Fast data in the era of big data: Twitter's real-time related query suggestion architecture

  • Authors:

  • Gilad Mishne;Jeff Dalton;Zhenghua Li;Aneesh Sharma;Jimmy Lin

  • Affiliations:

  • Twitter, San Francisco, CA, USA;Twitter, San Francisco, CA, USA;Twitter, San Francisco, CA, USA;Twitter, San Francisco, CA, USA;Twitter, San Francisco, CA, USA

  • Venue:
  • Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
  • Year:
  • 2013

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Abstract

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production today, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data.