vortex_array/aggregate_fn/fns/mean/
mod.rs

1// SPDX-License-Identifier: Apache-2.0
2// SPDX-FileCopyrightText: Copyright the Vortex contributors
3
4use vortex_error::VortexExpect;
5use vortex_error::VortexResult;
6use vortex_error::vortex_bail;
7use vortex_session::registry::CachedId;
8
9use crate::ArrayRef;
10use crate::ExecutionCtx;
11use crate::IntoArray;
12use crate::aggregate_fn::Accumulator;
13use crate::aggregate_fn::AggregateArgs;
14use crate::aggregate_fn::AggregateFnId;
15use crate::aggregate_fn::DynAccumulator;
16use crate::aggregate_fn::NumericalAggregateOpts;
17use crate::aggregate_fn::combined::BinaryCombined;
18use crate::aggregate_fn::combined::Combined;
19use crate::aggregate_fn::combined::CombinedOptions;
20use crate::aggregate_fn::combined::PairOptions;
21use crate::aggregate_fn::fns::count::Count;
22use crate::aggregate_fn::fns::sum::Sum;
23use crate::aggregate_fn::fns::sum::sum_decimal_dtype;
24use crate::arrays::ConstantArray;
25use crate::builtins::ArrayBuiltins;
26use crate::dtype::DType;
27use crate::dtype::DecimalDType;
28use crate::dtype::MAX_PRECISION;
29use crate::dtype::MAX_SCALE;
30use crate::dtype::Nullability;
31use crate::dtype::PType;
32use crate::dtype::i256;
33use crate::scalar::DecimalValue;
34use crate::scalar::Scalar;
35use crate::scalar_fn::fns::operators::Operator;
36
37/// Compute the arithmetic mean of an array.
38///
39/// See [`Mean`] for details.
40pub fn mean(array: &ArrayRef, ctx: &mut ExecutionCtx) -> VortexResult<Scalar> {
41    let mut acc = Accumulator::try_new(
42        Mean::combined(),
43        PairOptions(
44            NumericalAggregateOpts::default(),
45            NumericalAggregateOpts::default(),
46        ),
47        array.dtype().clone(),
48    )?;
49    acc.accumulate(array, ctx)?;
50    acc.finish()
51}
52
53/// Compute the arithmetic mean of an array.
54///
55/// Implemented as `Sum / Count` via [`BinaryCombined`].
56///
57/// Booleans and primitive numeric types produce nullable `f64` results.
58/// Decimals produce a nullable decimal result.
59#[derive(Clone, Debug)]
60pub struct Mean;
61
62impl Mean {
63    pub fn combined() -> Combined<Self> {
64        Combined(Mean)
65    }
66}
67
68impl BinaryCombined for Mean {
69    type Left = Sum;
70    type Right = Count;
71
72    fn id(&self) -> AggregateFnId {
73        static ID: CachedId = CachedId::new("vortex.mean");
74        *ID
75    }
76
77    fn left(&self) -> Sum {
78        Sum
79    }
80
81    fn right(&self) -> Count {
82        Count
83    }
84
85    fn left_name(&self) -> &'static str {
86        "sum"
87    }
88
89    fn right_name(&self) -> &'static str {
90        "count"
91    }
92
93    fn return_dtype(&self, _options: &CombinedOptions<Self>, input_dtype: &DType) -> Option<DType> {
94        Some(mean_output_dtype(input_dtype)?.with_nullability(Nullability::Nullable))
95    }
96
97    fn finalize(
98        &self,
99        args: AggregateArgs<'_, CombinedOptions<Self>>,
100        sum: ArrayRef,
101        count: ArrayRef,
102    ) -> VortexResult<ArrayRef> {
103        if let DType::Decimal(..) = sum.dtype() {
104            vortex_bail!("grouped mean over decimals is not yet supported");
105        }
106        let target = args.return_dtype.clone();
107        let sum = sum.cast(target.clone())?;
108        let count = count.cast(target.clone())?;
109
110        let non_zero = count
111            .binary(
112                ConstantArray::new(Scalar::zero_value(&target), count.len()).into_array(),
113                Operator::NotEq,
114            )?
115            .fill_null(false)?;
116        // if count is 0, dividing by 0 below produces NaN, and we need Null.
117        // mask values to skip 0 so on 0 count turns into Null, dividing by
118        // Null is always Null
119        let count = count.mask(non_zero)?;
120
121        sum.binary(count, Operator::Div)
122    }
123
124    fn finalize_scalar(
125        &self,
126        args: AggregateArgs<'_, CombinedOptions<Self>>,
127        left_scalar: Scalar,
128        right_scalar: Scalar,
129    ) -> VortexResult<Scalar> {
130        if let DType::Decimal(decimal_dtype, _) = *left_scalar.dtype() {
131            return finalize_decimal_scalar(
132                &left_scalar,
133                &right_scalar,
134                decimal_dtype,
135                args.return_dtype,
136            );
137        }
138
139        let target = args.return_dtype.clone();
140        let sum_cast = left_scalar.cast(&target)?;
141        let count_cast = right_scalar.cast(&target)?;
142
143        let sum = sum_cast.as_primitive().typed_value::<f64>();
144        let count = count_cast.as_primitive().typed_value::<f64>();
145        let value = match (sum, count) {
146            // None sum means sum overflowed, 0 count means empty input
147            (None, _) | (_, None) | (_, Some(0.0)) => return Ok(Scalar::null(target)),
148            (Some(s), Some(c)) => s / c,
149        };
150        Ok(Scalar::primitive(value, Nullability::Nullable))
151    }
152
153    fn serialize(&self, _options: &CombinedOptions<Self>) -> VortexResult<Option<Vec<u8>>> {
154        vortex_bail!("mean is not yet serializable");
155    }
156}
157
158fn mean_output_dtype(input_dtype: &DType) -> Option<DType> {
159    match input_dtype {
160        DType::Bool(_) | DType::Primitive(..) => {
161            Some(DType::Primitive(PType::F64, Nullability::Nullable))
162        }
163        DType::Decimal(decimal_dtype, _) => Some(DType::Decimal(
164            mean_decimal_dtype(&sum_decimal_dtype(decimal_dtype)),
165            Nullability::Nullable,
166        )),
167        _ => None,
168    }
169}
170
171/// mean() output decimal type mimicking Spark/DataFusion/MySQL: decimal(p+4, s+4)
172fn mean_decimal_dtype(sum: &DecimalDType) -> DecimalDType {
173    DecimalDType::new(
174        u8::min(MAX_PRECISION, sum.precision().saturating_sub(6)),
175        i8::min(MAX_SCALE, sum.scale() + 4),
176    )
177}
178
179fn finalize_decimal_scalar(
180    sum: &Scalar,
181    count: &Scalar,
182    sum_decimal: DecimalDType,
183    target_dtype: &DType,
184) -> VortexResult<Scalar> {
185    let target_decimal_dtype = *target_dtype
186        .as_decimal_opt()
187        .vortex_expect("decimal mean result dtype");
188
189    // overflow
190    let Some(sum_value) = sum.as_decimal().decimal_value() else {
191        return Ok(Scalar::null(target_dtype.clone()));
192    };
193    // empty input
194    let count = count.as_primitive().typed_value::<u64>().unwrap_or(0);
195    if count == 0 {
196        return Ok(Scalar::null(target_dtype.clone()));
197    }
198
199    let Ok(sum) = DecimalValue::rescale_i256(
200        sum_value.as_i256(),
201        sum_decimal.scale(),
202        target_decimal_dtype.scale(),
203    ) else {
204        return Ok(Scalar::null(target_dtype.clone()));
205    };
206    let mean = sum / i256::from_i128(i128::from(count));
207
208    let Ok(mean) = DecimalValue::try_from_i256(mean, target_decimal_dtype) else {
209        return Ok(Scalar::null(target_dtype.clone()));
210    };
211    Ok(Scalar::decimal(
212        mean,
213        target_decimal_dtype,
214        Nullability::Nullable,
215    ))
216}
217
218#[cfg(test)]
219mod tests {
220    use vortex_buffer::buffer;
221    use vortex_error::VortexResult;
222
223    use super::*;
224    use crate::VortexSessionExecute;
225    use crate::aggregate_fn::DynGroupedAccumulator;
226    use crate::aggregate_fn::GroupedAccumulator;
227    use crate::array_session;
228    use crate::arrays::BoolArray;
229    use crate::arrays::ChunkedArray;
230    use crate::arrays::DecimalArray;
231    use crate::arrays::FixedSizeListArray;
232    use crate::arrays::PrimitiveArray;
233    use crate::dtype::DecimalDType;
234    use crate::validity::Validity;
235
236    #[test]
237    fn mean_all_valid() -> VortexResult<()> {
238        let array = PrimitiveArray::new(buffer![1.0f64, 2.0, 3.0, 4.0, 5.0], Validity::NonNullable)
239            .into_array();
240        let mut ctx = array_session().create_execution_ctx();
241        let result = mean(&array, &mut ctx)?;
242        assert_eq!(result.as_primitive().as_::<f64>(), Some(3.0));
243        Ok(())
244    }
245
246    #[test]
247    fn mean_with_nulls() -> VortexResult<()> {
248        let array = PrimitiveArray::from_option_iter([Some(2.0f64), None, Some(4.0)]).into_array();
249        let mut ctx = array_session().create_execution_ctx();
250        let result = mean(&array, &mut ctx)?;
251        assert_eq!(result.as_primitive().as_::<f64>(), Some(3.0));
252        Ok(())
253    }
254
255    #[test]
256    fn mean_integers() -> VortexResult<()> {
257        let array = PrimitiveArray::new(buffer![10i32, 20, 30], Validity::NonNullable).into_array();
258        let mut ctx = array_session().create_execution_ctx();
259        let result = mean(&array, &mut ctx)?;
260        assert_eq!(result.as_primitive().as_::<f64>(), Some(20.0));
261        Ok(())
262    }
263
264    #[test]
265    fn mean_bool() -> VortexResult<()> {
266        let array: BoolArray = [true, false, true, true].into_iter().collect();
267        let mut ctx = array_session().create_execution_ctx();
268        let result = mean(&array.into_array(), &mut ctx)?;
269        assert_eq!(result.as_primitive().as_::<f64>(), Some(0.75));
270        Ok(())
271    }
272
273    #[test]
274    fn mean_constant_non_null() -> VortexResult<()> {
275        let array = ConstantArray::new(5.0f64, 4);
276        let mut ctx = array_session().create_execution_ctx();
277        let result = mean(&array.into_array(), &mut ctx)?;
278        assert_eq!(result.as_primitive().as_::<f64>(), Some(5.0));
279        Ok(())
280    }
281
282    #[test]
283    fn mean_chunked() -> VortexResult<()> {
284        let chunk1 = PrimitiveArray::from_option_iter([Some(1.0f64), None, Some(3.0)]);
285        let chunk2 = PrimitiveArray::from_option_iter([Some(5.0f64), None]);
286        let dtype = chunk1.dtype().clone();
287        let chunked = ChunkedArray::try_new(vec![chunk1.into_array(), chunk2.into_array()], dtype)?;
288        let mut ctx = array_session().create_execution_ctx();
289        let result = mean(&chunked.into_array(), &mut ctx)?;
290        assert_eq!(result.as_primitive().as_::<f64>(), Some(3.0));
291        Ok(())
292    }
293
294    #[test]
295    fn mean_skips_nans_by_default() -> VortexResult<()> {
296        // NaNs are excluded from both the sum and the count.
297        let array =
298            PrimitiveArray::new(buffer![1.0f64, f64::NAN, 3.0], Validity::NonNullable).into_array();
299        let mut ctx = array_session().create_execution_ctx();
300        let result = mean(&array, &mut ctx)?;
301        assert_eq!(result.as_primitive().as_::<f64>(), Some(2.0));
302        Ok(())
303    }
304
305    #[test]
306    fn mean_with_nan_not_skipping() -> VortexResult<()> {
307        let array =
308            PrimitiveArray::new(buffer![1.0f64, f64::NAN, 3.0], Validity::NonNullable).into_array();
309        let mut ctx = array_session().create_execution_ctx();
310        let keep_nans = NumericalAggregateOpts::include_nans();
311        let mut acc = Accumulator::try_new(
312            Mean::combined(),
313            PairOptions(keep_nans, keep_nans),
314            array.dtype().clone(),
315        )?;
316        acc.accumulate(&array, &mut ctx)?;
317        let result = acc.finish()?;
318        assert!(result.as_primitive().as_::<f64>().is_some_and(f64::is_nan));
319        Ok(())
320    }
321
322    #[test]
323    fn mean_all_null_returns_null() -> VortexResult<()> {
324        let array = PrimitiveArray::from_option_iter::<f64, _>([None, None, None]).into_array();
325        let mut ctx = array_session().create_execution_ctx();
326        let result = mean(&array, &mut ctx)?;
327        assert_eq!(result.as_primitive().as_::<f64>(), None);
328        Ok(())
329    }
330
331    #[test]
332    fn mean_decimal() -> VortexResult<()> {
333        let dtype = DecimalDType::new(6, 2);
334        let array =
335            DecimalArray::new(buffer![100i32, 200, 300], dtype, Validity::NonNullable).into_array();
336        let mut ctx = array_session().create_execution_ctx();
337        let result = mean(&array, &mut ctx)?;
338        assert_eq!(
339            result.dtype(),
340            &DType::Decimal(DecimalDType::new(10, 6), Nullability::Nullable)
341        );
342        // mean(1.00, 2.00, 3.00) = 2.000000
343        assert_eq!(
344            result.as_decimal().decimal_value(),
345            Some(DecimalValue::I256(i256::from_i128(2_000_000)))
346        );
347        Ok(())
348    }
349
350    #[test]
351    fn mean_decimal_null() -> VortexResult<()> {
352        let dtype = DecimalDType::new(6, 2);
353        let validity = Validity::from_iter([true, false, true]);
354        let array = DecimalArray::new(buffer![150i32, 0, 450], dtype, validity).into_array();
355        let mut ctx = array_session().create_execution_ctx();
356        let result = mean(&array, &mut ctx)?;
357        // mean(1.50, 4.50) = 3.000000
358        assert_eq!(
359            result.as_decimal().decimal_value(),
360            Some(DecimalValue::I256(i256::from_i128(3_000_000)))
361        );
362        Ok(())
363    }
364
365    #[test]
366    fn mean_decimal_chunked() -> VortexResult<()> {
367        let dtype = DecimalDType::new(6, 2);
368        let validity = Validity::NonNullable;
369        let chunk1 = DecimalArray::new(buffer![100i32, 200], dtype, validity.clone()).into_array();
370        let chunk2 = DecimalArray::new(buffer![300i32, 400, 500], dtype, validity).into_array();
371        let dtype = chunk1.dtype().clone();
372        let chunked = ChunkedArray::try_new(vec![chunk1, chunk2], dtype)?;
373        let mut ctx = array_session().create_execution_ctx();
374        let result = mean(&chunked.into_array(), &mut ctx)?;
375        // mean(1.00, 2.00, 3.00, 4.00, 5.00) = 3.000000
376        assert_eq!(
377            result.as_decimal().decimal_value(),
378            Some(DecimalValue::I256(i256::from_i128(3_000_000)))
379        );
380        Ok(())
381    }
382
383    #[test]
384    fn mean_decimal_33() -> VortexResult<()> {
385        let dtype = DecimalDType::new(6, 2);
386        let buf = buffer![100i32, 0, 0];
387        let array = DecimalArray::new(buf, dtype, Validity::NonNullable).into_array();
388        let mut ctx = array_session().create_execution_ctx();
389        let result = mean(&array, &mut ctx)?;
390        // mean(1.00, 0.00, 0.00) = 1/3 => 0.333333
391        assert_eq!(
392            result.as_decimal().decimal_value(),
393            Some(DecimalValue::I256(i256::from_i128(333_333)))
394        );
395        Ok(())
396    }
397
398    #[test]
399    fn mean_multi_batch() -> VortexResult<()> {
400        let mut ctx = array_session().create_execution_ctx();
401        let dtype = DType::Primitive(PType::F64, Nullability::NonNullable);
402        let mut acc = Accumulator::try_new(
403            Mean::combined(),
404            PairOptions(
405                NumericalAggregateOpts::default(),
406                NumericalAggregateOpts::default(),
407            ),
408            dtype,
409        )?;
410
411        let batch1 =
412            PrimitiveArray::new(buffer![1.0f64, 2.0, 3.0], Validity::NonNullable).into_array();
413        acc.accumulate(&batch1, &mut ctx)?;
414
415        let batch2 = PrimitiveArray::new(buffer![4.0f64, 5.0], Validity::NonNullable).into_array();
416        acc.accumulate(&batch2, &mut ctx)?;
417
418        let result = acc.finish()?;
419        assert_eq!(result.as_primitive().as_::<f64>(), Some(3.0));
420        Ok(())
421    }
422
423    fn mean_nan_null() -> Vec<(Vec<Option<f64>>, Option<f64>)> {
424        vec![
425            (vec![Some(f64::NAN), Some(1.0), None], Some(1.0)),
426            (vec![Some(f64::NAN), Some(1.0), Some(3.0)], Some(2.0)),
427            (vec![None, None, Some(f64::NAN)], None),
428            (vec![None, None, None], None),
429            (vec![Some(1.0), Some(2.0), Some(3.0)], Some(2.0)),
430        ]
431    }
432
433    #[test]
434    fn mean_combined_partials() -> VortexResult<()> {
435        let mut ctx = array_session().create_execution_ctx();
436        for (case, (group, expected)) in mean_nan_null().into_iter().enumerate() {
437            let mut acc = Accumulator::try_new(
438                Mean::combined(),
439                PairOptions(
440                    NumericalAggregateOpts::default(),
441                    NumericalAggregateOpts::default(),
442                ),
443                DType::Primitive(PType::F64, Nullability::Nullable),
444            )?;
445            let (head, tail) = group.split_at(2);
446            let head = PrimitiveArray::from_option_iter(head.iter().copied()).into_array();
447            let tail = PrimitiveArray::from_option_iter(tail.iter().copied()).into_array();
448            acc.accumulate(&head, &mut ctx)?;
449            acc.accumulate(&tail, &mut ctx)?;
450            let result = acc.finish()?;
451            assert_eq!(result.as_primitive().as_::<f64>(), expected, "case {case}");
452        }
453        Ok(())
454    }
455
456    #[test]
457    fn mean_grouped_finalize() -> VortexResult<()> {
458        let cases = mean_nan_null();
459        let elements = PrimitiveArray::from_option_iter(
460            cases.iter().flat_map(|(group, _)| group.iter().copied()),
461        )
462        .into_array();
463        let groups = FixedSizeListArray::try_new(elements, 3, Validity::NonNullable, cases.len())?;
464
465        let mut acc = GroupedAccumulator::try_new(
466            Mean::combined(),
467            PairOptions(
468                NumericalAggregateOpts::default(),
469                NumericalAggregateOpts::default(),
470            ),
471            DType::Primitive(PType::F64, Nullability::Nullable),
472        )?;
473        let mut ctx = array_session().create_execution_ctx();
474        acc.accumulate_list(&groups.into_array(), &mut ctx)?;
475        let result = acc.finish()?;
476
477        for (case, (_, expected)) in cases.into_iter().enumerate() {
478            let actual = result.execute_scalar(case, &mut ctx)?;
479            assert_eq!(actual.as_primitive().as_::<f64>(), expected, "case {case}");
480        }
481        Ok(())
482    }
483}
vortex_array/aggregate_fn/fns/mean/mod.rs

vortex_array/aggregate_fn/fns/mean/
mod.rs
