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Author SHA1 Message Date
Dylan M. Taylor cbb3849401 address review: skip unrelated spills a row at a time in the statistics scan 2026-11-03 23:10:59 +00:00
Dylan M. Taylor 825c522a84 address review: count stored and spilled cells of sparse ranges, skip statistics for unsupported colour scales 2026-11-03 21:39:01 +00:00
Dylan M. Taylor 89cf961a2f Conditional formatting: one scan budget per rule, and no thresholds for unsupported colour scales
numbers_in() scanned each of a rule's ranges separately with its own
2,000,000-cell limit, so a rule listing the same (or overlapping)
large range many times multiplied the scan while painting a single
cell. Colour scales also evaluated every stop's threshold, formulas
included, before discarding scales that are not 2- or 3-colour.

- A rule's statistics now share STATS_SCAN_LIMIT across all its ranges,
  and an exact repeat of a range is scanned once.
- A colour scale with a stop count other than 2 or 3 paints nothing
  and evaluates none of its thresholds.
2026-11-02 20:56:49 -05:00
4 changed files with 157 additions and 113 deletions
+133 -4
View File
@@ -1,7 +1,7 @@
//! Conditional formatting evaluation: what extra style, data bar, colour scale or icon a cell
//! gets.
use std::collections::HashMap;
use std::collections::{HashMap, HashSet};
use gridcraft_core::{CellRef, RangeRef, Value, compare};
use gridcraft_model::{CfOperator, CfRule, CfValueKind, Color, CondFormat, PatternType, Sheet, Style, Underline, Workbook, default_icon_thresholds};
@@ -47,6 +47,8 @@ enum StatsNeed {
fn stats_need(rule: &CfRule) -> Option<StatsNeed> {
match rule {
CfRule::Duplicate { .. } | CfRule::Unique { .. } => Some(StatsNeed::Counts),
// only two- and three-colour scales paint: no statistics for the others
CfRule::ColorScale { stops } if !matches!(stops.len(), 2 | 3) => None,
CfRule::Top10 { .. } | CfRule::AboveAverage { .. } | CfRule::ColorScale { .. } | CfRule::DataBar { .. } | CfRule::IconSet { .. } => {
Some(StatsNeed::Numbers)
}
@@ -70,15 +72,58 @@ thread_local! {
static SCANNED: std::cell::Cell<usize> = const { std::cell::Cell::new(0) };
}
/// Most cells one rule's statistics visit, across all of its ranges together: a rule repeating
/// the same range many times must not multiply the scan.
const STATS_SCAN_LIMIT: usize = 2_000_000;
/// Steps through the spilled cells inside `r`, row-major: `Some(cell)` for each, and `None` for a
/// jump past the spills left or right of the columns on a row (one step per row, however many
/// there are), so unrelated spills between a narrow range's corners cost little.
fn spill_steps(sh: &Sheet, r: RangeRef) -> impl Iterator<Item = Option<CellRef>> + '_ {
let mut cur = Some(r.start);
std::iter::from_fn(move || {
let at = cur.filter(|at| *at <= r.end)?;
let (&c, _) = sh.spill.range(at..=r.end).next()?;
if c.col < r.start.col {
cur = Some(CellRef::new(c.row, r.start.col));
Some(None)
} else if c.col > r.end.col {
cur = c.row.checked_add(1).map(|row| CellRef::new(row, r.start.col));
Some(None)
} else {
cur = c.col.checked_add(1).map(|col| CellRef::new(c.row, col));
Some(Some(c))
}
})
}
fn numbers_in(sh: &Sheet, ranges: &[RangeRef], need: StatsNeed) -> (Vec<f64>, HashMap<String, usize>) {
let mut nums = Vec::new();
let mut counts = HashMap::new();
let mut budget = STATS_SCAN_LIMIT;
let mut seen: HashSet<RangeRef> = HashSet::new();
let used = sh.used_range();
for r in ranges {
let r = match sh.used_range().and_then(|u| r.intersection(&u)) {
if budget == 0 {
break;
}
let r = match used.and_then(|u| r.intersection(&u)) {
Some(x) => x,
None => continue,
};
for c in r.iter().take(2_000_000) {
// The same range listed twice counts its cells once.
if !seen.insert(r) {
continue;
}
// Only stored and spilled cells hold values: blank cells of a sparse range cost nothing,
// so a value far down a huge range is still counted. Every cell looked at is charged.
let stored = sh.cells.iter_range(r).map(|(c, _)| (Some(c), true));
let spilled = spill_steps(sh, r).map(|c| (c, false));
for (c, is_stored) in stored.chain(spilled).take(budget) {
budget -= 1;
// a skip over spills outside the columns (charged, not read), or a spilled value
// under a stored cell, which was read with it
let Some(c) = c.filter(|c| is_stored || sh.cells.get(*c).is_none()) else { continue };
#[cfg(test)]
SCANNED.with(|n| n.set(n.get() + 1));
let v = sh.value(c);
@@ -278,7 +323,8 @@ impl CfCache {
_ => None,
},
CfRule::ColorScale { stops } => {
if let Some(n) = v.as_f64() {
// Only two- and three-colour scales paint: evaluate no thresholds for others.
if let Some(n) = v.as_f64().filter(|_| matches!(stops.len(), 2 | 3)) {
let pts: Vec<(f64, [u8; 3])> = stops
.iter()
.map(|(k, col)| (threshold(wb, sheet, c, st, k), col.resolve(&wb.theme).unwrap_or([255, 255, 255])))
@@ -560,6 +606,89 @@ mod tests {
assert_eq!(SCANNED.with(|n| n.get()), 10_000);
}
/// A rule listing the same range many times scans it once, and never more than the shared
/// limit however many ranges it holds.
#[test]
fn repeated_ranges_share_one_scan_budget() {
let mut s = crate::Session::new();
s.new_workbook();
s.execute("range.setValues", json!({"range": "A1", "values": [[1], [2], [3]]})).unwrap();
s.execute("range.setValues", json!({"range": "XFD1048576", "values": [[4]]})).unwrap();
let d = s.doc().unwrap();
let sh = d.wb.sheet(0).unwrap();
let dense = RangeRef::parse("A1:XFD1048576").unwrap();
let small = RangeRef::parse("A1:A3").unwrap();
SCANNED.with(|n| n.set(0));
let st = RuleStats::build(sh, &vec![small; 1000], StatsNeed::Numbers);
assert_eq!(st.sorted, vec![1.0, 2.0, 3.0]);
assert_eq!(SCANNED.with(|n| n.get()), 3);
let shifted: Vec<RangeRef> = (0..50).map(|i| RangeRef::new(CellRef::new(i, 0), dense.end)).collect();
SCANNED.with(|n| n.set(0));
RuleStats::build(sh, &shifted, StatsNeed::Numbers);
assert!(SCANNED.with(|n| n.get()) <= STATS_SCAN_LIMIT);
}
/// Blank cells of a sparse range are not charged to the scan budget: a value at the end of two
/// million-row columns still counts, so duplicates are found.
#[test]
fn sparse_ranges_keep_their_values_under_the_scan_budget() {
let mut s = crate::Session::new();
s.new_workbook();
s.execute("range.setValues", json!({"range": "A1", "values": [[7]]})).unwrap();
s.execute("range.setValues", json!({"range": "B1048576", "values": [[7]]})).unwrap();
let d = s.doc().unwrap();
let sh = d.wb.sheet(0).unwrap();
let ranges = [RangeRef::parse("A1:A1048576").unwrap(), RangeRef::parse("B1:B1048576").unwrap()];
SCANNED.with(|n| n.set(0));
let st = RuleStats::build(sh, &ranges, StatsNeed::Counts);
assert_eq!(st.counts.get("7"), Some(&2));
assert_eq!(SCANNED.with(|n| n.get()), 2);
}
/// Spills left and right of a narrow range, between its corners, are skipped a row at a time:
/// they do not use the budget that the in-range spills need.
#[test]
fn spills_outside_a_narrow_range_do_not_use_its_scan_budget() {
let mut s = crate::Session::new();
s.new_workbook();
let d = s.doc().unwrap();
let mut sh = d.wb.sheet(0).unwrap().clone();
for row in 0..1000 {
for col in [0u32, 5, 6, 7] {
sh.spill.insert(CellRef::new(row, col), Value::Number(1.0));
}
}
let r = RangeRef::parse("F1:G1000").unwrap();
let steps: Vec<_> = spill_steps(&sh, r).collect();
assert_eq!(steps.iter().flatten().count(), 2000);
// column A is skipped one step a row, never one per cell
assert!(steps.len() <= 2000 + 1000, "{}", steps.len());
let narrow = RangeRef::parse("C1:C1000").unwrap();
assert_eq!(spill_steps(&sh, narrow).flatten().count(), 0);
}
/// A colour scale with an unsupported number of stops paints nothing and evaluates none of them.
#[test]
fn colour_scales_with_other_stop_counts_paint_nothing() {
let mut s = crate::Session::new();
s.new_workbook();
s.execute("range.setValues", json!({"range": "A1", "values": [[1], [5]]})).unwrap();
let mut wb: Workbook = (*s.doc().unwrap().wb).clone();
let stop = (CfValueKind::Formula("=1".into()), Color::default());
let rule = |n: usize| CondFormat {
ranges: vec![RangeRef::parse("A1:A2").unwrap()],
rule: CfRule::ColorScale { stops: vec![stop.clone(); n] },
priority: 1,
stop_if_true: false,
};
wb.sheet_mut(0).unwrap().cond_formats.push(rule(4));
let mut cache = CfCache::new();
assert_eq!(cache.look(&wb, 0, CellRef::parse("A1").unwrap()).and_then(|l| l.scale_fill), None);
wb.sheet_mut(0).unwrap().cond_formats = vec![rule(2)];
let mut cache = CfCache::new();
assert!(cache.look(&wb, 0, CellRef::parse("A1").unwrap()).and_then(|l| l.scale_fill).is_some());
}
#[test]
fn cell_value_operator_names() {
let mut s = crate::Session::new();
+10 -43
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@@ -70,42 +70,27 @@ fn cell_criterion(v: &Value) -> Option<Criterion> {
/// when the label is unknown, criterion).
type CriteriaRow = (usize, Vec<(usize, Option<usize>, Criterion)>);
/// Most condition tests one selection may make (records times conditions): every record can be
/// tested against every criteria row when none matches, so a modest database and criteria table
/// multiply into billions of tests. Past it the function is `#NUM!`, as lifted lookups are; 5e8
/// is a few seconds of matching.
const SELECTION_WORK_BUDGET: usize = 500_000_000;
/// Rows (1-based within the database, excluding the header) of matching records.
fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> R<Vec<usize>> {
// The database column under each criteria label, found once per label rather than per cell.
let columns: Vec<Option<usize>> = (0..crit.cols)
.map(|c| match crit.get(0, c) {
Some(Value::Text(t)) => find_column(db, t),
Some(Value::Number(n)) => find_column(db, &gridcraft_core::number_to_text(*n)),
_ => None,
})
.collect();
fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> Vec<usize> {
// Per criteria row: its index and a list of (criteria column, database column or None when
// the label is unknown, criterion).
let mut rows: Vec<CriteriaRow> = Vec::new();
let mut tests = 0usize;
for r in 1..crit.rows {
let mut conds = Vec::new();
for c in 0..crit.cols {
let Some(cell) = crit.get(r, c) else { continue };
let Some(cr) = cell_criterion(cell) else { continue };
conds.push((c, columns.get(c).copied().flatten(), cr));
let col = match crit.get(0, c) {
Some(Value::Text(t)) => find_column(db, t),
Some(Value::Number(n)) => find_column(db, &gridcraft_core::number_to_text(*n)),
_ => None,
};
conds.push((c, col, cr));
}
tests = tests.saturating_add(conds.len().max(1));
rows.push((r, conds));
}
// Computed criteria (a formula evaluated per record) count like any other test.
if db.rows.saturating_sub(1).saturating_mul(tests) > SELECTION_WORK_BUDGET {
return Err(CellError::Num);
}
let all_match = rows.is_empty();
Ok((1..db.rows)
(1..db.rows)
.filter(|&rec| {
all_match
|| rows.iter().any(|(r, conds)| {
@@ -117,7 +102,7 @@ fn matching_records(db: &Array, crit: &Array, ctx: &mut dyn Ctx) -> R<Vec<usize>
})
})
})
.collect())
.collect()
}
struct Selection<'a> {
@@ -138,7 +123,7 @@ fn select<'a>(a: &'a [Arg], ctx: &mut dyn Ctx) -> R<Selection<'a>> {
let db = as_array(dbv);
let crit = as_array(critv);
let field = field_index(&db, a)?;
let records = matching_records(&db, &crit, ctx)?;
let records = matching_records(&db, &crit, ctx);
Ok(Selection { db, field, records })
}
@@ -367,24 +352,6 @@ mod tests {
is_err(d("DSUM", n(9.0)), CellError::Value);
}
/// Security: records times criteria conditions past the selection budget is `#NUM!` before
/// any record is tested; a nonmatching criteria table used to test every record against
/// every criteria row.
#[test]
fn selection_work_is_bounded() {
use gridcraft_core::Array;
let column = |label: &str, len: usize, v: Value| {
let mut data = vec![tv(label)];
data.extend(std::iter::repeat_n(v, len));
Value::from(Array::column(data))
};
let db = column("Tree", 100_000, tv("Apple"));
let wide = column("Tree", 5_001, tv("=Plum"));
is_err(ev("DCOUNTA", vec![rf(db.clone()), t("Tree"), rf(wide)]), CellError::Num);
let narrow = column("Tree", 2, tv("=Apple"));
close(ev("DCOUNTA", vec![rf(db), t("Tree"), rf(narrow)]), 100_000.0);
}
#[test]
fn simple_criteria() {
let c = |cells: Vec<Value>| arr(vec![vec![tv("Tree"), tv("Height")], cells]);
+1 -22
View File
@@ -416,17 +416,13 @@ fn workday_core(a: &[Arg], c: &mut dyn Ctx, mask: [bool; 7], hol: Option<&Arg>)
if days == 0 {
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);
+13 -44
View File
@@ -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,