DBMS > Altibase vs. Axibase vs. CouchDB vs. Google Cloud Datastore vs. SQLite

System Properties Comparison Altibase vs. Axibase vs. CouchDB vs. Google Cloud Datastore vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

altibase.com

axibase.com/docs/atsd/finance

couchdb.apache.org

cloud.google.com/datastore

www.sqlite.org

Technical documentation

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

docs.couchdb.org/en/stable

cloud.google.com/datastore/docs

www.sqlite.org/docs.html

Developer

Altibase

Axibase Corporation

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Dwayne Richard Hipp

Initial release

1999

2013

2005

2008

2000

Current release

v7.3, 2023, August 2023

15585

3.3.3, December 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Apache version 2

commercial

Open Source

Public Domain

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

C++

Java

Erlang

Server operating systems

AIX
HP-UX
Linux

Linux

Android
BSD
Linux
OS X
Solaris
Windows

hosted

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes, details here

yes

not rigid because of 'dynamic typing' concept.

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

via views

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like query language

SQL-like query language (GQL)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C++
Java

Go
Java
PHP
Python
R
Ruby

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

View functions in JavaScript

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

via file-system locks

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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