DBMS > IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Splice Machine vs. StarRocks

System Properties Comparison IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Splice Machine vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.95
Rank	#193	Overall
	#89	Relational DBMS

Website

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

splicemachine.com

www.starrocks.io

Technical documentation

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

splicemachine.com/how-it-works

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Splice Machine

The Linux Foundation

since Feb 2023

Initial release

2010

2014

2020

Current release

3.1, March 2021

3.3, June 2024

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

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

Java

C++, Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
Native Spark Datasource
ODBC

JDBC
MySQL protocol

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Java

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

yes

Java

user defined functions

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

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

Role based access control and fine grained access rights

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