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        0.7 (the default) is optimal.

    *   num_items* => *posint*

        Expected number of items to add to bloom filter.

    Returns an enveloped result (an array).

    First element (status) is an integer containing HTTP status code (200
    means OK, 4xx caller error, 5xx function error). Second element (msg) is
    a string containing error message, or 'OK' if status is 200. Third
    element (payload) is optional, the actual result. Fourth element (meta)
    is called result metadata and is optional, a hash that contains extra
    information.

    Return value: (any)

  check_with_bloom_filter
    Usage:

     check_with_bloom_filter(%args) -> [status, msg, payload, meta]

    Check with bloom filter.

    You supply the bloom filter in STDIN, items to check as arguments, and
    this utility will print lines containing 0 or 1 depending on whether
    items in the arguments are tested to be, respectively, not in the set
    (0) or probably in the set (1).

    This function is not exported.

    Arguments ('*' denotes required arguments):

    *   items* => *array[str]*

        Items to check.

    Returns an enveloped result (an array).

    First element (status) is an integer containing HTTP status code (200
    means OK, 4xx caller error, 5xx function error). Second element (msg) is
    a string containing error message, or 'OK' if status is 200. Third
    element (payload) is optional, the actual result. Fourth element (meta)
    is called result metadata and is optional, a hash that contains extra
    information.

    Return value: (any)

  gen_bloom_filter
    Usage:

     gen_bloom_filter(%args) -> [status, msg, payload, meta]

    Generate bloom filter.

    Examples:

    *   Create a bloom filter for 100k items and 0.1% maximum false-positive
        rate (actual bloom size and false-positive rate will be shown on
        stderr):

         gen_bloom_filter( false_positive_rate => "0.1%", num_items => 100000);

    You supply lines of text from STDIN and it will output the bloom filter
    bits on STDOUT. You can also customize "num_bits" ("m") and "num_hashes"
    ("k"), or, more easily, "num_items" and "fp_rate". Some rules of thumb
    to remember:

    *   One byte per item in the input set gives about a 2% false positive
        rate. So if you expect two have 1024 elements, create a 1KB bloom
        filter with about 2% false positive rate. For other false positive
        rates:

        10% - 4.8 bits per item 1% - 9.6 bits per item 0.1% - 14.4 bits per
        item 0.01% - 19.2 bits per item

    *   Optimal number of hash functions is 0.7 times number of bits per
        item. Note that the number of hashes dominate performance. If you
        want higher performance, pick a smaller number of hashes. But for
        most cases, use the the optimal number of hash functions.

    *   What is an acceptable false positive rate? This depends on your
        needs. 1% (1 in 100) or 0.1% (1 in 1,000) is a good start. If you
        want to make sure that user's chosen password is not in a known
        wordlist, a higher false positive rates will annoy your user more by
        rejecting her password more often, while lower false positive rates
        will require a higher memory usage.

    Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html

    FAQ

    *   Why does two different false positive rates (e.g. 1% and 0.1%) give
        the same bloom filter size?

        The parameter "m" is rounded upwards to the nearest power of 2 (e.g.
        1024*8 bits becomes 1024*8 bits but 1025*8 becomes 2048*8 bits), so
        sometimes two false positive rates with different "m" get rounded to
        the same value of "m". Use the "bloom_filter_calculator" routine to
        see the "actual_m" and "actual_p" (actual false-positive rate).

    This function is not exported.

    Arguments ('*' denotes required arguments):

    *   false_positive_rate => *float*

    *   num_bits => *posint*

        The default is 16384*8 bits (generates a ~16KB bloom filter). If you
        supply 16k items (meaning 1 byte per 1 item) then the false positive
        rate will be ~2%. If you supply fewer items the false positive rate
        is smaller and if you supply more than 16k items the false positive
        rate will be higher.

    *   num_hashes => *posint*

    *   num_items => *posint*

    Returns an enveloped result (an array).