DBMS > IBM Cloudant vs. IRONdb vs. Spark SQL vs. SQLite

System Properties Comparison IBM Cloudant vs. IRONdb vs. Spark SQL vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

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

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

Widely used embeddable, in-process RDBMS

Primary database model

Document store

Time Series DBMS

Relational DBMS

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

Score	4.35
Rank	#94	Overall
	#16	Document stores

Score	0.25
Rank	#337	Overall
	#29	Time Series DBMS

Score	19.74
Rank	#36	Overall
	#22	Relational DBMS

Score	132.67
Rank	#9	Overall
	#6	Relational DBMS

Website

www.ibm.com/cloud/cloudant

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

spark.apache.org/sql

www.sqlite.org

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.circonus.com/irondb

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

www.sqlite.org/docs.html

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Circonus LLC.

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2010

2017

2014

2000

Current release

V0.10.20, January 2018

3.3.0 ( 2.13), June 2022

3.40.1 (28 December 2022), December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Erlang

C and C++

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

server-less

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

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

RESTful HTTP/JSON API

HTTP API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

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

View functions (Map-Reduce) in JavaScript

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

via file-system locks

Durability

Support for making data persistent

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 for users can be defined per database

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