DBMS > Graph Engine vs. Hawkular Metrics vs. Qdrant vs. RDF4J

System Properties Comparison Graph Engine vs. Hawkular Metrics vs. Qdrant vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A high-performance vector database with neural network or semantic-based matching

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

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Vector DBMS

RDF store

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.graphengine.io

www.hawkular.org

github.com/qdrant/qdrant
qdrant.tech

rdf4j.org

Technical documentation

www.graphengine.io/docs/manual

www.hawkular.org/hawkular-metrics/docs/user-guide

qdrant.tech/documentation

rdf4j.org/documentation

Developer

Microsoft

Community supported by Red Hat

Qdrant

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

Initial release

2010

2014

2021

2004

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

.NET and C

Java

Rust

Java

Server operating systems

.NET

Linux
OS X
Windows

Docker
Linux
macOS
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

HTTP REST

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
C++
F#
Visual Basic

Go
Java
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Collection-level replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency, tunable consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Key-based authentication

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More resources

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