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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.
However, modern NoSQL databases often incorporate advanced features to optimize query performance. For example, MongoDB supports compound indexes and query-optimization strategies, Cassandra offers secondary indexes and materialized views, and Redis employs custom indexing mechanisms tailored to specific use cases.
Internally, Cosmos DB stores "items" in "containers", [3] with these two concepts being surfaced differently depending on the API used (these would be "documents" in "collections" when using the MongoDB-compatible API, for example). Containers are grouped in "databases", which are analogous to namespaces above containers.
In 2004, Google published a paper on a process called MapReduce that uses a similar architecture. The MapReduce concept provides a parallel processing model, and an associated implementation was released to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the "map ...
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 ...
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]