DBMS > IBM Cloudant vs. JaguarDB vs. StarRocks vs. Vertica

System Properties Comparison IBM Cloudant vs. JaguarDB vs. StarRocks vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Performant, highly scalable DBMS for AI and IoT applications

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

Forked from Apache Doris

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store

Key-value store
Vector DBMS

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

www.ibm.com/products/cloudant

www.jaguardb.com

www.starrocks.io

www.vertica.com

Technical documentation

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

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

vertica.com/documentation

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

The Linux Foundation

since Feb 2023

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2010

2015

2020

2005

Current release

3.3 July 2023

2.5.3, March 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

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Implementation language

Erlang

C++

the server part. Clients available in other languages

C++, Java

C++

Server operating systems

hosted

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

JDBC
MySQL protocol

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range and hash)

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional 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

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.

User concepts

Access control

Access rights for users can be defined per database

rights management via user accounts

Role based access control and fine grained access rights

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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