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

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

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Document store

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	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

impala.apache.org

www.coveo.com

www.esgyn.cn

github.com/techfort/LokiJS

azure.microsoft.com/services/cosmos-db

Technical documentation

impala.apache.org/impala-docs.html

docs.coveo.com

techfort.github.io/LokiJS

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Coveo

Esgyn

Microsoft

Initial release

2013

2012

2015

2014

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

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

C++

C++, Java

JavaScript

Server operating systems

Linux

hosted

Linux

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

hosted

Data scheme

yes

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

SQL-like DML and DDL statements

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

ADO.NET
JDBC
ODBC

JavaScript API

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

Supported programming languages

All languages supporting JDBC/ODBC

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

yes

user defined functions and integration of map-reduce

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

selectable replication factor

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

yes

query execution via MapReduce

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

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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