Struct hyperloglog_rs::hyperloglog::HyperLogLog

pub struct HyperLogLog<P: Precision + WordType<BITS>, const BITS: usize> { /* private fields */ }

A probabilistic algorithm for estimating the number of distinct elements in a set.

HyperLogLog is a probabilistic algorithm designed to estimate the number of distinct elements in a set. It does so by taking advantage of the fact that the representation of an element can be transformed into a uniform distribution in a space with a fixed range.

HyperLogLog works by maintaining a fixed-sized register array, where each register holds a counter. The algorithm splits the input set into subsets, applies a hash function to each element in the subset, and then updates the corresponding counter in the register array.

HyperLogLog uses a trick called “probabilistic counting” to estimate the number of distinct elements in the set. Each register counter is converted to a binary string, and the algorithm counts the number of leading zeros in each binary string. The maximum number of leading zeros over all counters is used to estimate the number of distinct elements in the set.

HyperLogLog has a tunable parameter called precision that determines the accuracy of the algorithm. Higher precision leads to better accuracy, but requires more memory. The error rate of the algorithm is guaranteed to be within a certain bound, depending on the chosen precision.

Examples

use hyperloglog_rs::prelude::*;

let mut hll = HyperLogLog::<Precision12, 6>::default();
hll.insert(&"apple");
hll.insert(&"banana");
hll.insert(&"cherry");

let estimated_cardinality = hll.estimate_cardinality();
assert!(estimated_cardinality >= 3.0_f32 * 0.9 &&
        estimated_cardinality <= 3.0_f32 * 1.1);

Citations

This implementation is based on the following papers:

• Flajolet, Philippe, et al. “HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm.” DMTCS Proceedings 1 (2007): 127-146.
• Heule, Stefan, Marc Nunkesser, and Alexander Hall. “HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm.” Proceedings of the 16th International Conference on Extending Database Technology. 2013.

Implementations

impl<P: Precision + WordType<BITS>, const BITS: usize> HyperLogLog<P, BITS>

pub fn from_words(words: &P::Words) -> Self

Create a new HyperLogLog counter from an array of words.

Arguments

• words - An array of u64 words to use for the HyperLogLog counter.

Returns

A new HyperLogLog counter initialized with the given words.

Examples

use hyperloglog_rs::prelude::*;
                                                                                                                                                                                                                                                                    
let words = [0_u32; 4];
let hll = HyperLogLog::<Precision4, 6>::from_words(&words);
assert_eq!(hll.len(), 16);

pub fn from_registers(registers: &[u32]) -> Self

Create a new HyperLogLog counter from an array of registers.

Arguments

• registers - An array of u32 registers to use for the HyperLogLog counter.

Returns

A new HyperLogLog counter initialized with the given registers.

Examples

use hyperloglog_rs::prelude::*;

let registers = [0_u32; 1 << 4];
let hll = HyperLogLog::<Precision4, 6>::from_registers(&registers);
assert_eq!(hll.len(), 1 << 4);

pub fn insert<T: Hash>(&mut self, rhs: T) -> bool

Adds an element to the HyperLogLog counter, and returns whether the counter has changed.

Arguments

• rhs - The element to add.

Examples

use hyperloglog_rs::prelude::*;

let mut hll = HyperLogLog::<Precision10, 6>::default();

hll.insert("Hello");
hll.insert("World");

assert!(hll.estimate_cardinality() >= 2.0);

Performance

The performance of this function depends on the size of the HyperLogLog counter (N), the number of distinct elements in the input, and the hash function used to hash elements. For a given value of N, the function has an average time complexity of O(1) and a worst-case time complexity of O(log N). However, the actual time complexity may vary depending on the distribution of the hashed elements.

Errors

This function does not return any errors.

Trait Implementations

impl<P: Precision + WordType<BITS>, const BITS: usize> BitAnd<&HyperLogLog<P, BITS>> for HyperLogLog<P, BITS>

fn bitand(self, rhs: &Self) -> Self

Computes the intersection between two HyperLogLog counters of the same precision and number of bits per register.

Caveats

Please be advised that HLL are not designed to compute intersections such as the one estimated by this operation. The resulting set will most likely be an overestimation of the real intersection. Operate with caution.

Implementation details

This operation is implemented by computing the minimum register-wise of the two HLL counters. This results in an estimation of the intersection because we obtain a new HLL counter that at most contain the elements present in both HLL counters.

Example

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let mut hll2 = HyperLogLog::<Precision14, 5>::default();
hll2.insert(&2);
hll2.insert(&3);

let hll_intersection = hll1 | hll2;

assert!(hll_intersection.estimate_cardinality() >= 3.0_f32 * 0.9 &&
        hll_intersection.estimate_cardinality() <= 3.0_f32 * 1.1);

Merging a set with an empty set should not change the cardinality.

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let hll_intersection = hll1.clone() | HyperLogLog::<Precision14, 5>::default();
assert_eq!(
    hll_intersection,
    hll1,
    concat!(
        "The cardinality of the intersection should ",
        "be the same as the cardinality of the first set."
   )
);

We can create the HLL counters from array from registers, so to be able to check that everything works as expected.

let first_registers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
let second_registers = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 19];
let expected = [9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 19];

let mut hll1 = HyperLogLog::<Precision4, 5>::from_registers(&first_registers);
let mut hll2 = HyperLogLog::<Precision4, 5>::from_registers(&second_registers);
let intersection = hll1 | &hll2;

assert_eq!(intersection.get_registers(), expected, "The registers are not the expected ones, got {:?} instead of {:?}.", intersection.get_registers(), expected);

type Output = HyperLogLog<P, BITS>

The resulting type after applying the & operator.

impl<P: Precision + WordType<BITS>, const BITS: usize> BitAnd for HyperLogLog<P, BITS>

fn bitand(self, rhs: Self) -> Self

Computes the intersection between two HyperLogLog counters of the same precision and number of bits per register.

Caveats

Please be advised that HLL are not designed to compute intersections such as the one estimated by this operation. The resulting set will most likely be an overestimation of the real intersection. Operate with caution.

Implementation details

This operation is implemented by computing the minimum register-wise of the two HLL counters. This results in an estimation of the intersection because we obtain a new HLL counter that at most contain the elements present in both HLL counters.

Example

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let mut hll2 = HyperLogLog::<Precision14, 5>::default();
hll2.insert(&2);
hll2.insert(&3);

let hll_intersection = hll1 & hll2;

assert!(hll_intersection.estimate_cardinality() >= 1.0_f32 * 0.9 &&
        hll_intersection.estimate_cardinality() <= 1.0_f32 * 1.1);

Executing the intersection between a set and an empty set should result in an empty set.

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let hll_intersection = hll1.clone() & HyperLogLog::<Precision14, 5>::default();
assert_eq!(
    HyperLogLog::<Precision14, 5>::default(),
    hll_intersection,
    concat!(
        "The cardinality of the intersection should ",
        "be the same as the empty test."
   )
);

We can create the HLL counters from array from registers, so to be able to check that everything works as expected.

let first_registers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
let second_registers = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 19];
let expected = [9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 19];

let mut hll1 = HyperLogLog::<Precision4, 5>::from_registers(&first_registers);
let mut hll2 = HyperLogLog::<Precision4, 5>::from_registers(&second_registers);
let intersection = hll1 | hll2;

assert_eq!(intersection.get_registers(), expected, "The registers are not the expected ones, got {:?} instead of {:?}.", intersection.get_registers(), expected);

type Output = HyperLogLog<P, BITS>

The resulting type after applying the & operator.

impl<P: Precision + WordType<BITS>, const BITS: usize> BitAndAssign<&HyperLogLog<P, BITS>> for HyperLogLog<P, BITS>

fn bitand_assign(&mut self, rhs: &Self)

Computes intersection between HLL counters.

Caveats

Please be advised that HLL are not designed to compute intersections such as the one estimated by this operation. The resulting set will most likely be an overestimation of the real intersection. Operate with caution.

Implementation details

This operation is implemented by computing the minimum register-wise of the two HLL counters. This results in an estimation of the intersection because we obtain a new HLL counter that at most contain the elements present in both HLL counters.

Example

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(2u8);

hll.bitand_assign(&hll2);

assert!(hll.estimate_cardinality() < 0.1, "The cardinality is {}, we were expecting 0.", hll.estimate_cardinality());

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(1u8);

hll.bitand_assign(&hll2);

assert!(hll.estimate_cardinality() > 1.0 - 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 1.0 + 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());

let mut hll3 = HyperLogLog::<Precision16, 6>::default();
hll3.insert(3u8);
hll3.insert(5u8);
hll3.insert(6u8);

let mut hll4 = HyperLogLog::<Precision16, 6>::default();
hll4.insert(5u8);
hll4.insert(6u8);

hll3.bitand_assign(&hll4);

assert!(hll3.estimate_cardinality() > 2.0 - 0.1, "Expected a value equal to around 2, got {}", hll3.estimate_cardinality());
assert!(hll3.estimate_cardinality() < 2.0 + 0.1, "Expected a value equal to around 2, got {}", hll3.estimate_cardinality());

Another example is that, if we allocate two example vectors which we will use to populate both two sets and the two HyperLogLog counter. All elements in the intersection of the two sets must also appear in the intersection of the two HyperLogLog counters. Usually, the problem is that it may over-estimate the cardinality of the intersection.

let first_vec: Vec<u64> = vec![1, 2, 3, 4, 4, 5, 6, 6, 7, 8];
let second_vec: Vec<u64> = vec![5, 6, 7, 8, 8, 9, 9, 10, 11, 11, 12];

let first_set = first_vec.iter().collect::<std::collections::HashSet<_>>();
let second_set = second_vec.iter().collect::<std::collections::HashSet<_>>();

let mut hll1 = HyperLogLog::<Precision8, 6>::default();
let mut hll2 = HyperLogLog::<Precision8, 6>::default();

for element in first_vec.iter() {
   hll1.insert(element);
}

for element in second_vec.iter() {
  hll2.insert(element);
}

let mut hll_intersection = hll1.clone();
hll_intersection &= &hll2;

let intersection = first_set.intersection(&second_set).collect::<std::collections::HashSet<_>>();

assert!(hll_intersection.estimate_cardinality() >= intersection.len() as f32 * 0.9 &&
       hll_intersection.estimate_cardinality() <= intersection.len() as f32 * 1.1);

for element in intersection.iter() {
   assert!(hll_intersection.may_contain(element));
}

impl<P: Precision + WordType<BITS>, const BITS: usize> BitAndAssign for HyperLogLog<P, BITS>

fn bitand_assign(&mut self, rhs: Self)

Computes intersection between HLL counters.

Caveats

Please be advised that HLL are not designed to compute intersections such as the one estimated by this operation. The resulting set will most likely be an overestimation of the real intersection. Operate with caution.

Implementation details

This operation is implemented by computing the minimum register-wise of the two HLL counters. This results in an estimation of the intersection because we obtain a new HLL counter that at most contain the elements present in both HLL counters.

Example

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(2u8);

hll.bitand_assign(hll2);

assert!(hll.estimate_cardinality() < 0.1, "The cardinality is {}, we were expecting 0.", hll.estimate_cardinality());

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(1u8);

hll.bitand_assign(hll2);

assert!(hll.estimate_cardinality() > 1.0 - 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 1.0 + 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());

let mut hll3 = HyperLogLog::<Precision16, 6>::default();
hll3.insert(3u8);
hll3.insert(5u8);

let mut hll4 = HyperLogLog::<Precision16, 6>::default();
hll4.insert(5u8);
hll4.insert(6u8);

hll3.bitand_assign(hll4);

assert!(hll3.estimate_cardinality() > 1.0 - 0.1, "Expected a value equal to around 1, got {}", hll3.estimate_cardinality());
assert!(hll3.estimate_cardinality() < 1.0 + 0.1, "Expected a value equal to around 1, got {}", hll3.estimate_cardinality());

impl<P: Precision + WordType<BITS>, const BITS: usize> BitOr<&HyperLogLog<P, BITS>> for HyperLogLog<P, BITS>

fn bitor(self, rhs: &Self) -> Self

Computes the union between two HyperLogLog counters of the same precision and number of bits per register.

Example

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let mut hll2 = HyperLogLog::<Precision14, 5>::default();
hll2.insert(&2);
hll2.insert(&3);

let hll_union = hll1 | hll2;

assert!(hll_union.estimate_cardinality() >= 3.0_f32 * 0.9 &&
        hll_union.estimate_cardinality() <= 3.0_f32 * 1.1);

Merging a set with an empty set should not change the cardinality.

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let hll_union = hll1.clone() | HyperLogLog::<Precision14, 5>::default();
assert_eq!(
    hll_union,
    hll1,
    concat!(
        "The cardinality of the union should ",
        "be the same as the cardinality of the first set."
   )
);

We can create the HLL counters from array from registers, so to be able to check that everything works as expected.

let first_registers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
let second_registers = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 19];
let expected = [9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 19];

let mut hll1 = HyperLogLog::<Precision4, 5>::from_registers(&first_registers);
let mut hll2 = HyperLogLog::<Precision4, 5>::from_registers(&second_registers);
let union = hll1 | &hll2;

assert_eq!(union.get_registers(), expected, "The registers are not the expected ones, got {:?} instead of {:?}.", union.get_registers(), expected);

type Output = HyperLogLog<P, BITS>

The resulting type after applying the | operator.

impl<Item: Hash, I: Iterator<Item = Item>, P: Precision + WordType<BITS>, const BITS: usize> BitOr<I> for HyperLogLog<P, BITS>

fn bitor(self, rhs: I) -> Self

Computes the union between an HLL counter and an iterator.

type Output = HyperLogLog<P, BITS>

The resulting type after applying the | operator.

impl<P: Precision + WordType<BITS>, const BITS: usize> BitOr for HyperLogLog<P, BITS>

fn bitor(self, rhs: Self) -> Self

Computes the union between two HyperLogLog counters of the same precision and number of bits per register.

Example

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let mut hll2 = HyperLogLog::<Precision14, 5>::default();
hll2.insert(&2);
hll2.insert(&3);

let hll_union = hll1 | hll2;

assert!(hll_union.estimate_cardinality() >= 3.0_f32 * 0.9 &&
        hll_union.estimate_cardinality() <= 3.0_f32 * 1.1);

Merging a set with an empty set should not change the cardinality.

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&1);
hll1.insert(&2);

let hll_union = hll1.clone() | HyperLogLog::<Precision14, 5>::default();
assert_eq!(
    hll_union,
    hll1,
    concat!(
        "The cardinality of the union should ",
        "be the same as the cardinality of the first set."
   )
);

We can create the HLL counters from array from registers, so to be able to check that everything works as expected.

let first_registers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
let second_registers = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 19];
let expected = [9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 11, 12, 13, 14, 15, 19];

let mut hll1 = HyperLogLog::<Precision4, 5>::from_registers(&first_registers);
let mut hll2 = HyperLogLog::<Precision4, 5>::from_registers(&second_registers);
let union = hll1 | hll2;

assert_eq!(union.get_registers(), expected, "The registers are not the expected ones, got {:?} instead of {:?}.", union.get_registers(), expected);

type Output = HyperLogLog<P, BITS>

The resulting type after applying the | operator.

impl<P: Precision + WordType<BITS>, const BITS: usize> BitOrAssign<&HyperLogLog<P, BITS>> for HyperLogLog<P, BITS>

fn bitor_assign(&mut self, rhs: &Self)

Computes union between HLL counters.

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(2u8);

hll.bitor_assign(&hll2);

assert!(hll.estimate_cardinality() > 2.0 - 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 2.0 + 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(1u8);

hll.bitor_assign(&hll2);

assert!(hll.estimate_cardinality() > 1.0 - 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 1.0 + 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());

let mut hll3 = HyperLogLog::<Precision16, 6>::default();
hll3.insert(3u8);
hll3.insert(4u8);

let mut hll4 = HyperLogLog::<Precision16, 6>::default();
hll4.insert(5u8);
hll4.insert(6u8);

hll3.bitor_assign(&hll4);

assert!(hll3.estimate_cardinality() > 4.0 - 0.1, "Expected a value equal to around 4, got {}", hll3.estimate_cardinality());
assert!(hll3.estimate_cardinality() < 4.0 + 0.1, "Expected a value equal to around 4, got {}", hll3.estimate_cardinality());

impl<Item: Hash, I: IntoIterator<Item = Item>, P: Precision + WordType<BITS>, const BITS: usize> BitOrAssign<I> for &mut HyperLogLog<P, BITS>

fn bitor_assign(&mut self, rhs: I)

Performs the |= operation.

impl<Item: Hash, I: IntoIterator<Item = Item>, P: Precision + WordType<BITS>, const BITS: usize> BitOrAssign<I> for HyperLogLog<P, BITS>

fn bitor_assign(&mut self, rhs: I)

Computes inplace union between an HLL counter and an iterator.

let mut hll = HyperLogLog::<Precision8, 6>::default();

hll |= [1u8, 2u8];

assert!(hll.estimate_cardinality() > 2.0 - 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 2.0 + 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());

hll |= [2u8, 3u8];

assert!(hll.estimate_cardinality() > 3.0 - 0.1, "Expected a value equal to around 3, got {}", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 3.0 + 0.1, "Expected a value equal to around 3, got {}", hll.estimate_cardinality());

impl<P: Precision + WordType<BITS>, const BITS: usize> BitOrAssign for HyperLogLog<P, BITS>

fn bitor_assign(&mut self, rhs: Self)

Computes union between HLL counters.

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(2u8);

hll.bitor_assign(hll2);

assert!(hll.estimate_cardinality() > 2.0 - 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 2.0 + 0.1, "The cardinality is {}, we were expecting 2.", hll.estimate_cardinality());

let mut hll = HyperLogLog::<Precision8, 6>::default();
hll.insert(1u8);

let mut hll2 = HyperLogLog::<Precision8, 6>::default();
hll2.insert(1u8);

hll.bitor_assign(hll2);

assert!(hll.estimate_cardinality() > 1.0 - 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());
assert!(hll.estimate_cardinality() < 1.0 + 0.1, "The cardinality is {}, we were expecting 1.", hll.estimate_cardinality());

let mut hll3 = HyperLogLog::<Precision16, 6>::default();
hll3.insert(3u8);
hll3.insert(4u8);

let mut hll4 = HyperLogLog::<Precision16, 6>::default();
hll4.insert(5u8);
hll4.insert(6u8);

hll3.bitor_assign(hll4);

assert!(hll3.estimate_cardinality() > 4.0 - 0.1, "Expected a value equal to around 4, got {}", hll3.estimate_cardinality());
assert!(hll3.estimate_cardinality() < 4.0 + 0.1, "Expected a value equal to around 4, got {}", hll3.estimate_cardinality());

impl<P: Clone + Precision + WordType<BITS>, const BITS: usize> Clone for HyperLogLog<P, BITS>

where P::Words: Clone, P::NumberOfZeros: Clone,

fn clone(&self) -> HyperLogLog<P, BITS>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<P: Debug + Precision + WordType<BITS>, const BITS: usize> Debug for HyperLogLog<P, BITS>

where P::Words: Debug, P::NumberOfZeros: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<P: Precision + WordType<BITS>, const BITS: usize> Default for HyperLogLog<P, BITS>

Implements the Default trait for HyperLogLog.

HyperLogLog is a probabilistic cardinality estimator that uses a fixed amount of memory to estimate the number of distinct elements in a set.

Examples

let hll: HyperLogLog<Precision10, 6> = Default::default();
assert_eq!(hll.len(), 1024);

fn default() -> Self

Returns a new HyperLogLog instance with default configuration settings.

impl<'de, P: Precision + WordType<BITS>, const BITS: usize> Deserialize<'de> for HyperLogLog<P, BITS>

fn deserialize<D: Deserializer<'de>>(deserializer: D) -> Result<Self, D::Error>

Deserializes the HyperLogLog counter using the given deserializer.

This method is part of the Deserialize trait implementation for the HyperLogLog struct, allowing the counter to be deserialized from a format supported by the deserializer.

Arguments

• deserializer: The deserializer used to deserialize the HyperLogLog counter.

Returns

The deserialization result, indicating success or failure.

Example

use serde::de::Deserialize;
use serde_json::Deserializer;
use hyperloglog_rs::prelude::*;

let words = [0, 0, 0, 0, 5, 0, 4, 0, 0, 3, 0, 0, 0];
let words_str = "[0, 0, 0, 0, 5, 0, 4, 0, 0, 3, 0, 0, 0]";
let mut deserializer = Deserializer::from_str(words_str);
let result = HyperLogLog::<Precision6, 6>::deserialize(&mut deserializer);
assert!(result.is_ok(), "Deserialization failed, error: {:?}", result.err());
let hll = result.unwrap();
hll.get_words().iter().zip(words.iter()).for_each(|(a, b)| assert_eq!(a, b, "Deserialized words do not match, expected: {}, actual: {}", b, a));

impl<P: Precision + WordType<BITS>, const BITS: usize> From<HyperLogLog<P, BITS>> for HyperLogLogWithMultiplicities<P, BITS>

fn from(hll: HyperLogLog<P, BITS>) -> Self

Converts to this type from the input type.

impl<P: Precision + WordType<BITS>, const BITS: usize> From<HyperLogLogWithMultiplicities<P, BITS>> for HyperLogLog<P, BITS>

fn from(hll: HyperLogLogWithMultiplicities<P, BITS>) -> Self

Converts to this type from the input type.

impl<P: Precision + WordType<BITS>, const BITS: usize, T: Hash> From<T> for HyperLogLog<P, BITS>

fn from(value: T) -> Self

Create a new HyperLogLog counter from a value.

This method creates a new empty HyperLogLog counter and inserts the hash of the given value into it. The value can be any type that implements the Hash trait.

Examples

let hll = HyperLogLog::<Precision14, 5>::from("test");

assert!(hll.estimate_cardinality() >=  1.0_f32);
assert!(!hll.is_empty());
assert!(hll.may_contain(&"test"));

impl<P: Precision + WordType<BITS>, const BITS: usize, A: Hash> FromIterator<A> for HyperLogLog<P, BITS>

fn from_iter<T: IntoIterator<Item = A>>(iter: T) -> Self

Creates a new HyperLogLog counter and adds all elements from an iterator to it.

Examples

use hyperloglog_rs::prelude::*;

let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
let hll: HyperLogLog<Precision12, 5> = data.iter().collect();
assert!(
    hll.estimate_cardinality() > 0.9 * data.len() as f32,
    concat!(
        "The estimate is too low, expected ",
        "at least {}, got {}",
    ),
    0.9 * data.len() as f32,
    hll.estimate_cardinality()
);
assert!(
    hll.estimate_cardinality() < 1.1 * data.len() as f32,
    concat!(
    "The estimate is too high, expected ",
    "at most {}, got {}",
   ),
    1.1 * data.len() as f32,
    hll.estimate_cardinality()
);

impl<P: Precision + WordType<BITS>, const BITS: usize> HyperLogLogTrait<P, BITS> for HyperLogLog<P, BITS>

fn get_number_of_zero_registers(&self) -> usize

Returns the number of registers with zero values. This value is used for computing a small correction when estimating the cardinality of a small set.

Examples

// Create a new HyperLogLog counter with precision 14 and 5 bits per register.
let mut hll = HyperLogLog::<Precision14, 5>::default();

// Add some elements to the counter.
hll.insert(&1);
hll.insert(&2);
hll.insert(&3);

// Get the number of zero registers.
let number_of_zero_registers = hll.get_number_of_zero_registers();

assert_eq!(number_of_zero_registers, 16381);

fn get_words(&self) -> &P::Words

Returns the array of words of the HyperLogLog counter.

const INTERMEDIATE_RANGE_CORRECTION_THRESHOLD: f32 = _

The threshold value used in the small range correction of the HyperLogLog algorithm.

const LINEAR_COUNT_THRESHOLD: f32 = _

const LOWER_REGISTER_MASK: u32 = _

The mask used to obtain the lower register bits in the HyperLogLog algorithm.

const LOWER_PRECISION_MASK: usize = _

The mask used to obtain the lower precision bits in the HyperLogLog algorithm.

const NOT_LOWER_PRECISION_MASK: u64 = _

const NUMBER_OF_PADDING_BITS: usize = _

const PADDING_BITS_MASK: u32 = _

The mask representing the bits that are never used in the u32 word in the cases where the number of bits is not a divisor of 32, such as 5 or 6. We set the LEADING bits as the padding bits, the unused one, so the leftmost bits.

const NUMBER_OF_PADDING_REGISTERS: usize = _

const LAST_WORD_PADDING_BITS_MASK: u32 = _

The mask representing the bits that are never used in the last u32 word in the cases where the number of registers is not a multiple of the number of registers in a word.

const UPPER_PRECISION_MASK: usize = _

The mask used to obtain the upper precision bits in the HyperLogLog algorithm.

const NUMBER_OF_REGISTERS_IN_WORD: usize = _

The number of registers that can fit in a single 32-bit word in the HyperLogLog algorithm.

fn adjust_estimate(raw_estimate: f32) -> f32

fn adjust_estimate_with_zeros(raw_estimate: f32, number_of_zeros: usize) -> f32

fn use_small_range_correction(&self) -> bool

Returns whether the cardinality of this HLL will be computed using the small-range correction.

fn estimate_cardinality(&self) -> f32

Estimates the cardinality of the set based on the HLL counter data.

fn estimate_union_cardinality(&self, other: &Self) -> f32

Returns an estimate of the cardinality of the union of two HyperLogLog counters.

fn estimate_union_and_sets_cardinality<F: Primitive<f32> + MaxMin>( &self, other: &Self ) -> EstimatedUnionCardinalities<F>

Returns an estimate of the cardinality of the two HLL counters union.

fn estimate_intersection_cardinality<F: Primitive<f32>>( &self, other: &Self ) -> F

Returns an estimate of the cardinality of the intersection of two HyperLogLog counters.

fn estimate_jaccard_index(&self, other: &Self) -> f32

Returns an estimate of the Jaccard index between two HyperLogLog counters.

fn estimate_difference_cardinality<F: Primitive<f32> + One>( &self, other: &Self ) -> F

Returns an estimate of the cardinality of the current HyperLogLog counter minus the provided one.

fn len(&self) -> usize

Returns the number of registers in the HLL counter.

fn is_empty(&self) -> bool

Returns whether no element was yet added to the HLL counter.

fn get_number_of_padding_registers() -> usize

Returns the number of extra registers that are not actually used.

fn get_number_of_non_zero_registers(&self) -> usize

fn get_registers(&self) -> P::Registers

Returns an array of registers of the HyperLogLog counter.

fn may_contain<T: Hash>(&self, rhs: &T) -> bool

Returns true if the HyperLogLog counter may contain the given element.

fn may_contain_all(&self, rhs: &Self) -> bool

Returns whether the provided HyperLogLog counter may be fully contained in the current HyperLogLog counter.

fn get_hash_and_index<T: Hash>(&self, value: &T) -> (u64, usize)

Returns the hash value and the corresponding register’s index for a given value.

fn estimate_cardinality_from_multiplicities( multiplicities: &P::RegisterMultiplicities ) -> f32

Returns the number of registers in the counter.

impl<P: Precision + WordType<BITS>, const BITS: usize, I: Primitive<f32>> HyperSpheresSketch<I> for HyperLogLog<P, BITS>

fn overlap_and_differences_cardinality_matrices<const L: usize, const R: usize>( left: &[Self; L], right: &[Self; R] ) -> ([[I; R]; L], [I; L], [I; R])

Returns the overlap and differences cardinality matrices of two lists of sets.

fn normalized_overlap_and_differences_cardinality_matrices<const L: usize, const R: usize>( left: &[Self; L], right: &[Self; R] ) -> ([[I; R]; L], [I; L], [I; R])

Returns the normalized overlap and differences cardinality matrices of two lists of sets.

impl<P: Precision + WordType<BITS>, const BITS: usize> PartialEq for HyperLogLog<P, BITS>

Implements PartialEq for HyperLogLog.

Implementative details

Two HyperLogLog counters are considered equal if they have the same words.

Examples

let mut hll1 = HyperLogLog::<Precision14, 5>::default();
hll1.insert(&2);

let mut hll2 = HyperLogLog::<Precision14, 5>::default();
hll2.insert(&2);
hll2.insert(&3);

assert_ne!(hll1, hll2);

hll1 |= hll2;

assert_eq!(hll1, hll2);

fn eq(&self, other: &Self) -> bool

Returns whether the two HyperLogLog counters are equal.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<P: Precision + WordType<BITS>, const BITS: usize> Serialize for HyperLogLog<P, BITS>

fn serialize<S: Serializer>(&self, serializer: S) -> Result<S::Ok, S::Error>

Serializes the HyperLogLog counter using the given serializer.

This method is part of the Serialize trait implementation for the HyperLogLog struct, allowing the counter to be serialized into a format supported by the serializer.

Arguments

• serializer: The serializer used to serialize the HyperLogLog counter.

Returns

The serialization result, indicating success or failure.

Example

use serde::Serialize;
use serde_json::Serializer;
use hyperloglog_rs::prelude::*;

let hll = HyperLogLog::<Precision12, 6>::default();
let mut serializer = Serializer::new(Vec::new());
let result = hll.serialize(&mut serializer);
assert!(result.is_ok(), "Serialization failed, error: {:?}", result.err());

impl<F: Primitive<f32>, P: Precision + WordType<BITS>, const BITS: usize> SetLike<F> for HyperLogLog<P, BITS>

fn get_estimated_union_cardinality( &self, self_cardinality: F, other: &Self, other_cardinality: F ) -> EstimatedUnionCardinalities<F>

Returns the estimated intersection and left and right difference cardinality between two sets.

fn get_cardinality(&self) -> F

Returns the cardinality of the set.

impl<P: Copy + Precision + WordType<BITS>, const BITS: usize> Copy for HyperLogLog<P, BITS>

where P::Words: Copy, P::NumberOfZeros: Copy,

impl<P: Precision + WordType<BITS>, const BITS: usize> Eq for HyperLogLog<P, BITS>