DBMS > HugeGraph vs. Lovefield vs. MarkLogic

System Properties Comparison HugeGraph vs. Lovefield vs. MarkLogic

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Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Embeddable relational database for web apps written in pure JavaScript

Operational and transactional Enterprise NoSQL database

Primary database model

Graph DBMS

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Website

github.com/hugegraph
hugegraph.apache.org

google.github.io/lovefield

www.marklogic.com

Technical documentation

hugegraph.apache.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.marklogic.com

Developer

Baidu

Google

MarkLogic Corp.

Initial release

2018

2014

2001

Current release

0.9

2.1.12, February 2017

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

restricted free version is 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

JavaScript

C++

Server operating systems

Linux
macOS
Unix

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Data scheme

yes

schema-free

Schema can be enforced

Typing

predefined data types such as float or date

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes

SQL92

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

Supported programming languages

Groovy
Java
Python

JavaScript

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

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

via XQuery or JavaScript

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes

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

Foreign keys

Referential integrity

yes

edges in graph

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes, with Range Indexes

User concepts

Access control

Users, roles and permissions

Role-based access control at the document and subdocument levels

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