feat: implement count_hash aggr function (#6342)

* feat: implement count_hash aggr function

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* sqlness case

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* change copyright year

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

* review changes

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

---------

Signed-off-by: Ruihang Xia <waynestxia@gmail.com>

src/common/function/Cargo.toml

@@ -33,6 +33,7 @@ common-version.workspace = true
 datafusion.workspace = true
 datafusion-common.workspace = true
 datafusion-expr.workspace = true
+datafusion-functions-aggregate-common.workspace = true
 datatypes.workspace = true
 derive_more = { version = "1", default-features = false, features = ["display"] }
 geo = { version = "0.29", optional = true }

src/common/function/src/aggrs.rs

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 pub mod approximate;
+pub mod count_hash;
 #[cfg(feature = "geo")]
 pub mod geo;
 pub mod vector;

src/common/function/src/aggrs/count_hash.rs

@@ -0,0 +1,647 @@
+// Copyright 2023 Greptime Team
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+//! `CountHash` / `count_hash` is a hash-based approximate distinct count function.
+//!
+//! It is a variant of `CountDistinct` that uses a hash function to approximate the
+//! distinct count.
+//! It is designed to be more efficient than `CountDistinct` for large datasets,
+//! but it is not as accurate, as the hash value may be collision.
+
+use std::collections::HashSet;
+use std::fmt::Debug;
+use std::sync::Arc;
+
+use ahash::RandomState;
+use datafusion_common::cast::as_list_array;
+use datafusion_common::error::Result;
+use datafusion_common::hash_utils::create_hashes;
+use datafusion_common::utils::SingleRowListArrayBuilder;
+use datafusion_common::{internal_err, not_impl_err, ScalarValue};
+use datafusion_expr::function::{AccumulatorArgs, StateFieldsArgs};
+use datafusion_expr::utils::{format_state_name, AggregateOrderSensitivity};
+use datafusion_expr::{
+    Accumulator, AggregateUDF, AggregateUDFImpl, EmitTo, GroupsAccumulator, ReversedUDAF,
+    SetMonotonicity, Signature, TypeSignature, Volatility,
+};
+use datafusion_functions_aggregate_common::aggregate::groups_accumulator::nulls::filtered_null_mask;
+use datatypes::arrow;
+use datatypes::arrow::array::{
+    Array, ArrayRef, AsArray, BooleanArray, Int64Array, ListArray, UInt64Array,
+};
+use datatypes::arrow::buffer::{OffsetBuffer, ScalarBuffer};
+use datatypes::arrow::datatypes::{DataType, Field};
+
+use crate::function_registry::FunctionRegistry;
+
+type HashValueType = u64;
+
+// read from /dev/urandom 4047821dc6144e4b2abddf23ad4171126a52eeecd26eff2191cf673b965a7875
+const RANDOM_SEED_0: u64 = 0x4047821dc6144e4b;
+const RANDOM_SEED_1: u64 = 0x2abddf23ad417112;
+const RANDOM_SEED_2: u64 = 0x6a52eeecd26eff21;
+const RANDOM_SEED_3: u64 = 0x91cf673b965a7875;
+
+impl CountHash {
+    pub fn register(registry: &FunctionRegistry) {
+        registry.register_aggr(CountHash::udf_impl());
+    }
+
+    pub fn udf_impl() -> AggregateUDF {
+        AggregateUDF::new_from_impl(CountHash {
+            signature: Signature::one_of(
+                vec![TypeSignature::VariadicAny, TypeSignature::Nullary],
+                Volatility::Immutable,
+            ),
+        })
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct CountHash {
+    signature: Signature,
+}
+
+impl AggregateUDFImpl for CountHash {
+    fn as_any(&self) -> &dyn std::any::Any {
+        self
+    }
+
+    fn name(&self) -> &str {
+        "count_hash"
+    }
+
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+
+    fn return_type(&self, _arg_types: &[DataType]) -> Result<DataType> {
+        Ok(DataType::Int64)
+    }
+
+    fn is_nullable(&self) -> bool {
+        false
+    }
+
+    fn state_fields(&self, args: StateFieldsArgs) -> Result<Vec<Field>> {
+        Ok(vec![Field::new_list(
+            format_state_name(args.name, "count_hash"),
+            Field::new_list_field(DataType::UInt64, true),
+            // For count_hash accumulator, null list item stands for an
+            // empty value set (i.e., all NULL value so far for that group).
+            true,
+        )])
+    }
+
+    fn accumulator(&self, acc_args: AccumulatorArgs) -> Result<Box<dyn Accumulator>> {
+        if acc_args.exprs.len() > 1 {
+            return not_impl_err!("count_hash with multiple arguments");
+        }
+
+        Ok(Box::new(CountHashAccumulator {
+            values: HashSet::default(),
+            random_state: RandomState::with_seeds(
+                RANDOM_SEED_0,
+                RANDOM_SEED_1,
+                RANDOM_SEED_2,
+                RANDOM_SEED_3,
+            ),
+            batch_hashes: vec![],
+        }))
+    }
+
+    fn aliases(&self) -> &[String] {
+        &[]
+    }
+
+    fn groups_accumulator_supported(&self, _args: AccumulatorArgs) -> bool {
+        true
+    }
+
+    fn create_groups_accumulator(
+        &self,
+        args: AccumulatorArgs,
+    ) -> Result<Box<dyn GroupsAccumulator>> {
+        if args.exprs.len() > 1 {
+            return not_impl_err!("count_hash with multiple arguments");
+        }
+
+        Ok(Box::new(CountHashGroupAccumulator::new()))
+    }
+
+    fn reverse_expr(&self) -> ReversedUDAF {
+        ReversedUDAF::Identical
+    }
+
+    fn order_sensitivity(&self) -> AggregateOrderSensitivity {
+        AggregateOrderSensitivity::Insensitive
+    }
+
+    fn default_value(&self, _data_type: &DataType) -> Result<ScalarValue> {
+        Ok(ScalarValue::Int64(Some(0)))
+    }
+
+    fn set_monotonicity(&self, _data_type: &DataType) -> SetMonotonicity {
+        SetMonotonicity::Increasing
+    }
+}
+
+/// GroupsAccumulator for `count_hash` aggregate function
+#[derive(Debug)]
+pub struct CountHashGroupAccumulator {
+    /// One HashSet per group to track distinct values
+    distinct_sets: Vec<HashSet<HashValueType, RandomState>>,
+    random_state: RandomState,
+    batch_hashes: Vec<HashValueType>,
+}
+
+impl Default for CountHashGroupAccumulator {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl CountHashGroupAccumulator {
+    pub fn new() -> Self {
+        Self {
+            distinct_sets: vec![],
+            random_state: RandomState::with_seeds(
+                RANDOM_SEED_0,
+                RANDOM_SEED_1,
+                RANDOM_SEED_2,
+                RANDOM_SEED_3,
+            ),
+            batch_hashes: vec![],
+        }
+    }
+
+    fn ensure_sets(&mut self, total_num_groups: usize) {
+        if self.distinct_sets.len() < total_num_groups {
+            self.distinct_sets
+                .resize_with(total_num_groups, HashSet::default);
+        }
+    }
+}
+
+impl GroupsAccumulator for CountHashGroupAccumulator {
+    fn update_batch(
+        &mut self,
+        values: &[ArrayRef],
+        group_indices: &[usize],
+        opt_filter: Option<&BooleanArray>,
+        total_num_groups: usize,
+    ) -> Result<()> {
+        assert_eq!(values.len(), 1, "count_hash expects a single argument");
+        self.ensure_sets(total_num_groups);
+
+        let array = &values[0];
+        self.batch_hashes.clear();
+        self.batch_hashes.resize(array.len(), 0);
+        let hashes = create_hashes(
+            &[ArrayRef::clone(array)],
+            &self.random_state,
+            &mut self.batch_hashes,
+        )?;
+
+        // Use a pattern similar to accumulate_indices to process rows
+        // that are not null and pass the filter
+        let nulls = array.logical_nulls();
+
+        match (nulls.as_ref(), opt_filter) {
+            (None, None) => {
+                // No nulls, no filter - process all rows
+                for (row_idx, &group_idx) in group_indices.iter().enumerate() {
+                    self.distinct_sets[group_idx].insert(hashes[row_idx]);
+                }
+            }
+            (Some(nulls), None) => {
+                // Has nulls, no filter
+                for (row_idx, (&group_idx, is_valid)) in
+                    group_indices.iter().zip(nulls.iter()).enumerate()
+                {
+                    if is_valid {
+                        self.distinct_sets[group_idx].insert(hashes[row_idx]);
+                    }
+                }
+            }
+            (None, Some(filter)) => {
+                // No nulls, has filter
+                for (row_idx, (&group_idx, filter_value)) in
+                    group_indices.iter().zip(filter.iter()).enumerate()
+                {
+                    if let Some(true) = filter_value {
+                        self.distinct_sets[group_idx].insert(hashes[row_idx]);
+                    }
+                }
+            }
+            (Some(nulls), Some(filter)) => {
+                // Has nulls and filter
+                let iter = filter
+                    .iter()
+                    .zip(group_indices.iter())
+                    .zip(nulls.iter())
+                    .enumerate();
+
+                for (row_idx, ((filter_value, &group_idx), is_valid)) in iter {
+                    if is_valid && filter_value == Some(true) {
+                        self.distinct_sets[group_idx].insert(hashes[row_idx]);
+                    }
+                }
+            }
+        }
+
+        Ok(())
+    }
+
+    fn evaluate(&mut self, emit_to: EmitTo) -> Result<ArrayRef> {
+        let distinct_sets: Vec<HashSet<u64, RandomState>> =
+            emit_to.take_needed(&mut self.distinct_sets);
+
+        let counts = distinct_sets
+            .iter()
+            .map(|set| set.len() as i64)
+            .collect::<Vec<_>>();
+        Ok(Arc::new(Int64Array::from(counts)))
+    }
+
+    fn merge_batch(
+        &mut self,
+        values: &[ArrayRef],
+        group_indices: &[usize],
+        _opt_filter: Option<&BooleanArray>,
+        total_num_groups: usize,
+    ) -> Result<()> {
+        assert_eq!(
+            values.len(),
+            1,
+            "count_hash merge expects a single state array"
+        );
+        self.ensure_sets(total_num_groups);
+
+        let list_array = as_list_array(&values[0])?;
+
+        // For each group in the incoming batch
+        for (i, &group_idx) in group_indices.iter().enumerate() {
+            if i < list_array.len() {
+                let inner_array = list_array.value(i);
+                let inner_array = inner_array.as_any().downcast_ref::<UInt64Array>().unwrap();
+                // Add each value to our set for this group
+                for j in 0..inner_array.len() {
+                    if !inner_array.is_null(j) {
+                        self.distinct_sets[group_idx].insert(inner_array.value(j));
+                    }
+                }
+            }
+        }
+
+        Ok(())
+    }
+
+    fn state(&mut self, emit_to: EmitTo) -> Result<Vec<ArrayRef>> {
+        let distinct_sets: Vec<HashSet<u64, RandomState>> =
+            emit_to.take_needed(&mut self.distinct_sets);
+
+        let mut offsets = Vec::with_capacity(distinct_sets.len() + 1);
+        offsets.push(0);
+        let mut curr_len = 0i32;
+
+        let mut value_iter = distinct_sets
+            .into_iter()
+            .flat_map(|set| {
+                // build offset
+                curr_len += set.len() as i32;
+                offsets.push(curr_len);
+                // convert into iter
+                set.into_iter()
+            })
+            .peekable();
+        let data_array: ArrayRef = if value_iter.peek().is_none() {
+            arrow::array::new_empty_array(&DataType::UInt64) as _
+        } else {
+            Arc::new(UInt64Array::from_iter_values(value_iter))
+        };
+        let offset_buffer = OffsetBuffer::new(ScalarBuffer::from(offsets));
+
+        let list_array = ListArray::new(
+            Arc::new(Field::new_list_field(DataType::UInt64, true)),
+            offset_buffer,
+            data_array,
+            None,
+        );
+
+        Ok(vec![Arc::new(list_array) as _])
+    }
+
+    fn convert_to_state(
+        &self,
+        values: &[ArrayRef],
+        opt_filter: Option<&BooleanArray>,
+    ) -> Result<Vec<ArrayRef>> {
+        // For a single hash value per row, create a list array with that value
+        assert_eq!(values.len(), 1, "count_hash expects a single argument");
+        let values = ArrayRef::clone(&values[0]);
+
+        let offsets = OffsetBuffer::new(ScalarBuffer::from_iter(0..values.len() as i32 + 1));
+        let nulls = filtered_null_mask(opt_filter, &values);
+        let list_array = ListArray::new(
+            Arc::new(Field::new_list_field(DataType::UInt64, true)),
+            offsets,
+            values,
+            nulls,
+        );
+
+        Ok(vec![Arc::new(list_array)])
+    }
+
+    fn supports_convert_to_state(&self) -> bool {
+        true
+    }
+
+    fn size(&self) -> usize {
+        // Base size of the struct
+        let mut size = size_of::<Self>();
+
+        // Size of the vector holding the HashSets
+        size += size_of::<Vec<HashSet<HashValueType, RandomState>>>()
+            + self.distinct_sets.capacity() * size_of::<HashSet<HashValueType, RandomState>>();
+
+        // Estimate HashSet contents size more efficiently
+        // Instead of iterating through all values which is expensive, use an approximation
+        for set in &self.distinct_sets {
+            // Base size of the HashSet
+            size += set.capacity() * size_of::<HashValueType>();
+        }
+
+        size
+    }
+}
+
+#[derive(Debug)]
+struct CountHashAccumulator {
+    values: HashSet<HashValueType, RandomState>,
+    random_state: RandomState,
+    batch_hashes: Vec<HashValueType>,
+}
+
+impl CountHashAccumulator {
+    // calculating the size for fixed length values, taking first batch size *
+    // number of batches.
+    fn fixed_size(&self) -> usize {
+        size_of_val(self) + (size_of::<HashValueType>() * self.values.capacity())
+    }
+}
+
+impl Accumulator for CountHashAccumulator {
+    /// Returns the distinct values seen so far as (one element) ListArray.
+    fn state(&mut self) -> Result<Vec<ScalarValue>> {
+        let values = self.values.iter().cloned().collect::<Vec<_>>();
+        let arr = Arc::new(UInt64Array::from(values)) as _;
+        let list_scalar = SingleRowListArrayBuilder::new(arr).build_list_scalar();
+        Ok(vec![list_scalar])
+    }
+
+    fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
+        if values.is_empty() {
+            return Ok(());
+        }
+
+        let arr = &values[0];
+        if arr.data_type() == &DataType::Null {
+            return Ok(());
+        }
+
+        self.batch_hashes.clear();
+        self.batch_hashes.resize(arr.len(), 0);
+        let hashes = create_hashes(
+            &[ArrayRef::clone(arr)],
+            &self.random_state,
+            &mut self.batch_hashes,
+        )?;
+        for hash in hashes.as_slice() {
+            self.values.insert(*hash);
+        }
+        Ok(())
+    }
+
+    /// Merges multiple sets of distinct values into the current set.
+    ///
+    /// The input to this function is a `ListArray` with **multiple** rows,
+    /// where each row contains the values from a partial aggregate's phase (e.g.
+    /// the result of calling `Self::state` on multiple accumulators).
+    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
+        if states.is_empty() {
+            return Ok(());
+        }
+        assert_eq!(states.len(), 1, "array_agg states must be singleton!");
+        let array = &states[0];
+        let list_array = array.as_list::<i32>();
+        for inner_array in list_array.iter() {
+            let Some(inner_array) = inner_array else {
+                return internal_err!(
+                    "Intermediate results of count_hash should always be non null"
+                );
+            };
+            let hash_array = inner_array.as_any().downcast_ref::<UInt64Array>().unwrap();
+            for i in 0..hash_array.len() {
+                self.values.insert(hash_array.value(i));
+            }
+        }
+        Ok(())
+    }
+
+    fn evaluate(&mut self) -> Result<ScalarValue> {
+        Ok(ScalarValue::Int64(Some(self.values.len() as i64)))
+    }
+
+    fn size(&self) -> usize {
+        self.fixed_size()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use datatypes::arrow::array::{Array, BooleanArray, Int32Array, Int64Array};
+
+    use super::*;
+
+    fn create_test_accumulator() -> CountHashAccumulator {
+        CountHashAccumulator {
+            values: HashSet::default(),
+            random_state: RandomState::with_seeds(
+                RANDOM_SEED_0,
+                RANDOM_SEED_1,
+                RANDOM_SEED_2,
+                RANDOM_SEED_3,
+            ),
+            batch_hashes: vec![],
+        }
+    }
+
+    #[test]
+    fn test_count_hash_accumulator() -> Result<()> {
+        let mut acc = create_test_accumulator();
+
+        // Test with some data
+        let array = Arc::new(Int32Array::from(vec![
+            Some(1),
+            Some(2),
+            Some(3),
+            Some(1),
+            Some(2),
+            None,
+        ])) as ArrayRef;
+        acc.update_batch(&[array])?;
+        let result = acc.evaluate()?;
+        assert_eq!(result, ScalarValue::Int64(Some(4)));
+
+        // Test with empty data
+        let mut acc = create_test_accumulator();
+        let array = Arc::new(Int32Array::from(vec![] as Vec<Option<i32>>)) as ArrayRef;
+        acc.update_batch(&[array])?;
+        let result = acc.evaluate()?;
+        assert_eq!(result, ScalarValue::Int64(Some(0)));
+
+        // Test with only nulls
+        let mut acc = create_test_accumulator();
+        let array = Arc::new(Int32Array::from(vec![None, None, None])) as ArrayRef;
+        acc.update_batch(&[array])?;
+        let result = acc.evaluate()?;
+        assert_eq!(result, ScalarValue::Int64(Some(1)));
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_count_hash_accumulator_merge() -> Result<()> {
+        // Accumulator 1
+        let mut acc1 = create_test_accumulator();
+        let array1 = Arc::new(Int32Array::from(vec![Some(1), Some(2), Some(3)])) as ArrayRef;
+        acc1.update_batch(&[array1])?;
+        let state1 = acc1.state()?;
+
+        // Accumulator 2
+        let mut acc2 = create_test_accumulator();
+        let array2 = Arc::new(Int32Array::from(vec![Some(3), Some(4), Some(5)])) as ArrayRef;
+        acc2.update_batch(&[array2])?;
+        let state2 = acc2.state()?;
+
+        // Merge state1 and state2 into a new accumulator
+        let mut acc_merged = create_test_accumulator();
+        let state_array1 = state1[0].to_array()?;
+        let state_array2 = state2[0].to_array()?;
+
+        acc_merged.merge_batch(&[state_array1])?;
+        acc_merged.merge_batch(&[state_array2])?;
+
+        let result = acc_merged.evaluate()?;
+        // Distinct values are {1, 2, 3, 4, 5}, so count is 5
+        assert_eq!(result, ScalarValue::Int64(Some(5)));
+
+        Ok(())
+    }
+
+    fn create_test_group_accumulator() -> CountHashGroupAccumulator {
+        CountHashGroupAccumulator::new()
+    }
+
+    #[test]
+    fn test_count_hash_group_accumulator() -> Result<()> {
+        let mut acc = create_test_group_accumulator();
+        let values = Arc::new(Int32Array::from(vec![1, 2, 1, 3, 2, 4, 5])) as ArrayRef;
+        let group_indices = vec![0, 1, 0, 0, 1, 2, 0];
+        let total_num_groups = 3;
+
+        acc.update_batch(&[values], &group_indices, None, total_num_groups)?;
+
+        let result_array = acc.evaluate(EmitTo::All)?;
+        let result = result_array.as_any().downcast_ref::<Int64Array>().unwrap();
+
+        // Group 0: {1, 3, 5} -> 3
+        // Group 1: {2} -> 1
+        // Group 2: {4} -> 1
+        assert_eq!(result.value(0), 3);
+        assert_eq!(result.value(1), 1);
+        assert_eq!(result.value(2), 1);
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_count_hash_group_accumulator_with_filter() -> Result<()> {
+        let mut acc = create_test_group_accumulator();
+        let values = Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5, 6])) as ArrayRef;
+        let group_indices = vec![0, 0, 1, 1, 2, 2];
+        let filter = BooleanArray::from(vec![true, false, true, true, false, true]);
+        let total_num_groups = 3;
+
+        acc.update_batch(&[values], &group_indices, Some(&filter), total_num_groups)?;
+
+        let result_array = acc.evaluate(EmitTo::All)?;
+        let result = result_array.as_any().downcast_ref::<Int64Array>().unwrap();
+
+        // Group 0: {1} (2 is filtered out) -> 1
+        // Group 1: {3, 4} -> 2
+        // Group 2: {6} (5 is filtered out) -> 1
+        assert_eq!(result.value(0), 1);
+        assert_eq!(result.value(1), 2);
+        assert_eq!(result.value(2), 1);
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_count_hash_group_accumulator_merge() -> Result<()> {
+        // Accumulator 1
+        let mut acc1 = create_test_group_accumulator();
+        let values1 = Arc::new(Int32Array::from(vec![1, 2, 3, 4])) as ArrayRef;
+        let group_indices1 = vec![0, 0, 1, 1];
+        acc1.update_batch(&[values1], &group_indices1, None, 2)?;
+        // acc1 state: group 0 -> {1, 2}, group 1 -> {3, 4}
+        let state1 = acc1.state(EmitTo::All)?;
+
+        // Accumulator 2
+        let mut acc2 = create_test_group_accumulator();
+        let values2 = Arc::new(Int32Array::from(vec![5, 6, 1, 3])) as ArrayRef;
+        // Merge into different group indices
+        let group_indices2 = vec![2, 2, 0, 1];
+        acc2.update_batch(&[values2], &group_indices2, None, 3)?;
+        // acc2 state: group 0 -> {1}, group 1 -> {3}, group 2 -> {5, 6}
+
+        // Merge state from acc1 into acc2
+        // We will merge acc1's group 0 into acc2's group 0
+        // and acc1's group 1 into acc2's group 2
+        let merge_group_indices = vec![0, 2];
+        acc2.merge_batch(&state1, &merge_group_indices, None, 3)?;
+
+        let result_array = acc2.evaluate(EmitTo::All)?;
+        let result = result_array.as_any().downcast_ref::<Int64Array>().unwrap();
+
+        // Final state of acc2:
+        // Group 0: {1} U {1, 2} -> {1, 2}, count = 2
+        // Group 1: {3}, count = 1
+        // Group 2: {5, 6} U {3, 4} -> {3, 4, 5, 6}, count = 4
+        assert_eq!(result.value(0), 2);
+        assert_eq!(result.value(1), 1);
+        assert_eq!(result.value(2), 4);
+
+        Ok(())
+    }
+
+    #[test]
+    fn test_size() {
+        let acc = create_test_group_accumulator();
+        // Just test it doesn't crash and returns a value.
+        assert!(acc.size() > 0);
+    }
+}

src/common/function/src/function_registry.rs

@@ -21,6 +21,7 @@ use once_cell::sync::Lazy;
 
 use crate::admin::AdminFunction;
 use crate::aggrs::approximate::ApproximateFunction;
+use crate::aggrs::count_hash::CountHash;
 use crate::aggrs::vector::VectorFunction as VectorAggrFunction;
 use crate::function::{AsyncFunctionRef, Function, FunctionRef};
 use crate::function_factory::ScalarFunctionFactory;
@@ -144,6 +145,9 @@ pub static FUNCTION_REGISTRY: Lazy<Arc<FunctionRegistry>> = Lazy::new(|| {
     // Approximate functions
     ApproximateFunction::register(&function_registry);
 
+    // CountHash function
+    CountHash::register(&function_registry);
+
     Arc::new(function_registry)
 });
 

src/common/query/src/lib.rs

@@ -14,7 +14,7 @@
 
 pub mod columnar_value;
 pub mod error;
-mod function;
+pub mod function;
 pub mod logical_plan;
 pub mod prelude;
 pub mod request;