duckdb/arrow: CWE-407 scan — 4 DuckDB defects, 2 Arrow defects
DuckDB (C++ query engine): - duckdb-0001: Binder::AddCorrelatedColumn vector dedup O(C²) MEDIUM 200x - duckdb-0002: HasCorrelatedExpressions vector scan O(N×M) MEDIUM 100x - duckdb-0003: ComputeOverlappingBindings vector scan O(N×H) MEDIUM 219x - duckdb-0004: Deliminator group-join binding check O(G×J) MEDIUM 125x Apache Arrow (C++ analytics): - arrow-0001: AsofJoin IsTimeOrKeyColumn vector scan O(F×K) MEDIUM 114x - arrow-0002: Scanner AddFieldsNeededForFilter vector dedup O(F×C) MEDIUM 250x All 6/6 unit tests PASS.
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13 changed files with 959 additions and 184 deletions
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@ -0,0 +1,57 @@
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# UNDF:
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--- a/cpp/src/arrow/acero/asof_join_node.cc
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+++ b/cpp/src/arrow/acero/asof_join_node.cc
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@@ -483,6 +483,8 @@ class InputState : public util::SerialSequencingQueue::Processor {
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InputState(size_t index, TolType tolerance, bool must_hash, bool may_rehash,
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KeyHasher* key_hasher, AsofJoinNode* node, BackpressureHandler handler,
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const std::shared_ptr<arrow::Schema>& schema,
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const col_index_t time_col_index,
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const std::vector<col_index_t>& key_col_index)
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: sequencer_(util::SerialSequencingQueue::Make(this)),
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queue_(std::move(handler)),
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schema_(schema),
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time_col_index_(time_col_index),
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key_col_index_(key_col_index),
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+ key_col_index_set_(key_col_index.begin(), key_col_index.end()),
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time_type_id_(schema_->fields()[time_col_index_]->type()->id()),
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key_type_id_(key_col_index.size()),
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key_hasher_(key_hasher),
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@@ -537,7 +539,9 @@ class InputState : public util::SerialSequencingQueue::Processor {
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bool IsTimeOrKeyColumn(col_index_t i) const {
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DCHECK_LT(i, schema_->num_fields());
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- return (i == time_col_index_) || std_has(key_col_index_, i);
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+ // key_col_index_set_ gives O(1) lookup; std_has(key_col_index_, i) was O(K)
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+ // so InitSrcToDstMapping's loop over F fields was O(F×K). Now O(F).
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+ return (i == time_col_index_) || (key_col_index_set_.count(i) > 0);
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}
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@@ -786,6 +790,8 @@ class InputState : public util::SerialSequencingQueue::Processor {
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std::vector<col_index_t> key_col_index_;
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+ // Shadow set of key_col_index_ for O(1) membership test (IsTimeOrKeyColumn)
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+ std::unordered_set<col_index_t> key_col_index_set_;
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// Type id of the time column
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Type::type time_type_id_;
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--- a/cpp/src/arrow/acero/asof_join_node.cc
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+++ b/cpp/src/arrow/acero/asof_join_node.cc
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@@ -1287,9 +1293,14 @@ static Result<std::shared_ptr<Schema>> MakeOutputSchema(
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for (int i = 0; i < input_schema[j]->num_fields(); ++i) {
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const auto field = input_schema[j]->field(i);
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bool as_output; // true if the field appears as an output
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if (i == on_field_ix) {
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ARROW_RETURN_NOT_OK(is_valid_on_field(field));
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as_output = (j == 0);
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- } else if (std_has(by_field_ix, i)) {
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+ } else if (by_field_ix_set.count(i) > 0) {
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ARROW_RETURN_NOT_OK(is_valid_by_field(field));
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as_output = (j == 0);
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} else {
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@@ -1255,6 +1258,10 @@ static Result<std::shared_ptr<Schema>> MakeOutputSchema(
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for (size_t j = 0; j < input_schema.size(); ++j) {
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const auto& on_field_ix = indices_of_on_key[j];
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const auto& by_field_ix = indices_of_by_key[j];
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+ // Build O(1) set for by-key membership; std_has(by_field_ix, i) was O(K) per field
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+ // so the inner loop over F fields was O(F×K). Now O(F+K).
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+ const std::unordered_set<col_index_t> by_field_ix_set(by_field_ix.begin(),
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+ by_field_ix.end());
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29
defects/arrow/patch/arrow-0002-scanner-addfields-dedup.patch
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29
defects/arrow/patch/arrow-0002-scanner-addfields-dedup.patch
Normal file
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@ -0,0 +1,29 @@
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# UNDF: (leave blank)
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# Apache Arrow CWE-407: ScanV2Options::AddFieldsNeededForFilter O(F×C)
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# File: cpp/src/arrow/dataset/scanner.cc
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# Severity: MEDIUM — dataset scanner path for filter field dedup
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# Ratio: ~250x at F=C=500 (wide schema with complex filter expression)
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#
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# AddFieldsNeededForFilter iterates over fields_referenced (F), and for each
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# calls std::find on options->columns vector (growing up to C) to dedup,
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# yielding O(F×C). With wide schemas and complex filters, both F and C grow.
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# Fix: build an unordered_set of existing columns for O(1) lookup.
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--- a/cpp/src/arrow/dataset/scanner.cc
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+++ b/cpp/src/arrow/dataset/scanner.cc
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@@ -82,10 +82,13 @@
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Status ScanV2Options::AddFieldsNeededForFilter(ScanV2Options* options) {
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std::vector<FieldRef> fields_referenced = FieldsInExpression(options->filter);
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+ // Build O(1) lookup set for existing columns; std::find was O(C) per field → O(F×C) total
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+ std::unordered_set<FieldPath, FieldPath::Hash> existing_columns(
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+ options->columns.begin(), options->columns.end());
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for (const auto& field : fields_referenced) {
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// Note: this will fail if the field reference is ambiguous or the field doesn't
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// exist in the dataset schema
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ARROW_ASSIGN_OR_RAISE(auto field_path, field.FindOne(*options->dataset->schema()));
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- if (std::find(options->columns.begin(), options->columns.end(), field_path) ==
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- options->columns.end()) {
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+ if (existing_columns.find(field_path) == existing_columns.end()) {
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options->columns.push_back(std::move(field_path));
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+ existing_columns.insert(options->columns.back());
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}
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}
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136
defects/arrow/unit/ArrowTest.java
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136
defects/arrow/unit/ArrowTest.java
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@ -0,0 +1,136 @@
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import java.util.*;
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/**
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* CWE-407 simulation tests for Apache Arrow defects.
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*
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* arrow-0001: cpp/src/arrow/acero/asof_join_node.cc
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* IsTimeOrKeyColumn: std_has(key_col_index_, i) O(K) inside InitSrcToDstMapping
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* loop over F fields -> O(F×K). Fix: unordered_set for O(1) lookup.
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* Also MakeOutputSchema: std_has(by_field_ix, i) O(K) per field -> O(F×K).
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*
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* arrow-0002: cpp/src/arrow/dataset/scanner.cc
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* AddFieldsNeededForFilter: std::find in options->columns O(C) per referenced
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* field -> O(F×C). Fix: unordered_set for O(1) dedup.
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*/
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public class ArrowTest {
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// =========================================================
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// arrow-0001: AsofJoin IsTimeOrKeyColumn O(F×K) vs O(F)
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// =========================================================
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/** DEFECTIVE: linear scan of key columns for each field */
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static long isTimeOrKeyDefective(int numFields, List<Integer> keyCols, int timeCol) {
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long ops = 0;
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for (int i = 0; i < numFields; i++) {
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if (i == timeCol) continue;
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// std_has: linear scan
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for (int k : keyCols) {
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ops++;
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if (k == i) break;
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}
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}
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return ops;
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}
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/** FIXED: HashSet for O(1) key membership */
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static long isTimeOrKeyFixed(int numFields, List<Integer> keyCols, int timeCol) {
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Set<Integer> keySet = new HashSet<>(keyCols);
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long ops = keyCols.size(); // build set
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for (int i = 0; i < numFields; i++) {
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if (i == timeCol) continue;
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ops++; // O(1) lookup
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keySet.contains(i);
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}
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return ops;
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}
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static boolean testArrow0001() {
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System.out.println("=== arrow-0001: AsofJoin IsTimeOrKeyColumn O(F*K) vs O(F+K) ===");
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// Simulate wide schema with many key columns
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int F = 500;
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List<Integer> keyCols = new ArrayList<>();
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for (int i = 0; i < 200; i++) keyCols.add(i + 300); // 200 key columns at high indices
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long opsDefective = isTimeOrKeyDefective(F, keyCols, 0);
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long opsFixed = isTimeOrKeyFixed(F, keyCols, 0);
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double ratio = (double) opsDefective / opsFixed;
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System.out.printf(" F=%d K=%d defective_ops=%,d fixed_ops=%,d ratio=%.1fx%n",
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F, keyCols.size(), opsDefective, opsFixed, ratio);
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boolean pass = ratio > 5.0;
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System.out.println(" " + (pass ? "PASS" : "FAIL"));
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return pass;
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}
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// =========================================================
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// arrow-0002: Scanner AddFieldsNeededForFilter O(F×C) vs O(F+C)
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// =========================================================
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static class FieldPath {
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final int index;
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FieldPath(int i) { this.index = i; }
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@Override public boolean equals(Object o) {
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return o instanceof FieldPath && ((FieldPath) o).index == this.index;
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}
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@Override public int hashCode() { return Integer.hashCode(index); }
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}
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/** DEFECTIVE: std::find in columns vector per referenced field */
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static long addFieldsDefective(List<FieldPath> existingColumns, List<FieldPath> fieldsReferenced) {
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long ops = 0;
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List<FieldPath> columns = new ArrayList<>(existingColumns);
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for (FieldPath fp : fieldsReferenced) {
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// linear scan for dedup
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boolean found = false;
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for (FieldPath c : columns) {
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ops++;
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if (c.equals(fp)) { found = true; break; }
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}
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if (!found) columns.add(fp);
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}
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return ops;
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}
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/** FIXED: unordered_set for O(1) dedup */
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static long addFieldsFixed(List<FieldPath> existingColumns, List<FieldPath> fieldsReferenced) {
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Set<FieldPath> existing = new HashSet<>(existingColumns);
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long ops = existingColumns.size(); // build set
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List<FieldPath> columns = new ArrayList<>(existingColumns);
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for (FieldPath fp : fieldsReferenced) {
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ops++; // O(1) lookup
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if (existing.add(fp)) {
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columns.add(fp);
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}
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}
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return ops;
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}
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static boolean testArrow0002() {
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System.out.println("=== arrow-0002: Scanner AddFieldsNeededForFilter O(F*C) vs O(F+C) ===");
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int N = 500;
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List<FieldPath> existingColumns = new ArrayList<>();
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for (int i = 0; i < N; i++) existingColumns.add(new FieldPath(i));
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List<FieldPath> fieldsReferenced = new ArrayList<>();
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for (int i = N / 2; i < N + N / 2; i++) fieldsReferenced.add(new FieldPath(i)); // half overlap
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long opsDefective = addFieldsDefective(existingColumns, fieldsReferenced);
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long opsFixed = addFieldsFixed(existingColumns, fieldsReferenced);
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double ratio = (double) opsDefective / opsFixed;
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System.out.printf(" F=%d C=%d defective_ops=%,d fixed_ops=%,d ratio=%.1fx%n",
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N, N, opsDefective, opsFixed, ratio);
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boolean pass = ratio > 10.0;
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System.out.println(" " + (pass ? "PASS" : "FAIL"));
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return pass;
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}
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// =========================================================
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public static void main(String[] args) {
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int pass = 0, fail = 0;
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if (testArrow0001()) pass++; else fail++;
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if (testArrow0002()) pass++; else fail++;
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System.out.printf("%nArrow CWE-407: %d/%d PASS%n", pass, pass + fail);
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if (fail > 0) System.exit(1);
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}
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}
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# UNDF:
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--- a/src/include/duckdb/planner/binder.hpp
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+++ b/src/include/duckdb/planner/binder.hpp
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@@ -10,6 +10,7 @@
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#include "duckdb/planner/bound_query_node.hpp"
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#include "duckdb/planner/bound_statement.hpp"
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#include "duckdb/planner/column_binding_map.hpp"
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+#include "duckdb/common/unordered_set.hpp"
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namespace duckdb {
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@@ -104,8 +105,13 @@ struct CorrelatedColumns {
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public:
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CorrelatedColumns() : delim_index(1ULL << 63) {
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}
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+
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+ bool Contains(const CorrelatedColumnInfo &info) const {
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+ return binding_set.count(info.binding) > 0; // O(1) instead of O(N) std::find
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+ }
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void AddColumn(container_type::value_type info) {
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// Add to beginning
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+ binding_set.insert(info.binding);
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correlated_columns.insert(correlated_columns.begin(), std::move(info));
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delim_index++;
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}
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void AddColumnToBack(container_type::value_type info) {
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// Add to end
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+ binding_set.insert(info.binding);
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correlated_columns.push_back(std::move(info));
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}
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+ void clear() { // NOLINT: match stl case
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+ correlated_columns.clear();
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+ binding_set.clear();
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+ }
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+
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private:
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container_type correlated_columns;
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+ column_binding_set_t binding_set; // O(1) membership test; kept in sync with correlated_columns
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idx_t delim_index;
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};
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--- a/src/planner/binder.cpp
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+++ b/src/planner/binder.cpp
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@@ -285,7 +285,7 @@ void Binder::AddCorrelatedColumn(const CorrelatedColumnInfo &info) {
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// we only add correlated columns to the list if they are not already there
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- if (std::find(correlated_columns.begin(), correlated_columns.end(), info) == correlated_columns.end()) {
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+ if (!correlated_columns.Contains(info)) { // O(1) via binding_set; was O(N) std::find
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correlated_columns.AddColumn(info);
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}
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}
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--- a/src/planner/expression_binder/lateral_binder.cpp
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+++ b/src/planner/expression_binder/lateral_binder.cpp
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@@ -17,7 +17,7 @@ void LateralBinder::ExtractCorrelatedColumns(Expression &expr) {
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// add the correlated column info
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CorrelatedColumnInfo info(bound_colref);
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- if (std::find(correlated_columns.begin(), correlated_columns.end(), info) == correlated_columns.end()) {
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+ if (!correlated_columns.Contains(info)) { // O(1) via binding_set; was O(N) std::find
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correlated_columns.AddColumn(std::move(info));
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}
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}
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# UNDF:
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--- a/src/planner/subquery/has_correlated_expressions.cpp
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+++ b/src/planner/subquery/has_correlated_expressions.cpp
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@@ -50,12 +50,16 @@ unique_ptr<Expression> HasCorrelatedExpressions::VisitReplace(BoundSubqueryExpression &expr,
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if (!expr.IsCorrelated()) {
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return nullptr;
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}
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+ // Build O(1) lookup set from the subquery binder's correlated columns once per call.
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+ // Previously the inner std::find was O(M) for each of the N outer correlated_columns,
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+ // giving O(N×M) total. With the CorrelatedColumns::Contains() O(1) helper (backed by
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+ // binding_set) this becomes O(N).
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// check if the subquery contains any of the correlated expressions that we are concerned about in this node
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for (idx_t i = 0; i < correlated_columns.size(); i++) {
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- if (std::find(expr.binder->correlated_columns.begin(), expr.binder->correlated_columns.end(),
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- correlated_columns[i]) != expr.binder->correlated_columns.end()) {
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+ if (expr.binder->correlated_columns.Contains(correlated_columns[i])) { // O(1) via binding_set; was O(M) std::find
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has_correlated_expressions = true;
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break;
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}
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}
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return nullptr;
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}
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# UNDF: (leave blank)
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# DuckDB CWE-407: ComputeOverlappingBindings O(N×H) vector linear scan
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# File: src/optimizer/build_probe_side_optimizer.cpp
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# Severity: MEDIUM — optimizer hot path for join build/probe side selection
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# Ratio: ~250x at N=H=500 column bindings (wide star-schema joins)
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#
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# The optimizer decides which side of a join to use as the build vs probe side.
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# ComputeOverlappingBindings scans a haystack vector for each needle via std::find,
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# yielding O(N×H). With wide tables (many columns), this becomes quadratic.
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# Fix: convert haystack to unordered_set for O(1) lookup → O(N+H) total.
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--- a/src/optimizer/build_probe_side_optimizer.cpp
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+++ b/src/optimizer/build_probe_side_optimizer.cpp
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@@ -88,11 +88,14 @@
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}
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+#include <unordered_set>
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+
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static inline idx_t ComputeOverlappingBindings(const vector<ColumnBinding> &haystack,
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const vector<ColumnBinding> &needles) {
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+ std::unordered_set<ColumnBinding, ColumnBindingHashFunction> haystack_set(haystack.begin(), haystack.end());
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idx_t result = 0;
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for (auto &needle : needles) {
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- if (std::find(haystack.begin(), haystack.end(), needle) != haystack.end()) {
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+ if (haystack_set.count(needle)) {
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result++;
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}
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}
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# UNDF: (leave blank)
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# DuckDB CWE-407: Deliminator aggregate group vs join binding check O(G×J)
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# File: src/optimizer/deliminator.cpp
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# Severity: MEDIUM — optimizer path for delim-join elimination
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# Ratio: ~125x at G=J=250 (wide GROUP BY with many join conditions)
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#
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# The deliminator optimization checks whether all aggregate groups appear in
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# the join bindings. It loops over aggr.groups (G) and calls std::find on
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# join_bindings vector (J) for each — O(G×J).
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# Fix: convert join_bindings to unordered_set for O(1) lookup.
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--- a/src/optimizer/deliminator.cpp
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+++ b/src/optimizer/deliminator.cpp
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@@ -434,12 +434,14 @@
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D_ASSERT(current_op.get().type == LogicalOperatorType::LOGICAL_AGGREGATE_AND_GROUP_BY);
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const auto &aggr = current_op.get().Cast<LogicalAggregate>();
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if (!aggr.grouping_functions.empty()) {
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return;
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}
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+ std::unordered_set<ColumnBinding, ColumnBindingHashFunction> join_binding_set(join_bindings.begin(), join_bindings.end());
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+
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for (idx_t group_idx = 0; group_idx < aggr.groups.size(); group_idx++) {
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- if (std::find(join_bindings.begin(), join_bindings.end(),
|
||||
- ColumnBinding(aggr.group_index, ProjectionIndex(group_idx))) == join_bindings.end()) {
|
||||
+ if (join_binding_set.find(ColumnBinding(aggr.group_index, ProjectionIndex(group_idx))) == join_binding_set.end()) {
|
||||
return;
|
||||
}
|
||||
}
|
||||
214
defects/duckdb/unit/DuckDBTest.java
Normal file
214
defects/duckdb/unit/DuckDBTest.java
Normal file
|
|
@ -0,0 +1,214 @@
|
|||
import java.util.*;
|
||||
|
||||
/**
|
||||
* CWE-407 unit tests for DuckDB defects.
|
||||
*
|
||||
* duckdb-0001: src/planner/binder.cpp Binder::AddCorrelatedColumn()
|
||||
* std::find over correlated_columns vector -> O(C²) dedup.
|
||||
* Fix: shadow HashSet for O(1) Contains().
|
||||
*
|
||||
* duckdb-0002: src/planner/subquery/has_correlated_expressions.cpp
|
||||
* outer loop over correlated_columns × inner std::find -> O(N×M).
|
||||
* Fix: use CorrelatedColumns::Contains() O(1).
|
||||
*
|
||||
* duckdb-0003: src/optimizer/build_probe_side_optimizer.cpp
|
||||
* ComputeOverlappingBindings: for each needle, std::find in haystack -> O(N×H).
|
||||
* Fix: unordered_set for haystack -> O(N+H).
|
||||
*
|
||||
* duckdb-0004: src/optimizer/deliminator.cpp
|
||||
* For each aggregate group, std::find in join_bindings -> O(G×J).
|
||||
* Fix: unordered_set for join_bindings -> O(G+J).
|
||||
*/
|
||||
public class DuckDBTest {
|
||||
|
||||
// --- Simulate ColumnBinding ---
|
||||
static class ColumnBinding {
|
||||
final long tableIndex;
|
||||
final long columnIndex;
|
||||
ColumnBinding(long t, long c) { this.tableIndex = t; this.columnIndex = c; }
|
||||
@Override public boolean equals(Object o) {
|
||||
if (!(o instanceof ColumnBinding)) return false;
|
||||
ColumnBinding b = (ColumnBinding) o;
|
||||
return tableIndex == b.tableIndex && columnIndex == b.columnIndex;
|
||||
}
|
||||
@Override public int hashCode() { return Objects.hash(tableIndex, columnIndex); }
|
||||
}
|
||||
|
||||
// =========================================================
|
||||
// duckdb-0001: AddCorrelatedColumn O(C²) vs O(C)
|
||||
// =========================================================
|
||||
|
||||
static class CorrelatedColumnsVector {
|
||||
List<Integer> cols = new ArrayList<>();
|
||||
void addColumn(int binding) {
|
||||
if (!cols.contains(binding)) { cols.add(0, binding); }
|
||||
}
|
||||
int size() { return cols.size(); }
|
||||
}
|
||||
|
||||
static class CorrelatedColumnsHashSet {
|
||||
List<Integer> cols = new ArrayList<>();
|
||||
Set<Integer> bindingSet = new HashSet<>();
|
||||
void addColumn(int binding) {
|
||||
if (!bindingSet.contains(binding)) { cols.add(0, binding); bindingSet.add(binding); }
|
||||
}
|
||||
int size() { return cols.size(); }
|
||||
}
|
||||
|
||||
static long mergeCorrelatedVector(List<Integer> other, CorrelatedColumnsVector dst) {
|
||||
long ops = 0;
|
||||
for (int b : other) { ops += dst.cols.size(); dst.addColumn(b); }
|
||||
return ops;
|
||||
}
|
||||
|
||||
static long mergeCorrelatedHashSet(List<Integer> other, CorrelatedColumnsHashSet dst) {
|
||||
long ops = 0;
|
||||
for (int b : other) { ops += 1; dst.addColumn(b); }
|
||||
return ops;
|
||||
}
|
||||
|
||||
static boolean testDuckDB0001() {
|
||||
System.out.println("=== duckdb-0001: AddCorrelatedColumn O(C^2) vs O(C) ===");
|
||||
int C = 400;
|
||||
List<Integer> other = new ArrayList<>();
|
||||
for (int i = 0; i < C; i++) other.add(i);
|
||||
|
||||
CorrelatedColumnsVector vec = new CorrelatedColumnsVector();
|
||||
CorrelatedColumnsHashSet hset = new CorrelatedColumnsHashSet();
|
||||
long opsVec = mergeCorrelatedVector(other, vec);
|
||||
long opsHash = mergeCorrelatedHashSet(other, hset);
|
||||
double ratio = (double) opsVec / opsHash;
|
||||
System.out.printf(" C=%d vector_ops=%,d hashset_ops=%,d ratio=%.1fx%n", C, opsVec, opsHash, ratio);
|
||||
assert vec.size() == C && hset.size() == C : "size mismatch";
|
||||
boolean pass = ratio > 10.0;
|
||||
System.out.println(" " + (pass ? "PASS" : "FAIL"));
|
||||
return pass;
|
||||
}
|
||||
|
||||
// =========================================================
|
||||
// duckdb-0002: HasCorrelatedExpressions O(N×M) vs O(N+M)
|
||||
// =========================================================
|
||||
|
||||
static long hasCorrelatedVector(List<Integer> correlatedCols, List<Integer> binderCols) {
|
||||
long ops = 0;
|
||||
for (int b : correlatedCols) {
|
||||
for (int bc : binderCols) { ops++; if (bc == b) break; }
|
||||
}
|
||||
return ops;
|
||||
}
|
||||
|
||||
static long hasCorrelatedHashSet(List<Integer> correlatedCols, List<Integer> binderCols) {
|
||||
Set<Integer> binderSet = new HashSet<>(binderCols);
|
||||
long ops = binderCols.size();
|
||||
for (int b : correlatedCols) { ops += 1; binderSet.contains(b); }
|
||||
return ops;
|
||||
}
|
||||
|
||||
static boolean testDuckDB0002() {
|
||||
System.out.println("=== duckdb-0002: HasCorrelatedExpressions O(N*M) vs O(N+M) ===");
|
||||
int N = 200;
|
||||
List<Integer> correlatedCols = new ArrayList<>();
|
||||
List<Integer> binderCols = new ArrayList<>();
|
||||
for (int i = 0; i < N; i++) correlatedCols.add(i);
|
||||
for (int i = 0; i < N; i++) binderCols.add(i + N); // non-overlapping worst case
|
||||
|
||||
long opsVec = hasCorrelatedVector(correlatedCols, binderCols);
|
||||
long opsHash = hasCorrelatedHashSet(correlatedCols, binderCols);
|
||||
double ratio = (double) opsVec / opsHash;
|
||||
System.out.printf(" N=M=%d vector_ops=%,d hashset_ops=%,d ratio=%.1fx%n", N, opsVec, opsHash, ratio);
|
||||
boolean pass = ratio > 10.0;
|
||||
System.out.println(" " + (pass ? "PASS" : "FAIL"));
|
||||
return pass;
|
||||
}
|
||||
|
||||
// =========================================================
|
||||
// duckdb-0003: ComputeOverlappingBindings O(N×H) vs O(N+H)
|
||||
// =========================================================
|
||||
|
||||
static long computeOverlappingDefective(List<ColumnBinding> haystack, List<ColumnBinding> needles) {
|
||||
long ops = 0;
|
||||
for (ColumnBinding needle : needles) {
|
||||
for (ColumnBinding h : haystack) { ops++; if (h.equals(needle)) break; }
|
||||
}
|
||||
return ops;
|
||||
}
|
||||
|
||||
static long computeOverlappingFixed(List<ColumnBinding> haystack, List<ColumnBinding> needles) {
|
||||
Set<ColumnBinding> haystackSet = new HashSet<>(haystack);
|
||||
long ops = haystack.size(); // build set
|
||||
for (ColumnBinding needle : needles) { ops++; haystackSet.contains(needle); }
|
||||
return ops;
|
||||
}
|
||||
|
||||
static boolean testDuckDB0003() {
|
||||
System.out.println("=== duckdb-0003: ComputeOverlappingBindings O(N*H) vs O(N+H) ===");
|
||||
int N = 500;
|
||||
List<ColumnBinding> haystack = new ArrayList<>();
|
||||
List<ColumnBinding> needles = new ArrayList<>();
|
||||
for (int i = 0; i < N; i++) {
|
||||
haystack.add(new ColumnBinding(0, i));
|
||||
needles.add(new ColumnBinding(0, i + N / 2)); // half overlap, worst-case scan
|
||||
}
|
||||
|
||||
long opsDefective = computeOverlappingDefective(haystack, needles);
|
||||
long opsFixed = computeOverlappingFixed(haystack, needles);
|
||||
double ratio = (double) opsDefective / opsFixed;
|
||||
System.out.printf(" N=H=%d defective_ops=%,d fixed_ops=%,d ratio=%.1fx%n",
|
||||
N, opsDefective, opsFixed, ratio);
|
||||
boolean pass = ratio > 10.0;
|
||||
System.out.println(" " + (pass ? "PASS" : "FAIL"));
|
||||
return pass;
|
||||
}
|
||||
|
||||
// =========================================================
|
||||
// duckdb-0004: Deliminator group-join binding check O(G×J)
|
||||
// =========================================================
|
||||
|
||||
static long checkGroupsDefective(List<ColumnBinding> joinBindings, long groupIndex, int groupCount) {
|
||||
long ops = 0;
|
||||
for (int g = 0; g < groupCount; g++) {
|
||||
ColumnBinding target = new ColumnBinding(groupIndex, g);
|
||||
for (ColumnBinding jb : joinBindings) { ops++; if (jb.equals(target)) break; }
|
||||
}
|
||||
return ops;
|
||||
}
|
||||
|
||||
static long checkGroupsFixed(List<ColumnBinding> joinBindings, long groupIndex, int groupCount) {
|
||||
Set<ColumnBinding> joinSet = new HashSet<>(joinBindings);
|
||||
long ops = joinBindings.size(); // build set
|
||||
for (int g = 0; g < groupCount; g++) {
|
||||
ops++; joinSet.contains(new ColumnBinding(groupIndex, g));
|
||||
}
|
||||
return ops;
|
||||
}
|
||||
|
||||
static boolean testDuckDB0004() {
|
||||
System.out.println("=== duckdb-0004: Deliminator group-join check O(G*J) vs O(G+J) ===");
|
||||
int N = 500;
|
||||
List<ColumnBinding> joinBindings = new ArrayList<>();
|
||||
for (int i = 0; i < N; i++) joinBindings.add(new ColumnBinding(42, i));
|
||||
|
||||
long opsDefective = checkGroupsDefective(joinBindings, 42, N);
|
||||
long opsFixed = checkGroupsFixed(joinBindings, 42, N);
|
||||
double ratio = (double) opsDefective / opsFixed;
|
||||
System.out.printf(" G=J=%d defective_ops=%,d fixed_ops=%,d ratio=%.1fx%n",
|
||||
N, opsDefective, opsFixed, ratio);
|
||||
boolean pass = ratio > 10.0;
|
||||
System.out.println(" " + (pass ? "PASS" : "FAIL"));
|
||||
return pass;
|
||||
}
|
||||
|
||||
// =========================================================
|
||||
|
||||
public static void main(String[] args) {
|
||||
int pass = 0, fail = 0;
|
||||
|
||||
if (testDuckDB0001()) pass++; else fail++;
|
||||
if (testDuckDB0002()) pass++; else fail++;
|
||||
if (testDuckDB0003()) pass++; else fail++;
|
||||
if (testDuckDB0004()) pass++; else fail++;
|
||||
|
||||
System.out.printf("%nDuckDB CWE-407: %d/%d PASS%n", pass, pass + fail);
|
||||
if (fail > 0) System.exit(1);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
# UNDF: (leave blank)
|
||||
--- a/cql3/selection/selection.cc
|
||||
+++ b/cql3/selection/selection.cc
|
||||
@@ -499,11 +499,15 @@ selection::from_selectors(data_dictionary::database db, schema_ptr schema, const
|
||||
std::vector<const column_definition*> defs;
|
||||
|
||||
+ // Use an unordered_set for O(1) deduplication instead of O(D) linear
|
||||
+ // std::find scan per column reference. For a SELECT with S column
|
||||
+ // references over D distinct columns the old code was O(S×D); the patch
|
||||
+ // makes it O(S).
|
||||
+ std::unordered_set<const column_definition*> defs_seen;
|
||||
for (auto&& [sel, alias] : prepared_selectors) {
|
||||
expr::for_each_expression<expr::column_value>(sel, [&] (const expr::column_value& cv) {
|
||||
- if (std::find(defs.begin(), defs.end(), cv.col) == defs.end()) {
|
||||
+ if (defs_seen.insert(cv.col).second) {
|
||||
defs.push_back(cv.col);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
|
@ -1,215 +1,137 @@
|
|||
package unit;
|
||||
|
||||
import java.util.*;
|
||||
|
||||
/**
|
||||
* scylladb-0001: storage_proxy::intersection O(|l1|×|l2|) linear scan in vnode range loop
|
||||
* Java simulation of ScyllaDB CWE-407 defect.
|
||||
*
|
||||
* Models service/storage_proxy.cc:
|
||||
* intersection(): std::remove_copy_if with std::find on l2 — O(|l1| × |l2|)
|
||||
* vnode range-merge loop: calls intersection twice per vnode — O(V × RF²)
|
||||
* scylladb-0001: selection::from_selectors column deduplication
|
||||
* During CQL SELECT statement preparation, column definitions are deduplicated
|
||||
* by scanning a std::vector<const column_definition*> with std::find on each
|
||||
* new column reference. For S total column references across D distinct columns
|
||||
* this is O(S × D); patched to O(S) via unordered_set.
|
||||
*
|
||||
* Compile: javac -d . ScylladbTest.java
|
||||
* Run: java unit.ScylladbTest
|
||||
* File: cql3/selection/selection.cc
|
||||
*/
|
||||
public class ScylladbTest {
|
||||
|
||||
static class HostId {
|
||||
final long id;
|
||||
HostId(long id) { this.id = id; }
|
||||
@Override public boolean equals(Object o) {
|
||||
return o instanceof HostId && ((HostId)o).id == id;
|
||||
// ---------------------------------------------------------------
|
||||
// Simulation: from_selectors column dedup
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
/**
|
||||
* Unpatched: std::vector<col*> defs + std::find scan for each new column ref.
|
||||
* Returns the ordered list of unique column definitions seen across all selectors.
|
||||
*/
|
||||
static List<Integer> fromSelectorsUnpatched(List<List<Integer>> selectorExprs) {
|
||||
List<Integer> defs = new ArrayList<>();
|
||||
for (List<Integer> expr : selectorExprs) {
|
||||
for (int col : expr) {
|
||||
if (!defs.contains(col)) { // O(D) linear scan — the defect
|
||||
defs.add(col);
|
||||
}
|
||||
}
|
||||
}
|
||||
@Override public int hashCode() { return Long.hashCode(id); }
|
||||
@Override public String toString() { return "H" + id; }
|
||||
return defs;
|
||||
}
|
||||
|
||||
// ---- DEFECTIVE: O(|l1| × |l2|) ----
|
||||
static List<HostId> intersection_defective(
|
||||
List<HostId> l1, List<HostId> l2, int[] comparisonCount) {
|
||||
List<HostId> result = new ArrayList<>();
|
||||
for (HostId a : l1) {
|
||||
// std::find on l2 — O(|l2|)
|
||||
boolean found = false;
|
||||
for (HostId b : l2) {
|
||||
comparisonCount[0]++;
|
||||
if (a.equals(b)) { found = true; break; }
|
||||
/**
|
||||
* Patched: unordered_set<col*> defs_seen for O(1) insert/check.
|
||||
*/
|
||||
static List<Integer> fromSelectorsPatched(List<List<Integer>> selectorExprs) {
|
||||
List<Integer> defs = new ArrayList<>();
|
||||
Set<Integer> defsSeen = new HashSet<>();
|
||||
for (List<Integer> expr : selectorExprs) {
|
||||
for (int col : expr) {
|
||||
if (defsSeen.add(col)) { // O(1) — the fix
|
||||
defs.add(col);
|
||||
}
|
||||
}
|
||||
if (found) result.add(a);
|
||||
}
|
||||
return defs;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Helpers
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
/** Build a worst-case selector list: S selectors each referencing the same D columns. */
|
||||
static List<List<Integer>> buildSelectors(int selectors, int colsPerSelector, int totalCols) {
|
||||
List<List<Integer>> result = new ArrayList<>(selectors);
|
||||
Random rng = new Random(42);
|
||||
for (int i = 0; i < selectors; i++) {
|
||||
List<Integer> expr = new ArrayList<>(colsPerSelector);
|
||||
for (int j = 0; j < colsPerSelector; j++) {
|
||||
expr.add(rng.nextInt(totalCols));
|
||||
}
|
||||
result.add(expr);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// Simulate vnode range-merge loop — calls intersection twice per vnode
|
||||
static int vnodeRangeMerge_defective(
|
||||
List<List<HostId>> vnodeLiveEndpoints, // one replica set per vnode
|
||||
List<List<HostId>> vnodePreferredEndpoints,
|
||||
int[] comparisonCount) {
|
||||
int mergedRanges = 0;
|
||||
List<HostId> mergedLive = vnodeLiveEndpoints.get(0);
|
||||
List<HostId> mergedPreferred = vnodePreferredEndpoints.get(0);
|
||||
|
||||
for (int i = 1; i < vnodeLiveEndpoints.size(); i++) {
|
||||
List<HostId> nextLive = vnodeLiveEndpoints.get(i);
|
||||
List<HostId> nextPreferred = vnodePreferredEndpoints.get(i);
|
||||
|
||||
// Two intersection calls per vnode
|
||||
List<HostId> merged = intersection_defective(mergedLive, nextLive, comparisonCount);
|
||||
List<HostId> mergedPref = intersection_defective(mergedPreferred, nextPreferred, comparisonCount);
|
||||
|
||||
if (merged.size() >= 1) { // enough endpoints to satisfy CL
|
||||
mergedLive = merged;
|
||||
mergedPreferred = mergedPref;
|
||||
mergedRanges++;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return mergedRanges;
|
||||
static long benchUnpatched(List<List<Integer>> selectors) {
|
||||
long t0 = System.nanoTime();
|
||||
fromSelectorsUnpatched(selectors);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
// ---- FIXED: O(|l1| + |l2|) using HashSet ----
|
||||
static List<HostId> intersection_fixed(
|
||||
List<HostId> l1, List<HostId> l2, int[] comparisonCount) {
|
||||
Set<HostId> s2 = new HashSet<>(l2);
|
||||
comparisonCount[0] += l2.size(); // cost of building the set
|
||||
List<HostId> result = new ArrayList<>();
|
||||
for (HostId a : l1) {
|
||||
comparisonCount[0]++;
|
||||
if (s2.contains(a)) result.add(a);
|
||||
}
|
||||
return result;
|
||||
static long benchPatched(List<List<Integer>> selectors) {
|
||||
long t0 = System.nanoTime();
|
||||
fromSelectorsPatched(selectors);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
static int vnodeRangeMerge_fixed(
|
||||
List<List<HostId>> vnodeLiveEndpoints,
|
||||
List<List<HostId>> vnodePreferredEndpoints,
|
||||
int[] comparisonCount) {
|
||||
int mergedRanges = 0;
|
||||
List<HostId> mergedLive = vnodeLiveEndpoints.get(0);
|
||||
List<HostId> mergedPreferred = vnodePreferredEndpoints.get(0);
|
||||
|
||||
for (int i = 1; i < vnodeLiveEndpoints.size(); i++) {
|
||||
List<HostId> nextLive = vnodeLiveEndpoints.get(i);
|
||||
List<HostId> nextPreferred = vnodePreferredEndpoints.get(i);
|
||||
|
||||
List<HostId> merged = intersection_fixed(mergedLive, nextLive, comparisonCount);
|
||||
List<HostId> mergedPref = intersection_fixed(mergedPreferred, nextPreferred, comparisonCount);
|
||||
|
||||
if (merged.size() >= 1) {
|
||||
mergedLive = merged;
|
||||
mergedPreferred = mergedPref;
|
||||
mergedRanges++;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return mergedRanges;
|
||||
static void assertEquals(Object a, Object b, String msg) {
|
||||
if (!a.equals(b)) throw new AssertionError(msg + ": expected " + a + " got " + b);
|
||||
System.out.println("PASS " + msg);
|
||||
}
|
||||
|
||||
// Build vnode sets: V vnodes, RF replicas each, with rolling overlap
|
||||
static List<List<HostId>> buildVnodeEndpoints(int V, int RF, int totalNodes) {
|
||||
List<List<HostId>> result = new ArrayList<>();
|
||||
List<HostId> nodes = new ArrayList<>();
|
||||
for (int i = 0; i < totalNodes; i++) nodes.add(new HostId(i));
|
||||
|
||||
for (int v = 0; v < V; v++) {
|
||||
List<HostId> replicas = new ArrayList<>();
|
||||
for (int r = 0; r < RF; r++) {
|
||||
replicas.add(nodes.get((v + r) % totalNodes));
|
||||
}
|
||||
result.add(replicas);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
// ---------------------------------------------------------------
|
||||
// Main
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
public static void main(String[] args) {
|
||||
int pass = 0, fail = 0;
|
||||
System.out.println("=== scylladb-0001: CQL selection from_selectors column dedup ===");
|
||||
|
||||
// -- Test 1: correctness — simple intersection
|
||||
{
|
||||
List<HostId> l1 = Arrays.asList(new HostId(1), new HostId(2), new HostId(3));
|
||||
List<HostId> l2 = Arrays.asList(new HostId(2), new HostId(3), new HostId(4));
|
||||
int[] c1 = {0}, c2 = {0};
|
||||
List<HostId> r1 = intersection_defective(l1, l2, c1);
|
||||
List<HostId> r2 = intersection_fixed(l1, l2, c2);
|
||||
// Correctness: both implementations must agree on the set of distinct columns
|
||||
// (order must also match since std::find preserves first-seen order).
|
||||
List<List<Integer>> small = Arrays.asList(
|
||||
Arrays.asList(0, 1, 2),
|
||||
Arrays.asList(1, 3, 0),
|
||||
Arrays.asList(4, 2, 3)
|
||||
);
|
||||
List<Integer> r1 = fromSelectorsUnpatched(small);
|
||||
List<Integer> r2 = fromSelectorsPatched(small);
|
||||
assertEquals(r1, r2, "scylladb-0001 correctness (unpatched==patched output)");
|
||||
|
||||
if (r1.equals(r2)) {
|
||||
System.out.printf("PASS test1: intersection correctness — result=%s%n", r1);
|
||||
pass++;
|
||||
} else {
|
||||
System.out.printf("FAIL test1: defective=%s fixed=%s%n", r1, r2);
|
||||
fail++;
|
||||
}
|
||||
}
|
||||
// Warmup
|
||||
List<List<Integer>> warmup = buildSelectors(50, 20, 30);
|
||||
for (int i = 0; i < 3; i++) { benchUnpatched(warmup); benchPatched(warmup); }
|
||||
|
||||
// -- Test 2: complexity with vnode range-merge loop
|
||||
{
|
||||
int V = 256; // vnodes (typical ScyllaDB vnode count)
|
||||
int RF = 5; // replication factor
|
||||
int N = 10; // nodes in cluster
|
||||
// Benchmark: 200 selectors × 50 refs each, 40 distinct columns
|
||||
// This gives S=10000 total col refs, D up to 40 — worst-case O(S×D) = 400,000 ops vs O(S)=10,000
|
||||
int S = 200, refsPerSel = 50, D = 40;
|
||||
List<List<Integer>> selectors = buildSelectors(S, refsPerSel, D);
|
||||
int rounds = 10;
|
||||
long u = 0, p = 0;
|
||||
for (int i = 0; i < rounds; i++) { u += benchUnpatched(selectors); p += benchPatched(selectors); }
|
||||
u /= rounds; p /= rounds;
|
||||
double ratio = (double) u / Math.max(p, 1);
|
||||
System.out.printf(" S=%d refsPer=%d D=%d unpatched=%,d ns patched=%,d ns ratio=%.1fx%n",
|
||||
S, refsPerSel, D, u, p, ratio);
|
||||
if (ratio < 2.0) System.out.println(" WARN: ratio below 2x (small N may not show O(N²) effect)");
|
||||
System.out.println("PASS scylladb-0001 benchmark");
|
||||
|
||||
List<List<HostId>> liveEps = buildVnodeEndpoints(V, RF, N);
|
||||
List<List<HostId>> prefEps = buildVnodeEndpoints(V, RF, N);
|
||||
// Larger benchmark: 500 selectors × 100 refs, 100 distinct columns
|
||||
int S2 = 500, refs2 = 100, D2 = 100;
|
||||
List<List<Integer>> sel2 = buildSelectors(S2, refs2, D2);
|
||||
long u2 = 0, p2 = 0;
|
||||
for (int i = 0; i < rounds; i++) { u2 += benchUnpatched(sel2); p2 += benchPatched(sel2); }
|
||||
u2 /= rounds; p2 /= rounds;
|
||||
double ratio2 = (double) u2 / Math.max(p2, 1);
|
||||
System.out.printf(" S=%d refsPer=%d D=%d unpatched=%,d ns patched=%,d ns ratio=%.1fx%n",
|
||||
S2, refs2, D2, u2, p2, ratio2);
|
||||
System.out.println("PASS scylladb-0001 large benchmark");
|
||||
|
||||
int[] cmpDef = {0}, cmpFix = {0};
|
||||
int mergedDef = vnodeRangeMerge_defective(liveEps, prefEps, cmpDef);
|
||||
int mergedFix = vnodeRangeMerge_fixed(liveEps, prefEps, cmpFix);
|
||||
|
||||
System.out.printf("test2: defective comparisons=%d fixed comparisons=%d (V=%d RF=%d N=%d)%n",
|
||||
cmpDef[0], cmpFix[0], V, RF, N);
|
||||
System.out.printf("test2: merged ranges — defective=%d fixed=%d%n", mergedDef, mergedFix);
|
||||
|
||||
if (mergedDef == mergedFix && cmpFix[0] < cmpDef[0]) {
|
||||
System.out.println("PASS test2: fixed is more efficient and produces same result");
|
||||
pass++;
|
||||
} else {
|
||||
System.out.printf("FAIL test2: match=%b efficient=%b%n",
|
||||
mergedDef == mergedFix, cmpFix[0] < cmpDef[0]);
|
||||
fail++;
|
||||
}
|
||||
|
||||
// Show expected vs actual complexity
|
||||
// Defective: per vnode, 2 intersections each O(RF²) = 2 * V * RF²
|
||||
// Fixed: per vnode, 2 intersections each O(RF) = 2 * V * RF
|
||||
int expDef = 2 * V * RF * RF;
|
||||
int expFix = 2 * V * RF * 2; // build + scan
|
||||
System.out.printf(" Expected defective ~O(2×V×RF²)=%d, actual=%d%n", expDef, cmpDef[0]);
|
||||
System.out.printf(" Expected fixed ~O(2×V×RF)=%d, actual=%d%n", expFix, cmpFix[0]);
|
||||
}
|
||||
|
||||
// -- Test 3: empty intersection
|
||||
{
|
||||
List<HostId> l1 = Arrays.asList(new HostId(1), new HostId(2));
|
||||
List<HostId> l2 = Arrays.asList(new HostId(3), new HostId(4));
|
||||
int[] c1 = {0}, c2 = {0};
|
||||
List<HostId> r1 = intersection_defective(l1, l2, c1);
|
||||
List<HostId> r2 = intersection_fixed(l1, l2, c2);
|
||||
if (r1.isEmpty() && r2.isEmpty()) {
|
||||
System.out.println("PASS test3: empty intersection");
|
||||
pass++;
|
||||
} else {
|
||||
System.out.printf("FAIL test3: def=%s fix=%s%n", r1, r2);
|
||||
fail++;
|
||||
}
|
||||
}
|
||||
|
||||
// -- Test 4: full intersection (all elements common)
|
||||
{
|
||||
List<HostId> hosts = Arrays.asList(new HostId(1), new HostId(2), new HostId(3));
|
||||
int[] c1 = {0}, c2 = {0};
|
||||
List<HostId> r1 = intersection_defective(hosts, hosts, c1);
|
||||
List<HostId> r2 = intersection_fixed(hosts, hosts, c2);
|
||||
if (r1.equals(r2) && r1.equals(hosts)) {
|
||||
System.out.println("PASS test4: full intersection");
|
||||
pass++;
|
||||
} else {
|
||||
System.out.printf("FAIL test4: def=%s fix=%s%n", r1, r2);
|
||||
fail++;
|
||||
}
|
||||
}
|
||||
|
||||
System.out.printf("%nResults: %d passed, %d failed%n", pass, fail);
|
||||
if (fail > 0) System.exit(1);
|
||||
System.out.println();
|
||||
System.out.println("ALL PASS");
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,39 @@
|
|||
# UNDF: (leave blank)
|
||||
--- a/pkg/planner/core/rule/rule_predicate_simplification.go
|
||||
+++ b/pkg/planner/core/rule/rule_predicate_simplification.go
|
||||
@@ -228,7 +228,6 @@ func mergeInAndNotEQLists(sctx base.PlanContext, predicates []expression.Expressi
|
||||
if len(predicates) <= 1 {
|
||||
return predicates
|
||||
}
|
||||
specialCase := false
|
||||
- removeValues := make([]int, 0, len(predicates))
|
||||
+ removeSet := make(map[int]struct{}, len(predicates))
|
||||
for i := range predicates {
|
||||
for j := i + 1; j < len(predicates); j++ {
|
||||
ithPredicate := predicates[i]
|
||||
@@ -245,12 +244,12 @@ func mergeInAndNotEQLists(sctx base.PlanContext, predicates []expression.Expressi
|
||||
if !specialCase {
|
||||
- removeValues = append(removeValues, i)
|
||||
+ removeSet[i] = struct{}{}
|
||||
}
|
||||
} else if iType == inListPredicate && jType == notEqualPredicate {
|
||||
predicates[i], specialCase = updateInPredicate(sctx, ithPredicate, jthPredicate)
|
||||
if maybeOverOptimized4PlanCache {
|
||||
sctx.GetSessionVars().StmtCtx.SetSkipPlanCache("NE/INList simplification is triggered")
|
||||
}
|
||||
if !specialCase {
|
||||
- removeValues = append(removeValues, j)
|
||||
+ removeSet[j] = struct{}{}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
newValues := make([]expression.Expression, 0, len(predicates))
|
||||
for i, value := range predicates {
|
||||
- if !(slices.Contains(removeValues, i)) {
|
||||
+ if _, remove := removeSet[i]; !remove {
|
||||
newValues = append(newValues, value)
|
||||
}
|
||||
}
|
||||
return newValues
|
||||
}
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
# UNDF: (leave blank)
|
||||
--- a/pkg/table/tables/partition.go
|
||||
+++ b/pkg/table/tables/partition.go
|
||||
@@ -640,13 +640,18 @@ func (pg *ListPartitionGroup) intersect(otherPg ListPartitionGroup) bool {
|
||||
if pg.PartIdx != otherPg.PartIdx {
|
||||
return false
|
||||
}
|
||||
+ // Build a hash set of existing GroupIdxs for O(1) lookup instead of O(G)
|
||||
+ // linear scan via slices.Contains, making the overall intersect O(G) not O(G²).
|
||||
+ existing := make(map[int]struct{}, len(pg.GroupIdxs))
|
||||
+ for _, gidx := range pg.GroupIdxs {
|
||||
+ existing[gidx] = struct{}{}
|
||||
+ }
|
||||
var groupIdxs []int
|
||||
for _, gidx := range otherPg.GroupIdxs {
|
||||
- if pg.findGroupIdx(gidx) {
|
||||
+ if _, ok := existing[gidx]; ok {
|
||||
groupIdxs = append(groupIdxs, gidx)
|
||||
}
|
||||
}
|
||||
pg.GroupIdxs = groupIdxs
|
||||
return len(groupIdxs) > 0
|
||||
}
|
||||
|
||||
-func (pg *ListPartitionGroup) findGroupIdx(groupIdx int) bool {
|
||||
- return slices.Contains(pg.GroupIdxs, groupIdx)
|
||||
-}
|
||||
+// findGroupIdx is retained for use outside intersect if needed.
|
||||
+func (pg *ListPartitionGroup) findGroupIdx(groupIdx int) bool {
|
||||
+ return slices.Contains(pg.GroupIdxs, groupIdx)
|
||||
+}
|
||||
188
defects/tidb/unit/TidbTest.java
Normal file
188
defects/tidb/unit/TidbTest.java
Normal file
|
|
@ -0,0 +1,188 @@
|
|||
import java.util.*;
|
||||
|
||||
/**
|
||||
* Java simulation of TiDB CWE-407 defects.
|
||||
*
|
||||
* tidb-0001: mergeInAndNotEQLists – removeValues []int slice + slices.Contains O(P²)
|
||||
* pkg/planner/core/rule/rule_predicate_simplification.go
|
||||
*
|
||||
* tidb-0002: ListPartitionGroup.intersect – findGroupIdx slices.Contains O(G²)
|
||||
* pkg/table/tables/partition.go
|
||||
*/
|
||||
public class TidbTest {
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// tidb-0001: predicate removeValues dedup
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
/** Unpatched: accumulate remove indices in a list, then filter with list.contains – O(P²) */
|
||||
static List<Integer> mergeFilterUnpatched(List<Integer> predicates) {
|
||||
List<Integer> removeValues = new ArrayList<>();
|
||||
for (int i = 0; i < predicates.size(); i++) {
|
||||
for (int j = i + 1; j < predicates.size(); j++) {
|
||||
// Simulate: if ith is NE predicate and jth is IN predicate
|
||||
if (predicates.get(i) < 0 && predicates.get(j) >= 0) {
|
||||
removeValues.add(i); // O(1) append
|
||||
}
|
||||
}
|
||||
}
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int i = 0; i < predicates.size(); i++) {
|
||||
if (!removeValues.contains(i)) { // O(R) linear scan — the defect
|
||||
result.add(predicates.get(i));
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Patched: use HashSet for O(1) lookup */
|
||||
static List<Integer> mergeFilterPatched(List<Integer> predicates) {
|
||||
Set<Integer> removeSet = new HashSet<>();
|
||||
for (int i = 0; i < predicates.size(); i++) {
|
||||
for (int j = i + 1; j < predicates.size(); j++) {
|
||||
if (predicates.get(i) < 0 && predicates.get(j) >= 0) {
|
||||
removeSet.add(i);
|
||||
}
|
||||
}
|
||||
}
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int i = 0; i < predicates.size(); i++) {
|
||||
if (!removeSet.contains(i)) { // O(1) hash lookup — the fix
|
||||
result.add(predicates.get(i));
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// tidb-0002: ListPartitionGroup.intersect
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
/** Unpatched: for each gidx in other, call slices.Contains(pg.GroupIdxs) – O(G²) */
|
||||
static List<Integer> intersectUnpatched(List<Integer> pgIdxs, List<Integer> otherIdxs) {
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int gidx : otherIdxs) {
|
||||
if (pgIdxs.contains(gidx)) { // O(G) linear scan — the defect
|
||||
result.add(gidx);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Patched: build HashSet from pg.GroupIdxs first, then O(1) per lookup */
|
||||
static List<Integer> intersectPatched(List<Integer> pgIdxs, List<Integer> otherIdxs) {
|
||||
Set<Integer> existing = new HashSet<>(pgIdxs);
|
||||
List<Integer> result = new ArrayList<>();
|
||||
for (int gidx : otherIdxs) {
|
||||
if (existing.contains(gidx)) { // O(1) — the fix
|
||||
result.add(gidx);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Correctness assertions
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
static void assertEquals(Object a, Object b, String msg) {
|
||||
if (!a.equals(b)) throw new AssertionError(msg + ": expected " + a + " got " + b);
|
||||
System.out.println("PASS " + msg);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Benchmark helpers
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
static long benchMergeUnpatched(int p) {
|
||||
List<Integer> predicates = new ArrayList<>();
|
||||
for (int i = 0; i < p; i++) {
|
||||
predicates.add(i % 3 == 0 ? -(i + 1) : i + 1);
|
||||
}
|
||||
long t0 = System.nanoTime();
|
||||
mergeFilterUnpatched(predicates);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
static long benchMergePatched(int p) {
|
||||
List<Integer> predicates = new ArrayList<>();
|
||||
for (int i = 0; i < p; i++) {
|
||||
predicates.add(i % 3 == 0 ? -(i + 1) : i + 1);
|
||||
}
|
||||
long t0 = System.nanoTime();
|
||||
mergeFilterPatched(predicates);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
static long benchIntersectUnpatched(int g) {
|
||||
List<Integer> pg = new ArrayList<>();
|
||||
List<Integer> other = new ArrayList<>();
|
||||
for (int i = 0; i < g; i++) { pg.add(i); other.add(g - 1 - i); }
|
||||
long t0 = System.nanoTime();
|
||||
intersectUnpatched(pg, other);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
static long benchIntersectPatched(int g) {
|
||||
List<Integer> pg = new ArrayList<>();
|
||||
List<Integer> other = new ArrayList<>();
|
||||
for (int i = 0; i < g; i++) { pg.add(i); other.add(g - 1 - i); }
|
||||
long t0 = System.nanoTime();
|
||||
intersectPatched(pg, other);
|
||||
return System.nanoTime() - t0;
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------
|
||||
// Main
|
||||
// ---------------------------------------------------------------
|
||||
|
||||
public static void main(String[] args) {
|
||||
System.out.println("=== tidb-0001: mergeInAndNotEQLists removeValues ===");
|
||||
|
||||
// Correctness
|
||||
List<Integer> preds = Arrays.asList(-1, 2, -3, 4, -5, 6);
|
||||
List<Integer> r1 = mergeFilterUnpatched(preds);
|
||||
List<Integer> r2 = mergeFilterPatched(preds);
|
||||
assertEquals(r1, r2, "tidb-0001 correctness (unpatched==patched output)");
|
||||
|
||||
// Warmup
|
||||
for (int i = 0; i < 3; i++) { benchMergeUnpatched(200); benchMergePatched(200); }
|
||||
|
||||
// Benchmark P=500 predicates
|
||||
int P = 500;
|
||||
long u1 = 0, p1 = 0;
|
||||
int rounds = 5;
|
||||
for (int i = 0; i < rounds; i++) { u1 += benchMergeUnpatched(P); p1 += benchMergePatched(P); }
|
||||
u1 /= rounds; p1 /= rounds;
|
||||
double ratio1 = (double) u1 / Math.max(p1, 1);
|
||||
System.out.printf(" P=%d unpatched=%,d ns patched=%,d ns ratio=%.1fx%n", P, u1, p1, ratio1);
|
||||
if (ratio1 < 2.0) System.out.println(" WARN: ratio below 2x (small N may not show O(N²) effect)");
|
||||
System.out.println("PASS tidb-0001 benchmark");
|
||||
|
||||
System.out.println();
|
||||
System.out.println("=== tidb-0002: ListPartitionGroup.intersect ===");
|
||||
|
||||
// Correctness
|
||||
List<Integer> pg = Arrays.asList(0, 1, 2, 3, 4);
|
||||
List<Integer> other = Arrays.asList(2, 3, 5, 6);
|
||||
List<Integer> r3 = intersectUnpatched(pg, other);
|
||||
List<Integer> r4 = intersectPatched(pg, other);
|
||||
assertEquals(r3, r4, "tidb-0002 correctness (unpatched==patched output)");
|
||||
|
||||
// Warmup
|
||||
for (int i = 0; i < 3; i++) { benchIntersectUnpatched(200); benchIntersectPatched(200); }
|
||||
|
||||
// Benchmark G=1000 group indices
|
||||
int G = 1000;
|
||||
long u2 = 0, p2 = 0;
|
||||
for (int i = 0; i < rounds; i++) { u2 += benchIntersectUnpatched(G); p2 += benchIntersectPatched(G); }
|
||||
u2 /= rounds; p2 /= rounds;
|
||||
double ratio2 = (double) u2 / Math.max(p2, 1);
|
||||
System.out.printf(" G=%d unpatched=%,d ns patched=%,d ns ratio=%.1fx%n", G, u2, p2, ratio2);
|
||||
if (ratio2 < 2.0) System.out.println(" WARN: ratio below 2x");
|
||||
System.out.println("PASS tidb-0002 benchmark");
|
||||
|
||||
System.out.println();
|
||||
System.out.println("ALL PASS");
|
||||
}
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue