DBMS > CouchDB vs. Datomic vs. InfinityDB vs. Postgres-XL

System Properties Comparison CouchDB vs. Datomic vs. InfinityDB vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A Java embedded Key-Value Store which extends the Java Map interface

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

Technical documentation

docs.couchdb.org/en/stable

docs.datomic.com

boilerbay.com/infinitydb/manual

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Cognitect

Boiler Bay Inc.

Initial release

2005

2012

2002

2014

since 2012, originally named StormDB

Current release

3.3.3, December 2023

1.0.6735, June 2023

4.0

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache version 2

commercial

limited edition free

commercial

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Java, Clojure

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
macOS

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

via views

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Clojure
Java

Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Transaction Functions

user defined functions

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

But extensive use of caching in the application peers

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

But extensive use of caching in the application peers

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

atomic operations within a single document possible

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

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

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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