DBMS > Microsoft SQL Server vs. OrientDB vs. Sphinx vs. STSdb vs. SurrealDB

System Properties Comparison Microsoft SQL Server vs. OrientDB vs. Sphinx vs. STSdb vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Multi-model DBMS (Document, Graph, Key/Value)

Open source search engine for searching in data from different sources, e.g. relational databases

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Search engine

Key-value store

Document store
Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

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

orientdb.org

sphinxsearch.com

github.com/STSSoft/STSdb4

surrealdb.com

Technical documentation

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

www.orientdb.com/docs/last/index.html

sphinxsearch.com/docs

surrealdb.com/docs

Developer

Microsoft

OrientDB LTD; CallidusCloud; SAP

Sphinx Technologies Inc.

STS Soft SC

SurrealDB Ltd

Initial release

1989

2010

2001

2011

2022

Current release

SQL Server 2022, November 2022

3.2.29, March 2024

3.5.1, February 2023

4.0.8, September 2015

v1.5.0, May 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache version 2

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Rust

Server operating systems

Linux
Windows

All OS with a Java JDK (>= JDK 6)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

schema-free

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language, no joins

SQL-like query language (SphinxQL)

SQL-like query language

APIs and other access methods

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Proprietary protocol

.NET Client API

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

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

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

Access rights for users and roles; record level security configurable

yes, based on authentication and database rules

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