DBMS > EsgynDB vs. gStore vs. Lovefield vs. Manticore Search vs. MySQL

System Properties Comparison EsgynDB vs. gStore vs. Lovefield vs. Manticore Search vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Embeddable relational database for web apps written in pure JavaScript

Multi-storage database for search, including full-text search.

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

www.esgyn.cn

en.gstore.cn

google.github.io/lovefield

manticoresearch.com

www.mysql.com

Technical documentation

en.gstore.cn/#/enDocs

github.com/google/lovefield/blob/master/docs/spec_index.md

manual.manticoresearch.com

dev.mysql.com/doc

Developer

Esgyn

Google

Manticore Software

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2016

2014

2017

1995

Current release

1.2, November 2023

2.1.12, February 2017

6.0, February 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 2.0

Open Source

GPL version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++, Java

C++

JavaScript

C++

C and C++

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP API
SPARQL 1.1

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

JavaScript

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions

yes

proprietary syntax

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

none

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.