DBMS > AnzoGraph DB vs. Couchbase vs. Hypertable vs. MySQL

System Properties Comparison AnzoGraph DB vs. Couchbase vs. Hypertable vs. MySQL

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

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
Spatial DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.couchbase.com

www.mysql.com

Technical documentation

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

docs.couchbase.com

dev.mysql.com/doc

Developer

Cambridge Semantics

Couchbase, Inc.

Hypertable Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2011

2009

1995

Current release

2.3, January 2021

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

0.9.8.11, March 2016

8.4.0, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

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

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Go and Erlang

C++

C and C++

Server operating systems

Linux

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
OS X
Solaris
Windows

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.

yes

Secondary indexes

yes

restricted

only exact value or prefix value scans

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

with proprietary extensions

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

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

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

yes

proprietary syntax

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Automatic Sharding

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

selectable replication factor on file system level

Multi-source replication
Source-replica 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

Foreign keys

Referential integrity

not needed in graphs

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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.

Users with fine-grained authorization concept

no user groups or roles

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