DBMS > atoti vs. Drizzle vs. Hazelcast vs. HEAVY.AI vs. JaguarDB

System Properties Comparison atoti vs. Drizzle vs. Hazelcast vs. HEAVY.AI vs. JaguarDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A widely adopted in-memory data grid

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Key-value store
Vector DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

atoti.io

hazelcast.com

github.com/heavyai/heavydb
www.heavy.ai

www.jaguardb.com

Technical documentation

docs.atoti.io

hazelcast.org/imdg/docs

docs.heavy.ai

www.jaguardb.com/support.html

Developer

ActiveViam

Drizzle project, originally started by Brian Aker

Hazelcast

HEAVY.AI, Inc.

DataJaguar, Inc.

Initial release

2008

2016

2015

Current release

7.2.4, September 2012

5.3.6, November 2023

5.10, January 2022

3.3 July 2023

License

Commercial or Open Source

commercial

free versions available

Open Source

GNU GPL

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL V3.0

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

C++ and CUDA

C++

the server part. Clients available in other languages

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

Linux

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

with proprietary extensions

SQL-like query language

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

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

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Python

yes

Event Listeners, Executor Services

Triggers

hooks for callbacks inside the server can be used.

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Replicated Map

Multi-source 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

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Role-based access control

fine grained access rights according to SQL-standard

rights management via user accounts

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More resources

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