DBMS > HyperSQL vs. MySQL vs. Prometheus vs. Spark SQL

System Properties Comparison HyperSQL vs. MySQL vs. Prometheus vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Widely used open source RDBMS

Open-source Time Series DBMS and monitoring system

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

dev.mysql.com/doc

prometheus.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2001

1995

2015

2014

Current release

2.7.2, June 2023

8.3.0, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

based on BSD license

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Scala

Server operating systems

All OS with a Java VM

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

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java, SQL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes

by Federation

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

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