DBMS > atoti vs. Hazelcast vs. Postgres-XL vs. Yanza

System Properties Comparison atoti vs. Hazelcast vs. Postgres-XL vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it 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.

A widely adopted in-memory data grid

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
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	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

atoti.io

hazelcast.com

www.postgres-xl.org

yanza.com

Technical documentation

docs.atoti.io

hazelcast.org/imdg/docs

www.postgres-xl.org/documentation

Developer

ActiveViam

Hazelcast

Yanza

Initial release

2008

2014

since 2012, originally named StormDB

2015

Current release

5.3.6, November 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache Version 2; commercial licenses available

Open Source

Mozilla public license

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

All OS with a Java VM

Linux
macOS

Windows

Data scheme

schema-free

yes

schema-free

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

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

any language that supports HTTP calls

Server-side scripts

Stored procedures

Python

yes

Event Listeners, Executor Services

user defined functions

Triggers

yes

Events

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

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

yes

Transaction concepts

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

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

ACID

MVCC

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

Role-based access control

fine grained access rights according to SQL-standard

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