DBMS > Amazon DocumentDB vs. Apache IoTDB vs. NuoDB vs. OrigoDB vs. SQLite

System Properties Comparison Amazon DocumentDB vs. Apache IoTDB vs. NuoDB vs. OrigoDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

NuoDB is a webscale distributed database that supports SQL and ACID transactions

A fully ACID in-memory object graph database

Widely used embeddable, in-process RDBMS

Primary database model

Document store

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/documentdb

iotdb.apache.org

www.3ds.com/nuodb-distributed-sql-database

origodb.com

www.sqlite.org

Technical documentation

aws.amazon.com/documentdb/resources

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

doc.nuodb.com

origodb.com/docs

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Dassault Systèmes

originally NuoDB, Inc.

Robert Friberg et al

Dwayne Richard Hipp

Initial release

2019

2018

2013

2009

under the name LiveDB

2000

Current release

1.1.0, April 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

Public Domain

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++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

not rigid because of 'dynamic typing' concept.

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
Native API

ADO.NET
JDBC
ODBC

.NET Client API
HTTP API
LINQ

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

.Net

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Java, SQL

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

data is dynamically stored/cached on the nodes where it is read/written

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Managed transparently by NuoDB

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

depending on model

yes

Transaction concepts

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

Atomic single-document operations

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

Durability

Support for making data persistent

yes

Write ahead log

yes

In-memory capabilities

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

yes

Temporary table

yes

User concepts

Access control

Access rights for users and roles

yes

Standard SQL roles/ privileges, Administrative Users

Role based authorization

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