DBMS > AgensGraph vs. Faircom EDGE vs. SAP HANA vs. Solr

System Properties Comparison AgensGraph vs. Faircom EDGE vs. SAP HANA vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

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

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

bitnine.net/agensgraph

www.faircom.com/products/faircom-edge

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

bitnine.net/documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

help.sap.com/hana

solr.apache.org/resources.html

Developer

Bitnine Global Inc.

FairCom Corporation

SAP

Apache Software Foundation

Initial release

2016

1979

2010

2006

Current release

2.1, December 2018

V3, October 2020

2.0 SPS07 (April 4, 2023), April 2023

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Java

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Appliance or cloud-service

All OS with a Java VM

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

Data scheme

depending on used data model

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

yes

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

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes

ANSI SQL queries

yes

Solr Parallel SQL Interface

APIs and other access methods

Cypher Query Language
JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

File partitioning

Customizable business rules for partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

yes

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