Storage is one part of the puzzle. The other is querying the stored data. Easily and effectively querying data is almost mandatory for any database to be considered seriously. It can be especially important when building the operational data store for applications with which people are interacting. An RDBMS thrives on SQL support, which makes accessing and querying data easy. Standardized syntax and semantics make it an attractive choice. The fi rst chapter in this book talks about the quest for a SQL-like query language in the world of NoSQL.
Among document databases, MongoDB provides the best querying capabilities. Best is a relative term, and developers argue about what they consider superior to alternatives, but I base my judgment on three factors: similarity to SQL, an easy syntax, and an easy learning curve. CouchDB’s querying capabilities are equally powerful and rather more straightforward once you understand the concepts of views and design documents. However, the concept of views as CouchDB defines it is new and can pose initial challenges to developers.
For key/value pairs and in-memory stores, nothing is more feature-rich than Redis as far as querying capabilities go. Redis has one of the most exhaustive sets of methods available for querying the data structures it stores. To add icing to the cake, it is all nicely documented. Read about the access methods at http://redis.io/commands.
Column-family stores like HBase have little to offer as far as rich querying capabilities go. However, an associated project called Hive makes it possible to query HBase using SQL-like syntax and semantics. Hypertable defines a query language called HQL and also supports Hive.
Bringing Hive into the mix raises the question of manipulating data for operational usage versus accessing it for batch processing and business intelligence. Hive is not an interactive tool in a way SQL is to RDBMS. Hive resembles SQL in syntax but is really a way to abstract MapReduce-style manipulations. Hive allows you to use SQL like predicate-driven syntax instead of map and reduce function definitions to carry batch data manipulation operations on the data set.
Source of Information : NoSQL
Among document databases, MongoDB provides the best querying capabilities. Best is a relative term, and developers argue about what they consider superior to alternatives, but I base my judgment on three factors: similarity to SQL, an easy syntax, and an easy learning curve. CouchDB’s querying capabilities are equally powerful and rather more straightforward once you understand the concepts of views and design documents. However, the concept of views as CouchDB defines it is new and can pose initial challenges to developers.
For key/value pairs and in-memory stores, nothing is more feature-rich than Redis as far as querying capabilities go. Redis has one of the most exhaustive sets of methods available for querying the data structures it stores. To add icing to the cake, it is all nicely documented. Read about the access methods at http://redis.io/commands.
Column-family stores like HBase have little to offer as far as rich querying capabilities go. However, an associated project called Hive makes it possible to query HBase using SQL-like syntax and semantics. Hypertable defines a query language called HQL and also supports Hive.
Bringing Hive into the mix raises the question of manipulating data for operational usage versus accessing it for batch processing and business intelligence. Hive is not an interactive tool in a way SQL is to RDBMS. Hive resembles SQL in syntax but is really a way to abstract MapReduce-style manipulations. Hive allows you to use SQL like predicate-driven syntax instead of map and reduce function definitions to carry batch data manipulation operations on the data set.
Source of Information : NoSQL
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