DBMS > CrateDB vs. InfinityDB vs. RDF4J vs. TinkerGraph

System Properties Comparison CrateDB vs. InfinityDB vs. RDF4J vs. TinkerGraph

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

A Java embedded Key-Value Store which extends the Java Map interface

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

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Key-value store

RDF store

Graph DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.00
Rank	#383	Overall
	#59	Key-value stores

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.08
Rank	#345	Overall
	#34	Graph DBMS

Website

cratedb.com

boilerbay.com

rdf4j.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

cratedb.com/docs

boilerbay.com/infinitydb/manual

rdf4j.org/documentation

Developer

Crate

Boiler Bay Inc.

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

Initial release

2013

2002

2004

2009

Current release

5.8.1, August 2024

4.0

License

Commercial or Open Source

Open Source

commercial

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

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

Implementation language

Java

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

All OS with a Java VM

Linux
OS X
Unix
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

TinkerPop 3

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Java

Java
PHP
Python

Groovy
Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

READ-COMMITTED or SERIALIZED

none

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Relationships in graphs

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

optional

In-memory capabilities

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

yes

User concepts

Access control

rights management via user accounts

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More resources

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