DBMS > H2 vs. Hazelcast vs. HyperSQL vs. VictoriaMetrics

System Properties Comparison H2 vs. Hazelcast vs. HyperSQL vs. VictoriaMetrics

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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.

A widely adopted in-memory data grid

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Secondary database models

Spatial DBMS

Document store

JSON support with IMDG 3.12

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

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

Technical documentation

www.h2database.com/html/main.html

hazelcast.org/imdg/docs

hsqldb.org/web/hsqlDocsFrame.html

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Thomas Mueller

Hazelcast

VictoriaMetrics

Initial release

2005

2008

2001

2018

Current release

2.2.220, July 2023

5.3.6, November 2023

2.7.2, June 2023

v1.91, May 2023

License

Commercial or Open Source

Open Source

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

Open Source

Apache Version 2; commercial licenses available

Open Source

based on BSD license

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

All OS with a Java VM

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

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

yes

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

Java

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

yes

Event Listeners, Executor Services

Java, SQL

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

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

Replicated Map

none

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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

Role-based access control

fine grained access rights according to SQL-standard

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