DBMS > Hazelcast vs. Tigris vs. VoltDB

System Properties Comparison Hazelcast vs. Tigris vs. VoltDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

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

Document store
Key-value store
Search engine
Time Series DBMS

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.05
Rank	#359	Overall
	#50	Document stores
	#52	Key-value stores
	#23	Search engines
	#37	Time Series DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

hazelcast.com

www.tigrisdata.com

www.voltdb.com

Technical documentation

hazelcast.org/imdg/docs

www.tigrisdata.com/docs

docs.voltdb.com

Developer

Hazelcast

Tigris Data, Inc.

VoltDB Inc.

Initial release

2008

2022

2010

Current release

5.3.6, November 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 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

Java

Java, C++

Server operating systems

All OS with a Java VM

Linux
macOS
Windows

Linux
OS X

for development

Data scheme

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

CLI Client
gRPC
RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Go
Java
JavaScript (Node.js)

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Java

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

yes, using FoundationDB

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

Role-based access control

Access rights for users and roles

Users and roles with access to stored procedures

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

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast Showcases Real-Time Data Platform at 2024 Gartner Summit
15 May 2024, Datanami

Hazelcast appoints Anthony Griffin as Chief Architect -
11 June 2024, Enterprise Times

Hazelcast Versus Redis: A Practical Comparison
4 January 2024, Database Trends and Applications

provided by Google News

Tigris Data Unveils Beta Launch of New Vector Search Tool
19 May 2023, Datanami

Tigris Data Launches All-in-One Developer Data Platform
27 September 2022, Datanami

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

Solutions Review Sits Down with VoltDB CEO David Flower
17 April 2018, Solutions Review

Stonebraker Seeks to Invert the Computing Paradigm with DBOS
12 March 2024, Datanami

provided by Google News

Share this page