DBMS > AgensGraph vs. Amazon DocumentDB vs. AnzoGraph DB vs. Hive vs. Microsoft SQL Server

System Properties Comparison AgensGraph vs. Amazon DocumentDB vs. AnzoGraph DB vs. Hive 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

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Microsofts flagship relational DBMS

Primary database model

Graph DBMS
Relational DBMS

Document store

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	1.91
Rank	#131	Overall
	#24	Document stores

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

bitnine.net/agensgraph

aws.amazon.com/documentdb

cambridgesemantics.com/anzograph

hive.apache.org

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

Technical documentation

bitnine.net/documentation

aws.amazon.com/documentdb/resources

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

cwiki.apache.org/confluence/display/Hive/Home

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

Developer

Bitnine Global Inc.

Cambridge Semantics

Apache Software Foundation

initially developed by Facebook

Microsoft

Initial release

2016

2019

2018

2012

1989

Current release

2.1, December 2018

2.3, January 2021

3.1.3, April 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

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux

All OS with a Java VM

Linux
Windows

Data scheme

depending on used data model

schema-free

Schema-free and OWL/RDFS-schema support

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

SQL

Support of SQL

yes

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

SQL-like DML and DDL statements

yes

APIs and other access methods

Cypher Query Language
JDBC

proprietary protocol using JSON (MongoDB compatible)

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

JDBC
ODBC
Thrift

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

Supported programming languages

C
Java
JavaScript
Python

Go
Java
JavaScript (Node.js)
PHP
Python

C++
Java
Python

C++
Java
PHP
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

yes

user defined functions and integration of map-reduce

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

none

Automatic sharding

Sharding

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-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication in MPP-Cluster

selectable replication factor

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Kerberos/HDFS data loading

yes

query execution via MapReduce

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

typically not used, however similar functionality with DBRef possible

not needed in graphs

yes

Transaction concepts

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

ACID

Atomic single-document operations

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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