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- W2950290221 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 processing in the era of 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, 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 data platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on analytics platforms that can handle big as well as fast data." @default.
- W2950290221 created "2019-06-27" @default.
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- W2950290221 date "2012-10-27" @default.
- W2950290221 modified "2023-09-27" @default.
- W2950290221 title "Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture" @default.
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