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Efficient Parallel Set-Similarity Joins Using MapReduce
Department of Computer Science University of California, Irvine Abstract In this paper we study how to efficiently perform set-similarity joins in parallel using the popular MapReduce framework. We propose a 3-stage approach for end-to-end set-similarity joins. We take as input a set of records and output a set of joined records based on a set-similarity condition. We efficiently partition the dat
GitHub - Yelp/mrjob: Run MapReduce jobs on Hadoop or Amazon Web Services
mrjob is a Python 2.7/3.4+ package that helps you write and run Hadoop Streaming jobs. Stable version (v0.7.4) documentation Development version documentation mrjob fully supports Amazon's Elastic MapReduce (EMR) service, which allows you to buy time on a Hadoop cluster on an hourly basis. mrjob has basic support for Google Cloud Dataproc (Dataproc) which allows you to buy time on a Hadoop cluster
GitHub - decster/nativetask: Hadoop task level native runtime
Introduction NativeTask is a high performance C++ API & runtime for Hadoop MapReduce. Why it is called NativeTask is that it is a native computing unit only focus on data processing, which is exactly what Task do in the Hadoop MapReduce context. In other word, NativeTask is not responsible for resource management, job Scheduling and fault-tolerance. Those are all managed by original Hadoop compone
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Hadoopã¨Mahoutã«ã‚ˆã‚Šã€ãƒ“ッグデータã§ã‚‚機械å¦ç¿’ã‚’è¡Œã†ã“ã¨ãŒã§ãã¾ã™ã€‚Mahoutã§å®Ÿè£…ã•ã‚Œã¦ã„る手法ã¯ã€å…¨ã¦åˆ†æ•£å‡¦ç†ã§ãるアルゴリズムã¨ã„ã†ã“ã¨ã«ãªã‚Šã¾ã™ã€‚Mahoutã§å®Ÿè£…ã•ã‚Œã¦ã„るアルゴリズムã¯ã€ã“ã“ã«åˆ—挙ã•ã‚Œã¦ã„ã¾ã™ã€‚è«–æ–‡ã¨ã—ã¦ã‚‚ã€2006å¹´ã«ã€ŒMap-Reduce for Machine Learning on Multicoreã€ã¨ã—ã¦ã„ãã¤ã‹ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãŒç´¹ä»‹ã•ã‚Œã¦ã„ã¾ã™ã€‚ ãã“ã§ä»Šå›žã¯ã€ï¼ˆä½•ç•ªç…Žã˜ã‹åˆ†ã‹ã‚Šã¾ã›ã‚“ãŒè‡ªåˆ†ã®ç†è§£ã®ãŸã‚ã«ã‚‚）ã“ã®è«–æ–‡ã§ç´¹ä»‹ã•ã‚Œã¦ã„るアルゴリズムã¨ã€ã©ã†ã‚„ã£ã¦åˆ†æ•£å‡¦ç†ã™ã‚‹ã®ã‹ã‚’ç°¡å˜ã«ãƒ¡ãƒ¢ã—ã¦ãŠããŸã„ã¨æ€ã„ã¾ã™ã€‚計算ã™ã‚‹ã¹ã統計é‡ãŒã€summation form（足ã—ç®—ã§è¡¨ç¾ã§ãる形）ã«ãªã£ã¦ã„ã‚‹ã‹ã©ã†ã‹ãŒã€é‡è¦ãªãƒã‚¤ãƒ³ãƒˆã§ã™ã€‚ãªã£ã¦ãªã„å ´åˆã¯ã€â€ã†ã¾ãâ€MapReduceã®å½¢ã«ãƒãƒ©ã™å¿…è¦ãŒã‚ã‚Šã¾ã™ã€‚ ※例ã«ã‚ˆã£ã¦ã€é–“é•ã„ãŒã‚ã£ãŸå ´åˆã¯éšæ™‚
Twister: Iterative MapReduce
Twister version 0.9 is now available. MapReduce programming model has simplified the implementations of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among parallel computing communities. From the years of experience in applying MapReduce programming model to various sci
MapReduce: A Flexible Data Processing Tool – Communications of the ACM
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs. Mapreduce is a programming model for processing and generating large data sets.4 Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs and a reduce function that merges all intermediate values associated with the sa
MapReduce Patterns, Algorithms, and Use Cases
In this article I digested a number of MapReduce patterns and algorithms to give a systematic view of the different techniques that can be found on the web or scientific articles. Several practical case studies are also provided. All descriptions and code snippets use the standard Hadoop’s MapReduce model with Mappers, Reduces, Combiners, Partitioners, and sorting. This framework is depicted in th
Welcome to YSmart's Homepage!
The homepage of YSmartOverview YSmart is a correlation aware SQL-to-MapReduce translator, which is built on top of the Hadoop platform. For a given SQL query and related table schemas, YSmart can automatically translate the query into a series of Hadoop MapReduce programs written in Java. Compared to other SQL-to-MapReduce translators, YSmart has been proved to have the following advantages: High
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Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) | atbrox
Mapreduce & Hadoop Algorithms in Academic Papers (5th update – Nov 2011) The prior update of this posting was in May, and a lot has happened related to Mapreduce and Hadoop since then, e.g. 1) big software companies have started offering hadoop-based software (Microsoft and Oracle), 2) Hadoop-startups have raised record amounts, and 3) nosql-landscape becoming increasingly datawarehouse’ish and sq
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About Hewlett Packard Enterprise: Information and Strategic Vision
Tall-and-skinny QR factorizations in MapReduce architectures
Alex Rasmussen
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GitHub - douban/dpark: Python clone of Spark, a MapReduce alike framework in Python
Apache Sparkâ„¢ - Unified Engine for large-scale data analytics
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