# 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.

### Parameters:

**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.

## Return

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

## Examples

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