DBMS > Amazon DynamoDB vs. KeyDB vs. Riak KV vs. SiteWhere vs. Sphinx

System Properties Comparison Amazon DynamoDB vs. KeyDB vs. Riak KV vs. SiteWhere vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Distributed, fault tolerant key-value store

M2M integration platform for persisting/querying time series data

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store
Key-value store

Key-value store

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

Time Series DBMS

Search engine

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/dynamodb

github.com/Snapchat/KeyDB
keydb.dev

github.com/sitewhere/sitewhere

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.keydb.dev

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

sitewhere1.sitewhere.io/index.html

sphinxsearch.com/docs

Developer

Amazon

EQ Alpha Technology Ltd.

OpenSource, formerly Basho Technologies

SiteWhere

Sphinx Technologies Inc.

Initial release

2012

2019

2009

2010

2001

Current release

3.2.0, December 2022

3.5.1, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSD-3

Open Source

Apache version 2, commercial enterprise edition

Open Source

Common Public Attribution License Version 1.0

Open Source

GPL version 2, commercial licence 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

C++

Erlang

Java

C++

Server operating systems

hosted

Linux

Linux
OS X

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

predefined scheme

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

Secondary indexes

yes

by using the Redis Search module

restricted

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

HTTP API
Native Erlang Interface

HTTP REST

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Lua

Erlang

Triggers

yes

by integration with AWS Lambda

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

based on HBase

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

The original contents of fields are not stored in the Sphinx index.

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)

simple password-based access control and ACL

yes, using Riak Security

Users with fine-grained authorization concept

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