DBMS > H2 vs. Heroic vs. Microsoft Azure SQL Database vs. Pinecone

System Properties Comparison H2 vs. Heroic vs. Microsoft Azure SQL Database vs. Pinecone

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Database as a Service offering with high compatibility to Microsoft SQL Server

A managed, cloud-native vector database

Primary database model

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	7.57
Rank	#47	Overall
	#30	Relational DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Website

www.h2database.com

github.com/spotify/heroic

azure.microsoft.com/en-us/products/azure-sql/database

www.pinecone.io

Technical documentation

www.h2database.com/html/main.html

spotify.github.io/heroic

docs.microsoft.com/en-us/azure/azure-sql

docs.pinecone.io/docs/overview

Developer

Thomas Mueller

Spotify

Microsoft

Pinecone Systems, Inc

Initial release

2005

2014

2010

2019

Current release

2.2.220, July 2023

V12

License

Commercial or Open Source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache 2.0

commercial

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

Java

C++

Server operating systems

All OS with a Java VM

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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.

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Java

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

Python

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

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More resources

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Recent citations in the news

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21 May 2024, Microsoft

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General availability: Azure SQL updates for late-May 2024
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Pinecone launches its serverless vector database out of preview
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