DBMS > Amazon Redshift vs. Dragonfly vs. HugeGraph

System Properties Comparison Amazon Redshift vs. Dragonfly vs. HugeGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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 fast-speed and highly-scalable Graph DBMS

Primary database model

Relational DBMS

Key-value store

Graph DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Website

aws.amazon.com/redshift

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

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.aws.amazon.com/redshift

www.dragonflydb.io/docs

hugegraph.apache.org/docs

Developer

Amazon (based on PostgreSQL)

DragonflyDB team and community contributors

Baidu

Initial release

2012

2023

2018

Current release

1.0, March 2023

0.9

License

Commercial or Open Source

commercial

Open Source

BSL 1.1

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

hosted

Linux

Linux
macOS
Unix

Data scheme

yes

scheme-free

yes

Typing

predefined data types such as float or date

yes

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

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

restricted

yes

also supports composite index and range index

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protocol

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

All languages supporting JDBC/ODBC

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

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

asynchronous Gremlin script jobs

Triggers

publish/subscribe channels provide some trigger functionality

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

Transaction concepts

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

ACID

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

User concepts

Access control

fine grained access rights according to SQL-standard

Password-based authentication

Users, roles and permissions

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