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21 Apr 09
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30 Jul 08
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23 Jul 08
fulvius longhiEscalando aplicações EE de forma horizontal, que é o mais dificil tipo de escala.
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Two JVM instances can't communicate through pipes, shared memory, semaphores or signals. The most effective way to communicate among different JVM processes is by socket. Simply put, if Java EE applications can be scalable on multiple JVM instances, they are also mostly scalable on multiple server nodes.
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The most scalable one is paired node replication, which is implemented by most popular vendors, such as BEA Weblogic, JBoss and IBM Websphere.
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The result was that the collocated structure scaled much better than the distributed one!
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An alternative way is to use an effective distributed file systems, such as Hadoop DFS
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When doing a memcached lookup, first the client hashes the key against the whole list of servers. Once it has chosen a server, the client then sends its request, and the server does an internal hash key lookup for the actual item data
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The big benefit, when dealing with giant systems, is memcached's ability to massively scale out. Since the client does one layer of hashing, it becomes entirely trivial to add dozens of nodes to the cluster. There's no interconnect to overload, or multicast protocol to implode.
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Don't cache objects who are updated more frequently than read.
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Try to avoid row based cache. Instead, cache complex object will make memcached more efficient.
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Choose the right hashing algorithms.
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Terracotta extends the Java Memory Model of a single JVM to include a cluster of virtual machines such that threads on one virtual machine can interact with threads on another virtual machine as if they were all on the same virtual machine with an unlimited amount of heap.
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I presume that must be some interconnect or multicast protocol to communicate among them. That may be the reason that the Terracotta failed to scale very well
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This customer resolved this problem by using Hadoop Map-Reduce to scale it horizontally. Now, this URL access patterns analyzing program can not only be run in a multiprocessed mode, but also parallelized to more than 10 nodes, and it only spends 7 minutes to finish all the work.
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Scalable write bandwidth: In this kind of applications, blogging, ranking and BBS will make the write bandwidth the major bottleneck for social networking websites. Distributed Cache system will help little on the performance for the database write actions. With data partitions, you can write in parallel which increases your write throughput. To support more registered users just add more database nodes, modify the meta table to reflect the new servers.
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Since database nodes can be added very dynamically, it is hard for Java EE applications in Tomcat to use the database connection pools.
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14 Jul 08
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DJHell .If you cannot satisfy the performance with one server node when concurrent users are increasing dramatically, or you cannot scale your Java applications with one JVM instance due to the limitation of garbage collection, your other choice is to run your sy
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