Kishore Gopalakrishna

Mountain View, California, United States Contact Info
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  • StarTree

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Publications

  • In Data Veritas -- Data Driven Testing for Distributed Systems

    DBTest2013 - Sixth International Workshop on Testing Database Systems

    The increasing deployment of distributed systems to solve large data and computational problems has not seen a concomitant increase in tools and techniques to test these systems. In this paper, we propose a data driven approach to testing. We translate our intuitions and expectations
    about how the system should behave into invariants, the truth of which can be verified from data emitted by the system. Our particular implementation of the invariants uses Q, a high-performance analytical…

    The increasing deployment of distributed systems to solve large data and computational problems has not seen a concomitant increase in tools and techniques to test these systems. In this paper, we propose a data driven approach to testing. We translate our intuitions and expectations
    about how the system should behave into invariants, the truth of which can be verified from data emitted by the system. Our particular implementation of the invariants uses Q, a high-performance analytical database, programmed with a vector language.

    To show the practical value of this approach, we describe how it was used to test Helix, a
    distributed cluster manager deployed at LinkedIn. We make the case that looking at testing as an exercise in data analytics has the following benefits. It (a) increases the expressivity of the tests (b) decreases their fragility and (c) suggests additional, insightful ways to understand the system under test.

    By obviating the need to build and maintain the scaffolding necessary for a test case repository, the testing process itself becomes more agile. This also reduces false positives/false negatives that can arise when the scaffolding code becomes both part of the test and the system under test.

    As the title of the paper suggests, there is truth in the data --- we only need to look for it.

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

    SIGMOD

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  • Untangling cluster management with Helix SOCC 2012

    ACM Symposium on Cloud Computing

    Generic cluster management framework for managing partitioned and replicated resources in distributed systems via declarative state model, constraints and objectives. Main features include partition management, failure handling and cluster expansion.

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