DBMS > HugeGraph vs. Ingres vs. MySQL vs. Solr vs. TerarkDB

System Properties Comparison HugeGraph vs. Ingres vs. MySQL vs. Solr vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Well established RDBMS

Widely used open source RDBMS

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Search engine

Key-value store

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

github.com/hugegraph
hugegraph.apache.org

www.actian.com/databases/ingres

www.mysql.com

solr.apache.org

github.com/bytedance/terarkdb

Technical documentation

hugegraph.apache.org/docs

docs.actian.com/ingres

dev.mysql.com/doc

solr.apache.org/resources.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Baidu

Actian Corporation

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

ByteDance, originally Terark

Initial release

2018

1974

originally developed at University Berkely in early 1970s

1995

2006

2016

Current release

0.9

11.2, May 2022

8.4.0, April 2024

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Java

C++

Server operating systems

Linux
macOS
Unix

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

FreeBSD
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

schema-free

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

also supports composite index and range index

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

C++ API
Java API

Supported programming languages

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

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

C++
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

proprietary syntax

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

yes

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

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Ingres Replicator

Multi-source replication
Source-replica replication

yes

none

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

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Users, roles and permissions

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

yes

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