DBMS > gStore vs. HugeGraph vs. LMDB vs. Microsoft SQL Server vs. Oracle

System Properties Comparison gStore vs. HugeGraph vs. LMDB vs. Microsoft SQL Server vs. Oracle

Editorial information provided by DB-Engines

Name

Description

A native Graph DBMS to store and maintain very large RDF datasets.

A fast-speed and highly-scalable Graph DBMS

A high performant, light-weight, embedded key-value database library

Microsofts flagship relational DBMS

Widely used RDBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

en.gstore.cn

github.com/hugegraph
hugegraph.apache.org

www.symas.com/symas-embedded-database-lmdb

www.microsoft.com/en-us/sql-server

www.oracle.com/database

Technical documentation

en.gstore.cn/#/enDocs

hugegraph.apache.org/docs

www.lmdb.tech/doc

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/en/database

Developer

Baidu

Symas

Microsoft

Oracle

Initial release

2016

2018

2011

1989

1980

Current release

1.2, November 2023

0.9

0.9.32, January 2024

SQL Server 2022, November 2022

23c, September 2023

License

Commercial or Open Source

Open Source

BSD

Open Source

Apache Version 2.0

Open Source

commercial

restricted free version is available

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

C++

Java

C++

C and C++

Server operating systems

Linux

Linux
macOS
Unix

Linux
Unix
Windows

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Schemaless in JSON and XML columns

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

yes

with proprietary extensions

APIs and other access methods

HTTP API
SPARQL 1.1

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C++
Java
JavaScript (Node.js)
PHP
Python

Groovy
Java
Python

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

can be realized in PL/SQL

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

yes

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Users, roles and permissions

fine grained access rights according to SQL-standard

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