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MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
Map-reduce can be used for batch processing of data and aggregation operations. However, according to MongoDB's documentation, the aggregation pipeline provides better performance for most aggregation operations. [41] The aggregation framework enables users to obtain results similar to those returned by queries that include the SQL GROUP BY clause.
Additionally, some NoSQL systems may exhibit lost writes and other forms of data loss. [14] Some NoSQL systems provide concepts such as write-ahead logging to avoid data loss. [15] For distributed transaction processing across multiple databases, data consistency is an even bigger challenge that is difficult for both NoSQL and relational databases.
Snappy is widely used in Google projects like Bigtable, MapReduce and in compressing data for Google's internal RPC systems. It can be used in open-source projects like MariaDB ColumnStore, [6] Cassandra, Couchbase, Hadoop, LevelDB, MongoDB, RocksDB, Lucene, Spark, InfluxDB, [7] and Ceph. [8] Firefox uses Snappy to compress data in localStorage ...
Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]
CouchDB is well suited for applications with accumulating, occasionally changing data, on which pre-defined queries are to be run and where versioning is important (CRM, CMS systems, by example). Master-master replication is an especially interesting feature, allowing easy multi-site deployments. [14]
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In MapReduce-based systems, data is normally stored on a distributed system, such as Hadoop Distributed File System (HDFS), and different data blocks might be stored in different machines. Thus, for column-store on MapReduce, different groups of columns might be stored on different machines, which introduces extra network costs when a query ...