DBMS > Brytlyt vs. Faircom EDGE vs. LeanXcale vs. Oracle Berkeley DB

System Properties Comparison Brytlyt vs. Faircom EDGE vs. LeanXcale vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Widely used in-process key-value store

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

brytlyt.io

www.faircom.com/products/faircom-edge

www.leanxcale.com

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.brytlyt.io

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

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Brytlyt

FairCom Corporation

LeanXcale

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2016

1979

2015

1994

Current release

5.0, August 2023

V3, October 2020

18.1.40, May 2020

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

commercial license available

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

C, C++ and CUDA

ANSI C, C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

specific XML-type available, but no XML query functionality.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

through Apache Derby

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

C
Java
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

.Net, JavaScript, C/C++

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

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

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

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

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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More resources

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