DBMS > BoltDB vs. EJDB vs. RDF4J vs. Spark SQL

System Properties Comparison BoltDB vs. EJDB vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Primary database model

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/boltdb/bolt

github.com/Softmotions/ejdb

rdf4j.org

spark.apache.org/sql

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

rdf4j.org/documentation

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

Developer

Softmotions

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Apache Software Foundation

Initial release

2013

2012

2004

2014

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

GPLv2

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

server-less

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL-like DML and DDL statements

APIs and other access methods

in-process shared library

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

yes

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

User concepts

Access control

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