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Karmona's Pragmatic Blog

The GeoSpatial Cloud

November 1st, 2010 by Moti Karmona | מוטי קרמונה · 3 Comments

In continue to my geo distance post, I have decided to post something on the simplest cloud architecture (RDS) for location based services (LBS)

IMHO, (to cut a long story short :) Amazon RDS is more than enough for most geo location applications.

Amazon Relational Database Service (Amazon RDS) is a web service that makes it easy to set up, operate, and scale a relational database in the cloud.

Amazon RDS gives you access to the full capabilities of a familiar MySQL database. This means the code, applications, and tools you already use today with your existing MySQL databases work seamlessly with Amazon RDS.

Two preliminary steps:
* Start a small DB instance with the kind help of AWS management console (image above)
* Use the RDS instance as if it is your “disruptive”  MySQL instance e.g. manage it using MySQL Workbench 5.2.29

“Flirting” with MySQL spatial capabilities (which seems to be “fully” supported by AWS RDS)

* Create the (MyISAM) Table with spatial index

CREATE TABLE `locations` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`lat` decimal(10,6) DEFAULT NULL,
`long` decimal(10,6) DEFAULT NULL,
`loc` point NOT NULL,
`name` varchar(45) DEFAULT NULL,
PRIMARY KEY (`id`),
SPATIAL KEY `loc` (`loc`)
) ENGINE=MyISAM AUTO_INCREMENT=6 DEFAULT CHARSET=utf8

* Insert few values to populate your table

INSERT INTO locations VALUES(1,40.748433, -73.985655, GeomFromText(‘POINT(40.748433 -73.985655)’), ‘The Empire State Building‘);
INSERT INTO locations VALUES(2, 40.689166, -74.044444, GeomFromText(‘POINT(40.689166 -74.044444)’), ‘The Statue of Liberty’);
INSERT INTO locations VALUES(3, 40.758611, -73.979166, GeomFromText(‘POINT(40.758611 -73.979166)’), ‘Rockefeller Center’);
INSERT INTO locations VALUES(4, 40.757266, -73.985838, GeomFromText(‘POINT(40.757266 -73.985838)’), ‘Times Square’);
INSERT INTO locations VALUES(5, 40.7527, -73.9818, GeomFromText(‘POINT(40.7527 -73.9818)’), ‘New York Public Library’);

* Execute a simple test drive query which returns all the locations and their distance from ‘The Empire State Building’ + Comparing two distance calculation methods (1) MySQL euclidean calculation (2) Haversine calculation (results below)

SELECT locations.name,
astext(locations.loc),
GLength(LineStringFromWKB(LineString(locations.loc, GeomFromText(‘POINT(40.748433 -73.985655)’))))*100
AS euclidean,
6378.1 * 2 * ASIN(SQRT(
POWER(SIN(RADIANS(40.748433 – locations.lat) / 2),2) +
COS(RADIANS(40.748433)) * COS(RADIANS(locations.lat) )
* POWER(SIN((RADIANS(-73.985655 – locations.long)) /2), 2) )) AS haversine
FROM locations
— HAVING euclidean < 1
— ORDER BY euclidean ASC LIMIT 10;


Two Surprises/Issues

(1) SRID (Spatial Reference Identifier) support in MySQL is a disgrace – In MySQL, the SRID value is just an integer associated with the geometry value. All calculations are done assuming Euclidean (planar) geometry.

Possible Workarounds: (1) Euclidean calculation can be enough (2) Use Haversine function if you need the accuracy

(2) MySQL ‘Where’ clause can’t use column aliases for filtering

Possible Workarounds: (1) Use ‘Having’ clause (see above) (2) Use the explicit function or field and not the alias

*** *** *** *** *** *** *** *** *** *** *** ***

Related/Interesting reference – Geo Distance Search with MySQL (Presentation | 2008)

Important note: To help new AWS customers get started in the cloud, AWS is introducing a new free usage tier. Beginning November 1, new AWScustomers will be able to run a free Amazon EC2 Micro Instance for a year, while also leveraging a new free usage tier for Amazon S3, Amazon Elastic Block Store, Amazon Elastic Load Balancing, and AWSdata transfer – Very Exciting Times!!!

Disturbing unrelated fact: Starting in 1931, every graduate of the Japanese Naval Academy was asked: “How would you carry out a surprise attack on Pearl Harbor?”

→ 3 CommentsTags: Cloud · Development · Disruptive Technology · Geo