DBMS > Apache Derby vs. Faircom EDGE vs. XTDB

System Properties Comparison Apache Derby vs. Faircom EDGE vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

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

Score	3.04
Rank	#85	Overall
	#46	Relational DBMS

Score	0.03
Rank	#364	Overall
	#56	Key-value stores
	#152	Relational DBMS

Score	0.20
Rank	#314	Overall
	#46	Document stores

Website

db.apache.org/derby

www.faircom.com/products/faircom-edge

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

db.apache.org/derby/manuals/index.html

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

www.xtdb.com/docs

Developer

Apache Software Foundation

FairCom Corporation

Juxt Ltd.

Initial release

1997

1979

2019

Current release

10.17.1.0, November 2023

V3, October 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Restricted, free version available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

ANSI C, C++

Clojure

Server operating systems

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes, extensible-data-notation format

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

yes

ANSI SQL queries

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC

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

HTTP REST
JDBC

Supported programming languages

Java

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

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

.Net, JavaScript, C/C++

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

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