Details

Bug

Status: Resolved

Major

Resolution: Fixed

None
Description
The current design of aggregates makes the calculation of the average incorrect.
Namely, if there are multiple input partitions, the result is average of the averages. For example if the input it in two batches [1,2], and [3,4,5], datafusion will say "average=3.25" rather than "average=3".
It also makes it impossible to compute the 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/sparksql/udafscala.html), i.e. have an `update`, a `merge` and an `evaluate`.
Code with a failing test (src/execution/context.rs)
#[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(()) }
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