BF.ADD key item

Available in: Redis Stack

Time complexity: O(k), where k is the number of hash functions used by the last sub-filter

Creates an empty Bloom Filter with a single sub-filter for the initial capacity requested 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.


  • key: The key under which the filter is found
  • error_rate: 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.
  • capacity: 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 parameters:

  • NONSCALING: 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: 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. If the number of elements to be stored in the filter is unknown, we recommend that you use an expansion of 2 or more to reduce the number of sub-filters. Otherwise, we recommend that you use an expansion of 1 to reduce memory consumption. The default expansion value is 2.


Integer reply - "1" if the item did not exist in the filter, "0" otherwise.


redis> BF.ADD bf item1
(integer) 0
redis> BF.ADD bf item_new
(integer) 1