BF.RESERVE key error_rate capacity [EXPANSION expansion]
Available in:
Redis Stack / Bloom 1.0.0
Time complexity:

Creates an empty Bloom filter with a single sub-filter for the initial specified capacity and with an upper bound error_rate.

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 expansion.

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 -log(error)/ln(2)^2. 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.

Required arguments


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 sub-filters.

Optional arguments


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.

EXPANSION expansion

When 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 expansion of 2 or more to reduce the number of sub-filters. Otherwise, you use an expansion of 1 to reduce memory consumption. The default value is 2.

Return value

Returns one of these replies:

  • Simple string reply - OK if filter created successfully
  • [] on error (invalid arguments, key already exists, etc.)


redis> BF.RESERVE bf 0.01 1000
redis> BF.RESERVE bf 0.01 1000
(error) ERR item exists
redis> BF.RESERVE bf_exp 0.01 1000 EXPANSION 2
redis> BF.RESERVE bf_non 0.01 1000 NONSCALING

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