My colleague, Romi Kuntsman, refered me to Big Fish Games software performance boosting case study that was published not long ago by MySQL (now SUN).
MySQL is a significant player in the internet based systems niche which includes the online advertisement, SaaS, games and gaming and many others. The internet based systems were not long ago considered as not so complex systsms as the enterprise systems.
This assumption is not longer true. Internet based system and mostly the top players in the market are facing challenges that require non conservative solutions. Players such as Facebook, Google, Big Fish and CJ are facing the challenge to handle millions of events per second. This is significantly larger than most enterprise software and n-tier architectures based systems were designed to handle with.
Therefore, MySQL based systems that were considered to be a low-mid range systems, are facing these days the most challenging requirements of traffic and load.
MySQL just released a good example for that (I will present several other cases we dealt and dealing with in future posts): BigFishGames.com is a fast-growing website with over 25 million unique customer accounts and over 2.5 million visitors per month and it well financed (over $80M in the last financial round).
Due to their high growth rate (100%-220% per year in last few years accurding to TechCrunch replys), Big Fish faced performance issues, although they used over 40 different MySQL servers...
What did Big Fish do?
1. Database Profiling and Tuning:
Big Fish DBA team used MySQL Query Analyzer. This product provides a consolidated view of query activities and execution details, and it enables quick identification of poorly running queries and tackle the root causes directly in the SQL code. The DBA team caught a "bad" query running 400K times overnight which never showed up in query logs. Since the Query Analyzer uses a Service Agent listening to application queries and performances metrics, the MySQL servers can always be live and operational when being analyzed. There is no need to switch the servers back and forth between on-line and off-line, which eliminates unnecessary risks to server availability and reliability. Moreover, it enables the DBA team gain a real time statistics, rather than make the analysis based on test environment simulations.
After deploying the MySQL Query Analyzer, Big Fish Games tripled (X3) its database performance within three days.
2. In Memory Database and Distributed Cache Layer
Big Fish Games used Memcached as an in memory database soluton and distributed caching layer. By using Memcached, all online queries are performed in the application server memory, and no online queries are processed in the database. Once in a while the Memcached is being syncronized with the database. This solution improved the end user response time, system scalability and database performance.
3. High Performance Servers
Big Fish Games chosen Sun Fire x64 Servers (not so surprising since MySQL is owned by SUN) in three different architectures:
- Sun Fire X2100: for applications which require lots of local disk space but less I/O or CPU speed
- Sun Fire X4100: for applications which demand fast processors but don't need speedy local disk I/O
- Sun Fire X4140: for applications where faster local disk I/O via RAID 10 and battery backed up write cache is essential (Memcached servers)
I think that this case study is a great example of how a poor performance system can be boosted in factors of X100 and more. Moreover, these cases are not so rare. Many players in the market gain success, and require better performing systems at the fraction of the cost of legacy enterprise systems, without major redesigning their existing servers, and while keeping their current code base.
RockeTier, the performance experts