DBMS > Apache Phoenix vs. Coveo vs. EsgynDB vs. LokiJS vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Phoenix vs. Coveo vs. EsgynDB vs. LokiJS vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

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

In-memory JavaScript DBMS

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.43
Rank	#264	Overall
	#42	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

phoenix.apache.org

www.coveo.com

www.esgyn.cn

github.com/techfort/LokiJS

azure.microsoft.com/services/cosmos-db

Technical documentation

phoenix.apache.org

docs.coveo.com

techfort.github.io/LokiJS

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Coveo

Esgyn

Microsoft

Initial release

2014

2012

2015

2014

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Java

JavaScript

Server operating systems

Linux
Unix
Windows

hosted

Linux

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

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

via views

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC

RESTful HTTP API

ADO.NET
JDBC
ODBC

JavaScript API

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

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Python

All languages supporting JDBC/ODBC/ADO.Net

JavaScript

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

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

View functions in JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Multi-source replication between multi datacenters

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

atomic operations within a single collection possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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