Lin Qiao

United States Contact Info
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Experience & Education

  • Fireworks AI

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Publications

  • Gobblin: Unifying Data Ingestion for Hadoop

    PVLDB

    Other authors
    See publication

  • Joins on Encoded and Partitioned Data

    PVLDB

    Other authors
    See publication

  • On brewing fresh espresso: LinkedIn's distributed data serving platform

    SIGMOD

    Other authors

  • Blink: Not Your Father's Database!

    Proceedings of the 5th International Workshop on Business Intelligence for the Real-Time Enterprises (BIRTE)

    The Blink project’s ambitious goals are to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. It takes a very innovative and counter-intuitive approach to processing BI queries, one that exploits several disruptive hardware and software technology trends. Specifically, it is a new, workload-optimized DBMS aimed primarily at BI query processing, and exploits scale-out of commodity multi-core processors…

    The Blink project’s ambitious goals are to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. It takes a very innovative and counter-intuitive approach to processing BI queries, one that exploits several disruptive hardware and software technology trends. Specifically, it is a new, workload-optimized DBMS aimed primarily at BI query processing, and exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Ignoring the general wisdom of the last three decades that the only way to scalably search large databases is with indexes, Blink always performs simple, “brute force” scans of the entire data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two products: (1) an accelerator appliance product for DB2 for z/OS (on the “mainframe”), called the IBM Smart Analytics Optimizer for DB2 for z/OS, V1.1, which was generally available in November 2010; and (2) the Informix Warehouse Accelerator (IWA), a software-only version that was generally available in March 2011. We are now working on the next generation of Blink, called BLink Ultra, or BLU, which will significantly expand the “sweet spot” of Blink technology to much larger, disk-based warehouses and allow BLU to “own” the data, rather than copies of it.

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  • Constant-Time Query Processing

    ICDE

    Other authors
    See publication

  • Main-memory scan sharing for multi-core CPUs

    PVLDB

    Other authors
    See publication

  • Integration of Server, Storage and Database Stack: Moving Processing Towards Data

    ICDE

    Other authors

  • A framework for enforcing application policies in database systems

    SIGMOD

    Other authors

  • Lazy, adaptive rid-list intersection, and its application to index anding

    SIGMOD

    Other authors
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Languages

  • English

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