DBMS > EDB Postgres vs. IRONdb vs. PouchDB vs. Spark SQL vs. SQLite

System Properties Comparison EDB Postgres vs. IRONdb vs. PouchDB vs. Spark SQL vs. SQLite

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

JavaScript DBMS with an API inspired by CouchDB

Spark SQL is a component on top of 'Spark Core' for structured data processing

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	1.90
Rank	#135	Overall
	#63	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.enterprisedb.com

www.circonus.com/solutions/time-series-database/

pouchdb.com

spark.apache.org/sql

www.sqlite.org

Technical documentation

www.enterprisedb.com/docs

docs.circonus.com/irondb/category/getting-started

pouchdb.com/guides

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

EnterpriseDB

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2005

2017

2012

2014

2000

Current release

14, December 2021

V0.10.20, January 2018

7.1.1, June 2019

3.5.0 ( 2.13), September 2023

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

BSD for PostgreSQL-components

commercial

Open Source

Apache 2.0

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 and C++

JavaScript

Scala

Server operating systems

Linux
Windows

Linux

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

Linux
OS X
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

standard with numerous extensions

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

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

HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

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

JavaScript

Java
Python
R
Scala

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

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes, in Lua

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

by hash, list or range

Automatic, metric affinity per node

Sharding

with a proxy-based framework, named couchdb-lounge

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

configurable replication factor, datacenter aware

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

Immediate consistency per node, eventual consistency across nodes

Eventual 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, multi-version concurrency control (MVCC)

yes

via file-system locks

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

fine grained access rights according to SQL-standard

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