance to observe no data loss because of backups and we should also notice that key ownerships remain well-balanced. [sent-35, score-0.236]
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