Building your own Facebook Realtime Analytics System

Recently, I was reading Todd Hoff's write-up on FaceBook real time analytics system. As usual, Todd did an excellent job in summarizing this video from Engineering Manager at Facebook Alex Himel.

In the first post, I’d like to summarize the case study, and consider some things that weren't mentioned in the summaries. This will lead to an architecture for building your own Realtime Time Analytics for Big-Data that might be easier to implement, using Facebook's experience as a starting point and guide as well as the experience gathered through a recent work with few of GigaSpaces customers. The second post provide a summary of that new approach as well as a pattern and a demo for building your own Real Time Analytics system..

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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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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