DBMS > Drizzle vs. Microsoft SQL Server vs. NSDb vs. PouchDB vs. TimescaleDB

System Properties Comparison Drizzle vs. Microsoft SQL Server vs. NSDb vs. PouchDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Microsofts flagship relational DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

JavaScript DBMS with an API inspired by CouchDB

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

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

nsdb.io

pouchdb.com

www.timescale.com

Technical documentation

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

nsdb.io/Architecture

pouchdb.com/guides

docs.timescale.com

Developer

Drizzle project, originally started by Brian Aker

Microsoft

Apache Software Foundation

Timescale

Initial release

2008

1989

2017

2012

2017

Current release

7.2.4, September 2012

SQL Server 2022, November 2022

7.1.1, June 2019

2.13.0, November 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, Scala

JavaScript

Server operating systems

FreeBSD
Linux
OS X

Linux
Windows

Linux
macOS

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

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

all fields are automatically indexed

yes

via views

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC

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

gRPC
HTTP REST
WebSocket

HTTP REST

only for PouchDB Server
JavaScript API

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

Supported programming languages

C
C++
Java
PHP

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

Java
Scala

JavaScript

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

View functions in JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

with a proxy-based framework, named couchdb-lounge

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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