Twitter’s optimizations

Data point: a few of the infrastructure pieces out of Twitter have been implemented in low-level, heavy metal C and they’re optimizing on individual machines instead of architecture. Today, twitter/fatcache, a memcached-on-SSDs:

To understand why network connected SSD makes sense, it is important to understand the role distributed memory plays in large-scale web architecture. In recent years, terabyte-scale, distributed, in-memory caches have become a fundamental building block of any web architecture. In-memory indexes, hash tables, key-value stores and caches are increasingly incorporated for scaling throughput and reducing latency of persistent storage systems. However, power consumption, operational complexity and single node DRAM cost make horizontally scaling this architecture challenging. The current cost of DRAM per server increases dramatically beyond approximately 150 GB, and power cost scales similarly as DRAM density increases.

It’s fascinating to observe Twitter’s architectural growth from the outside. They quickly exceeded the capacity of typical MySQL setups, then of Ruby and Rails, then memcached alone. They’ve got distributed filesystems, streaming distributed processing pipelines, and distributed databases. Now they’re optimizing down to the utilization of their hardware, taking advantage of the memory-like latencies of SSDs. When you start caring about power and the size of your index entries, you’ve reached a whole new level of Maslow’s hierarchy of scaling.

If trends continue and Twitter is a leader in how large-scale distributed systems are implemented, watch out. Twitter led many of us to Scala, ZooKeeper, and their own inventions like Storm and Finagle. Gird your programming and scaling fashion loins, because you’re about to learn a lot more about malloc, ERRNO, and processor architecture than you ever wanted to know!


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