A Solution -- First, the principles

Representation choices

GCDist -- Compute "Great Circle Distance"

Required TABLE Structure

FindNearest

The code for deg*10000 and 'miles'

Postlog

You want to find the nearest 10 pizza parlors, but you cannot figure out how to do it efficiently in your huge database. Database tables are good at one-dimensional indexing, but poor at two-dimensions.

You might have tried

⚈ INDEX(lat), INDEX(lng) -- but the optimizer used only one

⚈ INDEX(lat,lng) -- but it still had to work too hard

⚈ Sometimes you ended up with a full table scan -- Yuck.

PARTITIONs in MySQL give you a way to sort of have two clustered indexes. So, if we could slice up (partition) the globe in one dimension and use ordinary indexing in the other dimension, maybe we can get something approximating a 2D index.

It works. Not perfectly, but better than the alternatives.

What to PARTITION on? It seems like latitude or longitude would be a good idea. Note that longitudes vary in width, from 69 miles at the equator, to 0 at the poles. So, latitude seems like a better choice.

How many PARTITIONs? It does not matter a lot. Some thoughts:

⚈ 90 - 2 degrees each. (I don't like tables with too many partitions; 90 seems like plenty.)

⚈ 50-100 - evenly populated. (This requires code. For 2.7M placenames, 85 partitions varied from 0.5 degrees to very wide partitions at the poles.)

How to PARTITION? Well, MySQL is very picky. So FLOAT/DOUBLE are out. DECIMAL is out. So, we are stuck with some kludge. Essentially, we need to convert Lat/Lng to integers and use PARTITION BY RANGE.

To get to a datatype that can be used in PARTITION, you need to "scale" the latitude and longitude.

Datatype Bytes resolution ------------------ ----- -------------------------------- Deg*100 (SMALLINT) 4 1570 m 1.0 mi Cities DECIMAL(4,2)/(5,2) 5 1570 m 1.0 mi Cities SMALLINT scaled 4 682 m 0.4 mi Cities Deg*10000 (MEDIUMINT) 6 16 m 52 ft Houses/Businesses DECIMAL(6,4)/(7,4) 7 16 m 52 ft Houses/Businesses MEDIUMINT scaled 6 2.7 m 8.8 ft FLOAT 8 1.7 m 5.6 ft DECIMAL(8,6)/(9,6) 9 16cm 1/2 ft Friends in a mall Deg*10000000 (INT) 8 16mm 5/8 in Marbles DOUBLE 16 3.5nm ... Fleas on a dog(Sorted by resolution)

What these mean...

Deg*100 (SMALLINT) -- you take the lat/lng, multiply by 100, round, and store into a SMALLINT. That will take 2 bytes for each dimension, for a total of 4 bytes. Two items might be a mile apart, but register as having the same latitude and longitude.

DECIMAL(4,2) for Latitude and DECIMAL(5,2) for Longitude will take 2+3 bytes and have no better resolution than Deg*100.

SMALLINT scaled -- Convert Latitude into a SMALLINT SIGNED by doing (degrees / 90 * 32767) and rounding; Longitude by (degrees / 180 * 32767).

FLOAT has 24 significant bits; DOUBLE has 53. (They don't work with PARTITIONing but are included for completeness. Often people use DOUBLE without realizing how much an overkill it is, and how much space it takes.)

Sure, you could do DEG*1000 and other "in between" cases, but there is no advantage. DEG*1000 takes as much space as DEG*10000, but has less resolution.

So, go down the list to see how much resolution you need, then pick an encoding you are comfortable with.

GCDist is a helper FUNCTION that correctly computes the distance between two points on the globe.

The code has been benchmarked at about 20 microseconds per call on a 2011-vintage PC. If you had to check a million points, that would take 20 seconds -- far too much for a web application. So, one goal of the Procedure that uses it will be to minimize the usage. With the code presented here, the function need be called only a few dozen or few hundred times, except in pathological cases.

Sure, you could use the Pythagorean formula. And it would work for most applications. But it does not take extra effort to do the GC. Furthermore, GC works across a pole and across the dateline. And, a Pythagorean function is not that much faster.

For efficiency, GCDist understands the scaling you picked and has that stuff hardcoded. I am picking "Deg*10000", so the function expects 350000 for 35 degrees. If you choose a different scaling, you will need to change the code.

GCDist() takes 4 scaled DOUBLEs -- lat1, lon1, lat2, lon2 -- and returns a scaled number of "degrees" representing the distance.

The table of Representation Choices says 52 feet of resolution for DECIMAL(x,4). Here is how it was calculated: To measuring a diagonal between lat/lng (0,0) and (0.0001,00001) (one 'unit in the last place'):

⚈ 69.172 miles/degree of latitude

⚈ 10000 units per degree for the scaling chosen

⚈ 5280 feet / mile.

(No, this function does not compensate for the Earth being an oblate spheroid, etc.)

For various reasons, you must create your table this way...

Required fields:

⚈ id -- unique; probably an AUTO_INCREMENT; not necessarily 'owned' by this table

⚈ lat -- scaled (see above) latitude

⚈ lon -- scaled longitude

⚈ any fields needed filtering

⚈ PRIMARY KEY(lon, id, lat)

⚈ INDEX(id) -- (optional) if AUTO_INCREMENT here, or if need to JOIN from another table (for maintenance)

⚈ ENGINE=InnoDB -- so the PRIMARY KEY will be "clustered"

To elaborate on the PK:

⚈ lon -- must be first; the algorithm needs "clustering" on it.

⚈ lat -- because every UNIQUE key in a PARTITIONed table must include the partition 'key'

⚈ id -- because the PK must be unique.

Filtering... An argument to the FindNearest procedure includes a Boolean expression ("condition") for a WHERE clause. If you don't need any filtering, pass in "1". If you do need filtering, the fields you are going to test must be included in this table to avoid lots of JOINing to another table. To avoid "SQL injection", do not let web users put arbitrary expressions in the "condition" without making sure it is safe.

The FindNearest PROCEDURE will do multiple SELECTs something like this:

WHERE lat BETWEEN @my_lat - @dlat AND @my_lat + @dlat -- PARTITION Pruning and bounding box AND lon BETWEEN @my_lon - @dlon AND @my_lon + @dlon -- first part of PK AND _condition_The query planner will

⚈ Do PARTITION "pruning" based on the latitude;

⚈ Within a PARTITION (which is effectively a table), use lon do a 'clustered' range scan;

⚈ Then the "condition" come into play, and lat is rechecked.

This design leads to very few disk blocks needing to be read, which is the main goal of the design.

Note that this does not even call GCDist. That comes in the last pass when the ORDER BY and LIMIT are used.

There will be at least two SELECTs, but with proper tuning; usually no more than 6 SELECTs will be needed.

Here's the gist of it.

⚈ Make a guess at how close to "me" to look.

⚈ See how many items are in a 'square' around me, after filtering.

⚈ If not enough, repeat, doubling the width of the square.

⚈ After finding enough, or giving up because we are looking "too far", make one last pass to get all the data, ORDERed and LIMITed

Note that the loop merely uses 'squares' of lat/lng ranges. This is crude, but fits well with the partitioning and indexing, without needing excessive calls to GCDist. In the sample code, I picked 15 miles as starting value. Adjusting this will have some impact on the Procedure's performance, but the impact will vary with the use cases. A rough way to set the radius is to guess what will find the desired LIMIT about half the time. (This value is hardcoded in the PROCEDURE.)

Parameters passed into FindNearest():

⚈ Latitude -- -90..90 (not scaled -- see hardcoded conversion in PROCEDURE)

⚈ Longitude -- -180..180 (not scaled)

⚈ Max distance -- in miles or km -- see hardcoded conversion in PROCEDURE

⚈ Limit -- maximum number of items to return

⚈ Condition -- something to put after 'AND' (more discussion above)

The function will find the nearest items, up to Limit that meet the Condition. But it will give up at Max distance. (If you are at the South Pole, why bother searching very far for the tenth pizza parlor?)

Because of the "scaling", "hardcoding", "Condition", the table name, etc, this PROCEDURE is not truly generic and must be recoded for each application. Yes, I could have designed it to pass all that stuff in. But what a mess.

Timing: 10-30ms for "typical" usage; any dataset size. 300ms for pathological cases.

End-cases:

⚈ By using GC distance, not Pythagoras, distances are 'correct' even near poles.

⚈ Poles -- Even if the "nearest" is 180 degrees away (longitude), it can find it.

⚈ Dateline -- There is a small, 'contained', piece of code for crossing the Dateline. Example: you are at +179 deg longitude, and the nearest item is at -179.

The procedure returns one resultset,

⚈ Only rows that meet your Condition, within Max distance are returned

⚈ At most Limit rows are returned

⚈ The rows will be ordered, "closest" first.

⚈ "dist" will be in miles or km (based on a hardcoded constant in the SP)

This version is based on scaling "Deg*10000 (MEDIUMINT)".

DELIMITER // drop function if exists GCDist // CREATE FUNCTION GCDist ( _lat1 DOUBLE, -- Scaled Degrees north for one point _lon1 DOUBLE, -- Scaled Degrees west for one point _lat2 DOUBLE, -- other point _lon2 DOUBLE ) RETURNS DOUBLE DETERMINISTIC CONTAINS SQL -- SQL but does not read or write BEGIN -- Hardcoded constant: DECLARE _deg2rad DOUBLE DEFAULT PI()/1800000; -- For scaled by 1e4 to MEDIUMINT DECLARE _rlat1 DOUBLE DEFAULT _deg2rad * _lat1; DECLARE _rlat2 DOUBLE DEFAULT _deg2rad * _lat2; -- compute as if earth's radius = 1.0 DECLARE _rlond DOUBLE DEFAULT _deg2rad * (_lon1 - _lon2); DECLARE _m DOUBLE DEFAULT COS(_rlat2); DECLARE _x DOUBLE DEFAULT COS(_rlat1) - _m * COS(_rlond); DECLARE _y DOUBLE DEFAULT _m * SIN(_rlond); DECLARE _z DOUBLE DEFAULT SIN(_rlat1) - SIN(_rlat2); DECLARE _n DOUBLE DEFAULT SQRT( _x * _x + _y * _y + _z * _z ); RETURN 2 * ASIN(_n / 2) / _deg2rad; -- again--scaled degrees END; // -- FindNearest (about my 6th approach) drop procedure if exists FindNearest6 // CREATE PROCEDURE FindNearest ( IN _my_lat DOUBLE, -- Latitude of me [-90..90] (not scaled) IN _my_lon DOUBLE, -- Longitude [-180..180] IN _max_dist DOUBLE, -- Limit how far to search: miles or km IN _limit INT, -- How many items to try to get IN _condition VARCHAR(1111) -- ANDed; restricted to *Locations* fields ) DETERMINISTIC BEGIN -- lat and lng are in degrees -90..+90 and -180..+180 -- All computations done in Latitude degrees. -- Thing to tailor -- *id* = column that JOINs *Locations* and *Info* -- *Locations*, *Info* -- the two tables -- Scaling of lat, lon; here using *10000 in MEDIUMINT -- Table name -- Hardcoded constant: DECLARE _deg2rad DOUBLE DEFAULT PI()/1800000; -- For scaled by 1e4 to MEDIUMINT -- Cannot use params in PREPARE, so switch to @variables: -- Hardcoded constant: SET @my_lat := _my_lat * 10000, @my_lon := _my_lon * 10000, @deg2dist := 0.0069172, -- 69.172 for miles; 111.325 for km @start_deg := 15 / @deg2dist, -- Start with this radius first (15 miles) @max_deg := _max_dist / @deg2dist, @cutoff := @max_deg / SQRT(2), -- (slightly pessimistic) @dlat := @start_deg, -- note: must stay positive @lon2lat := COS(_deg2rad * @my_lat), @iterations := 0; -- just debugging -- Loop through, expanding search -- Search a 'square', repeat with bigger square until find enough rows -- If the inital probe found _limit rows, then probably the first -- iteration here will find the desired data. -- Hardcoded table name: SET @sql = CONCAT( "SELECT COUNT(*) INTO @near_ct FROM cities6 WHERE lat BETWEEN @my_lat - @dlat AND @my_lat + @dlat -- PARTITION Pruning and bounding box AND lon BETWEEN @my_lon - @dlon AND @my_lon + @dlon -- first part of PK AND ", _condition); PREPARE _sql FROM @sql; MainLoop: LOOP SET @iterations := @iterations + 1; -- The main probe: Search a 'square' SET @dlon := ABS(@dlat / @lon2lat); -- good enough for now -- note: must stay positive -- Hardcoded constants: SET @dlon := IF(ABS(@my_lat) + @dlat >= 900000, 3600001, @dlon); -- near a Pole EXECUTE _sql; IF ( @near_ct >= _limit OR -- Found enough @dlat >= @cutoff ) THEN -- Give up (too far) LEAVE MainLoop; END IF; -- Expand 'square': SET @dlat := LEAST(2 * @dlat, @cutoff); -- Double the radius to search END LOOP MainLoop; DEALLOCATE PREPARE _sql; -- Out of loop because found _limit items, or going too far. -- Expand range by about 1.4 (but not past _max_dist), -- then fetch details on nearest 10. -- Hardcoded constant: SET @dlat := IF( @dlat >= @max_deg OR @dlon >= 1800000, @max_deg, GCDist(ABS(@my_lat), @my_lon, ABS(@my_lat) - @dlat, @my_lon - @dlon) ); -- ABS: go toward equator to find farthest corner (also avoids poles) -- Dateline: not a problem (see GCDist code) -- Reach for longitude line at right angle: -- sin(dlon)*cos(lat) = sin(dlat) -- Hardcoded constant: SET @dlon := IFNULL(ASIN(SIN(_deg2rad * @dlat) / COS(_deg2rad * @my_lat)) / _deg2rad -- precise , 3600001); -- must be too near a pole -- Hardcoded constants: IF (ABS(@my_lon) + @dlon < 1800000 OR -- Usual case - not crossing dateline ABS(@my_lat) + @dlat < 900000) THEN -- crossing pole, so dateline not an issue -- Hardcoded table name: SET @sql = CONCAT( "SELECT *, @deg2dist * GCDist(@my_lat, @my_lon, lat, lon) AS dist FROM cities6 WHERE lat BETWEEN @my_lat - @dlat AND @my_lat + @dlat -- PARTITION Pruning and bounding box AND lon BETWEEN @my_lon - @dlon AND @my_lon + @dlon -- first part of PK AND ", _condition, " HAVING dist <= ", _max_dist, " ORDER BY dist LIMIT ", _limit ); PREPARE _sql FROM @sql; EXECUTE _sql; DEALLOCATE PREPARE _sql; ELSE -- Hardcoded constants and table name: -- Circle crosses dateline, do two SELECTs, one for each side SET @west_lon := IF(@my_lon < 0, @my_lon, @my_lon - 3600000); SET @east_lon := @west_lon + 3600000; -- One of those will be beyond +/- 180; this gets points beyond the dateline SET @sql = CONCAT( "( SELECT *, @deg2dist * GCDist(@my_lat, @west_lon, lat, lon) AS dist FROM cities6 WHERE lat BETWEEN @my_lat - @dlat AND @my_lat + @dlat -- PARTITION Pruning and bounding box AND lon BETWEEN @west_lon - @dlon AND @west_lon + @dlon -- first part of PK AND ", _condition, " HAVING dist <= ", _max_dist, " ) UNION ALL ( SELECT *, @deg2dist * GCDist(@my_lat, @east_lon, lat, lon) AS dist FROM cities6 WHERE lat BETWEEN @my_lat - @dlat AND @my_lat + @dlat -- PARTITION Pruning and bounding box AND lon BETWEEN @east_lon - @dlon AND @east_lon + @dlon -- first part of PK AND ", _condition, " HAVING dist <= ", _max_dist, " ) ORDER BY dist LIMIT ", _limit ); PREPARE _sql FROM @sql; EXECUTE _sql; DEALLOCATE PREPARE _sql; END IF; END; // DELIMITER ;

Original writing -- Apr, 2012 Requires at least MySQL 5.1

Akiban has a builtin mechanism for doing the equivalent. It involves a "Z-Order" index and can mix with other conditions in a WHERE clause. See Akiban Spatial Indexes

(Link may be broken now that FoundationDB and Akiban have merged.)

Contact me by posting a question at MySQL Forums :: Performance

-- Rick James

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Compound INDEXes plus other insights into the mysteries of INDEXing

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Entity-Attribute-Value -- a common, poorly performing, design patter; plus an alternative

Find the nearest 10 pizza parlors (efficient searching on Latitude + Longitude)

Alter of a Huge table

Latest 10 news articles -- how to optimize the schema and code for such

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Techniques on efficiently finding a random row (On beyond ORDER BY RAND())

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