So, You Wanna Make a Pokemon Go Clone?
I told you not to do it.
Well, if you absolutely insist, here’s how I’d go about it.
Step 1: Raise tons of money
You’re going to need it. And it’s not just for user acquisition. You’ll need a lot of dry powder for scaling costs in the unlikely event this game is as successful as you’ve claimed to your investors. For small apps, accessing something like the Foursquare API may be free–but it will require an expensive licensing deal to use it at the scale you’re thinking of and without restrictions.
Step 2: Buy every single location based game you can
Just having access to a places API such as Foursquare or Factual isn’t enough. You need location data relevant to a game–such as granular details about places inside of larger locations that are of interest to players. Pokemon Go has this from years of Ingress players submitting and verifying locations around the world.
Nearly 10 years ago, there was a frenzy of investment in location based games. The App Store is now littered with dead husks of old LBS games and ones that are on life support. With that pile of money you raised, it should be easy to go on a shopping spree and buy up these games. Not for their users, or even the technology, but for the data. Most of these games may have been fallow for years, making their location data stale. Yet, it may be possible with machine learning or old fashioned elbow grease to work that data into a layer of interesting sub-locations for your game to be designed around.
Step 3: Plan for Database Hell
Designing for scale at the start is a classic mistake for any startup. You’re effectively building a football stadium for a carload of people. That doesn’t mean you shouldn’t entertain the idea of scaling up a service once it’s successful.
Full disclosure, I’ve never built an app at the scale of Pokemon Go. Few people have. I suspect many of the server issues are related to scaling a geospatial database with that many users. It’s much harder to optimize your data around location than other usage patterns. Don’t take my word for it, check out this analysis.
It’s been years since I’ve looked at geospatial databases. Despite some announcements, it doesn’t look like a lot has changed. A cursory search suggests PostGIS is still a solid choice. Plus, there are a lot of Postgres experts out there that can help with scaling issues. MongoDB’s relatively new spatial features may also be an option.
As for fancier alternatives–Google App Engine is an easy way to “magically” scale an app. They have also started releasing really interesting new geospatial services. Not to mention some great support for mobile apps that may make integrating with Unity3D a bit easier. However, GAE is very expensive at scale, and the location features are still in alpha. Choosing Google App Engine is a risky decision, but also may be an easy way to get started.
To avoid vendor lock-in, have a migration strategy in mind. One of which may be using your pile of money to recruit backend people from startups with large amounts of users.
Step 4: Get Ready for the Disappointing State of Mobile AR
Pokemon Go has sparked a lot of renewed interest in AR. Much like geospatial databases, not much has changed in the past 5 years as far as what your average smartphone can do. Sure, beefier processors and higher res cameras can get away with some limited SLAM functionality. But, these features are very finicky. Your best bet is to keep AR to a minimum, as Pokemon Go smartly did. Placing virtual objects on real world surfaces in precise locations, especially outdoors, is the realm of next generation hardware.
Step 5: ??????
Ok, this isn’t a precise recipe for a Pokemon Go clone. But hey, if you’ve completed step one, maybe you should contact me for more details?