DBMS > Couchbase vs. H2 vs. HugeGraph vs. Riak KV

System Properties Comparison Couchbase vs. H2 vs. HugeGraph vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A fast-speed and highly-scalable Graph DBMS

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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

Spatial DBMS

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

Score	18.46
Rank	#36	Overall
	#5	Document stores

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Website

www.couchbase.com

www.h2database.com

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.couchbase.com

www.h2database.com/html/main.html

hugegraph.apache.org/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Couchbase, Inc.

Thomas Mueller

Baidu

OpenSource, formerly Basho Technologies

Initial release

2011

2005

2018

2009

Current release

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

2.2.220, July 2023

0.9

3.2.0, December 2022

License

Commercial or Open Source

Open Source

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

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
macOS
Unix

Linux
OS X

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.

Secondary indexes

yes

also supports composite index and range index

restricted

SQL

Support of SQL

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

yes

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

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Java

Groovy
Java
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

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

Java Stored Procedures and User-Defined Functions

asynchronous Gremlin script jobs

Erlang

Triggers

yes

via the TAP protocol

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

links between data sets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

Ephemeral buckets

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

Users, roles and permissions

yes, using Riak Security

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