DBMS > IRONdb vs. Lovefield vs. PouchDB vs. SQLite vs. XTDB

System Properties Comparison IRONdb vs. Lovefield vs. PouchDB vs. SQLite vs. XTDB

Editorial information provided by DB-Engines

Name

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

Description

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

Embeddable relational database for web apps written in pure JavaScript

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

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

google.github.io/lovefield

pouchdb.com

www.sqlite.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

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

pouchdb.com/guides

www.sqlite.org/docs.html

www.xtdb.com/docs

Developer

Circonus LLC.

Google

Apache Software Foundation

Dwayne Richard Hipp

Juxt Ltd.

Initial release

2017

2014

2012

2000

2019

Current release

V0.10.20, January 2018

2.1.12, February 2017

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

MIT License

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

Clojure

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

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

server-less

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

not rigid because of 'dynamic typing' concept.

yes, extensible-data-notation format

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

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

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

limited SQL, making use of Apache Calcite

APIs and other access methods

HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP REST
JDBC

Supported programming languages

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

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

Clojure
Java

Server-side scripts

Stored procedures

yes, in Lua

View functions in JavaScript

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

via file-system locks

yes

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

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