DBMS > JanusGraph vs. mSQL vs. SAP HANA vs. Solr vs. SWC-DB

System Properties Comparison JanusGraph vs. mSQL vs. SAP HANA vs. Solr vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

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

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

Time Series DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

janusgraph.org

hughestech.com.au/products/msql

www.sap.com/products/hana.html

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.janusgraph.org

help.sap.com/hana

solr.apache.org/resources.html

Developer

Linux Foundation; originally developed as Titan by Aurelius

Hughes Technologies

SAP

Apache Software Foundation

Alex Kashirin

Initial release

2017

1994

2010

2006

2020

Current release

0.6.3, February 2023

4.4, October 2021

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free licenses can be provided

commercial

Open Source

Apache Version 2

Open Source

GPL V3

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.

Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Appliance or cloud-service

All OS with a Java VM

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

Linux

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

Clojure
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

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

C++

Server-side scripts

Stored procedures

yes

SQLScript, R

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

yes

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