DBMS > IBM Cloudant vs. ReductStore vs. Spark SQL vs. Tkrzw

System Properties Comparison IBM Cloudant vs. ReductStore vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

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

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

www.ibm.com/products/cloudant

github.com/reductstore
www.reduct.store

spark.apache.org/sql

dbmx.net/tkrzw

Technical documentation

cloud.ibm.com/docs/Cloudant

www.reduct.store/docs

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

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

ReductStore LLC

Apache Software Foundation

Mikio Hirabayashi

Initial release

2010

2023

2014

2020

Current release

1.9, March 2024

3.5.0 ( 2.13), September 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

C++, Rust

Scala

C++

Server operating systems

hosted

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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 DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C++
JavaScript (Node.js)
Python
Rust

Java
Python
R
Scala

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

Foreign keys

Referential integrity

Transaction concepts

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

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

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

using specific database classes

User concepts

Access control

Access rights for users can be defined per database

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