I’ve continued building on my MariaDB GIS and node.js example application that I wrote about two weeks back, https://blog.mariadb.org/node-js-mariadb-and-gis/. The application shows how to load GPX information into MariaDB, using some MariaDB GIS functionality, and making use of the node.js platform together with MariaDB’s non-blocking client.

With the GPX data converted into GIS points in the MariaDB database, I wanted to further expand a little on both the GIS aspect and also look at how some additional data could be shown in the application by using jQuery’s Ajax calls to update a piece of the web based application UI.

To start with, an interesting thing to do when you have a bunch of GIS points in a …

The availability of the node.js binding for MariaDB’s non-blocking client library together with the GIS capabilities of MariaDB inspired me to make an example of using node.js and MariaDB to import so-called GPX tracks to a MariaDB database and then show them on a map. GPX tracks are what are stored by many GPS devices including running watches and smartphones.

My project makes use of MariaDB’s non-blocking client library together with the node.js platform and on top of that uses the GIS functionality found in MariaDB 5.5 and 10.0.

To start with let’s go through the software and components I’m using:

• Node.js – The popular Node.js platform built on Chrome’s JavaScript runtime. An event-driven and …

Almost every business process or decision includes a WHERE!

Like IP addresses encode the destination of a domain name, with a geographic information system (GIS) you can encode and analyze the location data allowing you to reveal relationships, patterns, and trends.

Weather Decision Technologies

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The world of GIS is confusing. Here is my understanding of how the peaces go together and how you can get started.

We describe locations on the earth in terms of Latitude and Longitude.  LatLng(35.22248, -97.44135).  There’s a third, Altitude.  GIS describes more then LatLng “point data”, it can also describe …

In my quest to understand spatial GIS functionality, I have come to the ultimate goal: evaluation the actual database products themselves.

PostgreSQL / PostGIS

PostGIS is a variant of PostgreSQL with spatial extensions. The main reason for maintaining the GIS feature set outside of PostgreSQL proper seems to be licensing: the spatial extensions are LGPL GPL licensed.

PostGIS is widely recognized as the most mature and feature-rich GIS implementation for SQL databases (and perhaps any database), matched only by the costly Spatial extension for the Oracle Enterprise database. (See comparisons in the links below.)

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While the underlying index should be opaque to the user of a DBMS with spatial features, the API used to define spatial types and operate on them is of course more visible. The relevant standard in this space is often referred to as "OpenGIS", however the Open Geospatial Consortium in fact defines a long list of standards. The standard relevant to SQL databases is known more precisely as "OpenGIS Implementation Specification for Geographic information - Simple feature access - Part 2: SQL option" aka "Simple feature access".

It is not meaningful to recite the standard at length in my blog, my focus is instead on actual implementations that I will blog about later. The following points are however worth noting:

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I work for a company that is the leading supplier of automotive maps, and wants to be the leading supplier of online maps. So it was only a matter of time that I needed to learn more about how spatial extensions work in different open source databases. Let's start from the beginning, understanding various spatial data structures that are used in implementations...

Links are provided to Wikipedia articles - which are both comprehensive, yet easy to understand - for those who want to get a deeper understanding of each structure. All Wikipedia articles on spatial indexes are listed here: http://en.wikipedia.org/wiki/Spatial_index#Spatial_index

B-tree

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Often application needs to know where a user is physically located. The easiest way to figure that out is by looking up their IP address in a special database. It can all be implemented in MySQL, but I often see it done inefficiently. In my post I will show how to implement a complete solution that offers great performance.

Importing Geo IP data

First you will require a database mapping network addresses to real locations. There are various resources available, but I chose the one nginx web server uses with its geoip module. GeoLite City comes in CSV format and is available for download with no charge from MaxMind.

The archive contains two files. GeoLiteCity-Blocks.csv lists all IP ranges and maps each one to the corresponding location identifier, while …

Here’s another MariaDB screencast, this time highlighting some of the GIS functionality in MariaDB.

(I recommend watching it in full screen 720p, so you can see the details.)

Some links and notes:

• GIS Functionality in MariaDB
• Data used in this screencast
• OpenJUMP
• JUMP database plugin
  • To use the database plugin, unpack it into the OpenJUMP lib/ext/ directory (not a subdirectory, but directly into that directory). Also place the mysql-connector-java.jar into the …

Parts of the world are already celebrating Christmas Eve and it’s time to relax and spend time with family and friends. Even if you don’t celebrate Christmas this is when there is time for less work. Here are a few words to round off MariaDB’s current state and where it’s heading.

This year culminated in MariaDB 5.3.3, the release candidate of 5.3. This is a significant release that makes years of work available by default in the database server. Earlier releases still required features to be explicitly switched on, but thanks to thorough testing assuring the quality of the new functionality we have now enabled them. It’s still called a release candidate which means it’s ready for general usage, but we want more user feedback before calling it stable. Make yourself familiar with the MariaDB 5.3.3 release notes.

Most of the new features and …

One of the query optimization scenarios I’ve seen a lot over the years is finding something within some distance from a point. For example, finding people within some distance of yourself, apartments in a radius from a postal code, and so on.

These queries usually use the great-circle formula. That might be because Google finds lots of pages claiming that this is the right way to do a radius search. “The earth is not flat!”, they all say. That’s true, but it doesn’t mean that the great-circle formula is a good approach. It’s usually a really bad approach, in fact. It’s needlessly precise for most things, not precise enough for others, and it’s an expensive query to execute; all the trig functions tend to eat a bunch of CPU, and make it impossible to use ordinary indexes. This is true for all of the databases I’ve used — MySQL, Postgres, and SQL Server.

The great-circle formula is needlessly precise for a few …