DBMS > Apache Jena - TDB vs. Riak TS vs. Vertica vs. Yaacomo

System Properties Comparison Apache Jena - TDB vs. Riak TS vs. Vertica vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	3.62
Rank	#83	Overall
	#3	RDF stores

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

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

www.vertica.com

yaacomo.com

Technical documentation

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

www.tiot.jp/riak-docs/riak/ts/latest

vertica.com/documentation

Developer

Apache Software Foundation

originally developed by HP Labs

Open Source, formerly Basho Technologies

OpenText

previously Micro Focus and Hewlett Packard

Q2WEB GmbH

Initial release

2000

2015

2005

2009

Current release

4.9.0, July 2023

3.0.0, September 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

Open Source

commercial

Limited community edition free

commercial

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

C++

Server operating systems

All OS with a Java VM

Linux
OS X

Linux

Android
Linux
Windows

Data scheme

yes

RDF Schemas

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes

Typing

predefined data types such as float or date

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

restricted

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

yes, limited

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

HTTP API
Native Erlang Interface

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

JDBC
ODBC

Supported programming languages

Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

yes

Erlang

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via event handler

yes

pre-commit hooks and post-commit hooks

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, hierarchical partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

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

ACID

TDB 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.

yes

User concepts

Access control

Access control via Jena Security

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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