DBMS > Datomic vs. etcd vs. Microsoft Azure Table Storage vs. PouchDB vs. SQLite

System Properties Comparison Datomic vs. etcd vs. Microsoft Azure Table Storage vs. PouchDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A distributed reliable key-value store

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

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

Primary database model

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.datomic.com

etcd.io
github.com/etcd-io/etcd

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

pouchdb.com

www.sqlite.org

Technical documentation

docs.datomic.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

pouchdb.com/guides

www.sqlite.org/docs.html

Developer

Cognitect

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2012

2000

Current release

1.0.6735, June 2023

3.4, August 2019

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2.0

commercial

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

Java, Clojure

JavaScript

Server operating systems

All OS with a Java VM

FreeBSD
Linux
Windows

experimental

hosted

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

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

gRPC
JSON over HTTP

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Clojure
Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

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

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

yes

Transaction Functions

View functions in JavaScript

Triggers

By using transaction functions

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

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

recommended only for testing and development

yes

User concepts

Access control

Access rights based on private key authentication or shared access signatures

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