DBMS > AgensGraph vs. AnzoGraph DB vs. CouchDB vs. HEAVY.AI vs. Microsoft SQL Server

System Properties Comparison AgensGraph vs. AnzoGraph DB vs. CouchDB vs. HEAVY.AI vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Microsofts flagship relational DBMS

Primary database model

Graph DBMS
Relational DBMS

Graph DBMS
RDF store

Document store

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

bitnine.net/agensgraph

cambridgesemantics.com/anzograph

couchdb.apache.org

github.com/heavyai/heavydb
www.heavy.ai

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

Technical documentation

bitnine.net/documentation

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

docs.couchdb.org/en/stable

docs.heavy.ai

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

Developer

Bitnine Global Inc.

Cambridge Semantics

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

HEAVY.AI, Inc.

Microsoft

Initial release

2016

2018

2005

2016

1989

Current release

2.1, December 2018

2.3, January 2021

3.3.3, December 2023

5.10, January 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free trial version available

Open Source

Apache version 2

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

C++ and CUDA

C++

Server operating systems

Linux
OS X
Windows

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux

Linux
Windows

Data scheme

depending on used data model

Schema-free and OWL/RDFS-schema support

schema-free

yes

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

via views

yes

SQL

Support of SQL

yes

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

yes

APIs and other access methods

Cypher Query Language
JDBC

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

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

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

Supported programming languages

C
Java
JavaScript
Python

C++
Java
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

View functions in JavaScript

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Automatic sharding

Sharding

improved architecture with release 2.0

Sharding

Round robin

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

Multi-source replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not needed in graphs

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users and roles

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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