DBMS > DuckDB vs. Lovefield vs. PouchDB vs. Splunk vs. SQLite

System Properties Comparison DuckDB vs. Lovefield vs. PouchDB vs. Splunk vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

Embeddable relational database for web apps written in pure JavaScript

JavaScript DBMS with an API inspired by CouchDB

Analytics Platform for Big Data

Widely used embeddable, in-process RDBMS

Primary database model

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

duckdb.org

google.github.io/lovefield

pouchdb.com

www.splunk.com

www.sqlite.org

Technical documentation

duckdb.org/docs

github.com/google/lovefield/blob/master/docs/spec_index.md

pouchdb.com/guides

docs.splunk.com/Documentation/Splunk

www.sqlite.org/docs.html

Developer

Google

Apache Software Foundation

Splunk Inc.

Dwayne Richard Hipp

Initial release

2018

2014

2012

2003

2000

Current release

1.0.0, June 2024

2.1.12, February 2017

7.1.1, June 2019

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

Open Source

commercial

Limited free edition and free developer edition available

Open Source

Public Domain

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

Server operating systems

server-less

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

tested with Chrome, Firefox, IE, Safari

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

Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

Splunk Search Processing Language for search commands

yes

SQL-92 is not fully supported

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

HTTP REST

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

JavaScript

C#
Java
JavaScript
PHP
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

View functions in JavaScript

yes

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

using MemoryDB

yes

User concepts

Access control

Access rights for users and roles

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