Related MicroZone Resources

Proven in Production: Clustrix Case Studies

INFOGRAPHIC: The Future of the Database

Clustrix Whitepapers

What We Offer: Clustrix Features

Like this piece? Share it with your friends:

| More

Originally written by Alexander Rubin

In my previous post about geo-spatial search in MySQL I described (along with other things) how to use geo-distance functions. In this post I will describe the geo-spatial distance functions in more details.

If you need to calculate an exact distance between 2 points on Earth in MySQL (very common for geo-enabled applications) you have at least 3 choices.

MySQL stored function for calculating distance on Earth

I previously gave an example for a MySQL-stored function which implements the haversine formula. However, the approach I used was not very precise: it was optimized for speed. If you need a more precise haversine formula implementation you can use this function (result will be in miles):

delimiter //create DEFINER = CURRENT_USER function haversine_distance_sp (lat1 double, lon1 double, lat2 double, lon2 double) returns double begin declare R int DEFAULT 3958.76; declare phi1 double; declare phi2 double; declare d_phi double; declare d_lambda double; declare a double; declare c double; declare d double; set phi1 = radians(lat1); set phi2 = radians(lat2); set d_phi = radians(lat2-lat1); set d_lambda = radians(lon2-lon1); set a = sin(d_phi/2) * sin(d_phi/2) + cos(phi1) * cos(phi2) * sin(d_lambda/2) * sin(d_lambda/2); set c = 2 * atan2(sqrt(a), sqrt(1-a)); set d = R * c; return d; end;//delimiter ;

(the algorithm is based on the standard formula, I’ve used the well-known Movable Type scripts calculator )

This is a slower implementation as it uses arctangent , however it is more precise.

MySQL UDF for Haversine distance

Another approach, which will give you much more performance is to use UDF. There are a number of implementations, I’ve used lib_mysqludf_haversine .

Here is the simple steps to install it in MySQL 5.6 (will also work with earlier versions):

$ wget 'https://github.com/lucasepe/lib_mysqludf_haversine/archive/master.zip'$ unzip master.zip$ cd lib_mysqludf_haversine-master/$ makemysql> show global variables like 'plugin%';+---------------+-------------------------+| Variable_name | Value |+---------------+-------------------------+| plugin_dir| /usr/lib/mysql/plugin |+---------------+-------------------------+1 row in set (0.00 sec)$ sudo cp lib_mysqludf_haversine.so /usr/lib/mysql/plugin/mysql> CREATE FUNCTION haversine_distance RETURNS REAL SONAME 'lib_mysqludf_haversine.so';mysql> select haversine_distance(37.4702954, -122.572938858498, 37.760150536, -122.20701914150199, 'mi') as dist_in_miles;+---------------+| dist_in_miles |+---------------+| 28.330467 |+---------------+1 row in set (0.00 sec)

Please note:

MySQL ST_distance function

In MySQL 5.6 you can use ST_distance function:

mysql> select st_distance(point(37.4702954, -122.572938858498), point( 37.760150536, -122.20701914150199)) as distance_plane;+---------------------+| distance_plane|+---------------------+| 0.46681174155173943 |+---------------------+1 row in set (0.00 sec)

As we can see it does not give us an actual distance in mile or kilometers as it does not take into account that we have latitude and longitude, rather than X and Y on plane.

Geo Distance Functions Performance

The stored procedures and functions in MySQL are known to be slower, especially with trigonometrical functions. I’ve did a quick test, using MySQL function benchmark .

First I set 2 points (10 miles from SFO airport)

set @rlon1 = 122.572938858498;set @rlat1 = 37.4702954;set @rlon2 = -122.20701914150199;set @rlat2 = 37.760150536;

Next I use 4 function to benchmark:

The benchmark function will execute the above function 100000 times.

Here are the results:

mysql>select benchmark(100000,haversine_old_sp(@rlat1, @rlon1, @rlat2, @rlon2)) as less_precise_mysql_stored_proc;+--------------------------------+| less_precise_mysql_stored_proc |+--------------------------------+|0 |+--------------------------------+1 row in set (1.46 sec)mysql>select benchmark(100000,haversine_distance_sp(@rlat1, @rlon1, @rlat2, @rlon2)) as more_precise_mysql_stored_proc;+--------------------------------+| more_precise_mysql_stored_proc |+--------------------------------+|0 |+--------------------------------+1 row in set (2.58 sec)mysql>select benchmark(100000,haversine_distance(@rlat1, @rlon1, @rlat2, @rlon2, 'mi')) as udf_haversine_function;+------------------------+| udf_haversine_function |+------------------------+|0 |+------------------------+1 row in set (0.17 sec)mysql> select benchmark(100000, st_distance(point(@rlat1, @rlon1), point(@rlat2, @rlon1))) as mysql_builtin_st_distance;+---------------------------+| mysql_builtin_st_distance |+---------------------------+| 0 |+---------------------------+1 row in set (0.10 sec)

As we can see the UDF gives much faster response time (which is comparable to built-in function).

Benchmark chart (smaller the better)

Conclusion

The lib_mysqludf_haversine UDF provides a good function for geo-distance search in MySQL. Please let me know in the comments what geo-distance functions or approaches do you use in your applications.

Published at DZone with permission ofPeter Zaitsev, author and DZone MVB. ( source )

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)