DBMS > Cubrid vs. EsgynDB vs. GreptimeDB vs. Lovefield

System Properties Comparison Cubrid vs. EsgynDB vs. GreptimeDB vs. Lovefield

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Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Embeddable relational database for web apps written in pure JavaScript

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

www.esgyn.cn

greptime.com

google.github.io/lovefield

Technical documentation

cubrid.org/manuals

docs.greptime.com

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

Developer

CUBRID Corporation, CUBRID Foundation

Esgyn

Greptime Inc.

Google

Initial release

2008

2015

2022

2014

Current release

11.0, January 2021

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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.

Implementation language

C, C++, Java

C++, Java

Rust

JavaScript

Server operating systems

Linux
Windows

Linux

Android
Docker
FreeBSD
Linux
macOS
Windows

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

tested with Chrome, Firefox, IE, Safari

Data scheme

yes

schema-free, schema definition possible

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

gRPC
HTTP API
JDBC

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C++
Erlang
Go
Java
JavaScript

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

Python

Triggers

yes

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

none

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

Simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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