DBMS > RDF4J vs. Snowflake vs. Solr vs. SpatiaLite vs. Sqrrl

System Properties Comparison RDF4J vs. Snowflake vs. Solr vs. SpatiaLite vs. Sqrrl

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

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

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

Spatial extension of SQLite

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Website

rdf4j.org

www.snowflake.com

solr.apache.org

www.gaia-gis.it/fossil/libspatialite/index

sqrrl.com

Technical documentation

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

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

Snowflake Computing Inc.

Apache Software Foundation

Alessandro Furieri

Amazon

originally Sqrrl Data, Inc.

Initial release

2004

2014

2006

2008

2012

Current release

9.6.1, May 2024

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache Version 2

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

commercial

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++

Java

Server operating systems

Linux
OS X
Unix
Windows

hosted

All OS with a Java VM

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

server-less

Linux

Data scheme

yes

RDF Schemas

yes

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

yes

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

yes

schema-free

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

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

Java
PHP
Python

JavaScript (Node.js)
Python

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

user defined functions

Java plugins

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

none

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

selectable replication factor

making use of Hadoop

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

optimistic locking

ACID

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

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.

yes

User concepts

Access control

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

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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