DBMS > GridGain vs. OpenTenBase vs. Vitess

System Properties Comparison GridGain vs. OpenTenBase vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

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

An enterprise-level distributed HTAP open source database based on PostgreSQL

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

www.gridgain.com

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

vitess.io

Technical documentation

www.gridgain.com/docs/index.html

docs.opentenbase.org
docs.opentenbase.org/en

vitess.io/docs

Developer

GridGain Systems, Inc.

OpenAtom Foundation, previously Tencent

The Linux Foundation, PlanetScale

Initial release

2007

2013

Current release

GridGain 8.5.1

2.5, January 2024

15.0.2, December 2022

License

Commercial or Open Source

commercial, open source

Open Source

BSD-3

Open Source

Apache Version 2.0, commercial licenses available

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++, .Net, Python, REST, SQL

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux

Docker
Linux
macOS

Data scheme

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

with proprietary extensions

APIs and other access methods

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
Go
Java
PHP
Python

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

Server-side scripts

Stored procedures

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

yes

proprietary syntax

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

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 across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control
Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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More resources

Recent citations in the news

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GridGain in-memory data and generative AI
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