DBMS > Hawkular Metrics vs. HugeGraph vs. Neo4j vs. NSDb vs. SAP HANA

System Properties Comparison Hawkular Metrics vs. HugeGraph vs. Neo4j vs. NSDb vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	44.89
Rank	#21	Overall
	#1	Graph DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.hawkular.org

github.com/hugegraph
hugegraph.apache.org

neo4j.com

nsdb.io

www.sap.com/products/hana.html

Technical documentation

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

hugegraph.apache.org/docs

neo4j.com/docs

nsdb.io/Architecture

help.sap.com/hana

Developer

Community supported by Red Hat

Baidu

Neo4j, Inc.

SAP

Initial release

2014

2018

2007

2017

2010

Current release

0.9

5.20, May 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

GPL version3, commercial licenses available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.

Implementation language

Java

Java, Scala

Server operating systems

Linux
OS X
Windows

Linux
macOS
Unix

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

Linux
macOS

Appliance or cloud-service

Data scheme

schema-free

yes

schema-free and schema-optional

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

also supports composite index and range index

yes

pluggable indexing subsystem, by default Apache Lucene

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP REST

Java API
RESTful HTTP API
TinkerPop Gremlin

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

gRPC
HTTP REST
WebSocket

JDBC
ODBC

Supported programming languages

Go
Java
Python
Ruby

Groovy
Java
Python

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

Java
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

User defined Procedures and Functions

SQLScript, R

Triggers

yes

via Hawkular Alerting

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

depending on used storage backend, e.g. Cassandra and HBase

yes using Neo4j Fabric

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

depending on used storage backend, e.g. Cassandra and HBase

Causal Clustering using Raft protocol

available in in Enterprise Version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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, roles and permissions

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

yes

More information provided by the system vendor

Specific characteristics

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Key customers

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Market metrics

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Licensing and pricing models

GPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
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