DBMS > Apache Jena - TDB vs. Faircom DB vs. GeoMesa vs. Google Cloud Bigtable vs. TerminusDB

System Properties Comparison Apache Jena - TDB vs. Faircom DB vs. GeoMesa vs. Google Cloud Bigtable vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

RDF store

Key-value store
Relational DBMS

Spatial DBMS

Key-value store
Wide column store

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	3.75
Rank	#84	Overall
	#3	RDF stores

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

jena.apache.org/documentation/tdb/index.html

www.faircom.com/products/faircom-db

www.geomesa.org

cloud.google.com/bigtable

terminusdb.com

Technical documentation

jena.apache.org/documentation/tdb/index.html

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigtable/docs

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

originally developed by HP Labs

FairCom Corporation

CCRi and others

Google

DataChemist Ltd.

Initial release

2000

1979

2014

2015

2018

Current release

4.9.0, July 2023

V12, November 2020

4.0.5, February 2024

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

commercial

Restricted, free version available

Open Source

Apache License 2.0

commercial

Open Source

GPL V3

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

Java

ANSI C, C++

Scala

Prolog, Rust

Server operating systems

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux

Data scheme

yes

RDF Schemas

schema free, schema optional, schema required, partial schema,

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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, ANSI SQL with proprietary extensions

SQL-like query language (WOQL)

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C#
C++
Go
Java
JavaScript (Node.js)
Python

JavaScript
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

depending on storage layer

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

depending on storage layer

Internal replication in Colossus, and regional replication between two clusters in different zones

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

depending on storage layer

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

TDB Transactions

tunable from ACID to Eventually Consistent

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

in-memory journaling

In-memory capabilities

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

yes

depending on storage layer

User concepts

Access control

Access control via Jena Security

Fine grained access rights according to SQL-standard with additional protections for files

yes

depending on the DBMS used for storage

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

Role-based access control

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More resources

DB-Engines blog posts

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