DBMS > HugeGraph vs. MarkLogic vs. NSDb vs. SQLite vs. Stardog

System Properties Comparison HugeGraph vs. MarkLogic vs. NSDb vs. SQLite vs. Stardog

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Operational and transactional Enterprise NoSQL database

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

Widely used embeddable, in-process RDBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Graph DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Time Series DBMS

Relational DBMS

Graph DBMS
RDF store

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

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

github.com/hugegraph
hugegraph.apache.org

Technical documentation

hugegraph.apache.org/docs

docs.marklogic.com

nsdb.io/Architecture

www.sqlite.org/docs.html

docs.stardog.com

Developer

Baidu

MarkLogic Corp.

Dwayne Richard Hipp

Stardog-Union

Initial release

2018

2001

2017

2000

2010

Current release

0.9

11.0, December 2022

3.46.0 (23 May 2024), May 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

Public Domain

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Java

C++

Java, Scala

Java

Server operating systems

Linux
macOS
Unix

Linux
OS X
Windows

Linux
macOS

server-less

Linux
macOS
Windows

Data scheme

yes

schema-free

Schema can be enforced

yes

dynamic column types

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

not rigid because of 'dynamic typing' concept.

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.

yes

Import/export of XML data possible

Secondary indexes

yes

also supports composite index and range index

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL92

SQL-like query language

yes

SQL-92 is not fully supported

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

gRPC
HTTP REST
WebSocket

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Groovy
Java
Python

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

via XQuery or JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

edges in graph

yes

relationships in graphs

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

yes, with Range Indexes

yes

User concepts

Access control

Users, roles and permissions

Role-based access control at the document and subdocument levels

Access rights for users and roles

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