DBMS > Amazon DocumentDB vs. HBase vs. InfinityDB vs. Microsoft SQL Server vs. SAP SQL Anywhere

System Properties Comparison Amazon DocumentDB vs. HBase vs. InfinityDB vs. Microsoft SQL Server vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

A Java embedded Key-Value Store which extends the Java Map interface

Microsofts flagship relational DBMS

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

aws.amazon.com/documentdb

hbase.apache.org

boilerbay.com

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

aws.amazon.com/documentdb/resources

hbase.apache.org/book.html

boilerbay.com/infinitydb/manual

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

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Boiler Bay Inc.

Microsoft

SAP

formerly Sybase

Initial release

2019

2008

2002

1989

1992

Current release

2.3.4, January 2021

4.0

SQL Server 2022, November 2022

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

schema-free, schema definition possible

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Java API
RESTful HTTP API
Thrift

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

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

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java

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

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

Server-side scripts

Stored procedures

yes

Coprocessors in Java

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

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

Atomic single-document operations

Single row ACID (across millions of columns)

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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