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https://issues.apache.org/jira/browse/ARROW-9937?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated ARROW-9937:
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Labels: pull-request-available (was: )
> [Rust] [DataFusion] Average is not correct
> ------------------------------------------
>
> Key: ARROW-9937
> URL: https://issues.apache.org/jira/browse/ARROW-9937
> Project: Apache Arrow
> Issue Type: Bug
> Components: Rust, Rust - DataFusion
> Reporter: Jorge
> Priority: Major
> Labels: pull-request-available
> Time Spent: 10m
> Remaining Estimate: 0h
>
> The current design of aggregates makes the calculation of the average
> incorrect.
> It also makes it impossible to compute the [geometric
> mean|https://en.wikipedia.org/wiki/Geometric_mean], distinct sum, and other
> operations.
> The central issue is that Accumulator returns a `ScalarValue` during partial
> aggregations via {{get_value}}, but very often a `ScalarValue` is not
> sufficient information to perform the full aggregation.
> A simple example is the average of 5 numbers, x1, x2, x3, x4, x5, that are
> distributed in batches of 2,
> {[x1, x2], [x3, x4], [x5]}
> . Our current calculation performs partial means,
> {(x1+x2)/2, (x3+x4)/2, x5}
> , and then reduces them using another average, i.e.
> {{((x1+x2)/2 + (x3+x4)/2 + x5)/3}}
> which is not equal to {{(x1 + x2 + x3 + x4 + x5)/5}}.
> I believe that our Accumulators need to pass more information from the
> partial aggregations to the final aggregation.
> We could consider taking an API equivalent to
> [spark]([https://docs.databricks.com/spark/latest/spark-sql/udaf-scala.html]),
> i.e. have an `update`, a `merge` and an `evaluate`.
> Code with a failing test ({{src/execution/context.rs}})
> {code:java}
> #[test]
> fn simple_avg() -> Result<()> {
> let schema = Schema::new(vec![
> Field::new("a", DataType::Int32, false),
> ]);
> let batch1 = RecordBatch::try_new(
> Arc::new(schema.clone()),
> vec![
> Arc::new(Int32Array::from(vec![1, 2, 3])),
> ],
> )?;
> let batch2 = RecordBatch::try_new(
> Arc::new(schema.clone()),
> vec![
> Arc::new(Int32Array::from(vec![4, 5])),
> ],
> )?;
> let mut ctx = ExecutionContext::new();
> let provider = MemTable::new(Arc::new(schema), vec![vec![batch1],
> vec![batch2]])?;
> ctx.register_table("t", Box::new(provider));
> let result = collect(&mut ctx, "SELECT AVG(a) FROM t")?;
> let batch = &result[0];
> assert_eq!(1, batch.num_columns());
> assert_eq!(1, batch.num_rows());
> let values = batch
> .column(0)
> .as_any()
> .downcast_ref::<Float64Array>()
> .expect("failed to cast version");
> assert_eq!(values.len(), 1);
> // avg(1,2,3,4,5) = 3.0
> assert_eq!(values.value(0), 3.0_f64);
> Ok(())
> }
> {code}
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