DBMS > Citus vs. HugeGraph vs. MySQL vs. SAP SQL Anywhere vs. Solr

System Properties Comparison Citus vs. HugeGraph vs. MySQL vs. SAP SQL Anywhere vs. Solr

Editorial information provided by DB-Engines

Name

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Search engine

Secondary database models

Document store

Document store
Spatial DBMS

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	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.citusdata.com

github.com/hugegraph
hugegraph.apache.org

www.mysql.com

www.sap.com/products/technology-platform/sql-anywhere.html

solr.apache.org

Technical documentation

docs.citusdata.com

hugegraph.apache.org/docs

dev.mysql.com/doc

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

Developer

Baidu

Oracle

since 2010, originally MySQL AB, then Sun

SAP

formerly Sybase

Apache Software Foundation

Initial release

2010

2018

1995

1992

2006

Current release

8.1, December 2018

0.9

8.4.0, April 2024

17, July 2015

9.6.0, April 2024

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

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Java

Server operating systems

Linux

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

standard, with numerous extensions

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

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

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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, in C/C++, Java, .Net or Perl

Java plugins

Triggers

yes

User configurable commands triggered on index changes

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

none

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

Source-replica replication

Database mirroring

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual 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

ACID

optimistic locking

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

fine grained access rights according to SQL-standard

yes

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