DBMS > Altibase vs. HBase vs. InfinityDB vs. Microsoft SQL Server vs. Quasardb

System Properties Comparison Altibase vs. HBase vs. InfinityDB vs. Microsoft SQL Server vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

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

Distributed, high-performance timeseries database

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	1.07
Rank	#186	Overall
	#87	Relational DBMS

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	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

altibase.com

hbase.apache.org

boilerbay.com

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

quasar.ai

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

hbase.apache.org/book.html

boilerbay.com/infinitydb/manual

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

doc.quasar.ai/master

Developer

Altibase

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Boiler Bay Inc.

Microsoft

quasardb

Initial release

1999

2008

2002

1989

2009

Current release

v7.3, 2023, August 2023

2.3.4, January 2021

4.0

SQL Server 2022, November 2022

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

AIX
HP-UX
Linux

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free, schema definition possible

yes

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

yes

schema-free

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

integer and binary

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

with tags

SQL

Support of SQL

ANSI SQL-92

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol

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)

HTTP API

Supported programming languages

C
C++
Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java

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

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

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

Coprocessors in Java

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

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

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

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

yes

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

yes

Transaction concepts

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

ACID

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

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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