DBMS > Amazon DynamoDB vs. Citus vs. Galaxybase vs. IRONdb vs. Typesense

System Properties Comparison Amazon DynamoDB vs. Citus vs. Galaxybase vs. IRONdb vs. Typesense

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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 distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Document store
Key-value store

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	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Score	0.79
Rank	#210	Overall
	#14	Search engines

Website

aws.amazon.com/dynamodb

www.citusdata.com

galaxybase.com

www.circonus.com/solutions/time-series-database/

typesense.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.citusdata.com

docs.circonus.com/irondb/category/getting-started

typesense.org/docs

Developer

Amazon

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

Circonus LLC.

Initial release

2012

2010

2017

2015

Current release

8.1, December 2018

Nov 20, November 2021

V0.10.20, January 2018

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

GPL V3

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

C and C++

C++

Server operating systems

hosted

Linux

Data scheme

schema-free

yes

Strong typed schema

schema-free

pre-defined schema optional

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

yes

standard, with numerous extensions

SQL-like query language (Circonus Analytics Query Language: CAQL)

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

HTTP API

RESTful HTTP API

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

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

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

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

yes, in Lua

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

other methods possible by using 3rd party extensions

configurable replication factor, datacenter aware

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Relationships in graphs

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)

fine grained access rights according to SQL-standard

Role-based access control

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