DBMS > CouchDB vs. gStore vs. MarkLogic vs. Redis vs. Riak TS

System Properties Comparison CouchDB vs. gStore vs. MarkLogic vs. Redis vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A native Graph DBMS to store and maintain very large RDF datasets.

Operational and transactional Enterprise NoSQL database

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store

Graph DBMS
RDF store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Time Series DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	0.03
Rank	#367	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	149.43
Rank	#6	Overall
	#1	Key-value stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

couchdb.apache.org

en.gstore.cn

www.progress.com/marklogic

redis.com
redis.io

Technical documentation

docs.couchdb.org/en/stable

en.gstore.cn/#/enDocs

www.progress.com/marklogic/documentation

docs.redis.com/latest/index.html
redis.io/docs

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

MarkLogic Corp.

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Open Source, formerly Basho Technologies

Initial release

2005

2016

2001

2009

2015

Current release

3.3.3, December 2023

1.2, November 2023

11.0, December 2022

7.2.5, May 2024

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

BSD

commercial

restricted free version is available

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

Open Source

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

Erlang

C++

Erlang

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
OS X

Data scheme

schema-free

schema-free and OWL/RDFS-schema support

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

via views

yes

with RediSearch module

restricted

SQL

Support of SQL

yes

SQL92

with RediSQL module

yes, limited

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
SPARQL 1.1

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

proprietary protocol

RESP - REdis Serialization Protocol

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

View functions in JavaScript

yes

via XQuery or JavaScript

Lua; Redis Functions coming in Redis 7 (slides and Github)

Erlang

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

through RedisGears

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

atomic operations within a single document possible

yes

ACID

can act as a resource manager in an XA/JTA transaction

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes, with Range Indexes

yes

User concepts

Access control

Access rights for users can be defined per database

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Role-based access control at the document and subdocument levels

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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