DBMS > EsgynDB vs. Hazelcast vs. Microsoft Azure Table Storage vs. Riak KV vs. TimesTen

System Properties Comparison EsgynDB vs. Hazelcast vs. Microsoft Azure Table Storage vs. Riak KV vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A widely adopted in-memory data grid

A Wide Column Store for rapid development using massive semi-structured datasets

Distributed, fault tolerant key-value store

In-Memory RDBMS compatible to Oracle

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.esgyn.cn

hazelcast.com

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

hazelcast.org/imdg/docs

www.tiot.jp/riak-docs/riak/kv/latest

docs.oracle.com/database/timesten-18.1

Developer

Esgyn

Hazelcast

Microsoft

OpenSource, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2008

2012

2009

1998

Current release

5.3.6, November 2023

3.2.0, December 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Java

Erlang

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP API

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Event Listeners, Executor Services

Erlang

PL/SQL

Triggers

yes

Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Replicated Map

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Access rights based on private key authentication or shared access signatures

yes, using Riak Security

fine grained access rights according to SQL-standard

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