DBMS > IBM Cloudant vs. Spark SQL vs. Splice Machine

System Properties Comparison IBM Cloudant vs. Spark SQL vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store

Relational DBMS

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

Score	2.73
Rank	#108	Overall
	#20	Document stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

www.ibm.com/products/cloudant

spark.apache.org/sql

splicemachine.com

Technical documentation

cloud.ibm.com/docs/Cloudant

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

splicemachine.com/how-it-works

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Apache Software Foundation

Splice Machine

Initial release

2010

2014

Current release

3.5.0 ( 2.13), September 2023

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

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

Scala

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
Python
R
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

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

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles according to SQL-standard

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