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RedisBloom Bloom Filter Command Documentation

Based on Space/Time Trade-offs in Hash Coding with Allowable Errors by Burton H. Bloom.

BF.RESERVE

Format:

BF.RESERVE {key} {error_rate} {capacity} [EXPANSION {expansion}] [NONSCALING]

Description:

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.

Complexity

O(1)

Returns

OK on success, error otherwise.

BF.ADD

Format

BF.ADD {key} {item}

Description

Adds an item to the Bloom Filter, creating the filter if it does not yet exist.

Parameters

  • key : The name of the filter
  • item : The item to add

Complexity

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

Returns

"1" if the item was newly inserted, or "0" if it may have existed previously.

BF.MADD

Format

BF.MADD {key} {item ...}

Description

Adds one or more items to the Bloom Filter and creates the filter if it does not exist yet. This command operates identically to BF.ADD except that it allows multiple inputs and returns multiple values.

Parameters

  • key : The name of the filter
  • item : One or more items to add

Complexity

O(k * n), where k is the number of hash functions used by the last sub-filter and m the number of items that are added.

Returns

An array of booleans (integers). Each element is either true or false depending on whether the corresponding input element was newly added to the filter or may have previously existed.

BF.INSERT

BF.INSERT {key} [CAPACITY {cap}] [ERROR {error}] [EXPANSION {expansion}] [NOCREATE]
[NONSCALING] ITEMS {item ...}

Description

BF.INSERT is a sugarcoated combination of BF.RESERVE and BF.ADD. It creates a new filter if the key does not exist using the relevant arguments (see BF.RESERVE). Next, all ITEMS are inserted.

Parameters

  • key : The name of the filter
  • item : One or more items to add. The ITEMS keyword must precede the list of items to add.

Optional parameters:

  • NOCREATE : (Optional) Indicates that the filter should not be created if it does not already exist. If the filter does not yet exist, an error is returned rather than creating it automatically. This may be used where a strict separation between filter creation and filter addition is desired. It is an error to specify NOCREATE together with either CAPACITY or ERROR .
  • capacity : (Optional) Specifies the desired capacity for the filter to be created. This parameter is ignored if the filter already exists. If the filter is automatically created and this parameter is absent, then the module-level capacity is used. See BF.RESERVE for more information about the impact of this value.
  • error : (Optional) Specifies the error ratio of the newly created filter if it does not yet exist. If the filter is automatically created and error is not specified then the module-level error rate is used. See BF.RESERVE for more information about the format of this value.
  • NONSCALING : Prevents the filter from creating additional sub-filters if initial capacity is reached. Non-scaling filters require 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.

Examples

Add three items to a filter with default parameters if the filter does not already exist:

BF.INSERT filter ITEMS foo bar baz

Add one item to a filter with a capacity of 10000 if the filter does not already exist:

BF.INSERT filter CAPACITY 10000 ITEMS hello

Add 2 items to a filter with an error if the filter does not already exist:

BF.INSERT filter NOCREATE ITEMS foo bar

Complexity

O(k * n), where k is the number of hash functions used by the last sub-filter and m the number of items that are added.

Returns

An array of booleans (integers). Each element is either true or false depending on whether the corresponding input element was newly added to the filter or may have previously existed.

BF.EXISTS

Format

BF.EXISTS {key} {item}

Description

Determines whether an item may exist in the Bloom Filter or not.

Parameters

  • key : The name of the filter
  • item : The item to check for

Complexity

O(k * n), where k is the number of hash functions and n is the number of sub-filters . On average, a sub-filter returns FALSE after 2 bits are tested. Therefore the average complexity for a FALSE reply is O(2 * n) . For a TRUE reply the complexity is O((2 * 1/2 * n) + k) .

Returns

"0" if the item certainly does not exist, "1" if the item may exist.

BF.MEXISTS

Format

BF.MEXISTS {key} {item ...}

Description

Determines if one or more items may exist in the filter or not.

Parameters

  • key : The name of the filter
  • items : One or more items to check

Complexity

O(m * k * n), where m is the number of added elements, k is the number of hash functions and n is the number of sub-filters . On average, a sub-filter returns FALSE after 2 bits are tested. Therefore the average complexity for a FALSE reply is O(m * 2 * n) . For a TRUE reply, the complexity is O(m * ((2 * 1/2 * n ) + k)) .

Returns

An array of boolean values (actually integers). A true value means the corresponding item may exist in the filter, and a false value means it does not exist in the filter.

BF.SCANDUMP

Format

BF.SCANDUMP {key} {iter}

Description

Begins an incremental save of the bloom filter. This is useful for large bloom filters which cannot fit into the normal SAVE and RESTORE model.

The first time this command is called, the value of iter should be 0. This command returns successive (iter, data) pairs until (0, NULL) to indicate completion.

A demonstration in python-flavored pseudocode:

chunks = []
iter = 0
while True:
    iter, data = BF.SCANDUMP(key, iter)
    if iter == 0:
        break
    else:
        chunks.append([iter, data])

# Load it back
for chunk in chunks:
    iter, data = chunk
    BF.LOADCHUNK(key, iter, data)

Parameters

  • key : Name of the filter
  • iter : Iterator value; either 0 or the iterator from a previous invocation of this command

Complexity

O(n), where n is the capacity.

Returns

An array of Iterator and Data . The Iterator is passed as input to the next invocation of SCANDUMP . If Iterator is 0, then it means iteration has completed.

The iterator-data pair should also be passed to LOADCHUNK when restoring the filter.

BF.LOADCHUNK

Format

BF.LOADCHUNK {key} {iter} {data}

Description

Restores a filter previously saved using SCANDUMP . See the SCANDUMP command for example usage.

This command overwrites any bloom filter stored under key . Make sure that the bloom filter is not be changed between invocations.

Parameters

  • key : Name of the key to restore
  • iter : Iterator value associated with data (returned by SCANDUMP )
  • data : Current data chunk (returned by SCANDUMP )

Complexity

O(n), where n is the capacity.

Returns

OK on success, or an error on failure.

BF.INFO

Format

BF.INFO {key}

Description

Return information about key

Parameters

  • key : Name of the key to restore

Complexity O

O(1)

Returns

127.0.0.1:6379> BF.INFO cf
1) Capacity
2) (integer) 1709
3) Size
4) (integer) 2200
5) Number of filters
6) (integer) 1
7) Number of items inserted
8) (integer) 0
9) Expansion rate
10) (integer) 1