DBMS > AnzoGraph DB vs. Dolt vs. Microsoft SQL Server vs. Sequoiadb vs. Spark SQL

System Properties Comparison AnzoGraph DB vs. Dolt vs. Microsoft SQL Server vs. Sequoiadb vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

A MySQL compatible DBMS with Git-like versioning of data and schema

Microsofts flagship relational DBMS

NewSQL database with distributed OLTP and SQL

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

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

cambridgesemantics.com/anzograph

github.com/dolthub/dolt
www.dolthub.com

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

www.sequoiadb.com

spark.apache.org/sql

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.dolthub.com

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

www.sequoiadb.com/en/index.php?m=Files&a=index

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

Developer

Cambridge Semantics

DoltHub Inc

Microsoft

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2018

1989

2013

2014

Current release

2.3, January 2021

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Scala

Server operating systems

Linux

Linux
macOS
Windows

Linux
Windows

Linux

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

CLI Client
HTTP REST

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

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

C++
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

currently in alpha release

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

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

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

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes, but depending on the SQL-Server Edition

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Document is locked during a transaction

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

User concepts

Access control

Access rights for users and roles

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

simple password-based access control

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