DBMS > Altibase vs. Google Cloud Datastore vs. Microsoft Access vs. PostgreSQL vs. XTDB

System Properties Comparison Altibase vs. Google Cloud Datastore vs. Microsoft Access vs. PostgreSQL vs. XTDB

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Document store

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.85
Rank	#204	Overall
	#94	Relational DBMS

Score	3.62
Rank	#71	Overall
	#12	Document stores

Score	95.91
Rank	#13	Overall
	#9	Relational DBMS

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Score	0.20
Rank	#314	Overall
	#46	Document stores

Website

altibase.com

cloud.google.com/datastore

www.microsoft.com/en-us/microsoft-365/access

www.postgresql.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

cloud.google.com/datastore/docs

developer.microsoft.com/en-us/access

www.postgresql.org/docs

www.xtdb.com/docs

Developer

Altibase

Google

Microsoft

PostgreSQL Global Development Group

www.postgresql.org/developer

Juxt Ltd.

Initial release

1999

2008

1992

1989

1989: Postgres, 1996: PostgreSQL

2019

Current release

v7.3, 2023, August 2023

1902 (16.0.11328.20222), March 2019

16.4, August 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Bundled with Microsoft Office

Open Source

BSD

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Clojure

Server operating systems

AIX
HP-UX
Linux

hosted

Windows

Not a real database server, but making use of DLLs

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

yes, extensible-data-notation format

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like query language (GQL)

yes

but not compliant to any SQL standard

yes

standard with numerous extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Proprietary protocol

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

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST
JDBC

Supported programming languages

C
C++
Java

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

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Clojure
Java

Server-side scripts

Stored procedures

stored procedures and stored functions

using Google App Engine

yes

since Access 2010 using the ACE-engine

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Callbacks using the Google Apps Engine

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

Source-replica replication

other methods possible by using 3rd party extensions

yes, each node contains all data

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

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.

Immediate Consistency

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

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