DBMS > LokiJS vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Solr vs. Valentina Server

System Properties Comparison LokiJS vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Solr vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

In-memory JavaScript DBMS

Globally distributed, horizontally scalable, multi-model database service

JavaScript DBMS with an API inspired by CouchDB

A widely used distributed, scalable search engine based on Apache Lucene

Object-relational database and reports server

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

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

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

github.com/techfort/LokiJS

azure.microsoft.com/services/cosmos-db

pouchdb.com

solr.apache.org

www.valentina-db.net

Technical documentation

techfort.github.io/LokiJS

learn.microsoft.com/azure/cosmos-db

pouchdb.com/guides

solr.apache.org/resources.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Microsoft

Apache Software Foundation

Paradigma Software

Initial release

2014

2012

2006

1999

Current release

7.1.1, June 2019

9.6.0, April 2024

5.7.5

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache Version 2

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

JavaScript

Java

Server operating systems

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

hosted

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

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

supports customizable data types and automatic typing

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

via views

yes

All properties auto-indexed by default

yes

via views

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

yes

APIs and other access methods

JavaScript API

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

HTTP REST

only for PouchDB Server
JavaScript API

Java API
RESTful HTTP/JSON API

ODBC

Supported programming languages

JavaScript

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

JavaScript

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

View functions in JavaScript

JavaScript

View functions in JavaScript

Java plugins

yes

Triggers

yes

JavaScript

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single collection possible

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

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

by using IndexedDB, WebSQL or LevelDB as backend

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 can be defined down to the item level

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.