DBMS > Amazon DynamoDB vs. Dragonfly vs. Google Cloud Datastore vs. Trafodion

System Properties Comparison Amazon DynamoDB vs. Dragonfly vs. Google Cloud Datastore vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines 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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Key-value store

Key-value store

Document store

Relational DBMS

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	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

aws.amazon.com/dynamodb

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

cloud.google.com/datastore

trafodion.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

www.dragonflydb.io/docs

cloud.google.com/datastore/docs

trafodion.apache.org/documentation.html

Developer

Amazon

DragonflyDB team and community contributors

Google

Apache Software Foundation, originally developed by HP

Initial release

2012

2023

2008

2014

Current release

1.0, March 2023

2.3.0, February 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

BSL 1.1

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++

C++, Java

Server operating systems

hosted

Linux

hosted

Linux

Data scheme

schema-free

scheme-free

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes, details here

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

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
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Lua

using Google App Engine

Java Stored Procedures

Triggers

yes

by integration with AWS Lambda

publish/subscribe channels provide some trigger functionality

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication using Paxos

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

User concepts

Access control

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

Password-based authentication

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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