DBMS > Google Cloud Datastore vs. ObjectBox vs. PostGIS vs. Tibero vs. TimescaleDB

System Properties Comparison Google Cloud Datastore vs. ObjectBox vs. PostGIS vs. Tibero vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

Extremely fast embedded database for small devices, IoT and Mobile

Spatial extension of PostgreSQL

A secure RDBMS, designed for easy portability from Oracle

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

cloud.google.com/datastore

objectbox.io

postgis.net

us.tmaxsoft.com/products/tibero

www.timescale.com

Technical documentation

cloud.google.com/datastore/docs

docs.objectbox.io

postgis.net/documentation

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

docs.timescale.com

Developer

Google

ObjectBox Limited

TmaxSoft

Timescale

Initial release

2008

2017

2005

2003

2017

Current release

3.4.2, February 2024

6, April 2015

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

GPL v2.0

commercial

Open Source

Apache 2.0

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++

C and Assembler

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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)

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

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

Supported programming languages

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Persistent Stored Procedure (PSM)

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

based on PostgreSQL

horizontal partitioning

by range, hash, list or composite

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

online/offline synchronization between client and server

yes

based on PostgreSQL

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

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

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

Concurrency

Support for concurrent manipulation of data

yes

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.

planned for next version

User concepts

Access control

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

yes

based on PostgreSQL

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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