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We present the architecture behind Twitter's real-time related querysuggestion and spelling correction service. Although these tasks have receivedmuch attention in the web search literature, the Twitter context introduces areal-time "twist": after significant breaking news events, we aim to providerelevant results within minutes. This paper provides a case study illustratingthe challenges of real-time data processing in the era of "big data". We tellthe story of how our system was built twice: our first implementation was builton a typical Hadoop-based analytics stack, but was later replaced because itdid not meet the latency requirements necessary to generate meaningfulreal-time results. The second implementation, which is the system deployed inproduction, is a custom in-memory processing engine specifically designed forthe task. This experience taught us that the current typical usage of Hadoop asa "big data" platform, while great for experimentation, is not well suited tolow-latency processing, and points the way to future work on data analyticsplatforms that can handle "big" as well as "fast" data.

Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture