DBMS > Amazon DocumentDB vs. Apache Pinot vs. EsgynDB vs. Oracle Coherence

System Properties Comparison Amazon DocumentDB vs. Apache Pinot vs. EsgynDB vs. Oracle Coherence

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Oracles in-memory data grid solution

Primary database model

Document store

Relational DBMS

Key-value store

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.35
Rank	#274	Overall
	#128	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	1.90
Rank	#127	Overall
	#21	Key-value stores

Website

aws.amazon.com/documentdb

pinot.apache.org

www.esgyn.cn

www.oracle.com/java/coherence

Technical documentation

aws.amazon.com/documentdb/resources

docs.pinot.apache.org

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation and contributors

Esgyn

Oracle

Initial release

2019

2015

2007

Current release

1.0.0, September 2023

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

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

Java

C++, Java

Java

Server operating systems

hosted

All OS with a Java JDK11 or higher

Linux

All OS with a Java VM

Data scheme

schema-free

yes

schema-free

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

SQL-like query language

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC

ADO.NET
JDBC
ODBC

JCache
JPA
RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Go
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication between multi datacenters

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

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