DBMS > AnzoGraph DB vs. Couchbase vs. Fauna vs. IBM Db2 Event Store

System Properties Comparison AnzoGraph DB vs. Couchbase vs. Fauna vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Graph DBMS
RDF store

Document store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

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

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

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	18.46
Rank	#36	Overall
	#5	Document stores

Score	1.55
Rank	#154	Overall
	#26	Document stores
	#14	Graph DBMS
	#72	Relational DBMS
	#13	Time Series DBMS

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

cambridgesemantics.com/anzograph

www.couchbase.com

fauna.com

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

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.couchbase.com

docs.fauna.com

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

Developer

Cambridge Semantics

Couchbase, Inc.

Fauna, Inc.

IBM

Initial release

2018

2011

2014

2017

Current release

2.3, January 2021

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

2.0

License

Commercial or Open Source

commercial

free trial version available

Open Source

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

commercial

free developer edition available

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, C++, Go and Erlang

Scala

C and C++

Server operating systems

Linux

Linux
OS X
Windows

hosted

Linux

Linux, macOS, Windows for the developer addition

Data scheme

Schema-free and OWL/RDFS-schema support

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.

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

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

yes

through the embedded Spark runtime

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

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

RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++
Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

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

Server-side scripts

Stored procedures

user defined functions and aggregates

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

user defined functions

yes

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Automatic Sharding

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

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

Access rights for users and roles

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

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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