DBMS > H2GIS vs. LevelDB vs. MySQL vs. PostGIS vs. Solr

System Properties Comparison H2GIS vs. LevelDB vs. MySQL vs. PostGIS vs. Solr

Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used open source RDBMS

Spatial extension of PostgreSQL

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Spatial DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Spatial DBMS

Search engine

Secondary database models

Relational DBMS

Document store
Spatial DBMS

Relational DBMS

Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.h2gis.org

github.com/google/leveldb

www.mysql.com

postgis.net

solr.apache.org

Technical documentation

www.h2gis.org/docs/home

github.com/google/leveldb/blob/main/doc/index.md

dev.mysql.com/doc

postgis.net/documentation

solr.apache.org/resources.html

Developer

CNRS

Google

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2013

2011

1995

2005

2006

Current release

1.23, February 2021

8.4.0, April 2024

3.4.2, February 2024

9.6.0, April 2024

License

Commercial or Open Source

Open Source

LGPL 3.0

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL v2.0

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

Java

Server operating systems

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

Java

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

based on H2

yes

proprietary syntax

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

based on PostgreSQL

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

none

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

optimistic locking

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

with automatic compression on writes

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

yes

based on PostgreSQL

yes

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