DBMS > Amazon Redshift vs. DolphinDB vs. MarkLogic vs. RethinkDB

System Properties Comparison Amazon Redshift vs. DolphinDB vs. MarkLogic vs. RethinkDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Operational and transactional Enterprise NoSQL database

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	2.74
Rank	#105	Overall
	#19	Document stores

Website

aws.amazon.com/redshift

www.dolphindb.com

www.marklogic.com

rethinkdb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.dolphindb.cn/en/help200/index.html

docs.marklogic.com

rethinkdb.com/docs

Developer

Amazon (based on PostgreSQL)

DolphinDB, Inc

MarkLogic Corp.

The Linux Foundation

since July 2017

Initial release

2012

2018

2001

2009

Current release

v2.00.4, January 2022

11.0, December 2022

2.4.1, August 2020

License

Commercial or Open Source

commercial

free community version available

commercial

restricted free version is available

Open Source

Apache Version 2

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++

Server operating systems

hosted

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

SQL92

APIs and other access methods

JDBC
ODBC

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via XQuery or JavaScript

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

ACID

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

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes, with Range Indexes

User concepts

Access control

fine grained access rights according to SQL-standard

Administrators, Users, Groups

Role-based access control at the document and subdocument levels

yes

users and table-level permissions

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