This guide will show you how to import GeoIP2 or GeoLite2 databases into MySQL so that they can be easily queried and manipulated on your server.
Importing a CSV database consists of downloading the database, extracting it, creating tables to hold the data, and indexing those tables for faster querying.
Begin by making sure you have downloaded the latest version of the GeoIP2 or GeoLite2 database that you want to import. You can download databases through your account portal. CSV format databases are shipped as a single zip file. See our CSV database documentation for details about the zip structure and contents of the archive. For the purposes of this tutorial, we will be using the GeoIP2 City CSV file, but you can get information about any of our CSV format databases and adapt the following instructions accordingly.
If you are importing databases for use in an ongoing manner, you will want to automate the process of downloading and extracting the CSV files so that you can be sure that your database is always up to date.
Once you have downloaded the database, unpack the zip file in your desired directory. If you are importing the GeoIP2 City database, you will have a number of files. In this tutorial we will be working with the following files:
If you are working in a language other than English, you can choose the
Locations file from the zip archive. For example, if you want to
load Chinese location names into MySQL, you can use
GeoIP2-City-Locations-zh-CN.csv instead of
documentation on CSV format databases includes a list of all location files
currently included in GeoIP2 and GeoLite2
First we create a table to hold the network information contained in
You can find the full description of the schema for these files in the section
Blocks files for GeoIP2 and GeoLite2 CSV
databases. If you are
working with a different database, you can find the
Blocks file schema for
the appropriate database, and adapt the table to meet
network field of the database uses CIDR notation. Unfortunately MySQL
doesn't offer any capabilities to work with data in that format, so we'll first
have to convert the networks into something else that we can easily query later
on. We chose to represent networks as a pair of IP addresses that are the first
and last address in the network, respectively. We will convert this field so
that both of these IP addresses will be represented as hexadecimal numbers.
We can use the database conversion tool to convert this field to hexadecimal numbers. Once you have downloaded the program and installed it to the same directory where you have extracted your CSV files, you can run it:
We can now create a table called
geoip2_networkto hold the data we just
converted. We'll represent the IP addresses using the type
which will be large enough to represent 128-bit (16 byte) IPv6 addresses.
Note that we're adding two separate indexes for
network_end. If we used a composite index over both columns, we wouldn't be
able to speed up the queries we'll be using later on.
We can now import the contents of
GeoIP2-City-Blocks-IPv6.csv into the table we just created.
We'll start by loading the converted IPv6 data:
We can load the converted IPv4 data in the same way:
With everything loaded we're now ready to look up an IP address in the database.
As we're representing IP addresses as
varbinary(16), we'll first have to
convert the textual representation of the IP address we're interested in to that
same type using MySQL's built-in
While that yields the correct results, we notice that the query performed so poorly that it will not be practical in many applications.
One way of speeding up the query is the addition of
order by network_end like
While that addresses the performance concern we've had with the previous query, this query will still perform poorly for addresses not contained in our GeoIP2 database:
We can work around this problem by breaking our query up into two parts such that MySQL will be able to use the indexes we've created more efficiently:
Using that construct gives us good query performance for all addresses, regardless of whether or not the GeoIP2 database contains any information about them. Depending on your application, you might want to consider encapsulating this complexity/verbosity in a function. Alternatively, MySQL also offers spatial data types that can be used to achieve similar performance while allowing for queries to be expressed more naturally.
accuracy_radius are everything
we're interested in we'd be done at this point and our application would be
easily able to query what it needs. However, GeoIP2 databases provide additional
location information. Note the
geoname_id field in our network table. This
field can be used to look up additional information about the geolocation from
Locations files we downloaded before. We'll load this data into MySQL
We start by creating a table as before. As with the
Blocks files, the schema
for GeoIP2 and GeoLite2 City locations files can be found in the CSV section of
We'll name this table
We then populate our
geoip2_location table from a
Locations CSV file. In
this case we will be populating the table from
Using the file with the
-en suffix will give us the names of geolocations in
Note that there's a number of different
Locations files available. The other
files with different language suffixes contain localized versions of the
data in different languages for some of the
geoname_ids. Depending on your
application's needs you may decide to import additional
Locations files into
localized tables. For example, you could load
into a table called
geoip2_location-en, and load
GeoIP2-City-Locations-zh-CN.csv into a table called
could then query whichever locations table you needed for English or Chinese
We can now use our
geoip2_location table to resolve the
provided by our
geoip2_network table. For example:
Here we were only interested in English results, but we can adjust our join condition if we were interested in different or additional languages.
Note how a left outer join is used. This is because additional location
information might not be available for any given row of our
table. For example, some IP addresses cannot be resolved to a city or
subdivision. With the left join we'd still receive
accuracy_radius as a result of our query if available, while an inner join
would result in zero rows if no additional location information was available.
In addition to the
geoname_id column that provides location information for a
network, there's also
represented_country_geoname_id, which provide location information about the
country in which the ISP has registered the network, and the country which is
represented by users of the IP address, respectively. The location data for both
can be included by additional joins:
This page was last updated on September 17, 2021.