Month: April 2010

MySQL – Enterprise readiness

Google runs critical business systems with MySQL.

“Google runs critical business systems with MySQL and InnoDB. The systems require 24×7 operation with minimal downtime. The systems support large OLTP and reporting workloads. We are very happy with the scalability, reliability and manageability of this software.”

Chris DiBona, Open Source Programs Manager, Google Inc.

Yahoo Financial runs on MySQL

MySQL at Yahoo!
Some Technical Details:
Operating system used: FreeBSD and Linux, synchronized using MySQL Replication
Size of database: 25 GB
Average number of concurrent connections: 60
Max number of concurrent connections: 250

Ticketmaster runs on MySQL

“We migrated the Event database from Microsoft SQL Server to MySQL for lower costs and higher scalability. Thanks to MySQL, we are able to scale 4 times better while constantly maintaining the replication latency of less than 1 second across our 250 MySQL servers.”

Ed Presz, Sr. Director of Database Engineering, Ticketmaster Entertainment

 It is time for all other innovative companies in financial, retail, manufacturing, health care, services sectors to look into MySQL as their database..

Note: This page is used for google’s page rank emprical analysis. The links will be created based on the random graph created.  This is node #3 which has the key word:  xysivabodzinyx , xysivabodzinxy . As per the graph, it links to page 1, page 5

Differentiating Technologies

IT industry’s adoption to open source, cloud computing, out sourcing and software as a service are increasing exponential. These adoption reduces cost, residual risk and increases value,agility and enables organization for known and unknown unknowns.  It is possible, adoption’s value proposition could be  reversed,  if commodity and differentiating technologies and services are not properly categorized.

Differentiating Technologies:

  • Security Technologies
    • Identity Management
      • User provisioning & de-provisioning
      • Access management
      • Role Management – Role mining, engineering
      • Federation – Service & Identity
    • Data loss prevention
    • Network security – intrusion prevention, firewall, load balancer, proxy server,
    • Managed File Transfer
  • Integration Technologies
    • XML Appliance
    • SOA/Cloud security appliance
  • Internal tools for compliance
    • Service Desk
    • incident & problem management
    • Release Mangement  – Source code control management
    • ITIL
  • Collaboration Platform
    • Web 2.0  – blog, wiki, corporate twitter – for both internal and external users
    • Email – integration with iPhone, blackberry
  • Next generation platform
    • Application running in iPad, iPhone,
    • Virtual desktop

Note: This page is used for google’s page rank emprical analysis. The links will be created based on the random graph created.  This is node #2 which has the key word:  xysivabodzinyx , xysivabodzinxy . As per the graph, it links to page 1

Google Pagerank – An emprical analysis

Pagerank is an algorithm used by google to assign importance of each page in world wide web. The order of search results  depends on the page rank assigned to each page.  A web site has multiple pages and page rank assigned to each page in the web site. The page rank of the page depends on number of external links coming to the page and number of links going out of the page.  The page rank of an incoming link also plays a role on determining the page rank of the page.

Page rank is used to decide the sequence of search results for a google search. Companies want their page to be at the top of the search result. The easiest way to be at top of the search result is to pay google for the adwords. The sponsored web sites appear above and to the right hand side of its regular search results. 

Not all companies can afford to pay google for their Adwords. There is cost effective alternative which requires an understanding of how google works.  Google’s internal working knowledge will aid to define  analytical (or mathematical) strategies and implemented in the web site to improve the page rank and eventually with more visitors to the web site.

To validate the theory with emprical analysis, a plan with following steps drafted.

  1. Find a key word that is not in google’s index server
  2. Create a graph (random graph) and find the initial transition probability
  3. Evaluate the steady state of transition probability matrix using power methods – Which is the page rank of the graph (I will post the technical/mathematical details in a pdf. It is time-consuming to write matrix and other math notation in word press editor)
  4. Develop a set of web sites (pages) adhering to the random graph and each web site to contain the new key word
  5. Allow google crawler to include the new sites in their index server for the new key words
  6. Search for the new key word and absorb the order of search results
  7. Report the results

1. A new key word was selected and  is given below.   There is no google search result for the key word.

2. The random graph  (a representation of how web sites are linked to each other) and the graph will be implemented  with various blog post and  each blog post will have the key word  adhering to the graph. Each node of the graph denote a blog posting. The links connecting the nodes are the hyperlinks connecting the nodes.

Note: This page is used for google’s page rank emprical analysis. The links will be created based on the random graph created.  This is node #1 which has the key word:  xysivabodzinyx , xysivabodzinxy . As per the graph, it links out to page 2, page 4