DBMS > Amazon DynamoDB vs. Dragonfly vs. Elasticsearch vs. FatDB vs. TimescaleDB

System Properties Comparison Amazon DynamoDB vs. Dragonfly vs. Elasticsearch vs. FatDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Key-value store

Search engine

Document store
Key-value store

Time Series DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Relational DBMS

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	128.79
Rank	#8	Overall
	#1	Search engines

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/dynamodb

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.elastic.co/elasticsearch

www.timescale.com

Technical documentation

docs.aws.amazon.com/dynamodb

www.dragonflydb.io/docs

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

docs.timescale.com

Developer

Amazon

DragonflyDB team and community contributors

Elastic

FatCloud

Timescale

Initial release

2012

2023

2010

2012

2017

Current release

1.0, March 2023

8.6, January 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSL 1.1

Open Source

Elastic License

commercial

Open Source

Apache 2.0

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++

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Windows

Linux
OS X
Windows

Data scheme

schema-free

scheme-free

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Via inetgration in SQL Server

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

Java API
RESTful HTTP/JSON API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua

yes

via applications

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

by integration with AWS Lambda

publish/subscribe channels provide some trigger functionality

yes

by using the 'percolation' feature

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

ES-Hadoop Connector

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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

Memcached and Redis integration

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Password-based authentication

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

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