DBMS > Amazon DocumentDB vs. Faircom EDGE vs. IRONdb vs. Vertica

System Properties Comparison Amazon DocumentDB vs. Faircom EDGE vs. IRONdb vs. Vertica

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

aws.amazon.com/documentdb

www.faircom.com/products/faircom-edge

www.circonus.com/solutions/time-series-database/

www.vertica.com

Technical documentation

aws.amazon.com/documentdb/resources

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

docs.circonus.com/irondb/category/getting-started

vertica.com/documentation

Developer

FairCom Corporation

Circonus LLC.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2019

1979

2017

2005

Current release

V3, October 2020

V0.10.20, January 2018

12.0.3, January 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

ANSI C, C++

C and C++

C++

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Data scheme

schema-free

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

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

text, numeric, histograms

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language (Circonus Analytics Query Language: CAQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

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

HTTP API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in Lua

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

Automatic, metric affinity per node

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

Synchronous and asynchronous realtime replication based on transaction logs

configurable replication factor, datacenter aware

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

when using SQL

yes

Transaction concepts

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

Atomic single-document operations

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

Access rights for users and roles

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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