DBMS > Apache Pinot vs. H2GIS vs. MySQL vs. Spark SQL

System Properties Comparison Apache Pinot vs. H2GIS vs. MySQL vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Spatial extension of H2

Widely used open source RDBMS

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

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.pinot.apache.org

www.h2gis.org/docs/home

dev.mysql.com/doc

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

Developer

Apache Software Foundation and contributors

CNRS

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2015

2013

1995

2014

Current release

1.0.0, September 2023

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

LGPL 3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Scala

Server operating systems

All OS with a Java JDK11 or higher

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

Go
Java
Python

Java

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

based on H2

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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, multi-version concurrency control (MVCC)

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

yes

based on H2

Users with fine-grained authorization concept

no user groups or roles

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