DBMS > Amazon DynamoDB vs. Apache Phoenix vs. Citus vs. Galaxybase vs. HugeGraph

System Properties Comparison Amazon DynamoDB vs. Apache Phoenix vs. Citus vs. Galaxybase vs. HugeGraph

Editorial information provided by DB-Engines

Name

Description

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Scalable, ACID-compliant native distributed parallel graph platform

A fast-speed and highly-scalable Graph DBMS

Primary database model

Document store
Key-value store

Relational DBMS

Graph DBMS

Secondary database models

Document store

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	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

aws.amazon.com/dynamodb

phoenix.apache.org

www.citusdata.com

galaxybase.com

github.com/hugegraph
hugegraph.apache.org

Technical documentation

docs.aws.amazon.com/dynamodb

phoenix.apache.org

docs.citusdata.com

hugegraph.apache.org/docs

Developer

Amazon

Apache Software Foundation

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Baidu

Initial release

2012

2014

2010

2017

2018

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

8.1, December 2018

Nov 20, November 2021

0.9

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

Open Source

AGPL, commercial license also available

commercial

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

Java

C and Java

Java

Server operating systems

hosted

Linux
Unix
Windows

Linux

Linux
macOS
Unix

Data scheme

schema-free

yes

late-bound, schema-on-read capabilities

yes

Strong typed schema

yes

Typing

predefined data types such as float or date

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

standard, with numerous extensions

APIs and other access methods

RESTful HTTP API

JDBC

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

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Go
Java
Python

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined procedures and functions

asynchronous Gremlin script jobs

Triggers

yes

by integration with AWS Lambda

yes

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

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Hadoop integration

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

edges in graph

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

Concurrency

Support for concurrent manipulation of data

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)

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

fine grained access rights according to SQL-standard

Role-based access control

Users, roles and permissions

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