Taba is a distributed aggregation service, currently handling over 6M events per second, with low end-to-end latency. Similar in concept to statsd, Taba can handle orders of magnitude more data, with stronger consistency guarantees. It is built atop Redis using Python with gevent and Cython. We will discuss the data model used, and how its design impacts process flows, the database schema, network usage, cacheability, etc. We will also describe the cluster architecture, which is based on many identical distributed processes, and uses master-less coordination to automatically distribute load across the cluster. Finally, we will discuss some of the interesting challenges and solutions encountered. These include: - Memory fragmentation and ‘ratcheting’ in CPython - Using generators, greenlets, and queues to great effect in reducing memory usage without sacrificing performance. - Using Cython to optimize critical sections - Optimizing CPU intensive operations (e.g. distributed reservoir sampling) - Hybrid manual / automatic memory management techniques