DBMS > Apache Jena - TDB vs. Apache Phoenix vs. Google BigQuery vs. Spark SQL vs. VoltDB

System Properties Comparison Apache Jena - TDB vs. Apache Phoenix vs. Google BigQuery vs. Spark SQL vs. VoltDB

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 scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

Spark SQL is a component on top of 'Spark Core' for structured data processing

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

RDF store

Relational DBMS

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

Score	3.62
Rank	#83	Overall
	#3	RDF stores

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

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

phoenix.apache.org

cloud.google.com/bigquery

spark.apache.org/sql

www.voltdb.com

Technical documentation

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

phoenix.apache.org

cloud.google.com/bigquery/docs

spark.apache.org/docs/latest/sql-programming-guide.html

docs.voltdb.com

Developer

Apache Software Foundation

originally developed by HP Labs

Apache Software Foundation

Google

Apache Software Foundation

VoltDB Inc.

Initial release

2000

2014

2010

2014

2010

Current release

4.9.0, July 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.5.0 ( 2.13), September 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Java, C++

Server operating systems

All OS with a Java VM

Linux
Unix
Windows

hosted

Linux
OS X
Windows

Linux
OS X

for development

Data scheme

yes

RDF Schemas

yes

late-bound, schema-on-read capabilities

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

JDBC

RESTful HTTP/JSON API

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Java

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Java

Triggers

yes

via event handler

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

TDB Transactions

ACID

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users and roles with access to stored procedures

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