It always depends, I'll give you my advice to offer you a new perspective
What to store? Storing every event vs. only storing aggregates
(Event log style) log every event and count them later, vs.
If you plan to don't miss any detail, even though now they are not relevant, on my eyes that's the best approach, because sometimes, as the results comes, then you find some other events that for X or Y they were not relevant, or they didn't bring any extra information, but after some analysis, it simply does, and you need to also track that one, then because its recorded but not accounted it would take you some time before you can add it to the picture.
(Time-series style) store a single aggregated "count of event E for date D" for every day
If you want to implement and use it tomorrow, it can work, but then if you have a new requirements, or you find a correlation with another event that you omitted for any reason, then you need to add this new event and then wait some long time to have nice aggregation levels
Where to store the data
In a relational database (particularly MySQL)
The first option can be heavy for a DB if you go for recording all events, so MySQL I'm afraid can become too small, and if you want to go for RDBMS solutions you may think bigger, like PostgreSQL or proprietary like Oracle or DB2.
But for the aggregation would be a good choice, depending of the load generated you can aggregate in code and insert those aggregations in the DB.
In a non-relational (NoSQL) database
If you go for this solution, you need to see which approach you want to follow nice read on wikipedia may help you, I can't help you much on that topic because I simply don't have enough experience, I mostly use rdbms.
In flat log files (collected centrally over the network via syslog-ng)
I personally would discourage you to go for that option, If the file grows too much, it would be more difficult to parse, but still I don't know the main purpose, is to follow up on a system, or simply check a log file ...
Hope it helps!
