DBMS > Amazon DocumentDB vs. Hive vs. Microsoft SQL Server vs. Vitess

System Properties Comparison Amazon DocumentDB vs. Hive vs. Microsoft SQL Server vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

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

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

Microsofts flagship relational DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

aws.amazon.com/documentdb

hive.apache.org

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

vitess.io

Technical documentation

aws.amazon.com/documentdb/resources

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

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

vitess.io/docs

Developer

Apache Software Foundation

initially developed by Facebook

Microsoft

The Linux Foundation, PlanetScale

Initial release

2019

2012

1989

2013

Current release

3.1.3, April 2022

SQL Server 2022, November 2022

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

Docker
Linux
macOS

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
Thrift

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C++
Java
PHP
Python

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

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

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

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-document operations

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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