DBMS > IBM Cloudant vs. JaguarDB vs. Microsoft Azure Table Storage vs. SWC-DB vs. Tarantool

System Properties Comparison IBM Cloudant vs. JaguarDB vs. Microsoft Azure Table Storage vs. SWC-DB vs. Tarantool

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

A Wide Column Store for rapid development using massive semi-structured datasets

A high performance, scalable Wide Column DBMS

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Document store

Key-value store
Vector DBMS

Wide column store

Document store
Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

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	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

www.ibm.com/products/cloudant

www.jaguardb.com

azure.microsoft.com/en-us/services/storage/tables

github.com/kashirin-alex/swc-db
www.swcdb.org

www.tarantool.io

Technical documentation

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

www.tarantool.io/en/doc

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

Microsoft

Alex Kashirin

Initial release

2010

2015

2012

2020

2008

Current release

3.3 July 2023

0.5, April 2021

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

Open Source

GPL V3

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

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

C++

the server part. Clients available in other languages

C++

C and C++

Server operating systems

hosted

Linux

hosted

Linux

BSD
Linux
macOS

Data scheme

schema-free

yes

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

RESTful HTTP API

Proprietary protocol
Thrift

Open binary protocol

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

Lua, C and SQL stored procedures

Triggers

yes

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

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

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

atomic operations within a document possible

optimistic locking

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Access rights for users can be defined per database

rights management via user accounts

Access rights based on private key authentication or shared access signatures

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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