polars_core/frame/group_by/

perfect.rs

use std::fmt::Debug;
use std::mem::MaybeUninit;

use num_traits::{FromPrimitive, ToPrimitive};
use polars_utils::idx_vec::IdxVec;
use polars_utils::sync::SyncPtr;
use rayon::prelude::*;

use crate::POOL;
#[cfg(all(feature = "dtype-categorical", feature = "performant"))]
use crate::config::verbose;
use crate::datatypes::*;
use crate::prelude::*;

impl<T> ChunkedArray<T>
where
    T: PolarsIntegerType,
    T::Native: ToPrimitive + FromPrimitive + Debug,
{
    /// Use the indexes as perfect groups.
    ///
    /// # Safety
    /// This ChunkedArray must contain each value in [0..num_groups) at least
    /// once, and nothing outside this range.
    pub unsafe fn group_tuples_perfect(
        &self,
        num_groups: usize,
        mut multithreaded: bool,
        group_capacity: usize,
    ) -> GroupsType {
        multithreaded &= POOL.current_num_threads() > 1;
        // The latest index will be used for the null sentinel.
        let len = if self.null_count() > 0 {
            // We add one to store the null sentinel group.
            num_groups + 1
        } else {
            num_groups
        };
        let null_idx = len.saturating_sub(1);

        let n_threads = POOL.current_num_threads();
        let chunk_size = len / n_threads;

        let (groups, first) = if multithreaded && chunk_size > 1 {
            let mut groups: Vec<IdxVec> = Vec::new();
            groups.resize_with(len, || IdxVec::with_capacity(group_capacity));
            let mut first: Vec<IdxSize> = Vec::with_capacity(len);

            // Round up offsets to nearest cache line for groups to reduce false sharing.
            let groups_start = groups.as_ptr();
            let mut per_thread_offsets = Vec::with_capacity(n_threads + 1);
            per_thread_offsets.push(0);
            for t in 0..n_threads {
                let ideal_offset = (t + 1) * chunk_size;
                let cache_aligned_offset =
                    ideal_offset + groups_start.wrapping_add(ideal_offset).align_offset(128);
                if t == n_threads - 1 {
                    per_thread_offsets.push(len);
                } else {
                    per_thread_offsets.push(std::cmp::min(cache_aligned_offset, len));
                }
            }

            let groups_ptr = unsafe { SyncPtr::new(groups.as_mut_ptr()) };
            let first_ptr = unsafe { SyncPtr::new(first.as_mut_ptr()) };
            POOL.install(|| {
                (0..n_threads).into_par_iter().for_each(|thread_no| {
                    // We use raw pointers because the slices would overlap.
                    // However, each thread has its own range it is responsible for.
                    let groups = groups_ptr.get();
                    let first = first_ptr.get();
                    let start = per_thread_offsets[thread_no];
                    let start = T::Native::from_usize(start).unwrap();
                    let end = per_thread_offsets[thread_no + 1];
                    let end = T::Native::from_usize(end).unwrap();

                    if start == end && thread_no != n_threads - 1 {
                        return;
                    };

                    let push_to_group = |cat, row_nr| unsafe {
                        debug_assert!(cat < len);
                        let buf = &mut *groups.add(cat);
                        buf.push(row_nr);
                        if buf.len() == 1 {
                            *first.add(cat) = row_nr;
                        }
                    };

                    let mut row_nr = 0 as IdxSize;
                    for arr in self.downcast_iter() {
                        if arr.null_count() == 0 {
                            for &cat in arr.values().as_slice() {
                                if cat >= start && cat < end {
                                    push_to_group(cat.to_usize().unwrap(), row_nr);
                                }

                                row_nr += 1;
                            }
                        } else {
                            for opt_cat in arr.iter() {
                                if let Some(&cat) = opt_cat {
                                    if cat >= start && cat < end {
                                        push_to_group(cat.to_usize().unwrap(), row_nr);
                                    }
                                } else if thread_no == n_threads - 1 {
                                    // Last thread handles null values.
                                    push_to_group(null_idx, row_nr);
                                }

                                row_nr += 1;
                            }
                        }
                    }
                });
            });
            unsafe {
                first.set_len(len);
            }
            (groups, first)
        } else {
            let mut groups = Vec::with_capacity(len);
            let mut first = Vec::with_capacity(len);
            let first_out = first.spare_capacity_mut();
            groups.resize_with(len, || IdxVec::with_capacity(group_capacity));

            let mut push_to_group = |cat, row_nr| unsafe {
                let buf: &mut IdxVec = groups.get_unchecked_mut(cat);
                buf.push(row_nr);
                if buf.len() == 1 {
                    *first_out.get_unchecked_mut(cat) = MaybeUninit::new(row_nr);
                }
            };

            let mut row_nr = 0 as IdxSize;
            for arr in self.downcast_iter() {
                for opt_cat in arr.iter() {
                    if let Some(cat) = opt_cat {
                        push_to_group(cat.to_usize().unwrap(), row_nr);
                    } else {
                        push_to_group(null_idx, row_nr);
                    }

                    row_nr += 1;
                }
            }
            unsafe {
                first.set_len(len);
            }
            (groups, first)
        };

        // NOTE! we set sorted here!
        // this happens to be true for `fast_unique` categoricals
        GroupsType::Idx(GroupsIdx::new(first, groups, true))
    }
}

#[cfg(all(feature = "dtype-categorical", feature = "performant"))]
// Special implementation so that cats can be processed in a single pass
impl CategoricalChunked {
    // Use the indexes as perfect groups
    pub fn group_tuples_perfect(&self, multithreaded: bool, sorted: bool) -> GroupsType {
        let rev_map = self.get_rev_map();
        if self.is_empty() {
            return GroupsType::Idx(GroupsIdx::new(vec![], vec![], true));
        }
        let cats = self.physical();

        let mut out = match &**rev_map {
            RevMapping::Local(cached, _) => {
                if self._can_fast_unique() {
                    assert!(cached.len() <= self.len(), "invalid invariant");
                    if verbose() {
                        eprintln!("grouping categoricals, run perfect hash function");
                    }
                    // on relative small tables this isn't much faster than the default strategy
                    // but on huge tables, this can be > 2x faster
                    unsafe { cats.group_tuples_perfect(cached.len(), multithreaded, 0) }
                } else {
                    self.physical().group_tuples(multithreaded, sorted).unwrap()
                }
            },
            RevMapping::Global(_mapping, _cached, _) => {
                // TODO! see if we can optimize this
                // the problem is that the global categories are not guaranteed packed together
                // so we might need to deref them first to local ones, but that might be more
                // expensive than just hashing (benchmark first)
                self.physical().group_tuples(multithreaded, sorted).unwrap()
            },
        };
        if sorted {
            out.sort()
        }
        out
    }
}