DBMS > BoltDB vs. Drizzle vs. Heroic vs. LeanXcale vs. RDF4J

System Properties Comparison BoltDB vs. Drizzle vs. Heroic vs. LeanXcale vs. RDF4J

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An embedded key-value store for Go.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

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

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

RDF store

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

github.com/boltdb/bolt

github.com/spotify/heroic

www.leanxcale.com

rdf4j.org

Technical documentation

spotify.github.io/heroic

rdf4j.org/documentation

Developer

Drizzle project, originally started by Brian Aker

Spotify

LeanXcale

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

Initial release

2013

2008

2014

2015

2004

Current release

7.2.4, September 2012

License

Commercial or Open Source

Open Source

MIT License

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

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

via Elasticsearch

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

through Apache Derby

APIs and other access methods

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C++
Java
PHP

C
Java
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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