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Entries in in-memory (4)

Monday
May182015

How MySQL is able to scale to 200 Million QPS - MySQL Cluster

This is a guest post by Andrew Morgan, MySQL Principal Product Manager at Oracle.

MySQL Cluster logo

The purpose of this post is to introduce MySQL Cluster - which is the in-memory, real-time, scalable, highly available version of MySQL. Before addressing the incredible claim in the title of 200 Million Queries Per Second it makes sense to go through an introduction of MySQL Cluster and its architecture in order to understand how it can be achieved.

Introduction to MySQL Cluster

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Tuesday
Apr122011

Caching and Processing 2TB Mozilla Crash Reports in memory with Hazelcast

Mozilla processes TB's of Firefox crash reports daily using HBase, Hadoop, Python and Thrift protocol. The project is called Socorro, a system for collecting, processing, and displaying crash reports from clients. Today the Socorro application stores about 2.6 million crash reports per day. During peak traffic, it receives about 2.5K crashes per minute. 

In this article we are going to demonstrate a proof of concept showing how Mozilla could integrate Hazelcast into Socorro and achieve caching and processing 2TB of crash reports with 50 node Hazelcast cluster. The video for the demo is available here.

 

To read the rest of the article please click below...

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Monday
May032010

100 Node Hazelcast cluster on Amazon EC2

Deploying, running and monitoring application on a big cluster is a challenging task. Recently Hazelcast team deployed a demo application on Amazon EC2 platform to show how Hazelcast p2p cluster scales and screen recorded the entire process from deployment to monitoring.

Hazelcast is open source (Apache License), transactional, distributed caching solution for Java. It is a little more than a cache though as it provides distributed implementation of map, multimap, queue, topic, lock and executor service. 

Details of running 100 node Hazelcast cluster on Amazon EC2 can be found here. Make sure to watch the screencast!

Thursday
Dec172009

Oracle and IBM databases: Disk-based vs In-memory databases 

Current disk based RDBMS can run out of steam when processing large data. Can these problems be solved by migrating from a disk based RDBMS to an IMDB? Any limitations? To find out, I tested one of each from the two leading vendors who together hold 70% of the market share - Oracle's 11g and TimesTen 11g, and IBM's DB2 v9.5 and solidDB 6.3.

read more at BigDataMatters.com