DBMS > LevelDB vs. Microsoft Azure Table Storage vs. Postgres-XL vs. PouchDB vs. SQLite

System Properties Comparison LevelDB vs. Microsoft Azure Table Storage vs. Postgres-XL vs. PouchDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A Wide Column Store for rapid development using massive semi-structured datasets

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

github.com/google/leveldb

azure.microsoft.com/en-us/services/storage/tables

www.postgres-xl.org

pouchdb.com

www.sqlite.org

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

www.postgres-xl.org/documentation

pouchdb.com/guides

www.sqlite.org/docs.html

Developer

Google

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2011

2012

2014

since 2012, originally named StormDB

2012

2000

Current release

1.23, February 2021

10 R1, October 2018

7.1.1, June 2019

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Mozilla public license

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

JavaScript

Server operating systems

Illumos
Linux
OS X
Windows

hosted

Linux
macOS

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

server-less

Data scheme

schema-free

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

XML type, but no XML query functionality

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

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

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

user defined functions

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

with automatic compression on writes

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

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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