DBMS > Citus vs. HugeGraph vs. MySQL vs. Splunk vs. Trafodion

System Properties Comparison Citus vs. HugeGraph vs. MySQL vs. Splunk vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

A fast-speed and highly-scalable Graph DBMS

Widely used open source RDBMS

Analytics Platform for Big Data

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

www.citusdata.com

github.com/hugegraph
hugegraph.apache.org

www.mysql.com

www.splunk.com

trafodion.apache.org

Technical documentation

docs.citusdata.com

hugegraph.apache.org/docs

dev.mysql.com/doc

docs.splunk.com/Documentation/Splunk

trafodion.apache.org/documentation.html

Developer

Baidu

Oracle

since 2010, originally MySQL AB, then Sun

Splunk Inc.

Apache Software Foundation, originally developed by HP

Initial release

2010

2018

1995

2003

2014

Current release

8.1, December 2018

0.9

8.4.0, April 2024

2.3.0, February 2019

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Limited free edition and free developer edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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Implementation language

Java

C and C++

C++, Java

Server operating systems

Linux

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

with proprietary extensions

Splunk Search Processing Language for search commands

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Groovy
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
Java
JavaScript
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

asynchronous Gremlin script jobs

yes

proprietary syntax

yes

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

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

Multi-source replication
Source-replica replication

Multi-source replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users, roles and permissions

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles

fine grained access rights according to SQL-standard

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