DBMS > HyperSQL vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Tarantool

System Properties Comparison HyperSQL vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Globally distributed, horizontally scalable, multi-model database service

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

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	3.90
Rank	#85	Overall
	#46	Relational DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	1.67
Rank	#150	Overall
	#25	Document stores
	#25	Key-value stores
	#69	Relational DBMS

Website

hsqldb.org

azure.microsoft.com/services/cosmos-db

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

www.tarantool.io

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

learn.microsoft.com/azure/cosmos-db

www.tarantool.io/en/doc

Developer

Microsoft

Initial release

2001

2014

2012

2008

Current release

2.7.2, June 2023

2.10.0, May 2022

License

Commercial or Open Source

Open Source

based on BSD license

commercial

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

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

BSD
Linux
macOS

Data scheme

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

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

HTTP API

JDBC via HTTP
JDBC
ODBC

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

RESTful HTTP API

Open binary protocol

Supported programming languages

All languages supporting JDBC/ODBC
Java

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

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

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

Server-side scripts

Stored procedures

Java, SQL

JavaScript

Lua, C and SQL stored procedures

Triggers

yes

JavaScript

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

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

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

none

yes

Implicit feature of the cloud service

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

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

yes, full featured in-memory storage engine with persistence

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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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