To calculate aggregates, we have two use cases. Either we’ll calculate aggregates over all of the information we know about, or we’ll calculate over a subset. We’ll start by calculating aggregates over the entire population, and then we’ll write code that calculates aggregates over a smaller group.
To calculate aggregates over everyone we have information for, we’ll recycle some code that we wrote back in section 6.6.4, specifically the readblocks() function, which reads blocks of data from a given key. Using this function, we can perform a single command and round trip with Redis to fetch information about thousands of users at one time. Our function to calculate aggregates with this block-reading function is shown next.
This function to calculate aggregates over country- and state-level information for everyone uses a structure called a defaultdict, which we also first used in chapter 6 to calculate aggregates about location information before writing back to Redis. Inside this function, we refer to a helper function that actually updates the aggregates and decodes location codes back into their original ISO3 country codes and local state abbreviations, which can be seen in this next listing.
With a function to convert location codes back into useful location information and update aggregate information, we have the building blocks to perform aggregates over a subset of users. As an example, say that we have location information for many Twitter users. And also say that we have follower information for each user. To discover information about where the followers of a given user are located, we’d only need to fetch location information for those users and compute aggregates similar to our global aggregates. The next listing shows a function that will aggregate location information over a provided list of user IDs.
This technique of storing fixed-length data in sharded STRINGs can be useful. Though we stored multiple bytes of data per user, we can use GETBIT and SETBIT identically to store individual bits, or even groups of bits.
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