DBMS > HBase vs. InfinityDB vs. Microsoft SQL Server vs. SAP SQL Anywhere vs. Vitess

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

Editorial information provided by DB-Engines

Name

Description

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Wide column store

Key-value 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	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

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

hbase.apache.org

boilerbay.com

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

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

vitess.io

Technical documentation

hbase.apache.org/book.html

boilerbay.com/infinitydb/manual

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

help.sap.com/docs/SAP_SQL_Anywhere

vitess.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Boiler Bay Inc.

Microsoft

SAP

formerly Sybase

The Linux Foundation, PlanetScale

Initial release

2008

2002

1989

1992

2013

Current release

2.3.4, January 2021

4.0

SQL Server 2022, November 2022

17, July 2015

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

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

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

Data scheme

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

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

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

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

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

Coprocessors in Java

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

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

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

Source-replica replication

Database mirroring

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

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

yes

not for MyISAM storage engine

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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