DBMS > Couchbase vs. FatDB vs. Hazelcast vs. IBM Db2 Event Store

System Properties Comparison Couchbase vs. FatDB vs. Hazelcast vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A widely adopted in-memory data grid

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store

Document store
Key-value store

Key-value store

Event Store
Time Series DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store

JSON support with IMDG 3.12

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

www.couchbase.com

hazelcast.com

www.ibm.com/products/db2-event-store

Technical documentation

docs.couchbase.com

hazelcast.org/imdg/docs

www.ibm.com/docs/en/db2-event-store

Developer

Couchbase, Inc.

FatCloud

Hazelcast

IBM

Initial release

2011

2012

2008

2017

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

5.3.6, November 2023

2.0

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

free developer edition 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

C, C++, Go and Erlang

Java

C and C++

Server operating systems

Linux
OS X
Windows

Windows

All OS with a Java VM

Linux

Linux, macOS, Windows for the developer addition

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++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

Via inetgration in SQL Server

SQL-like query language

yes

through the embedded Spark runtime

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

via applications

yes

Event Listeners, Executor Services

yes

Triggers

yes

via the TAP protocol

yes

via applications

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

yes

Replicated Map

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

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

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Can implement custom security layer via applications

Role-based access control

fine grained access rights according to SQL-standard

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