DBMS > Axibase vs. Ignite vs. Stardog vs. XTDB

System Properties Comparison Axibase vs. Ignite vs. Stardog vs. XTDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

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.

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

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

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Graph DBMS
RDF store

Document store

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

axibase.com/docs/atsd/finance

ignite.apache.org

www.stardog.com

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

Technical documentation

apacheignite.readme.io/docs

docs.stardog.com

www.xtdb.com/docs

Developer

Axibase Corporation

Apache Software Foundation

Stardog-Union

Juxt Ltd.

Initial release

2013

2015

2010

2019

Current release

15585

Apache Ignite 2.6

7.3.0, May 2020

1.19, September 2021

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

MIT 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

Java

C++, Java, .Net

Java

Clojure

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

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

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-like query language

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

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

HTTP REST
JDBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

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

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

yes (cache interceptors and events)

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

Multi-source replication in HA-Cluster

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

Access rights for users and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

The Ultimate ATV Test: Suzuki's King Quad 750 AXI Rugged Package vs. Alaska's Hunting Season
14 October 2020, Outdoor Life

Time Series Databases Software Market - A comprehensive study by Key Players | Warp 10, Amazon Timestream ...
6 February 2020, openPR

provided by Google News

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

GridGain Showcases Power of Apache Ignite at Community Over Code Conference
5 October 2023, Datanami

Apache Ignite: An Overview
6 September 2023, Open Source For You

GridGain Unified Real-Time Data Platform Version 8.9 Addresses Today's More Complex Real-Time Data Processing ...
12 October 2023, GlobeNewswire

What is Apache Ignite? How is Apache Ignite Used?
18 July 2022, The Stack

provided by Google News

Share this page