MRQL (the Map-Reduce Query Language) is an SQL-like query language for map-reduce computations. It is implemented on top of Apache's Hadoop. MRQL is powerful enough to express most common data analysis tasks over many different kinds of raw data, including hierarchical data and nested collections, such as XML data. It is more powerful than other current languages, such as Hive and Pig Latin, since it can operate on more complex data and supports more powerful query constructs, thus eliminating the need for using explicit map-reduce code.
Almost everyone has heard of Google's MapReduce framework, but very few have ever hacked around with the idea of map and reduce. These two idioms are borrowed from functional programming, and form the basis of Google's framework. Although Python is not a functional programming language, it has built-in support for both of these concepts. A…
Sqoop is a tool designed to import data from relational databases into Hadoop. Sqoop uses JDBC to connect to a database. It examines each table’s schema and automatically generates the necessary classes to import data into the Hadoop Distributed File System (HDFS). Sqoop then creates and launches a MapReduce job to read tables from the database via DBInputFormat, the JDBC-based InputFormat. Tables are read into a set of files in HDFS. Sqoop supports both SequenceFile and text-based target and includes performance enhancements for loading data from MySQL.
M. Bayir, I. Toroslu, A. Cosar, und G. Fidan. WWW '09: Proceedings of the 18th international conference on World wide web, Seite 161--170. New York, NY, USA, ACM, (2009)
M. Becker, H. Mewes, A. Hotho, D. Dimitrov, F. Lemmerich, und M. Strohmaier. International Conference Companion on World Wide Web, Seite 17--18. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2016)
C. Bellettini, M. Camilli, L. Capra, und M. Monga. Reachability Problems, Volume 8169 von Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2013)
C. Bellettini, M. Camilli, L. Capra, und M. Monga. Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2012 14th International Symposium on, Seite 295-302. IEEE Computer Society, (September 2012)
Q. Chen, A. Therber, M. Hsu, H. Zeller, B. Zhang, und R. Wu. Proceedings of the 2009 International Database Engineering & Applications Symposium, Seite 43--53. New York, NY, USA, ACM, (2009)
F. Chierichetti, R. Kumar, und A. Tomkins. WWW '10: Proceedings of the 19th international conference on World wide web, Seite 231--240. New York, NY, USA, ACM, (2010)
F. Chierichetti, R. Kumar, und A. Tomkins. WWW '10: Proceedings of the 19th international conference on World wide web, Seite 231--240. New York, NY, USA, ACM, (2010)
H. chih Yang, A. Dasdan, R. Hsiao, und D. Parker. SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Seite 1029--1040. New York, NY, USA, ACM, (2007)
H. chih Yang, A. Dasdan, R. Hsiao, und D. Parker. SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, Seite 1029--1040. New York, NY, USA, ACM, (2007)
C. Chu, S. Kim, Y. Lin, Y. Yu, G. Bradski, A. Ng, und K. Olukotun. Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems Vancouver, British Columbia, Canada, December 4-7, 2006, Seite 281-288. MIT Press, (2006)
R. Cordeiro, C. Jr., A. Traina, J. López, U. Kang, und C. Faloutsos. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 21-24, 2011, Seite 690-698. ACM, (2011)