DBMS > DolphinDB vs. Microsoft SQL Server vs. Riak KV vs. Spark SQL vs. Tkrzw

System Properties Comparison DolphinDB vs. Microsoft SQL Server vs. Riak KV vs. Spark SQL vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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.

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

Spark SQL is a component on top of 'Spark Core' for structured data processing

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

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

Relational DBMS

Key-value store

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

www.dolphindb.com

www.microsoft.com/en-us/sql-server

spark.apache.org/sql

dbmx.net/tkrzw

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DolphinDB, Inc

Microsoft

OpenSource, formerly Basho Technologies

Apache Software Foundation

Mikio Hirabayashi

Initial release

2018

1989

2009

2014

2020

Current release

v2.00.4, January 2022

SQL Server 2022, November 2022

3.2.0, December 2022

3.5.0 ( 2.13), September 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

free community version available

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

C++

Erlang

Scala

C++

Server operating systems

Linux
Windows

Linux
OS X

Linux
OS X
Windows

Linux
macOS

Data scheme

yes

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.

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

JDBC
ODBC

Supported programming languages

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
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

Java
Python
R
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

no "single point of failure"

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

yes

ACID

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

using specific database classes

User concepts

Access control

Administrators, Users, Groups

fine grained access rights according to SQL-standard

yes, using Riak Security

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