DBMS > HugeGraph vs. JanusGraph vs. Spark SQL vs. SQLite vs. STSdb

System Properties Comparison HugeGraph vs. JanusGraph vs. Spark SQL vs. SQLite vs. STSdb

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

A Graph DBMS optimized for distributed clusters

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Graph DBMS

Relational DBMS

Key-value store

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

github.com/hugegraph
hugegraph.apache.org

janusgraph.org

spark.apache.org/sql

www.sqlite.org

github.com/STSSoft/STSdb4

Technical documentation

hugegraph.apache.org/docs

docs.janusgraph.org

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

Baidu

Linux Foundation; originally developed as Titan by Aurelius

Apache Software Foundation

Dwayne Richard Hipp

STS Soft SC

Initial release

2018

2017

2014

2000

2011

Current release

0.9

0.6.3, February 2023

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux
macOS
Unix

Linux
OS X
Unix
Windows

Linux
OS X
Windows

server-less

Windows

Data scheme

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.NET Client API

Supported programming languages

Groovy
Java
Python

Clojure
Java
Python

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C#
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

yes

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

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

via file-system locks

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

Users, roles and permissions

User authentification and security via Rexster Graph Server

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