DBMS > Amazon Redshift vs. BoltDB vs. Faircom EDGE vs. Postgres-XL vs. Vitess

System Properties Comparison Amazon Redshift vs. BoltDB vs. Faircom EDGE vs. Postgres-XL vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

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

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

aws.amazon.com/redshift

github.com/boltdb/bolt

www.faircom.com/products/faircom-edge

www.postgres-xl.org

vitess.io

Technical documentation

docs.aws.amazon.com/redshift

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

www.postgres-xl.org/documentation

vitess.io/docs

Developer

Amazon (based on PostgreSQL)

FairCom Corporation

The Linux Foundation, PlanetScale

Initial release

2012

2013

1979

2014

since 2012, originally named StormDB

2013

Current release

V3, October 2020

10 R1, October 2018

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Restricted, free version available

Open Source

Mozilla public license

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS

Docker
Linux
macOS

Data scheme

yes

schema-free

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

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

XML type, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

ANSI SQL queries

yes

distributed, parallel query execution

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

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

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

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

user defined functions

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

File partitioning

Customizable business rules for partitioning

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
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

none

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

when using SQL

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

ACID

MVCC

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

table locks or row locks depending on storage engine

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.

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.