DBMS > EsgynDB vs. Microsoft Azure Cosmos DB vs. OceanBase vs. PlanetScale

System Properties Comparison EsgynDB vs. Microsoft Azure Cosmos DB vs. OceanBase vs. PlanetScale

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Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

A distributed, high available RDBMS compatible with Oracle and MySQL

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Wide column store

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	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Website

www.esgyn.cn

azure.microsoft.com/services/cosmos-db

en.oceanbase.com

planetscale.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

en.oceanbase.com/docs/oceanbase-database

planetscale.com/docs

Developer

Esgyn

Microsoft

OceanBase

previously Alibaba and Ant Group

PlanetScale

Initial release

2015

2014

2010

2020

Current release

4.3.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

C++

Server operating systems

Linux

hosted

Linux

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

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

JavaScript

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

yes

proprietary syntax

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Implicit feature of the cloud service

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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