DBMS > Amazon DocumentDB vs. HyperSQL vs. Microsoft Azure Table Storage vs. RocksDB

System Properties Comparison Amazon DocumentDB vs. HyperSQL vs. Microsoft Azure Table Storage vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A Wide Column Store for rapid development using massive semi-structured datasets

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

aws.amazon.com/documentdb

hsqldb.org

azure.microsoft.com/en-us/services/storage/tables

rocksdb.org

Technical documentation

aws.amazon.com/documentdb/resources

hsqldb.org/web/hsqlDocsFrame.html

github.com/facebook/rocksdb/wiki

Developer

Microsoft

Facebook, Inc.

Initial release

2019

2001

2012

2013

Current release

2.7.2, June 2023

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

based on BSD license

commercial

Open Source

BSD

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

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

Linux

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC
Java

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

optimistic locking

yes

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

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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