We have loaded Open Street Map points of interests in the article The Most Popular Pub Names — which compares PostgreSQL with MongoDB for simple geographical queries, and is part of our PostgreSQL Extensions article series. In today’s article, look at how to geolocalize an IP address and locate the nearest pub, all within a single SQL query!

For that, we are going to use the awesome ip4r extension from RhodiumToad.

Geolocation Data Loading

The first step is to find an geolocation database, and several providers are offering that. The one I did choose for that example is the http://www.maxmind.com free database available at GeoLite Free Downloadable Databases.

After having had a look at the files in there, we define the table schema we want and load the archive, using pgloader. So, first, the target schema is created using the following script:

create extension if not exists ip4r; create schema if not exists geolite; create table if not exists geolite. location ( locid integer primary key , country text , region text , city text , postalcode text , location point, metrocode text , areacode text ); create table if not exists geolite.blocks ( iprange ip4r, locid integer ); create index blocks_ip4r_idx on geolite.blocks using gist(iprange);

And the data can now be imported to those target tables thanks to the following pgloader command, quite involved:

/* * Loading from a ZIP archive containing CSV files. */ LOAD ARCHIVE FROM http://geolite.maxmind.com/download/geoip/database/GeoLiteCity_CSV/GeoLiteCity-latest.zip INTO postgresql://[email protected]/appdev BEFORE LOAD EXECUTE 'geolite.sql' LOAD CSV FROM FILENAME MATCHING ~/GeoLiteCity-Location.csv/ WITH ENCODING iso-8859-1 ( locId, country, region [ null if blanks ], city [ null if blanks ], postalCode [ null if blanks ], latitude, longitude, metroCode [ null if blanks ], areaCode [ null if blanks ] ) INTO postgresql://[email protected]/appdev TARGET TABLE geolite.location ( locid,country,region,city,postalCode, location point using (format nil "(~a,~a)" longitude latitude), metroCode,areaCode ) WITH skip header = 2, drop indexes, fields optionally enclosed by '"', fields escaped by double-quote, fields terminated by ',' AND LOAD CSV FROM FILENAME MATCHING ~/GeoLiteCity-Blocks.csv/ WITH ENCODING iso-8859-1 ( startIpNum, endIpNum, locId ) INTO postgresql://[email protected]/appdev TARGET TABLE geolite.blocks ( iprange ip4r using (ip-range startIpNum endIpNum), locId ) WITH skip header = 2, drop indexes, fields optionally enclosed by '"', fields escaped by double-quote, fields terminated by ',';

The pgloader command describe the file format so that pgloader can parse the data from the CSV file and transform it in memory to the format we expect in PostgreSQL. The location in the CSV file is given as two separate fields latitude and longitude , which we use to form a single point column.

In the same vein, in the pgloader command we also describe how to transform a IP address range from a couple of integers to a more classic representation of the same data:

CL-USER> ( pgloader.transforms::ip-range "16777216" "16777471" ) "1.0.0.0-1.0.0.255"

The pgloader command also finds the files we want to load independantly of the real name of the directory, here GeoLiteCity_20180327 . So when there’s a new release of the Geolite files, you can run the pgloader all over again and expect it to load the new data.

And here’s what the output of the pgloader command looks like. Note that I have stripped the timestamps from the logs output, in order for the line to make sense when printed in those pages:

$ pgloader --verbose geolite.load NOTICE Starting pgloader, log system is ready. LOG Data errors in '/private/tmp/pgloader/' LOG Parsing commands from file #P"/Users/dim/dev/yesql/src/1-application-development/data/geolite/geolite.load" LOG Fetching 'http://geolite.maxmind.com/download/geoip/database/GeoLiteCity_CSV/GeoLiteCity-latest.zip' LOG Extracting files from archive '/var/folders/bh/t1wcr6cx37v4h87yj3qj009r0000gn/T/GeoLiteCity-latest.zip' NOTICE unzip -o "/var/folders/bh/t1wcr6cx37v4h87yj3qj009r0000gn/T/GeoLiteCity-latest.zip" -d "/var/folders/bh/t1wcr6cx37v4h87yj3qj009r0000gn/T/GeoLiteCity-latest/" NOTICE Executing SQL block for before load NOTICE ALTER TABLE "geolite"."location" DROP CONSTRAINT IF EXISTS "location_pkey"; NOTICE COPY "geolite"."location" NOTICE Opening #P"/private/var/folders/bh/t1wcr6cx37v4h87yj3qj009r0000gn/T/GeoLiteCity-latest/GeoLiteCity_20180327/GeoLiteCity-Location.csv" NOTICE copy "geolite"."location": 234105 rows done, 11.5 MB, 2.1 MBps NOTICE copy "geolite"."location": 495453 rows done, 24.3 MB, 2.2 MBps NOTICE copy "geolite"."location": 747550 rows done, 37.1 MB, 2.2 MBps NOTICE CREATE UNIQUE INDEX location_pkey ON geolite.location USING btree (locid) NOTICE ALTER TABLE "geolite"."location" ADD PRIMARY KEY USING INDEX "location_pkey"; NOTICE DROP INDEX IF EXISTS "geolite"."blocks_ip4r_idx"; NOTICE COPY "geolite"."blocks" NOTICE Opening #P"/private/var/folders/bh/t1wcr6cx37v4h87yj3qj009r0000gn/T/GeoLiteCity-latest/GeoLiteCity_20180327/GeoLiteCity-Blocks.csv" NOTICE copy "geolite"."blocks": 227502 rows done, 7.0 MB, 1.8 MBps NOTICE copy "geolite"."blocks": 492894 rows done, 15.2 MB, 1.9 MBps NOTICE copy "geolite"."blocks": 738483 rows done, 22.9 MB, 2.0 MBps NOTICE copy "geolite"."blocks": 986719 rows done, 30.7 MB, 2.1 MBps NOTICE copy "geolite"."blocks": 1246450 rows done, 38.9 MB, 2.2 MBps NOTICE copy "geolite"."blocks": 1489726 rows done, 47.1 MB, 2.2 MBps NOTICE copy "geolite"."blocks": 1733633 rows done, 55.1 MB, 2.2 MBps NOTICE copy "geolite"."blocks": 1985222 rows done, 63.3 MB, 2.2 MBps NOTICE CREATE INDEX blocks_ip4r_idx ON geolite.blocks USING gist (iprange) LOG report summary reset table name errors read imported bytes total time ----------------------- --------- --------- --------- --------- -------------- download 0 0 0 0.793s extract 0 0 0 0.855s before load 0 5 5 0.033s fetch 0 0 0 0.005s ----------------------- --------- --------- --------- --------- -------------- "geolite"."location" 0 928138 928138 46.4 MB 20.983s "geolite"."blocks" 0 2108310 2108310 67.4 MB 30.695s ----------------------- --------- --------- --------- --------- -------------- Files Processed 0 2 2 0.024s COPY Threads Completion 0 4 4 51.690s Index Build Completion 0 0 0 49.363s Create Indexes 0 2 2 49.265s Constraints 0 1 1 0.002s ----------------------- --------- --------- --------- --------- -------------- Total import time ✓ 3036448 3036448 113.8 MB 2m30.344s

We can see that pgloader has dropped the indexes before loading the data, and created them again once the data is loaded, in parallel to loading data from the next table. This parallel processing can be a huge benefit on beefy servers.

So we now have the following tables to play with:

List of relations Schema │ Name │ Type │ Owner │ Size │ Description ═════════╪══════════╪═══════╪════════╪═══════╪═════════════ geolite │ blocks │ table │ appdev │ 89 MB │ geolite │ location │ table │ appdev │ 64 MB │ (2 rows)

Finding an IP Address in our Ranges

Here’s what the main data look like:

table geolite.blocks limit 10 ;

The TABLE command is per SQL standard, so we might as well use it:

iprange │ locid ═════════════════════╪════════ 1.0.0.0/24 │ 617943 1.0.1.0-1.0.3.255 │ 104084 1.0.4.0/22 │ 17 1.0.8.0/21 │ 47667 1.0.16.0/20 │ 879228 1.0.32.0/19 │ 47667 1.0.64.0-1.0.81.255 │ 885221 1.0.82.0/24 │ 902132 1.0.83.0-1.0.86.255 │ 885221 1.0.87.0/24 │ 873145 (10 rows)

What we have here is a classic ip range column where we can see that the datatype output function is smart enough to display ranges either in their CIDR notation or in the more general start-end notation when no CIDR applies.

The ip4r extension provides several operators to work with the dataset we have, some of those operators are supported by the index we just created. And just for the fun of it here’s a catalog query to inquire about them:

select amopopr::regoperator from pg_opclass c join pg_am am on am.oid = c .opcmethod join pg_amop amop on amop.amopfamily = c .opcfamily where opcintype = 'ip4r' ::regtype and am.amname = 'gist' ;

The catalog query above joins the PostgreSQL catalogs for operator classes, index access methods on the notion of an operator family in order to retrieve the list of operators associated with the ip4r data type and the GiST access method:

amopopr ════════════════ >>=(ip4r,ip4r) <<=(ip4r,ip4r) >>(ip4r,ip4r) <<(ip4r,ip4r) &&(ip4r,ip4r) =(ip4r,ip4r) (6 rows)

Note that we could have been using the psql \dx+ ip4r command instead of course, but that query directly list operators that the GiST index knows how to solve. The operator >>= reads as contains and is the one we’re going to use here.

select iprange, locid from geolite.blocks where iprange >>= '91.121.37.122' ;

And here’s the range in which we find the IP address 91.121.37.122, and the location it’s associated with:

iprange │ locid ══════════════════════════╪═══════ 91.121.0.0-91.121.71.255 │ 75

This lookup is fast, thanks to our specialized GiST index, its timing is under a millisecond.

Geolocation meta-data

Now with the MaxMind schema that we are using in that example, the interesting data actually is to be found in the other table, the geolite.location one. Let’s use another IP address now, I’m told by the unix command host that google.us has address 74.125.195.147 and we can inquire where is that IP from:

select * from geolite.blocks join geolite. location using (locid) where iprange >>= '74.125.195.147' ;

Our data locates the Google IP address in Mountain View, which is credible:

─[ RECORD 1 ]─────────────────────────── locid │ 2703 iprange │ 74.125.191.0-74.125.223.255 country │ US region │ CA city │ Mountain View postalcode │ 94043 location │ (-122.0574,37.4192) metrocode │ 807 areacode │ 650

Now you can actually draw that on a map as you have the location information as a point datatype containing both the longitude and latitude.

Emergency Pub

What if you want to make an application to help lost souls find the nearest pub from where they are currently? Now that you know their location from the IP address they are using in their browser, it should be easy enough right?

As we downloaded a list of pubs from the UK, we are going to use an IP address that should be in the UK too.

See my project https://github.com/dimitri/pubnames for loading the same set of data on your local database, or read the article The Most Popular Pub Names for more about this OpenStreetMap data loading.

$ host www.ox.ac.uk www.ox.ac.uk has address 129 .67.242.154 www.ox.ac.uk has address 129 .67.242.155

Knowing that, we can search the geolocation of this IP address:

select * from geolite. location l join geolite.blocks using (locid) where iprange >>= '129.67.242.154' ;

And the Oxford University is actually hosted in Oxford, it seems:

─[ RECORD 1 ]───────────── locid │ 375290 country │ GB region │ K2 city │ Oxford postalcode │ OX1 location │ (-1.25,51.75) metrocode │ ¤ areacode │ ¤ iprange │ 129.67.0.0/16

What are the ten nearest pubs around if you’re just out of the Oxford University? Well, let’s figure that out before we get thirsty!

select pubs.name, round((pubs.pos <@> l. location ):: numeric , 3 ) as miles, ceil( 1609 . 34 * (pubs.pos <@> l. location ):: numeric ) as meters from geolite. location l join geolite.blocks using (locid) left join lateral ( select name, pos from pubnames order by pos <-> l. location limit 10 ) as pubs on true where blocks.iprange >>= '129.67.242.154' order by meters;

And here’s the list, obtained in around about a millisecond on my laptop:

name │ miles │ meters ════════════════════╪═══════╪════════ The Bear │ 0.268 │ 431 The Half Moon │ 0.280 │ 451 The Wheatsheaf │ 0.295 │ 475 The Chequers │ 0.314 │ 506 The Old Tom │ 0.315 │ 507 Turl Bar │ 0.321 │ 518 St Aldate's Tavern │ 0.329 │ 530 The Mad Hatter │ 0.337 │ 542 King's Arms │ 0.397 │ 639 White Horse │ 0.402 │ 647 (10 rows)

The first run of this query wasn’t very fast, until we realized with friends on the IRC channel for PostgreSQL help that the plan was using parallel processing. The size of the dataset means that the parallel setup cost is going to ruin our performance here, so I’ve been doing the following to get the query to run in 1ms rather than 15ms: set parallel_setup_cost to 10000 ;

So with PostgreSQL and some easily available extensionsm, we are actually capable of performing advanced geolocation lookups in a single SQL query. The timing of this query makes it possible to use it in production and serve users requests directly from it, too!

Conclusion

While some geolocation data provider are giving you some libs and code to do quite fast lookups, any interesting thing you want to do with the geolocation data is about the meta data. And that’s where yet again PostgreSQL shines: you can actually use specialized data types and operators, JOINs and kNN searches, all from within a single query. You get back only those results you are interested into, and the application is then responsible for adding value to that, rather than processing the data itself.

Typically what the application here would be doing is drawing a map and locating the pubs on it, adding maybe descriptions and votes and notes on each address, maybe even the draft menu. An ideal application might even be able to join the draft menu of each nearby pub against your own preferences and offer you a nice short list ordered by what you’re most likely to want to drink at this hour.

Living in the future is both exciting and frightening!