DBMS > DolphinDB vs. PouchDB vs. Prometheus vs. SQLite vs. Warp 10

System Properties Comparison DolphinDB vs. PouchDB vs. Prometheus vs. SQLite vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

JavaScript DBMS with an API inspired by CouchDB

Open-source Time Series DBMS and monitoring system

Widely used embeddable, in-process RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

Technical documentation

docs.dolphindb.cn/en/help200/index.html

pouchdb.com/guides

prometheus.io/docs

www.sqlite.org/docs.html

www.warp10.io/content/02_Getting_started

Developer

DolphinDB, Inc

Apache Software Foundation

Dwayne Richard Hipp

SenX

Initial release

2018

2012

2015

2000

2015

Current release

v2.00.4, January 2022

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache License 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++

JavaScript

Java

Server operating systems

Linux
Windows

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

Linux
Windows

server-less

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

via views

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

JavaScript

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

by Federation

none

selectable replication factor

based on HBase

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

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

Administrators, Users, Groups

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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