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The Hadoop Performance Myth
The wish lists of many data-driven organizations seem reasonable enough. They’d like to capitalize on real-time data analysis, move beyond batch processing for time-critical insights, allow multiple users to share cluster resources, and provide predictable service levels. However, fundamental performance limitations of complex distributed systems such as Hadoop prevent much of this from happening.
In this report, Courtney Webster examines the root cause of these performance problems and explains why best practices for mitigating them—cluster tuning, provisioning, and even cluster isolation for mission critical jobs—don’t provide viable, scalable, or long-term solutions.
Organizations have been pushing Hadoop and other distributed systems to their performance breaking points as they seek to use clusters as shared resources across multiple business units and individual users. Once they hit this performance wall, companies will find it difficult to deliver on the big data promise at scale.
In this report, Courtney Webster examines the root cause of these performance problems and explains why best practices for mitigating them—cluster tuning, provisioning, and even cluster isolation for mission critical jobs—don’t provide viable, scalable, or long-term solutions.
Organizations have been pushing Hadoop and other distributed systems to their performance breaking points as they seek to use clusters as shared resources across multiple business units and individual users. Once they hit this performance wall, companies will find it difficult to deliver on the big data promise at scale.
23 pages, ebook
Published April 1, 2016
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