Facebook just published this summary of a summit for database researchers held at Menlo Park last September. I recommend it. It contains a clear and concise description of Facebook's data infrastructure, and a description of the open problems they are thinking about, which is even more interesting.
https://research.facebook.com/blog/1522692927972019/facebook-s-top-open-data-problems/ To whet your appetite, here are the problems (the summaries mostly my own paraphrase): * Mobile: How should the shift toward mobile devices affect Facebookâs data infrastructure? * Reducing replication: How can we reduce the number of round trips between the application and data layers? * Impact of Caching on Availability (aka "oh no, we just restarted memcached"): How do we harness the efficiency gains provided by caching without being brought to our knees by a sudden drop in cache hit rate? * Sampling at logging time in a distributed environment: How should we sample log streams if we want to maintain accuracy and flexibility to answer post-hoc queries? * Trading storage space and CPU: TL;DR: gzip --best or gzip --fast? * Reliability of pipelines: Pipelines are less reliable than the sum of their parts. A pipeline composed of two systems, each 0.999 reliable, is 0.989 reliable. Much sadness. What to do? * Globally distributed warehouse: consistency models and synchronization problems. * Time series correlation and anomaly detection: AKA: I want an alert for that massive memcached bytes_out spike that doesn't also wake me up with false positives at 2AM.
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