Use Cases and Factors Affecting the Choice of Postgres or MongoDB With the introduction of its WiredTiger database engine, MongoDB 3.0 offered improvements in write speeds (between seven and 10 times as fast), together with data compression of 50% to cut disk space. Still, performance ratings are made to be beaten. Postgres also consumed 25% less disk space. Postgres was about twice as fast in data ingestion, two-and-half times as fast in data selection, and three times as fast in data inserts. The tests were based on selecting, loading, and inserting complex document data to the tune of 50 million records. However, NoSQL fans got a shock when performance ratings from EnterpriseDB () in 2014 showed Postgres performance to be significantly better than that of MongoDB. While they may lack the ACID (atomicity, consistency, isolation, and durability) properties needed for financial transactions, for example, they may offer advantages in handling larger volumes of unstructured data more rapidly. Operating with simpler data structures than those of SQL databases, NoSQL database systems have often shown faster speeds of storage and retrieval. One of the advantages frequently cited for NoSQL database management systems is their performance. Native JSON Data Stores do not Always Have the Best Performance Data can be routed to a JSON column for possible data modeling afterwards, or to a table using an SQL schema, all within the same Postgres database. In offering both SQL as well as JSON storage, Postgres lets users keep their options open. The tradeoff is the potential loss of data, but this may suit users who have less need to persist their data. MongoDB also offers the possibility of increasing write throughput by deferring writing to disk.Horizontal scaling of Postgres is also possible, but tends to be more involved or use an additional third-party solution. Scaling of Postgres installations has often been vertical. MongoDB offers automatic database sharding for easy horizontal scaling of JSON data storage.Postgres provides data constraint and validation functions to help ensure that JSON documents are more meaningful: for example, preventing attempts to store alphabetical characters where numerical values are expected.
#Mongodb or postgresql 64 bits#
Since then, MongoDB and Postgres have both been enhancing their JSON storage capabilities. JSON document storage and management for Postgres arrived somewhat later, after MongoDB began life in 2009 as a native JSON document DBMS. In the fast-moving world of unstructured data, does it make more sense to use a database management system (DBMS) built from the start to handle the widely accepted JSON data format? Or can a SQL database that now includes JSON functionality be a better choice? Postgres, with its SQL roots, started offering NoSQL functionality early on with its key-value store functionality, called hstore, introduced in 2006.
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