DBMS > Google Cloud Datastore vs. ObjectBox vs. SQLite vs. SQream DB vs. Stardog

System Properties Comparison Google Cloud Datastore vs. ObjectBox vs. SQLite vs. SQream DB vs. Stardog

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

Widely used embeddable, in-process RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Time Series DBMS

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

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Score	0.73
Rank	#228	Overall
	#105	Relational DBMS

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

cloud.google.com/datastore

Technical documentation

cloud.google.com/datastore/docs

docs.objectbox.io

www.sqlite.org/docs.html

docs.sqream.com

docs.stardog.com

Developer

Google

ObjectBox Limited

Dwayne Richard Hipp

SQream Technologies

Stardog-Union

Initial release

2008

2017

2000

2017

2010

Current release

3.45.3 (15 April 2024), April 2024

2022.1.6, December 2022

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

Public Domain

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++, CUDA, Haskell, Java, Scala

Java

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

server-less

Linux

Linux
macOS
Windows

Data scheme

schema-free

yes

dynamic column types

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes, details here

yes

not rigid because of 'dynamic typing' concept.

yes, ANSI Standard SQL Types

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-92 is not fully supported

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

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

Proprietary native API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

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

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

C++
Java
JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

user defined functions in Python

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

Callbacks using the Google Apps Engine

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

online/offline synchronization between client and server

none

Multi-source replication in HA-Cluster

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

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

relationships in graphs

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

via file-system locks

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.

yes

User concepts

Access control

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

yes

Access rights for users and roles

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.