DBMS > FeatureBase vs. HugeGraph vs. Microsoft SQL Server vs. Oracle vs. RocksDB

System Properties Comparison FeatureBase vs. HugeGraph vs. Microsoft SQL Server vs. Oracle vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

A fast-speed and highly-scalable Graph DBMS

Microsofts flagship relational DBMS

Widely used RDBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.featurebase.com

github.com/hugegraph
hugegraph.apache.org

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

www.oracle.com/database

rocksdb.org

Technical documentation

docs.featurebase.com

hugegraph.apache.org/docs

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

docs.oracle.com/en/database

github.com/facebook/rocksdb/wiki

Developer

Molecula and Pilosa Open Source Contributors

Baidu

Microsoft

Oracle

Facebook, Inc.

Initial release

2017

2018

1989

1980

2013

Current release

2022, May 2022

0.9

SQL Server 2022, November 2022

23c, September 2023

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

restricted free version is available

Open Source

BSD

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

C++

Server operating systems

Linux
macOS

Linux
macOS
Unix

Linux
Windows

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

Linux

Data scheme

yes

Schemaless in JSON and XML columns

schema-free

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 queries

yes

with proprietary extensions

APIs and other access methods

gRPC
JDBC
Kafka Connector
ODBC

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)

C++ API
Java API

Supported programming languages

Java
Python

Groovy
Java
Python

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

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

Sharding

yes

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

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

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

yes

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using Linux fsync

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'

yes

User concepts

Access control

Users, roles and permissions

fine grained access rights according to SQL-standard

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