As others have said, this is a solved problem in a lot of companies. Basic answers are:
1. Queuing
2. Asynchronous APIs (don't wait for the 'real' response, just submit the transaction)
3. Call-backs to the Client
A good async setup can easily handle 100k+ TPS
If you want to go the synchronous route, it's more complicated but amounts to partitioning and creating separate swim-lanes (copies of the system, both at the compute and data layers)
Note that the client doesn't need to know about async operations/you don't need an async api at the http layer. Put the queue in memory. Have your queue workers wait up to ~5 ms to build a batch, or run the transaction when a batch is big enough (at 100k RPS, you already have a batch of 100 every ms). You're adding ~1-5 ms latency, so no reason not to respond synchronously to the client. Conceptually, the queue and workers are an implementation detail within the model. As far as the controller knows, the db query just took an extra ms (or under any real load, responded more quickly).
Sure, but no matter how many async request you accept you still only have 50k items available. You also presumably take people's money, having them input their personal and card information so not waiting for real response means what? Thank you for your money and the data, we'll be in touch soon; pinky promise?
> Thank you for your money and the data, we'll be in touch soon; pinky promise?
That's very much an option when it's something this popular - the Olympics I went to did an even more extreme version of that ("Thank you for putting in which events you wanted to see, your card may be charged up to x some time within the next month").
Or you can do it like plane seats: allocate 50k provisional tickets during the initial release (async but on a small timescale), and then if a provisional ticket isn't paid for within e.g. 3 days you put it back on sale.
Ultimately if it takes you x minutes to confirm payment details then you have to either take payment details from some people who then don't get tickets, or put some tickets back on sale when payment for them fails. But that's not really a scaling issue - you have the same problem trying to sell 1 thing to 5 people on an online shop.
You have 50,000 tickets to spread between one million people, you can partition people to tickets and only have 20 people per ticket. You won't have strict ordering (e.g., someone who applied after may get a ticket where someone who applied earlier doesn't), but we'd be talking about errors in the milliseconds.
A good async setup can easily handle 100k+ TPS
If you want to go the synchronous route, it's more complicated but amounts to partitioning and creating separate swim-lanes (copies of the system, both at the compute and data layers)