DBMS > Apache Pinot vs. EsgynDB vs. Oracle Coherence vs. Quasardb

System Properties Comparison Apache Pinot vs. EsgynDB vs. Oracle Coherence vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Key-value store

Time Series DBMS

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

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

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Website

pinot.apache.org

www.esgyn.cn

www.oracle.com/java/coherence

quasar.ai

Technical documentation

docs.pinot.apache.org

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

doc.quasar.ai/master

Developer

Apache Software Foundation and contributors

Esgyn

Oracle

quasardb

Initial release

2015

2007

2009

Current release

1.0.0, September 2023

14.1, August 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

Java

C++, Java

Java

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux

All OS with a Java VM

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

JCache
JPA
RESTful HTTP API

HTTP API

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

.Net
C++
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes, with selectable consistency level

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

Cryptographically strong user authentication and audit trail

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