DBMS > Microsoft SQL Server vs. Riak KV vs. SAP SQL Anywhere vs. Splice Machine vs. STSdb

System Properties Comparison Microsoft SQL Server vs. Riak KV vs. SAP SQL Anywhere vs. Splice Machine vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Document store
Graph DBMS
Spatial DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

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

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

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

www.sap.com/products/technology-platform/sql-anywhere.html

splicemachine.com

github.com/STSSoft/STSdb4

Technical documentation

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

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

help.sap.com/docs/SAP_SQL_Anywhere

splicemachine.com/how-it-works

Developer

Microsoft

OpenSource, formerly Basho Technologies

SAP

formerly Sybase

Splice Machine

STS Soft SC

Initial release

1989

2009

1992

2014

2011

Current release

SQL Server 2022, November 2022

3.2.0, December 2022

17, July 2015

3.1, March 2021

4.0.8, September 2015

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

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

C++

Erlang

Java

Server operating systems

Linux
Windows

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

yes

SQL

Support of SQL

yes

APIs and other access methods

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

HTTP API
Native Erlang Interface

ADO.NET
HTTP API
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

.NET Client API

Supported programming languages

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

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

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

Erlang

yes, in C/C++, Java, .Net or Perl

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

no "single point of failure"

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

selectable replication factor

Source-replica replication

Database mirroring

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

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

yes

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes, using Riak Security

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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