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Counting at scale in a distributed environment is surprisingly hard. And it's a subject we've covered before in various ways: Big Data Counting: How to count a billion distinct objects using only 1.5KB of Memory, How to update video views count effectively?, Numbers Everyone Should Know (sharded counters).
Kellabyte (which is an excellent blog) in Scalable Eventually Consistent Counters talks about how the Cassandra counter implementation scores well on the scalability and high availability front, but in so doing has "over and under counting problem in partitioned environments."
Which is often fine. But if you want more accuracy there's a PN-counter, which is a CRDT (convergent replicated data type) where "all the changes made to a counter on each node rather than storing and modifying a single value so that you can merge all the values into the proper final value. Of course the trade-off here is additional storage and processing but there are ways to optimize this."
And there's a paper you can count on that goes into more details: Scalable Eventually Consistent Counters over Unreliable Networks:
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