DBMS > Apache Jena - TDB vs. Percona Server for MongoDB vs. Snowflake vs. Spark SQL

System Properties Comparison Apache Jena - TDB vs. Percona Server for MongoDB vs. Snowflake vs. Spark SQL

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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 drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

RDF store

Document 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	0.60
Rank	#246	Overall
	#39	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

www.percona.com/mongodb/software/percona-server-for-mongodb

www.snowflake.com

spark.apache.org/sql

Technical documentation

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

docs.percona.com/percona-distribution-for-mongodb

docs.snowflake.net/manuals/index.html

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

Developer

Apache Software Foundation

originally developed by HP Labs

Percona

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2000

2015

2014

Current release

4.9.0, July 2023

3.4.10-2.10, November 2017

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

Open Source

GPL Version 2

commercial

Open Source

Apache 2.0

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

C++

Scala

Server operating systems

All OS with a Java VM

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

proprietary protocol using JSON

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions

Triggers

yes

via event handler

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

none

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

Immediate Consistency

Foreign keys

Referential integrity

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

via In-Memory Engine

User concepts

Access control

Access control via Jena Security

Access rights for users and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

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