DBMS > Apache Ignite vs. Dolt vs. PostgreSQL vs. XTDB

System Properties Comparison Apache Ignite vs. Dolt vs. PostgreSQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

A MySQL compatible DBMS with Git-like versioning of data and schema

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store
Relational DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Document store

Secondary database models

Document store

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	3.23
Rank	#81	Overall
	#10	Key-value stores
	#44	Relational DBMS

Score	0.60
Rank	#232	Overall
	#106	Relational DBMS

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Score	0.20
Rank	#314	Overall
	#46	Document stores

Website

ignite.apache.org

github.com/dolthub/dolt
www.dolthub.com

www.postgresql.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

apacheignite.readme.io/docs

docs.dolthub.com

www.postgresql.org/docs

www.xtdb.com/docs

Developer

Apache Software Foundation

DoltHub Inc

PostgreSQL Global Development Group

www.postgresql.org/developer

Juxt Ltd.

Initial release

2015

2018

1989

1989: Postgres, 1996: PostgreSQL

2019

Current release

2.16.0, December 2023

16.4, August 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C++, Java, .Net

Clojure

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

standard with numerous extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

CLI Client
HTTP REST

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

HTTP REST
JDBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Clojure
Java

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

currently in alpha release

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Source-replica replication

other methods possible by using 3rd party extensions

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Security Hooks for custom implementations

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

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