DBMS > IBM Cloudant vs. Ingres vs. Spark SQL vs. XTDB

System Properties Comparison IBM Cloudant vs. Ingres vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Well established RDBMS

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

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

Primary database model

Document store

Relational DBMS

Document store

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

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.ibm.com/products/cloudant

www.actian.com/databases/ingres

spark.apache.org/sql

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

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.actian.com/ingres

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

www.xtdb.com/docs

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Actian Corporation

Apache Software Foundation

Juxt Ltd.

Initial release

2010

1974

originally developed at University Berkely in early 1970s

2014

2019

Current release

11.2, May 2022

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

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

Clojure

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Ingres Replicator

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

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

MVCC

yes

Durability

Support for making data persistent

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.

User concepts

Access control

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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