DBMS > Google Cloud Datastore vs. jBASE vs. PostGIS vs. Transbase vs. Vitess

System Properties Comparison Google Cloud Datastore vs. jBASE vs. PostGIS vs. Transbase vs. Vitess

Editorial information provided by DB-Engines

Name

Description

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

A robust multi-value DBMS comprising development tools and middleware

Spatial extension of PostgreSQL

A resource-optimized, high-performance, universally applicable RDBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

cloud.google.com/datastore

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

postgis.net

www.transaction.de/en/products/transbase.html

vitess.io

Technical documentation

cloud.google.com/datastore/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

postgis.net/documentation

www.transaction.de/en/products/transbase/features.html

vitess.io/docs

Developer

Google

Rocket Software (formerly Zumasys)

Transaction Software GmbH

The Linux Foundation, PlanetScale

Initial release

2008

1991

2005

1987

2013

Current release

5.7

3.4.2, February 2024

Transbase 8.3, 2022

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

commercial

free development license

Open Source

Apache Version 2.0, commercial licenses available

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 and C++

Server operating systems

hosted

AIX
Linux
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

optional

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

SQL

Support of SQL

SQL-like query language (GQL)

Embedded SQL for jBASE in BASIC

yes

with proprietary extensions

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net
Basic
Jabbascript
Java

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

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

using Google App Engine

yes

user defined functions

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

based on PostgreSQL

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

based on PostgreSQL

Source-replica replication

Multi-source replication
Source-replica replication

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 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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

yes

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

Access rights can be defined down to the item level

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

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