DBMS > Apache Jena - TDB vs. IRONdb vs. MongoDB

System Properties Comparison Apache Jena - TDB vs. IRONdb vs. MongoDB

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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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

RDF store

Time Series DBMS

Document store

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0

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

Score	5.61
Rank	#78	Overall
	#2	RDF stores

Score	0.30
Rank	#341	Overall
	#31	Time Series DBMS

Score	425.36
Rank	#5	Overall
	#1	Document stores

Website

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

www.circonus.com/solutions/time-series-database

www.mongodb.com

Technical documentation

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

docs.circonus.com/irondb

docs.mongodb.com/manual

Developer

Apache Software Foundation

originally developed by HP Labs

Circonus LLC.

MongoDB, Inc

Initial release

2000

2017

2009

Current release

4.6.1, September 2022

V0.10.20, January 2018

6.0.5, March 2023

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

commercial

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

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Implementation language

Java

C and C++

C++

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

RDF Schemas

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

SQL-like query language (Circonus Analytics Query Language: CAQL)

Read-only SQL queries via the MongoDB Connector for BI

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

HTTP API

proprietary protocol using JSON

Supported programming languages

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Server-side scripts

Stored procedures

yes

yes, in Lua

JavaScript

Triggers

yes

via event handler

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

ACID

TDB Transactions

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Access control via Jena Security

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

Internet of Things and Time Series (Bosch, GE Healthcare, Jaguar Land Rover) Mobile...
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Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

365+ million downloads, over 150,000+ Atlas clusters provisioned every month handling...
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Licensing and pricing models

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
» more

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