DBMS > LokiJS vs. Microsoft Azure Cosmos DB vs. MySQL vs. Quasardb vs. Spark SQL

System Properties Comparison LokiJS vs. Microsoft Azure Cosmos DB vs. MySQL vs. Quasardb vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

In-memory JavaScript DBMS

Globally distributed, horizontally scalable, multi-model database service

Widely used open source RDBMS

Distributed, high-performance timeseries database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/techfort/LokiJS

azure.microsoft.com/services/cosmos-db

www.mysql.com

quasar.ai

spark.apache.org/sql

Technical documentation

techfort.github.io/LokiJS

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

doc.quasar.ai/master

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

Apache Software Foundation

Initial release

2014

1995

2009

2014

Current release

8.4.0, April 2024

3.14.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

JavaScript

C and C++

C++

Scala

Server operating systems

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

hosted

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

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

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

JDBC
ODBC

Supported programming languages

JavaScript

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

View functions in JavaScript

JavaScript

yes

proprietary syntax

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

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

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 LevelDB

yes

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

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

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