DBMS > IBM Db2 vs. Newts vs. PouchDB vs. SQLite vs. StarRocks

System Properties Comparison IBM Db2 vs. Newts vs. PouchDB vs. SQLite vs. StarRocks

Editorial information provided by DB-Engines

Name

Description

Common in IBM host environments, 2 different versions for host and Windows/Linux

Time Series DBMS based on Cassandra

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	123.05
Rank	#9	Overall
	#6	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Website

www.ibm.com/products/db2

opennms.github.io/newts

pouchdb.com

www.sqlite.org

www.starrocks.io

Technical documentation

www.ibm.com/docs/en/db2

github.com/OpenNMS/newts/wiki

pouchdb.com/guides

www.sqlite.org/docs.html

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

IBM

OpenNMS Group

Apache Software Foundation

Dwayne Richard Hipp

The Linux Foundation

since Feb 2023

Initial release

1983

host version

2014

2012

2000

2020

Current release

12.1, October 2016

7.1.1, June 2019

3.46.1 (13 August 2024), August 2024

3.3, June 2024

License

Commercial or Open Source

commercial

free version is available

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache Version 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 and C++

Java

JavaScript

C++, Java

Server operating systems

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X
Windows

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

server-less

Linux

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

via views

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

yes

APIs and other access methods

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

HTTP REST
Java API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
MySQL protocol

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Java

JavaScript

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

Java

Server-side scripts

Stored procedures

yes

View functions in JavaScript

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding

based on Cassandra

Sharding

with a proxy-based framework, named couchdb-lounge

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

selectable replication factor

based on Cassandra

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

via file-system locks

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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