DBMS > Apache Druid vs. PouchDB vs. SQLite vs. StarRocks vs. Valentina Server

System Properties Comparison Apache Druid vs. PouchDB vs. SQLite vs. StarRocks vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Object-relational database and reports server

Primary database model

Relational DBMS
Time Series DBMS

Document store

Relational DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

pouchdb.com/guides

www.sqlite.org/docs.html

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

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Apache Software Foundation and contributors

Apache Software Foundation

Dwayne Richard Hipp

The Linux Foundation

since Feb 2023

Paradigma Software

Initial release

2012

2000

2020

1999

Current release

29.0.1, April 2024

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

2.5.3, March 2023

5.7.5

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Public Domain

Open Source

Apache Version 2.0

commercial

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

Java

JavaScript

C++, Java

Server operating systems

Linux
OS X
Unix

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

server-less

Linux

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

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

SQL for querying

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
MySQL protocol

ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

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, via HDFS, S3 or other storage engines

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

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard

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