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DBMS > Apache Druid vs. Faircom EDGE vs. Manticore Search vs. SQL.JS

System Properties Comparison Apache Druid vs. Faircom EDGE vs. Manticore Search vs. SQL.JS

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Multi-storage database for search, including full-text search.

Port of SQLite to JavaScript

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Website

druid.apache.org

www.faircom.com/products/faircom-edge

manticoresearch.com

Technical documentation

druid.apache.org/docs/latest/design

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

manual.manticoresearch.com

sql.js.org/documentation/index.html

Developer

Apache Software Foundation and contributors

FairCom Corporation

Manticore Software

Alon Zakai

enhancements implemented by others

Initial release

2012

1979

2017

2012

Current release

29.0.1, April 2024

V3, October 2020

6.0, February 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Restricted, free version available

Open Source

GPL version 2

Open Source

Cloud-based only

Only available as a cloud service

no

no

no

no

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

ANSI C, C++

C++

JavaScript

Server operating systems

Linux
OS X
Unix

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
macOS
Windows

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

Data scheme

yes

schema-less columns are supported

Flexible Schema (defined schema, partial schema, schema free)

Fixed schema

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

no

yes

Can index from XML

no

Secondary indexes

yes

yes

yes

full-text index on all search fields

SQL

Support of SQL

SQL for querying

yes

ANSI SQL queries

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

JavaScript API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

JavaScript

Server-side scripts

Stored procedures

no

yes

.Net, JavaScript, C/C++

user defined functions

no

Triggers

no

yes

no

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

File partitioning

Customizable business rules for partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Synchronous and asynchronous realtime replication based on transaction logs

Synchronous replication based on Galera library

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

no

no

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

none

Foreign keys

Referential integrity

no

yes

when using SQL

no

yes

Transaction concepts

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

no

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

yes

yes

The original contents of fields are not stored in the Manticore index.

no

except by serializing a db to a file

In-memory capabilities

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

no

yes

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

no

no

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