DBMS > BigchainDB vs. BoltDB vs. Heroic vs. Solr vs. Virtuoso

System Properties Comparison BigchainDB vs. BoltDB vs. Heroic vs. Solr vs. Virtuoso

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

A widely used distributed, scalable search engine based on Apache Lucene

Virtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs

Primary database model

Document store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine

Secondary database models

Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	4.26
Rank	#78	Overall
	#13	Document stores
	#4	Graph DBMS
	#2	Native XML DBMS
	#42	Relational DBMS
	#2	RDF stores
	#9	Search engines

Website

www.bigchaindb.com

github.com/boltdb/bolt

github.com/spotify/heroic

solr.apache.org

virtuoso.openlinksw.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

spotify.github.io/heroic

solr.apache.org/resources.html

docs.openlinksw.com/virtuoso

Developer

Spotify

Apache Software Foundation

OpenLink Software

Initial release

2016

2013

2014

2006

1998

Current release

9.6.0, April 2024

7.2.11, September 2023

License

Commercial or Open Source

Open Source

AGPL v3

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

GPLv2, extended commercial license available

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

Python

Java

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

AIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

SQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

via Elasticsearch

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

yes

SQL-92, SQL-200x, SQL-3, SQLX

APIs and other access methods

CLI Client
RESTful HTTP API

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

Java API
RESTful HTTP/JSON API

ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

Java plugins

yes

Virtuoso PL

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Chain, star, and bi-directional replication
Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

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

yes

Fine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

More information provided by the system vendor

Specific characteristics

Virtuoso is a modern multi-model RDBMS for managing data represented as tabular relations...
» more

Competitive advantages

Performance & Scale — as exemplified by DBpedia and the LOD Cloud it spawned, i.e.,...
» more

Typical application scenarios

Used for — Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
» more

Key customers

Broad use across enterprises and governments including — European Union (EU) US Government...
» more

Market metrics

Largest installed-base of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
» more

Licensing and pricing models

Available in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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