DBMS > Amazon DynamoDB vs. Faircom EDGE vs. Riak KV vs. Solr

System Properties Comparison Amazon DynamoDB vs. Faircom EDGE vs. Riak KV vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Distributed, fault tolerant key-value store

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Key-value store

Key-value store
Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Search engine

Secondary database models

Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

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

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/dynamodb

www.faircom.com/products/faircom-edge

solr.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

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

www.tiot.jp/riak-docs/riak/kv/latest

solr.apache.org/resources.html

Developer

Amazon

FairCom Corporation

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2012

1979

2009

2006

Current release

V3, October 2020

3.2.0, December 2022

9.6.1, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Restricted, free version available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

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++

Erlang

Java

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

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

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

supports customizable data types and automatic typing

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

ANSI SQL queries

Solr Parallel SQL Interface

APIs and other access methods

RESTful HTTP API

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

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Erlang

Java plugins

Triggers

yes

by integration with AWS Lambda

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency
Tunable Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

links between data sets can be stored

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

optimistic locking

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 can be defined via the AWS Identity and Access Management (IAM)

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

yes, using Riak Security

yes

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