DBMS > Apache Druid vs. Badger vs. Faircom EDGE vs. Oracle NoSQL vs. searchxml

System Properties Comparison Apache Druid vs. Badger vs. Faircom EDGE vs. Oracle NoSQL vs. searchxml

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

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS
Time Series DBMS

Key-value store

Key-value store
Relational DBMS

Document store
Key-value store
Relational DBMS

Native XML DBMS
Search engine

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	0.14
Rank	#331	Overall
	#49	Key-value stores

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

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

druid.apache.org

github.com/dgraph-io/badger

www.faircom.com/products/faircom-edge

www.oracle.com/database/nosql/technologies/nosql

www.searchxml.net/category/products

Technical documentation

druid.apache.org/docs/latest/design

godoc.org/github.com/dgraph-io/badger

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

www.searchxml.net/support/handouts

Developer

Apache Software Foundation and contributors

DGraph Labs

FairCom Corporation

Oracle

informationpartners gmbh

Initial release

2012

2017

1979

2011

2015

Current release

29.0.1, April 2024

V3, October 2020

23.3, December 2023

1.0

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

commercial

Restricted, free version available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

ANSI C, C++

Java

C++

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Solaris
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux
Solaris SPARC/x86

Windows

Data scheme

yes

schema-less columns are supported

schema-free

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

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

optional

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

SQL

Support of SQL

SQL for querying

yes

ANSI SQL queries

SQL-like DML and DDL statements

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

RESTful HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C
C#
Go
Java
JavaScript (Node.js)
Python

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

File partitioning

Customizable business rules for partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

off heap cache

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.

Access rights for users and roles

Domain, group and role-based access control at the document level and for application services

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