DBMS > EJDB vs. Firebird vs. Google Cloud Datastore vs. RDF4J vs. TempoIQ

System Properties Comparison EJDB vs. Firebird vs. Google Cloud Datastore vs. RDF4J vs. TempoIQ

Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Firebird is an open source RDBMS forked from Borland's InterBase

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Primary database model

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

github.com/Softmotions/ejdb

www.firebirdsql.org

cloud.google.com/datastore

rdf4j.org

tempoiq.com (offline)

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.firebirdsql.org/en/reference-manuals

cloud.google.com/datastore/docs

rdf4j.org/documentation

Developer

Softmotions

Firebird Foundation

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

TempoIQ

Initial release

2012

2000

As fork of Borland's InterBase

2008

2004

2012

Current release

5.0.0, January 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Initial Developer's Public License

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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++

Java

Server operating systems

server-less

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

yes, details here

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GQL)

APIs and other access methods

in-process shared library

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

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

Java
PHP
Python

C#
Java
JavaScript

Node.js
Python
Ruby

Server-side scripts

Stored procedures

PSQL

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Realtime Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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 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

typically not needed, however similar functionality with collection joins possible

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Users with fine-grained authorization concept

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

simple authentication-based access control

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