Get Data: refuse an import whose dense rows would be too large

data.getData and data.fromTextCsv built every row densely before
writing it: JSON records were padded to the union of all keys (found
with a quadratic Vec::contains scan), and CSV cells were expanded over
the whole used rectangle. A few sparse records or one far-away CSV
cell therefore allocated billions of slots.

The importer now collects JSON keys with a HashSet, refuses more keys
than a sheet has columns, and checks the projected rows x columns
against MAX_IMPORT_CELLS (10,000,000) before building any row.
This commit is contained in:
Dylan M. Taylor
2026-11-02 20:34:32 -05:00
parent 85e51e009c
commit 052ee60218
+52 -1
View File
@@ -42,6 +42,15 @@ pub(super) fn table_resize(s: &mut Session, p: &Json) -> Result<Json> {
})
}
/// Most cells (rows × columns, blanks included) Get Data imports. The rows are built densely
/// before they are written, so a few sparse records or one far-away CSV cell must not expand
/// into a rectangle of billions of slots.
const MAX_IMPORT_CELLS: u64 = 10_000_000;
fn too_many_cells() -> EngineError {
EngineError::Other(format!("The data is too large to import ({MAX_IMPORT_CELLS} cells at most)."))
}
pub(super) fn from_csv(s: &mut Session, p: &Json) -> Result<Json> {
let bytes = if let Some(t) = str_param(p, "text") {
t.as_bytes().to_vec()
@@ -57,15 +66,22 @@ pub(super) fn from_csv(s: &mut Session, p: &Json) -> Result<Json> {
if text.trim_start().starts_with('[') {
let arr: Vec<Json> = serde_json::from_str(&text).map_err(|e| bad("data.getData", e.to_string()))?;
let mut keys: Vec<String> = Vec::new();
let mut seen: std::collections::HashSet<&str> = std::collections::HashSet::new();
for o in &arr {
if let Some(m) = o.as_object() {
for k in m.keys() {
if !keys.contains(k) {
if seen.insert(k.as_str()) {
keys.push(k.clone());
}
}
}
}
if keys.len() > gridcraft_core::MAX_COLS as usize {
return Err(EngineError::Other("The data has more fields than a sheet has columns.".into()));
}
if (arr.len() as u64 + 1).saturating_mul(keys.len().max(1) as u64) > MAX_IMPORT_CELLS {
return Err(too_many_cells());
}
let mut out = vec![keys.iter().map(|k| json!(k)).collect::<Vec<_>>()];
for o in &arr {
out.push(keys.iter().map(|k| o.get(k).cloned().unwrap_or(Json::Null)).collect());
@@ -79,6 +95,9 @@ pub(super) fn from_csv(s: &mut Session, p: &Json) -> Result<Json> {
let wb = gridcraft_xlsx::read_csv(&bytes, &opts).map_err(|e| EngineError::Other(e.to_string()))?;
let sh = wb.sheet(0).ok_or(EngineError::NoDocument)?;
let Some(u) = sh.used_range() else { return Err(EngineError::Other("The file is empty.".into())) };
if u.count() > MAX_IMPORT_CELLS {
return Err(too_many_cells());
}
(u.start.row..=u.end.row)
.map(|r| {
(u.start.col..=u.end.col)
@@ -129,3 +148,35 @@ pub(super) fn queries(s: &mut Session, _: &Json) -> Result<Json> {
.collect(),
))
}
#[cfg(test)]
mod tests {
use serde_json::json;
use crate::Session;
fn s() -> Session {
let mut s = Session::new();
s.new_workbook();
s
}
#[test]
fn sparse_imports_are_refused_before_their_rows_are_built() {
let mut s = s();
let sheets = s.doc().unwrap().wb.sheets.len();
// 5,000 records with one field each: 5,001 × 5,000 slots once every record gets every field.
let records: Vec<_> = (0..5_000).map(|i| json!({ format!("k{i}"): i })).collect();
let text = serde_json::to_string(&records).unwrap();
let err = s.execute("data.getData", json!({"text": text})).expect_err("too many cells");
assert!(err.to_string().contains("too large"), "{err}");
// A CSV whose last line reaches 16,001 columns, 1,000 lines down.
let text = format!("1{}{}x", "\n".repeat(999), ",".repeat(16_000));
let err = s.execute("data.fromTextCsv", json!({"text": text})).expect_err("too many cells");
assert!(err.to_string().contains("too large"), "{err}");
assert_eq!(s.doc().unwrap().wb.sheets.len(), sheets);
// Small imports still work.
let out = s.execute("data.getData", json!({"text": r#"[{"a": 1}, {"b": 2}]"#})).unwrap();
assert_eq!((out["rows"].as_u64(), out["cols"].as_u64()), (Some(3), Some(2)), "{out}");
}
}