Learn more the requirements for keeping data up to date.
#MAXMIND TORRENT LICENSE#
In addition, all database customers and users are required to maintain up to date databases as part of their license agreement. Accuracy will reduce over time if database updates are not applied.
#MAXMIND TORRENT UPDATE#
Using a little bit-twiddling magic, we take our table and convert it into one that’s fast and optimized.Users should always promptly update to the latest database release. These sections act almost like a database index, allowing Redshift to narrow down which networks to check for each IP. Some networks are wide enough that they will go into more than one section. We will organize it into distinct sections using the first half of the IPs and place each network into it’s proper section.
We will build a new table in Redshift that will replace maxmind_ipv4_to_geo that’s optimized for GeoIP lookups. Once there they taste-test a minuscule number of flavors in comparison to before. Laid out this way, customers head to the one section that matches their preference. To speed up our ice cream shop, we are going to organize it into distinct sections - the chocolates over here, the vanillas over there, and a special spot for the minty flavors. This results in a nested loop join, one of the quickest ways to make a database cry. What if each customer in line had to taste-test every single flavor before they could choose one? That’s what happens if we attempt to combine our log data (customers) to the MaxMind data (flavors) directly using our BETWEEN join (taste-test). Suppose you ran an ice cream shop with millions of delicious flavors. What’s wrong with this query? A quick peek at the EXPLAIN plan, the list of steps Redshift takes to execute the query, tells all. Using the geoip2-csv-converter tool provided by MaxMind, we will add the integer range representation of each network to our CSV. To do so, we need a way to convert IPs and IP networks to numbers. Similarly, IP networks are just ranges of really big numbers. You can think of an IP as a fancy representation of a really big number. Instead we will use knowledge of the math behind IPs to do the work ourselves. If this MaxMind table were in Redshift, how would we join to it? Redshift doesn’t include any handy network address types likes modern Postgres or INET functions like MySQL. A subnet mask can be any number between 1 and 32 and the smaller the number, the wider the network. So if we observed a user with the IP 1.2.3.95, that would fall in the network 1.2.3.0/24 and thus is located in geoname_id of 6252001. and has any number between 0 and 255 at the end. If we had the network 1.2.3.0/24 that would mean “every IP that starts with 1.2.3. It’s like how you might describe a street of physical addresses in New York City by saying “The 500 block of west 23rd street.” It’s composed of an IP, a slash, and a number after the slash called a subnet mask. This is an IP network represented in CIDR notation and it represents a range of IPs. Right away we notice a problem - this data has something that looks like an IP but has a slash and an extra number at the end. Upon opening the zip, we find a number of CSV files, the most important among them being GeoLite2-City-Blocks-IPv4.csv. So off we go to download MaxMind’s GeoLite2 City data. One less thing to bother your boss about. We will use MaxMind data because it’s reliable and robust. Together we will use their IP-to-City dataset to enrich our log data and determine what city and country our users are from. The place folks commonly start is MaxMind, mostly because it’s the first Google result for “GeoIP”. What you need is geolocation using IPs, aka GeoIP. But Redshift doesn’t have a way to do that. You know there’s user IP addresses in your log data, you just need to turn them into locations.
If you want to report on those retention rates, lifetime values, or repeat behaviors by geo, you need something you can query with SQL, something that lives in your data warehouse. You know Google Analytics has traffic by geographic location, but that’s not gonna cut it. You naturally throw out a guess or two and discuss why that might be. “We get a lot of traffic from Boston,” your boss remarks. It always starts with an innocent observation.
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