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engine: PivotTable filters test each record against a set of selected labels (#3279)
Every source record scanned the filter's whole selection, so a large selection multiplied by many records was slow even when nothing passed. Each filter's labels are lowercased into a set once per computation.
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@@ -168,6 +168,19 @@ fn filters_and_report_filter_rows() {
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assert!(s.execute("pivot.filter", json!({"field": "Product", "selected": []})).is_err());
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}
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/// A selection of the most labels allowed (100,000) is looked up once per record, not scanned: the
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/// report still filters case-insensitively.
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#[test]
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fn a_huge_filter_selection_is_matched_through_a_set() {
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let mut s = sales();
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s.execute("insert.pivotTable", json!({"rows": ["Product"], "values": ["Sales"], "filters": ["Region"]})).unwrap();
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let mut selected: Vec<String> = (0..99_999).map(|i| format!("Region {i}")).collect();
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selected.push("EAST".into());
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s.execute("pivot.filter", json!({"field": "Region", "selected": selected})).unwrap();
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let gt = find_row(&s, "Grand Total").unwrap();
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assert_eq!(val(&s, &CellRef::new(gt, 1).a1()), Value::Number(415.0));
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}
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#[test]
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fn show_values_as() {
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let mut s = sales();
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@@ -77,6 +77,8 @@ struct AggregateInput<'a> {
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extra: &'a [usize],
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filters: &'a [(usize, &'a PivotFilter, PivotDateGroup, bool)],
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filter_fmts: &'a [String],
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/// Each filter's selected labels, ASCII-lowercased, so a record's test is one probe.
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filter_sets: &'a [Option<HashSet<String>>],
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row_fmts: &'a [String],
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col_fmts: &'a [String],
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empty: bool,
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@@ -99,10 +101,10 @@ fn aggregate_record(
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ri: usize,
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rec: &[Value],
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) -> Result<(), String> {
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for ((c, field, group, _), fmt) in input.filters.iter().zip(input.filter_fmts) {
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if let Some(selected) = &field.selected {
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for (((c, _, group, _), fmt), selected) in input.filters.iter().zip(input.filter_fmts).zip(input.filter_sets) {
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if let Some(selected) = selected {
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let label = item_label(input.wb, rec.get(*c).unwrap_or(&Value::Empty), *group, fmt);
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if !selected.iter().any(|value| value.eq_ignore_ascii_case(&label)) {
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if !selected.contains(&label.to_ascii_lowercase()) {
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return Ok(());
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}
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}
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@@ -475,6 +477,8 @@ pub(super) fn compute_from(wb: &Workbook, pt: &PivotTable, src: &Source, mut pro
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// ---- aggregate
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let filter_fmts: Vec<String> = filters.iter().map(|f| fmt_of(f.0)).collect();
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let filter_sets: Vec<Option<HashSet<String>>> =
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filters.iter().map(|(_, field, _, _)| field.selected.as_ref().map(|s| s.iter().map(|v| v.to_ascii_lowercase()).collect())).collect();
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let row_fmts: Vec<String> = rows.iter().map(|(c, _)| fmt_of(*c)).collect();
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let col_fmts: Vec<String> = cols.iter().map(|(c, _)| fmt_of(*c)).collect();
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let aggregate = aggregate_source(
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@@ -488,6 +492,7 @@ pub(super) fn compute_from(wb: &Workbook, pt: &PivotTable, src: &Source, mut pro
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extra: &extra,
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filters: &filters,
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filter_fmts: &filter_fmts,
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filter_sets: &filter_sets,
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row_fmts: &row_fmts,
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col_fmts: &col_fmts,
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empty,
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