DBMS > Amazon DocumentDB vs. HugeGraph vs. Microsoft SQL Server

System Properties Comparison Amazon DocumentDB vs. HugeGraph vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

Microsofts flagship relational DBMS

Primary database model

Document store

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.89
Rank	#137	Overall
	#24	Document stores

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Website

aws.amazon.com/documentdb

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

aws.amazon.com/documentdb/resources

hugegraph.apache.org/docs

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

Developer

Baidu

Microsoft

Initial release

2019

2018

1989

Current release

0.9

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

Linux
macOS
Unix

Linux
Windows

Data scheme

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Groovy
Java
Python

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

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

depending on used storage backend, e.g. Cassandra and HBase

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)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

edges in graph

yes

Transaction concepts

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

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

Access rights for users and roles

Users, roles and permissions

fine grained access rights according to SQL-standard

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