DBMS > Datomic vs. MongoDB vs. Splice Machine
System Properties Comparison Datomic vs. MongoDB vs. Splice Machine
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|Editorial information provided by DB-Engines|
|Name||Datomic Xexclude from comparison||MongoDB Xexclude from comparison||Splice Machine Xexclude from comparison|
|Description||Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability||One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure||Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark|
|Primary database model||Relational DBMS||Document store||Relational DBMS|
|Secondary database models||Spatial DBMS|
Search engine integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS Time Series Collections introduced in Release 5.0
Vector DBMS currently available in the MongoDB Atlas cloud service only
|Developer||Cognitect||MongoDB, Inc||Splice Machine|
|Current release||1.0.6735, June 2023||6.0.7, June 2023||3.1, March 2021|
|License Commercial or Open Source||commercial limited edition free||Open Source MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.||Open Source AGPL 3.0, commercial license available|
|Cloud-based only Only available as a cloud service||no||no MongoDB available as DBaaS (MongoDB Atlas)||no|
|DBaaS offerings (sponsored links) Database as a Service|
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|MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.|
|Implementation language||Java, Clojure||C++||Java|
|Server operating systems||All OS with a Java VM||Linux|
|Data scheme||yes||schema-free Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.||yes|
|Typing predefined data types such as float or date||yes||yes string, integer, double, decimal, boolean, date, object_id, geospatial||yes|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no|
|SQL Support of SQL||no||Read-only SQL queries via the MongoDB Atlas SQL Interface||yes|
|APIs and other access methods||RESTful HTTP API||GraphQL|
proprietary protocol using JSON
Native Spark Datasource
|Supported programming languages||Clojure|
|Actionscript unofficial driver|
Clojure unofficial driver
ColdFusion unofficial driver
D unofficial driver
Dart unofficial driver
Delphi unofficial driver
Groovy unofficial driver
Lisp unofficial driver
Lua unofficial driver
MatLab unofficial driver
PowerShell unofficial driver
Prolog unofficial driver
R unofficial driver
Smalltalk unofficial driver
|Triggers||By using transaction functions||yes in MongoDB Atlas only||yes|
|Partitioning methods Methods for storing different data on different nodes||none But extensive use of caching in the application peers||Sharding Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.||Shared Nothhing Auto-Sharding, Columnar Partitioning|
|Replication methods Methods for redundantly storing data on multiple nodes||none But extensive use of caching in the application peers||Multi-Source deployments with MongoDB Atlas Global Clusters|
|MapReduce Offers an API for user-defined Map/Reduce methods||no||yes||Yes, via Full Spark Integration|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate Consistency||Eventual Consistency can be individually decided for each read operation|
Immediate Consistency default behaviour
|Foreign keys Referential integrity||no||no typically not used, however similar functionality with DBRef possible||yes|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||ACID||Multi-document ACID Transactions with snapshot isolation||ACID|
|Concurrency Support for concurrent manipulation of data||yes||yes||yes, multi-version concurrency control (MVCC)|
|Durability Support for making data persistent||yes using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)||yes optional, enabled by default||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||yes recommended only for testing and development||yes In-memory storage engine introduced with MongoDB version 3.2||yes|
|User concepts Access control||no||Access rights for users and roles||Access rights for users, groups and roles according to SQL-standard|
|More information provided by the system vendor|
|Specific characteristics||MongoDB provides an integrated suite of cloud database and data services to accelerate...|
|Competitive advantages||Built around the flexible document data model and unified API, MongoDB is a developer...|
|Typical application scenarios||AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...|
|Key customers||ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...|
|Market metrics||Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...|
|Licensing and pricing models||MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...|
We invite representatives of system vendors to contact us for updating and extending the system information,
|Related products and services|
|3rd parties||Navicat for MongoDB gives you a highly effective GUI interface for MongoDB database management, administration and development.|
CData: Connect to Big Data & NoSQL through standard Drivers.
Studio 3T: The world's favorite IDE for working with MongoDB
We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
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