DBMS > Amazon DynamoDB vs. Citus vs. Galaxybase vs. HugeGraph vs. InfluxDB

System Properties Comparison Amazon DynamoDB vs. Citus vs. Galaxybase vs. HugeGraph vs. InfluxDB

Editorial information provided by DB-Engines

Name

Description

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

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

DBMS for storing time series, events and metrics

Primary database model

Document store
Key-value store

Relational DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Document store

Spatial DBMS

with GEO package

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

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	0.04
Rank	#375	Overall
	#40	Graph DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	26.56
Rank	#28	Overall
	#1	Time Series DBMS

Website

aws.amazon.com/dynamodb

www.citusdata.com

galaxybase.com

github.com/hugegraph
hugegraph.apache.org

www.influxdata.com/products/influxdb-overview

Technical documentation

docs.aws.amazon.com/dynamodb

docs.citusdata.com

hugegraph.apache.org/docs

docs.influxdata.com/influxdb

Developer

Amazon

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

Baidu

Initial release

2012

2010

2017

2018

2013

Current release

8.1, December 2018

Nov 20, November 2021

0.9

2.7.5, January 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache Version 2.0

Open Source

MIT-License; commercial enterprise version 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 and Java

Java

Server operating systems

hosted

Linux

Linux
macOS
Unix

Linux
OS X

through Homebrew

Data scheme

schema-free

yes

Strong typed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

HTTP API
JSON over UDP

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

Go
Java
Python

Groovy
Java
Python

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Sharding

in enterprise version only

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

other methods possible by using 3rd party extensions

yes

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

selectable replication factor

in enterprise version only

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

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

Depending on used storage engine

User concepts

Access control

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

fine grained access rights according to SQL-standard

Role-based access control

Users, roles and permissions

simple rights management via user accounts

More information provided by the system vendor

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