DBMS > Apache Pinot vs. CouchDB vs. CrateDB vs. Microsoft SQL Server vs. PouchDB

System Properties Comparison Apache Pinot vs. CouchDB vs. CrateDB vs. Microsoft SQL Server vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Distributed Database based on Lucene

Microsofts flagship relational DBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Relational DBMS

Document store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Document store

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

pinot.apache.org

couchdb.apache.org

cratedb.com

www.microsoft.com/en-us/sql-server

pouchdb.com

Technical documentation

docs.pinot.apache.org

docs.couchdb.org/en/stable

cratedb.com/docs

learn.microsoft.com/en-US/sql/sql-server

pouchdb.com/guides

Developer

Apache Software Foundation and contributors

Apache Software Foundation

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

Crate

Microsoft

Apache Software Foundation

Initial release

2015

2005

2013

1989

2012

Current release

1.0.0, September 2023

3.3.3, December 2023

SQL Server 2022, November 2022

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Erlang

Java

C++

JavaScript

Server operating systems

All OS with a Java JDK11 or higher

Android
BSD
Linux
OS X
Solaris
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

yes

via views

yes

via views

SQL

Support of SQL

SQL-like query language

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Go
Java
Python

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

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions (Javascript)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

improved architecture with release 2.0

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Configurable replication on table/partition-level

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 can be defined per database

rights management via user accounts

fine grained access rights according to SQL-standard

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