æ€§èƒ½æ¸¬å®šé“ å®Ÿè·µç·¨2014/2/1ã«é–‹å‚¬ã•ã‚ŒãŸæ—¥æœ¬PostgreSQLユーザ会 第27回ã—ãã¿ï¼‹ã‚¢ãƒ—リケーション勉強会ã§ã®è¬›æ¼”ã§ã™ã€‚ http://www.postgresql.jp/wg/shikumi/shikumi28Read less
New Results Posted for the SQL “Big Data Benchmarkâ€
Back in June, Patrick Wendell posted a first set of results in a “Big Data Benchmark†for large-scale query engines.  Obviously a lot has happened in the space since then and so we have updated those results, re-running the tests on the latest versions of the previously tested systems (Redshift, Impala, Spark, and Hive) and including numbers for the Tez (Stinger) system. While all the systems exa
pumabenchmarks - Faraz Ahmad
MapReduce Benchmarks by Faraz Ahmad, Seyong Lee, Mithuna Thottethodi, T. N. Vijaykumar MapReduce is a well-known programming model, developed within Google, for processing large amounts of raw data, for example, crawled documents or web request logs. This data is usually so large that it must be distributed across thousands of machines in order to be processed in a reasonable time. The ease of pro
Kazuho at Work: Q4M - 0.6 release and benchmarks
Today I have uploaded Q4M (a Queue for MySQL) 0.6, which is basically a performance-improvement from previous releases. Instead of using pread's and a small user-level cache, Q4M (in default configuration) now uses mmap for reads with a reader/writer lock to improve concurrency. I also noticed that it would be possible to consume a queued row in one SQL statement. SELECT * FROM queue_table WHERE q
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Tutorial: Micro 2014 BigDataBench
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