BITCOUNT key [start end [BYTE | BIT]]
Time complexity: O(N)
Count the number of set bits (population counting) in a string.
By default all the bytes contained in the string are examined. It is possible to specify the counting operation only in an interval passing the additional arguments start and end.
Like for the
GETRANGE command start and end can contain negative values in
order to index bytes starting from the end of the string, where -1 is the last
byte, -2 is the penultimate, and so forth.
Non-existent keys are treated as empty strings, so the command will return zero.
By default, the additional arguments start and end specify a byte index.
We can use an additional argument
BIT to specify a bit index.
So 0 is the first bit, 1 is the second bit, and so forth.
For negative values, -1 is the last bit, -2 is the penultimate, and so forth.
The number of bits set to 1.
dragonfly> SET mykey "foobar"
dragonfly> BITCOUNT mykey
dragonfly> BITCOUNT mykey 0 0
dragonfly> BITCOUNT mykey 1 1
dragonfly> BITCOUNT mykey 1 1 BYTE
dragonfly> BITCOUNT mykey 5 30 BIT
Pattern: real-time metrics using bitmaps
Bitmaps are a very space-efficient representation of certain kinds of information. One example is a Web application that needs the history of user visits, so that for instance it is possible to determine what users are good targets of beta features.
SETBIT command this is trivial to accomplish, identifying every day
with a small progressive integer.
For instance day 0 is the first day the application was put online, day 1 the
next day, and so forth.
Every time a user performs a page view, the application can register that in
the current day the user visited the web site using the
SETBIT command setting
the bit corresponding to the current day.
Later it will be trivial to know the number of single days the user visited the
web site simply calling the
BITCOUNT command against the bitmap.
A similar pattern where user IDs are used instead of days is described in the article called "Fast easy realtime metrics using Redis bitmaps".
In the above example of counting days, even after 10 years the application is
online we still have just
365*10 bits of data per user, that is just 456 bytes
With this amount of data
BITCOUNT is still as fast as any other O(1) Redis
When the bitmap is big, there are two alternatives:
- Taking a separated key that is incremented every time the bitmap is modified. This can be very efficient and atomic using a small Redis Lua script.
- Running the bitmap incrementally using the
BITCOUNTstart and end optional parameters, accumulating the results client-side, and optionally caching the result into a key.