DBMS > CouchDB vs. Fujitsu Enterprise Postgres vs. OrigoDB vs. QuestDB vs. Snowflake

System Properties Comparison CouchDB vs. Fujitsu Enterprise Postgres vs. OrigoDB vs. QuestDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A fully ACID in-memory object graph database

A high performance open source SQL database for time series data

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store

Relational DBMS

Document store
Object oriented DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

Relational DBMS

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

Score	7.46
Rank	#51	Overall
	#7	Document stores

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	0.00
Rank	#385	Overall
	#54	Document stores
	#21	Object oriented DBMS

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

couchdb.apache.org

www.postgresql.fastware.com

origodb.com

questdb.io

www.snowflake.com

Technical documentation

docs.couchdb.org/en/stable

www.postgresql.fastware.com/product-manuals

origodb.com/docs

questdb.io/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Robert Friberg et al

QuestDB Technology Inc

Snowflake Computing Inc.

Initial release

2005

2009

under the name LiveDB

2014

Current release

3.3.3, December 2023

Fujitsu Enterprise Postgres 14, January 2022

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java (Zero-GC), C++, Rust

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux
macOS
Windows

hosted

Data scheme

schema-free

yes

schema-free via InfluxDB Line Protocol

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL with time-series extensions

yes

APIs and other access methods

RESTful HTTP/JSON API

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

.NET Client API
HTTP API
LINQ

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

CLI Client
JDBC
ODBC

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

.Net

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

yes

user defined functions

Triggers

yes

Domain Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

partitioning by range, list and by hash

horizontal partitioning

client side managed; servers are not synchronized

horizontal partitioning (by timestamps)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Source-replica replication with eventual consistency

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

yes

Transaction concepts

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

atomic operations within a single document possible

ACID

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes, multi-version concurrency control (MVCC)

yes

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

through memory mapped files

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Role based authorization

Users with fine-grained authorization concept, user roles and pluggable authentication

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