BF.RESERVE key error_rate capacity [EXPANSION expansion] [NONSCALING]
Creates an empty Bloom filter with a single sub-filter for the initial specified capacity and with an upper bound
By default, the filter auto-scales by creating additional sub-filters when
capacity is reached.
The new sub-filter is created with size of the previous sub-filter multiplied by
Though the filter can scale up by creating sub-filters, it is recommended to reserve the estimated required
capacity since maintaining and querying
sub-filters requires additional memory (each sub-filter uses an extra bits and hash function) and consume further CPU time than an equivalent filter that had
the right capacity at creation time.
The number of hash functions is
The number of bits per item is
-log(error)/ln(2) ≈ 1.44.
- 1% error rate requires 7 hash functions and 10.08 bits per item.
- 0.1% error rate requires 10 hash functions and 14.4 bits per item.
- 0.01% error rate requires 14 hash functions and 20.16 bits per item.
is key name for the the Bloom filter to be created.
The desired probability for false positives. The rate is a decimal value between 0 and 1. For example, for a desired false positive rate of 0.1% (1 in 1000), error_rate should be set to 0.001.
The number of entries intended to be added to the filter.
If your filter allows scaling, performance will begin to degrade after adding more items than this number.
The actual degradation depends on how far the limit has been exceeded. Performance degrades linearly with the number of
Prevents the filter from creating additional sub-filters if initial capacity is reached.
Non-scaling filters requires slightly less memory than their scaling counterparts. The filter returns an error when
capacity is reached.
capacity is reached, an additional sub-filter is created.
The size of the new sub-filter is the size of the last sub-filter multiplied by
expansion, specified as a positive integer.
If the number of elements to be stored in the filter is unknown, you use an
2 or more to reduce the number of sub-filters.
Otherwise, you use an
1 to reduce memory consumption. The default value is
Returns one of these replies:
- Simple string reply -
OKif filter created successfully
-  on error (invalid arguments, key already exists, etc.)
redis> BF.RESERVE bf 0.01 1000 OK
redis> BF.RESERVE bf 0.01 1000 (error) ERR item exists
redis> BF.RESERVE bf_exp 0.01 1000 EXPANSION 2 OK
redis> BF.RESERVE bf_non 0.01 1000 NONSCALING OK