DBMS > AnzoGraph DB vs. Couchbase vs. HBase

System Properties Comparison AnzoGraph DB vs. Couchbase vs. HBase

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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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Graph DBMS
RDF store

Document store

Wide column store

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	31.25
Rank	#26	Overall
	#2	Wide column stores

Website

cambridgesemantics.com/anzograph

www.couchbase.com

hbase.apache.org

Technical documentation

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

docs.couchbase.com

hbase.apache.org/book.html

Developer

Cambridge Semantics

Couchbase, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2018

2011

2008

Current release

2.3, January 2021

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

2.3.4, January 2021

License

Commercial or Open Source

commercial

free trial version available

Open Source

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

Open Source

Apache version 2

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

Java

Server operating systems

Linux

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

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

Java API
RESTful HTTP API
Thrift

Supported programming languages

C++
Java
Python

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

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

yes

Coprocessors in Java

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Automatic Sharding

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
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 or Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

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

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.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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