Accelerate large-scale applications with BOLT
Highly complex services, such as those here at Facebook, have large source code bases in order to deliver a wide range of features and functionality. Even after the machine code for one of these services is compiled, it can range from 10s to 100s of megabytes in size, which is often too large to fit in any modern CPU instruction cache. As a result, the hardware spends a considerable amount of proc
Apache Spark @Scale: A 60 TB+ production use case
Facebook often uses analytics for data-driven decision making. Over the past few years, user and product growth has pushed our analytics engines to operate on data sets in the tens of terabytes for a single query. Some of our batch analytics is executed through the venerable Hive platform (contributed to Apache Hive by Facebook in 2009) and Corona, our custom MapReduce implementation. Facebook has
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