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https://github.com/storytold/gridcraft.git
synced 2026-11-06 02:19:41 -06:00
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3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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cbb3849401 | ||
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825c522a84 | ||
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89cf961a2f |
+133
-4
@@ -1,7 +1,7 @@
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//! Conditional formatting evaluation: what extra style, data bar, colour scale or icon a cell
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//! gets.
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use std::collections::HashMap;
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use std::collections::{HashMap, HashSet};
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use gridcraft_core::{CellRef, RangeRef, Value, compare};
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use gridcraft_model::{CfOperator, CfRule, CfValueKind, Color, CondFormat, PatternType, Sheet, Style, Underline, Workbook, default_icon_thresholds};
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@@ -47,6 +47,8 @@ enum StatsNeed {
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fn stats_need(rule: &CfRule) -> Option<StatsNeed> {
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match rule {
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CfRule::Duplicate { .. } | CfRule::Unique { .. } => Some(StatsNeed::Counts),
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// only two- and three-colour scales paint: no statistics for the others
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CfRule::ColorScale { stops } if !matches!(stops.len(), 2 | 3) => None,
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CfRule::Top10 { .. } | CfRule::AboveAverage { .. } | CfRule::ColorScale { .. } | CfRule::DataBar { .. } | CfRule::IconSet { .. } => {
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Some(StatsNeed::Numbers)
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}
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@@ -70,15 +72,58 @@ thread_local! {
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static SCANNED: std::cell::Cell<usize> = const { std::cell::Cell::new(0) };
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}
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/// Most cells one rule's statistics visit, across all of its ranges together: a rule repeating
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/// the same range many times must not multiply the scan.
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const STATS_SCAN_LIMIT: usize = 2_000_000;
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/// Steps through the spilled cells inside `r`, row-major: `Some(cell)` for each, and `None` for a
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/// jump past the spills left or right of the columns on a row (one step per row, however many
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/// there are), so unrelated spills between a narrow range's corners cost little.
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fn spill_steps(sh: &Sheet, r: RangeRef) -> impl Iterator<Item = Option<CellRef>> + '_ {
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let mut cur = Some(r.start);
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std::iter::from_fn(move || {
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let at = cur.filter(|at| *at <= r.end)?;
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let (&c, _) = sh.spill.range(at..=r.end).next()?;
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if c.col < r.start.col {
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cur = Some(CellRef::new(c.row, r.start.col));
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Some(None)
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} else if c.col > r.end.col {
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cur = c.row.checked_add(1).map(|row| CellRef::new(row, r.start.col));
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Some(None)
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} else {
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cur = c.col.checked_add(1).map(|col| CellRef::new(c.row, col));
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Some(Some(c))
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}
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})
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}
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fn numbers_in(sh: &Sheet, ranges: &[RangeRef], need: StatsNeed) -> (Vec<f64>, HashMap<String, usize>) {
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let mut nums = Vec::new();
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let mut counts = HashMap::new();
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let mut budget = STATS_SCAN_LIMIT;
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let mut seen: HashSet<RangeRef> = HashSet::new();
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let used = sh.used_range();
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for r in ranges {
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let r = match sh.used_range().and_then(|u| r.intersection(&u)) {
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if budget == 0 {
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break;
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}
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let r = match used.and_then(|u| r.intersection(&u)) {
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Some(x) => x,
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None => continue,
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};
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for c in r.iter().take(2_000_000) {
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// The same range listed twice counts its cells once.
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if !seen.insert(r) {
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continue;
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}
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// Only stored and spilled cells hold values: blank cells of a sparse range cost nothing,
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// so a value far down a huge range is still counted. Every cell looked at is charged.
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let stored = sh.cells.iter_range(r).map(|(c, _)| (Some(c), true));
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let spilled = spill_steps(sh, r).map(|c| (c, false));
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for (c, is_stored) in stored.chain(spilled).take(budget) {
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budget -= 1;
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// a skip over spills outside the columns (charged, not read), or a spilled value
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// under a stored cell, which was read with it
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let Some(c) = c.filter(|c| is_stored || sh.cells.get(*c).is_none()) else { continue };
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#[cfg(test)]
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SCANNED.with(|n| n.set(n.get() + 1));
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let v = sh.value(c);
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@@ -278,7 +323,8 @@ impl CfCache {
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_ => None,
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},
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CfRule::ColorScale { stops } => {
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if let Some(n) = v.as_f64() {
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// Only two- and three-colour scales paint: evaluate no thresholds for others.
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if let Some(n) = v.as_f64().filter(|_| matches!(stops.len(), 2 | 3)) {
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let pts: Vec<(f64, [u8; 3])> = stops
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.iter()
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.map(|(k, col)| (threshold(wb, sheet, c, st, k), col.resolve(&wb.theme).unwrap_or([255, 255, 255])))
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@@ -560,6 +606,89 @@ mod tests {
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assert_eq!(SCANNED.with(|n| n.get()), 10_000);
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}
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/// A rule listing the same range many times scans it once, and never more than the shared
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/// limit however many ranges it holds.
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#[test]
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fn repeated_ranges_share_one_scan_budget() {
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let mut s = crate::Session::new();
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s.new_workbook();
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s.execute("range.setValues", json!({"range": "A1", "values": [[1], [2], [3]]})).unwrap();
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s.execute("range.setValues", json!({"range": "XFD1048576", "values": [[4]]})).unwrap();
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let d = s.doc().unwrap();
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let sh = d.wb.sheet(0).unwrap();
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let dense = RangeRef::parse("A1:XFD1048576").unwrap();
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let small = RangeRef::parse("A1:A3").unwrap();
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SCANNED.with(|n| n.set(0));
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let st = RuleStats::build(sh, &vec![small; 1000], StatsNeed::Numbers);
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assert_eq!(st.sorted, vec![1.0, 2.0, 3.0]);
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assert_eq!(SCANNED.with(|n| n.get()), 3);
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let shifted: Vec<RangeRef> = (0..50).map(|i| RangeRef::new(CellRef::new(i, 0), dense.end)).collect();
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SCANNED.with(|n| n.set(0));
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RuleStats::build(sh, &shifted, StatsNeed::Numbers);
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assert!(SCANNED.with(|n| n.get()) <= STATS_SCAN_LIMIT);
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}
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/// Blank cells of a sparse range are not charged to the scan budget: a value at the end of two
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/// million-row columns still counts, so duplicates are found.
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#[test]
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fn sparse_ranges_keep_their_values_under_the_scan_budget() {
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let mut s = crate::Session::new();
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s.new_workbook();
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s.execute("range.setValues", json!({"range": "A1", "values": [[7]]})).unwrap();
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s.execute("range.setValues", json!({"range": "B1048576", "values": [[7]]})).unwrap();
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let d = s.doc().unwrap();
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let sh = d.wb.sheet(0).unwrap();
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let ranges = [RangeRef::parse("A1:A1048576").unwrap(), RangeRef::parse("B1:B1048576").unwrap()];
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SCANNED.with(|n| n.set(0));
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let st = RuleStats::build(sh, &ranges, StatsNeed::Counts);
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assert_eq!(st.counts.get("7"), Some(&2));
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assert_eq!(SCANNED.with(|n| n.get()), 2);
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}
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/// Spills left and right of a narrow range, between its corners, are skipped a row at a time:
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/// they do not use the budget that the in-range spills need.
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#[test]
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fn spills_outside_a_narrow_range_do_not_use_its_scan_budget() {
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let mut s = crate::Session::new();
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s.new_workbook();
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let d = s.doc().unwrap();
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let mut sh = d.wb.sheet(0).unwrap().clone();
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for row in 0..1000 {
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for col in [0u32, 5, 6, 7] {
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sh.spill.insert(CellRef::new(row, col), Value::Number(1.0));
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}
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}
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let r = RangeRef::parse("F1:G1000").unwrap();
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let steps: Vec<_> = spill_steps(&sh, r).collect();
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assert_eq!(steps.iter().flatten().count(), 2000);
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// column A is skipped one step a row, never one per cell
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assert!(steps.len() <= 2000 + 1000, "{}", steps.len());
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let narrow = RangeRef::parse("C1:C1000").unwrap();
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assert_eq!(spill_steps(&sh, narrow).flatten().count(), 0);
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}
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/// A colour scale with an unsupported number of stops paints nothing and evaluates none of them.
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#[test]
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fn colour_scales_with_other_stop_counts_paint_nothing() {
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let mut s = crate::Session::new();
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s.new_workbook();
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s.execute("range.setValues", json!({"range": "A1", "values": [[1], [5]]})).unwrap();
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let mut wb: Workbook = (*s.doc().unwrap().wb).clone();
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let stop = (CfValueKind::Formula("=1".into()), Color::default());
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let rule = |n: usize| CondFormat {
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ranges: vec![RangeRef::parse("A1:A2").unwrap()],
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rule: CfRule::ColorScale { stops: vec![stop.clone(); n] },
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priority: 1,
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stop_if_true: false,
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};
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wb.sheet_mut(0).unwrap().cond_formats.push(rule(4));
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let mut cache = CfCache::new();
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assert_eq!(cache.look(&wb, 0, CellRef::parse("A1").unwrap()).and_then(|l| l.scale_fill), None);
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wb.sheet_mut(0).unwrap().cond_formats = vec![rule(2)];
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let mut cache = CfCache::new();
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assert!(cache.look(&wb, 0, CellRef::parse("A1").unwrap()).and_then(|l| l.scale_fill).is_some());
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}
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#[test]
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fn cell_value_operator_names() {
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let mut s = crate::Session::new();
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@@ -70,42 +70,27 @@ fn cell_criterion(v: &Value) -> Option<Criterion> {
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/// when the label is unknown, criterion).
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type CriteriaRow = (usize, Vec<(usize, Option<usize>, Criterion)>);
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/// Most condition tests one selection may make (records times conditions): every record can be
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/// tested against every criteria row when none matches, so a modest database and criteria table
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/// multiply into billions of tests. Past it the function is `#NUM!`, as lifted lookups are; 5e8
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/// is a few seconds of matching.
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const SELECTION_WORK_BUDGET: usize = 500_000_000;
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/// Rows (1-based within the database, excluding the header) of matching records.
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fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> R<Vec<usize>> {
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// The database column under each criteria label, found once per label rather than per cell.
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let columns: Vec<Option<usize>> = (0..crit.cols)
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.map(|c| match crit.get(0, c) {
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Some(Value::Text(t)) => find_column(db, t),
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Some(Value::Number(n)) => find_column(db, &gridcraft_core::number_to_text(*n)),
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_ => None,
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})
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.collect();
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fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> Vec<usize> {
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// Per criteria row: its index and a list of (criteria column, database column or None when
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// the label is unknown, criterion).
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let mut rows: Vec<CriteriaRow> = Vec::new();
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let mut tests = 0usize;
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for r in 1..crit.rows {
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let mut conds = Vec::new();
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for c in 0..crit.cols {
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let Some(cell) = crit.get(r, c) else { continue };
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let Some(cr) = cell_criterion(cell) else { continue };
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conds.push((c, columns.get(c).copied().flatten(), cr));
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let col = match crit.get(0, c) {
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Some(Value::Text(t)) => find_column(db, t),
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Some(Value::Number(n)) => find_column(db, &gridcraft_core::number_to_text(*n)),
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_ => None,
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};
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conds.push((c, col, cr));
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}
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tests = tests.saturating_add(conds.len().max(1));
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rows.push((r, conds));
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}
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// Computed criteria (a formula evaluated per record) count like any other test.
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if db.rows.saturating_sub(1).saturating_mul(tests) > SELECTION_WORK_BUDGET {
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return Err(CellError::Num);
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}
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let all_match = rows.is_empty();
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Ok((1..db.rows)
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(1..db.rows)
|
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.filter(|&rec| {
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all_match
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|| rows.iter().any(|(r, conds)| {
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@@ -117,7 +102,7 @@ fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> R<Vec<usize>
|
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})
|
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})
|
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})
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.collect())
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.collect()
|
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}
|
||||
|
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struct Selection<'a> {
|
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@@ -138,7 +123,7 @@ fn select<'a>(a: &'a [Arg], ctx: &mut dyn Ctx) -> R<Selection<'a>> {
|
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let db = as_array(dbv);
|
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let crit = as_array(critv);
|
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let field = field_index(&db, a)?;
|
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let records = matching_records(&db, &crit, ctx)?;
|
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let records = matching_records(&db, &crit, ctx);
|
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Ok(Selection { db, field, records })
|
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}
|
||||
|
||||
@@ -367,24 +352,6 @@ mod tests {
|
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is_err(d("DSUM", n(9.0)), CellError::Value);
|
||||
}
|
||||
|
||||
/// Security: records times criteria conditions past the selection budget is `#NUM!` before
|
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/// any record is tested; a nonmatching criteria table used to test every record against
|
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/// every criteria row.
|
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#[test]
|
||||
fn selection_work_is_bounded() {
|
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use gridcraft_core::Array;
|
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let column = |label: &str, len: usize, v: Value| {
|
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let mut data = vec![tv(label)];
|
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data.extend(std::iter::repeat_n(v, len));
|
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Value::from(Array::column(data))
|
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};
|
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let db = column("Tree", 100_000, tv("Apple"));
|
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let wide = column("Tree", 5_001, tv("=Plum"));
|
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is_err(ev("DCOUNTA", vec![rf(db.clone()), t("Tree"), rf(wide)]), CellError::Num);
|
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let narrow = column("Tree", 2, tv("=Apple"));
|
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close(ev("DCOUNTA", vec![rf(db), t("Tree"), rf(narrow)]), 100_000.0);
|
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}
|
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|
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#[test]
|
||||
fn simple_criteria() {
|
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let c = |cells: Vec<Value>| arr(vec![vec![tv("Tree"), tv("Height")], cells]);
|
||||
|
||||
@@ -416,17 +416,13 @@ fn workday_core(a: &[Arg], c: &mut dyn Ctx, mask: [bool; 7], hol: Option<&Arg>)
|
||||
if days == 0 {
|
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return num_val(start as f64);
|
||||
}
|
||||
// The holidays on working days, still sorted (`holidays` sorts them), so each step counts
|
||||
// the ones it passed with two binary searches rather than a scan of every holiday: a run of
|
||||
// consecutive holidays takes one step per holiday.
|
||||
let hs: Vec<i64> = hs.into_iter().filter(|&h| !mask[weekday0(sys, h)]).collect();
|
||||
let dir = days.signum();
|
||||
let mut cur = start;
|
||||
let mut rem = days.abs();
|
||||
while rem > 0 {
|
||||
let next = advance(sys, cur, rem, dir, &mask, per_week);
|
||||
let (lo, hi) = if dir > 0 { (cur + 1, next) } else { (next, cur - 1) };
|
||||
rem = hs.partition_point(|&h| h <= hi).saturating_sub(hs.partition_point(|&h| h < lo)) as i64;
|
||||
rem = hs.iter().filter(|&&h| h >= lo && h <= hi && !mask[weekday0(sys, h)]).count() as i64;
|
||||
cur = next;
|
||||
if !(0..=MAX_SERIAL as i64).contains(&cur) {
|
||||
return Err(CellError::Num);
|
||||
@@ -759,23 +755,6 @@ mod tests {
|
||||
is_err(ev("TIMEVALUE", vec![t("x")]), CellError::Value);
|
||||
}
|
||||
|
||||
/// Security: WORKDAY counts the holidays each step passes by binary search, so a long run of
|
||||
/// consecutive holidays is linear work, not one scan of every holiday per holiday.
|
||||
#[test]
|
||||
fn workday_over_many_consecutive_holidays() {
|
||||
let start = d(2000.0, 1.0, 3.0);
|
||||
let hol: Vec<f64> = (1..=200_000).map(|i| start + f64::from(i)).collect();
|
||||
let out = ev("WORKDAY.INTL", vec![n(start), n(1.0), n(11.0), av(col(&hol))]);
|
||||
// Sundays are the only weekend (11): 200,000 days of holidays are skipped, landing on the
|
||||
// first working day after them.
|
||||
let after = start + 200_001.0;
|
||||
let sunday = ev("WEEKDAY", vec![n(after), n(2.0)]) == nv(7.0);
|
||||
close(out, if sunday { after + 1.0 } else { after });
|
||||
// Holidays on a weekend don't push the result out.
|
||||
let fri = d(2026.0, 10.0, 9.0);
|
||||
close(ev("WORKDAY", vec![n(fri), n(1.0), av(col(&[fri + 1.0, fri + 3.0]))]), fri + 4.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn day_counts() {
|
||||
close(ev("DAYS", vec![n(d(2021.0, 3.0, 15.0)), n(d(2021.0, 2.0, 1.0))]), 42.0);
|
||||
|
||||
@@ -178,10 +178,6 @@ fn sort(args: &[Arg], _c: &mut dyn Ctx) -> R<Value> {
|
||||
return Err(CellError::Value);
|
||||
}
|
||||
let orders: Vec<i64> = (0..idx.len()).map(|i| *ord.get(i).or(ord.first()).unwrap_or(&1)).collect();
|
||||
// A column repeated in sort_index can't break a tie its first use left, so only first uses
|
||||
// stay: the keys copied per row are then at most the row itself, however long sort_index is.
|
||||
let mut seen = std::collections::HashSet::new();
|
||||
let (idx, orders): (Vec<i64>, Vec<i64>) = idx.into_iter().zip(orders).filter(|(i, _)| seen.insert(*i)).unzip();
|
||||
let mut lines: Vec<(Vec<Value>, Vec<Value>)> =
|
||||
rows_of(&a).into_iter().map(|r| (idx.iter().map(|&i| r.get(i as usize - 1).cloned().unwrap_or(Value::Empty)).collect(), r)).collect();
|
||||
lines.sort_by(|x, y| cmp_keys(&x.0, &y.0, &orders));
|
||||
@@ -838,16 +834,9 @@ fn pivotby(args: &[Arg], ctx: &mut dyn Ctx) -> R<Value> {
|
||||
let ckeys: Vec<Vec<Value>> = p.rows.iter().map(|&r| key_row(&cf, r)).collect();
|
||||
let rk = distinct_keys(&rkeys, if row_sort.unsigned_abs() as usize <= rf.cols { row_sort } else { 0 });
|
||||
let ck = distinct_keys(&ckeys, if col_sort.unsigned_abs() as usize <= cf.cols { col_sort } else { 0 });
|
||||
// Source rows of every row key, column key and pair, built in one pass, so a total reads
|
||||
// its rows instead of rescanning the whole source once per total.
|
||||
let mut cell: HashMap<(String, String), Vec<usize>> = HashMap::new();
|
||||
let mut by_row: HashMap<String, Vec<usize>> = HashMap::new();
|
||||
let mut by_col: HashMap<String, Vec<usize>> = HashMap::new();
|
||||
for ((r, c), &i) in rkeys.iter().zip(ckeys.iter()).zip(p.rows.iter()) {
|
||||
let (r, c) = (row_key(r), row_key(c));
|
||||
by_row.entry(r.clone()).or_default().push(i);
|
||||
by_col.entry(c.clone()).or_default().push(i);
|
||||
cell.entry((r, c)).or_default().push(i);
|
||||
cell.entry((row_key(r), row_key(c))).or_default().push(i);
|
||||
}
|
||||
let mut agg = |rows: &[usize], c: usize| -> Value {
|
||||
if rows.is_empty() {
|
||||
@@ -857,12 +846,14 @@ fn pivotby(args: &[Arg], ctx: &mut dyn Ctx) -> R<Value> {
|
||||
aggregate_with(f, &vs, ctx)
|
||||
};
|
||||
let rows_for = |r: Option<&Vec<Value>>, c: Option<&Vec<Value>>| -> Vec<usize> {
|
||||
match (r, c) {
|
||||
(Some(r), Some(c)) => cell.get(&(row_key(r), row_key(c))).cloned().unwrap_or_default(),
|
||||
(Some(r), None) => by_row.get(&row_key(r)).cloned().unwrap_or_default(),
|
||||
(None, Some(c)) => by_col.get(&row_key(c)).cloned().unwrap_or_default(),
|
||||
(None, None) => p.rows.clone(),
|
||||
}
|
||||
let rkey = r.map(|k| row_key(k));
|
||||
let ckey = c.map(|k| row_key(k));
|
||||
p.rows
|
||||
.iter()
|
||||
.zip(rkeys.iter().zip(ckeys.iter()))
|
||||
.filter(|(_, (a, b))| rkey.as_ref().is_none_or(|k| *k == row_key(a)) && ckey.as_ref().is_none_or(|k| *k == row_key(b)))
|
||||
.map(|(&i, _)| i)
|
||||
.collect()
|
||||
};
|
||||
let mut cols: Vec<Option<&Vec<Value>>> = ck.iter().map(Some).collect();
|
||||
if col_total > 0 {
|
||||
@@ -912,7 +903,10 @@ fn pivotby(args: &[Arg], ctx: &mut dyn Ctx) -> R<Value> {
|
||||
}
|
||||
};
|
||||
for c in &cols {
|
||||
let rows = rows_for(r, *c);
|
||||
let rows = match (r, c) {
|
||||
(Some(rk), Some(ck)) => cell.get(&(row_key(rk), row_key(ck))).cloned().unwrap_or_default(),
|
||||
_ => rows_for(r, *c),
|
||||
};
|
||||
for vc in 0..vals.cols {
|
||||
line.push(agg(&rows, vc));
|
||||
}
|
||||
@@ -1116,21 +1110,6 @@ mod tests {
|
||||
assert_eq!(rows_of(&r), vec![vec![nv(0.0), nv(5.0)], vec![nv(1.0), nv(2.0)], vec![nv(1.0), nv(1.0)]]);
|
||||
}
|
||||
|
||||
/// Security: a column repeated in sort_index keys the sort once. The keys copied per row stay
|
||||
/// within the row, however long sort_index is, and a repeat (even with the other order) can't
|
||||
/// change the result, since its first use already ordered the rows.
|
||||
#[test]
|
||||
fn sort_drops_repeated_sort_indices() {
|
||||
let m = || av(arr(vec![vec![nv(1.0), nv(2.0)], vec![nv(1.0), nv(1.0)], vec![nv(0.0), nv(5.0)]]));
|
||||
let want = vec![vec![nv(0.0), nv(5.0)], vec![nv(1.0), nv(2.0)], vec![nv(1.0), nv(1.0)]];
|
||||
let r = ev("SORT", vec![m(), av(row(&[1.0, 1.0, 2.0, 1.0])), av(row(&[1.0, -1.0, -1.0, 1.0]))]);
|
||||
assert_eq!(rows_of(&r), want);
|
||||
let idx = vec![1.0; 1_000_000];
|
||||
let tall = av(gridcraft_core::Array::filled(2_000, 1, nv(1.0)).into());
|
||||
let r = ev("SORT", vec![tall, av(row(&idx))]);
|
||||
assert_eq!(rows_of(&r).len(), 2_000);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sortby() {
|
||||
let r = ev("SORTBY", vec![av(col(&[1.0, 2.0, 3.0])), av(col(&[30.0, 10.0, 20.0]))]);
|
||||
@@ -1302,16 +1281,6 @@ mod tests {
|
||||
);
|
||||
}
|
||||
|
||||
/// Security: PIVOTBY totals read their source rows from indexes built in the grouping pass,
|
||||
/// rather than rescanning the source once per total, so many distinct keys stay linear.
|
||||
#[test]
|
||||
fn pivotby_totals_with_many_groups() {
|
||||
let keys: Vec<f64> = (0..20_000).map(f64::from).collect();
|
||||
let r = rows_of(&ev("PIVOTBY", vec![av(col(&keys)), av(col(&vec![1.0; 20_000])), av(col(&vec![2.0; 20_000])), t("SUM")]));
|
||||
assert_eq!(r.get(1), Some(&vec![nv(0.0), nv(2.0), nv(2.0)]));
|
||||
assert_eq!(r.last(), Some(&vec![tv("Total"), nv(40_000.0), nv(40_000.0)]));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pivotby_size_check_counts_column_header_rows() {
|
||||
// One row, n column fields and n value columns: n header rows + value-name row + body row + total row,
|
||||
|
||||
Reference in New Issue
Block a user