DBMS > Apache Ignite vs. CrateDB vs. VoltDB

System Properties Comparison Apache Ignite vs. CrateDB vs. VoltDB

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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.

Distributed Database based on Lucene

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Key-value store
Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	3.00
Rank	#90	Overall
	#13	Key-value stores
	#48	Relational DBMS

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

ignite.apache.org

cratedb.com

www.voltdb.com

Technical documentation

apacheignite.readme.io/docs

cratedb.com/docs

docs.voltdb.com

Developer

Apache Software Foundation

Crate

VoltDB Inc.

Initial release

2015

2013

2010

Current release

2.16.0, December 2023

5.8.1, August 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++, Java, .Net

Java

Java, C++

Server operating systems

Linux
OS X
Solaris
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X

for development

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

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

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

only a subset of SQL 99

APIs and other access methods

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

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

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

user defined functions (Javascript)

Java

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Configurable replication on table/partition-level

Multi-source replication
Source-replica replication

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

Eventual Consistency
Read-after-write consistency on record level

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

rights management via user accounts

Users and roles with access to stored procedures

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More resources

Recent citations in the news

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

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

Real-time in-memory OLTP and Analytics with Apache Ignite on AWS
14 May 2016, AWS Blog

Fire up big data processing with Apache Ignite
27 October 2016, InfoWorld

GridGain In-Memory Data Fabric Becomes Apache Ignite
9 April 2015, Linux.com

provided by Google News

CrateDB Appoints Jeff Olson as New CRO
9 July 2024, PR Newswire

CrateDB Announces Availability of CrateDB on Google Cloud Marketplace
8 April 2024, Datanami

CrateDB Appoints Sergey Gerasimenko as New CTO
19 February 2024, PR Newswire

How We Designed CrateDB as a Realtime SQL DBMS for the Internet of Things
29 August 2017, The New Stack

Taleo Consulting and CrateDB Announce Strategic Partnership to Transform Data Management and Analytics in Financial Services markets
29 July 2024, PR Newswire

provided by Google News

VoltDB Launches Active(N) Lossless Cross Data Center Replication
31 August 2021, PR Newswire

Unveiling Volt Active Data’s game-changing approach to limitless app performance
16 October 2023, YourStory

VoltDB Turns to Real-Time Analytics with NewSQL Database
30 January 2014, Datanami

VoltDB Aims for Fast Big Data Development
29 January 2015, ADT Magazine

VoltDB Upgrades Power, Security of Its In-Memory Database
1 February 2017, eWeek

provided by Google News

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