DBMS > IBM Cloudant vs. KeyDB vs. Spark SQL vs. Yanza

System Properties Comparison IBM Cloudant vs. KeyDB vs. Spark SQL vs. Yanza

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Editorial information provided by DB-Engines

Name

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

Description

Database as a Service offering based on Apache CouchDB

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

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

Time Series DBMS for IoT Applications

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	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.ibm.com/products/cloudant

github.com/Snapchat/KeyDB
keydb.dev

spark.apache.org/sql

yanza.com

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.keydb.dev

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

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

EQ Alpha Technology Ltd.

Apache Software Foundation

Yanza

Initial release

2010

2019

2014

2015

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Erlang

C++

Scala

Server operating systems

hosted

Linux

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC

HTTP API

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Java
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Lua

Triggers

yes

Timer and event based

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

Eventual Consistency
Strong eventual consistency with CRDTs

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

Optimistic locking, atomic execution of commands blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

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