DBMS > Amazon DynamoDB vs. Cachelot.io vs. IRONdb vs. RDF4J vs. SurrealDB

System Properties Comparison Amazon DynamoDB vs. Cachelot.io vs. IRONdb vs. RDF4J vs. SurrealDB

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

In-memory caching system

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Key-value store

Key-value store

Time Series DBMS

RDF store

Document store
Graph 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	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/dynamodb

cachelot.io

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

rdf4j.org

surrealdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

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

rdf4j.org/documentation

surrealdb.com/docs

Developer

Amazon

Circonus LLC.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

SurrealDB Ltd

Initial release

2012

2015

2017

2004

2022

Current release

V0.10.20, January 2018

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Simplified BSD License

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

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 and C++

Java

Rust

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux

Linux
OS X
Unix
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query language

APIs and other access methods

RESTful HTTP API

Memcached protocol

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

Java
PHP
Python

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes, in Lua

yes

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

configurable replication factor, datacenter aware

none

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

none

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

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

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

yes, based on authentication and database rules

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