DBMS > AntDB vs. Couchbase vs. GridGain vs. VoltDB

System Properties Comparison AntDB vs. Couchbase vs. GridGain vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

GridGain is an in-memory computing platform, built on Apache Ignite

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

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	0.23
Rank	#317	Overall
	#141	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

www.couchbase.com

www.gridgain.com

www.voltdb.com

Technical documentation

docs.couchbase.com

www.gridgain.com/docs/index.html

docs.voltdb.com

Developer

AsiaInfo Technologies Limited

Couchbase, Inc.

GridGain Systems, Inc.

VoltDB Inc.

Initial release

2011

2007

2010

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

GridGain 8.5.1

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

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, C++, Go and Erlang

Java, C++, .Net

Java, C++

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
OS X

for development

Data scheme

yes

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

SQL2016 compliant

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

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

yes

only a subset of SQL 99

APIs and other access methods

JDBC
MySQL protocol compliant
ODBC

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

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

Java

Triggers

yes

via the TAP protocol

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes (replicated cache)

Multi-source replication
Source-replica replication

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

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Security Hooks for custom implementations

Users and roles with access to stored procedures

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