chore(planner): consider limit when planning

packages/zql-benchmarks/src/planner-hydration.bench.ts

@@ -75,6 +75,7 @@ function benchmarkQuery<TTable extends keyof typeof schema.tables & string>(
 
   // Map to server names, plan, then map back to client names
   const mappedAST = mapAST(unplannedAST, clientToServerMapper);
+
   const plannedServerAST = planQuery(mappedAST, costModel);
   const plannedClientAST = mapAST(plannedServerAST, serverToClientMapper);
 

packages/zql-integration-tests/src/chinook/planner.pg.test.ts

@@ -71,7 +71,9 @@ describe('Chinook planner tests', () => {
 
   test('playlist with track', () => {
     const ast = getPlanAST(queries.sqlite.playlist.whereExists('tracks'));
-    expect(pick(ast, ['where', 'flip'])).toBe(true);
+    // No flip because:
+    // all playlists have a track so we must scan all playlists!
+    expect(pick(ast, ['where', 'flip'])).toBe(false);
     expect(pick(ast, ['where', 'related', 'subquery', 'where', 'flip'])).toBe(
       false,
     );
@@ -80,8 +82,14 @@ describe('Chinook planner tests', () => {
   test('tracks with playlist', () => {
     const ast = getPlanAST(queries.sqlite.track.whereExists('playlists'));
 
-    // playlist table is smaller. Should be flipped.
-    expect(pick(ast, ['where', 'flip'])).toBe(true);
+    // TODO:
+    // This is where SELECTIVITY_PLAN.md would help.
+    // We assume a selectivity of 1 on playlist_track and playlist
+    // because there are no filters.
+    // but a track may not be part of a playlist!
+    // If many tracks are not part of any playlist
+    // then flipping would be better.
+    expect(pick(ast, ['where', 'flip'])).toBe(false);
   });
 
   test('has album a or album b', () => {

packages/zql/src/planner/SELECTIVITY_PLAN.md

@@ -0,0 +1,336 @@
+# Semi-Join Selectivity Forward Plan
+
+## Problem Statement
+
+The current planner implementation conflates two fundamentally different concepts when estimating costs for semi-joins (EXISTS clauses with LIMIT):
+
+1. **Filter Selectivity**: The fraction of child rows that pass filter predicates
+2. **Semi-Join Selectivity**: The fraction of parent rows that have at least one matching child
+
+This conflation leads to incorrect cost estimates for queries with LIMIT clauses on the outer table.
+
+## Background
+
+### Example Scenario
+
+Consider the query:
+
+```sql
+SELECT * FROM users
+WHERE EXISTS (
+  SELECT 1 FROM posts
+  WHERE posts.userId = users.id
+  AND posts.published = true
+)
+LIMIT 10
+```
+
+The planner needs to estimate: **How many users must we scan to find 10 users that have at least one published post?**
+
+### Current Implementation (Incorrect)
+
+```typescript
+// In planner-connection.ts:129-139
+const costWithFilters = model(table, sort, filters, undefined);
+const costWithoutFilters = model(table, sort, undefined, undefined);
+this.selectivity = costWithFilters / costWithoutFilters;
+
+// In planner-join.ts:210-214
+scanEst = Math.min(scanEst, parentCost.limit / childCost.selectivity);
+```
+
+**What it calculates**: If 50% of posts are published → selectivity = 0.5
+**What it estimates**: Need to scan 10 / 0.5 = 20 users
+
+**Problem**: This assumes 50% of users have published posts, but the actual probability depends on how posts are distributed across users (the "fan-out").
+
+### The Two Selectivities Compared
+
+| Scenario                                                                                      | Filter Selectivity                 | Semi-Join Selectivity                             | Reality                                 |
+| --------------------------------------------------------------------------------------------- | ---------------------------------- | ------------------------------------------------- | --------------------------------------- |
+| Even distribution<br/>1000 users, 10 posts each<br/>50% published                             | 0.5<br/>(50% of posts pass filter) | 0.999<br/>(P(user has ≥1 published) = 1 - 0.5^10) | Almost all users have ≥1 published post |
+| Skewed distribution<br/>900 users with 0 posts<br/>100 users with 100 posts<br/>50% published | 0.5<br/>(still 50% of posts pass)  | 0.1<br/>(only 100/1000 users have any posts)      | Only 10% of users have posts at all     |
+
+**Key insight**: Filter selectivity tells us about child rows; semi-join selectivity tells us about parent rows. They're only equal when fan-out = 1 (foreign key from parent to child).
+
+## The Fan-Out Formula
+
+The relationship between filter and semi-join selectivity is:
+
+```
+semiJoinSelectivity = 1 - (1 - filterSelectivity)^fanOut
+```
+
+Where **fan-out** = average number of child rows per parent row.
+
+### Proof Intuition
+
+For a parent with N children, each with independent probability P of matching:
+
+- Probability NO children match: (1 - P)^N
+- Probability ≥1 child matches: 1 - (1 - P)^N
+
+### Edge Cases
+
+**When fan-out = 1** (foreign key from parent → child):
+
+```
+semiJoinSelectivity = 1 - (1 - filterSelectivity)^1
+                    = filterSelectivity
+```
+
+Current implementation is correct! ✅
+
+**When fan-out = 10** (typical one-to-many):
+
+- filterSelectivity = 0.5 → semiJoinSelectivity = 0.999
+- filterSelectivity = 0.1 → semiJoinSelectivity = 0.651
+- filterSelectivity = 0.01 → semiJoinSelectivity = 0.096
+
+**When fan-out = 100** (high cardinality):
+
+- filterSelectivity = 0.5 → semiJoinSelectivity ≈ 1.0
+- filterSelectivity = 0.1 → semiJoinSelectivity ≈ 1.0
+- filterSelectivity = 0.01 → semiJoinSelectivity = 0.634
+
+## Solution: Query sqlite_stat1 for Fan-Out
+
+### What sqlite_stat1 Provides
+
+After running `ANALYZE`, SQLite populates `sqlite_stat1` with index statistics:
+
+```sql
+SELECT tbl, idx, stat FROM sqlite_stat1;
+
+-- Example result:
+-- tbl='posts', idx='idx_posts_userId', stat='10000 100'
+--                                             ^     ^
+--                                             |     |
+--                                    total rows   avg rows per distinct userId
+```
+
+The `stat` column format: `"totalRows avgRowsPerDistinct [avgRowsPerDistinct2 ...]"`
+
+For an index on `posts.userId`:
+
+- First number: Total rows in the index (10,000 posts)
+- Second number: Average rows per distinct value of userId (100 posts per user)
+- **The second number IS the fan-out!**
+
+### Implementation Strategy
+
+```typescript
+function getFanOutFromStats(
+  db: Database,
+  tableName: string,
+  columnName: string,
+): number | undefined {
+  // 1. Find indexes containing the column
+  const indexes = db
+    .prepare(
+      `
+    SELECT name FROM sqlite_master
+    WHERE type='index'
+    AND tbl_name=?
+    AND sql LIKE '%' || ? || '%'
+  `,
+    )
+    .all(tableName, columnName);
+
+  if (indexes.length === 0) {
+    return undefined; // No index, can't get stats
+  }
+
+  // 2. Query sqlite_stat1 for the first matching index
+  for (const {name: indexName} of indexes) {
+    const result = db
+      .prepare(
+        `
+      SELECT stat FROM sqlite_stat1
+      WHERE tbl=? AND idx=?
+    `,
+      )
+      .get(tableName, indexName);
+
+    if (result && result.stat) {
+      const parts = result.stat.split(' ');
+      if (parts.length >= 2) {
+        return parseInt(parts[1], 10); // Average rows per distinct value
+      }
+    }
+  }
+
+  return undefined; // Stats not available (ANALYZE not run)
+}
+```
+
+### Fallback Strategy
+
+```typescript
+const DEFAULT_FANOUT = 3; // Conservative middle ground
+
+const fanOut = getFanOutFromStats(db, table, joinColumn) ?? DEFAULT_FANOUT;
+const semiJoinSelectivity = 1 - Math.pow(1 - filterSelectivity, fanOut);
+```
+
+**Why 3?**
+
+- SQLite's default is 10 (might be too optimistic)
+- fan-out = 1 is common for FK relationships
+- fan-out = 3 is a conservative middle ground
+- Overestimating selectivity → slightly less efficient but still correct
+- Underestimating selectivity → risk of bad plans
+
+## Design Decisions
+
+### Default Fan-Out Value
+
+**Options**:
+
+- 1: Conservative, assumes FK relationships (current behavior)
+- 3: Moderate, safe middle ground
+- 10: SQLite's default, optimistic
+
+**Recommendation**: Use 3
+
+- Safer than 10 for sparse relationships
+- More accurate than 1 for typical one-to-many
+- Easy to tune based on production data
+
+## Edge Cases and Considerations
+
+### 1. No Index on Join Column
+
+If the child table has no index on the join column:
+
+- `getFanOutFromStats()` returns `undefined`
+- Fall back to `DEFAULT_FANOUT`
+- Cost estimates may be less accurate but still safe
+
+### 2. ANALYZE Not Run
+
+If `sqlite_stat1` is empty:
+
+- `getFanOutFromStats()` returns `undefined`
+- Fall back to `DEFAULT_FANOUT`
+- Document recommendation to run ANALYZE periodically
+
+### 3. Composite Indexes
+
+If join column is part of a multi-column index:
+
+- `sqlite_stat1` provides stats at each position
+- Use the stat corresponding to the join column's position
+- May require more sophisticated parsing
+
+### 4. Multiple Indexes on Same Column
+
+If multiple indexes contain the column:
+
+- Use first index found (arbitrary but consistent)
+- Could enhance to prefer indexes where column is leftmost
+
+### 5. Zero Selectivity
+
+Current code has a bug where `semiJoinSelectivity = 0` (EXISTS can never succeed):
+
+```typescript
+if (childCost.semiJoinSelectivity === 0) {
+  // EXISTS can never succeed - result set is empty
+  return {
+    baseCardinality: 0,
+    runningCost: parentCost.runningCost, // Cost to discover emptiness
+    filterSelectivity: 0,
+    semiJoinSelectivity: 0,
+    limit: parentCost.limit,
+  };
+}
+```
+
+This should be fixed in the same PR.
+
+### 6. NOT EXISTS
+
+For `NOT EXISTS`, we don't short-circuit on first match:
+
+- Must scan ALL children to verify none match
+- Don't apply limit or use semi-join selectivity
+- Current code correctly doesn't set limit for NOT EXISTS
+
+## Future Enhancements
+
+### Enhancement 1: Use sqlite_stat4 for Distribution
+
+**Problem**: `sqlite_stat1` provides average fan-out, but distributions can be highly skewed.
+
+**Solution**: Sample actual distribution from `sqlite_stat4`:
+
+```typescript
+function getSemiJoinSelectivityWithStat4(
+  db: Database,
+  childTable: string,
+  childColumn: string,
+  filterSelectivity: number,
+): number {
+  // Query sqlite_stat4 for distribution samples
+  const samples = db
+    .prepare(
+      `
+    SELECT CAST(neq AS INTEGER) as fanOut
+    FROM sqlite_stat4
+    WHERE tbl=? AND idx LIKE '%' || ? || '%'
+  `,
+    )
+    .all(childTable, childColumn);
+
+  if (samples.length === 0) {
+    return fallbackToStat1();
+  }
+
+  // Calculate selectivity for each sample's fan-out
+  const selectivities = samples.map(
+    s => 1 - Math.pow(1 - filterSelectivity, s.fanOut),
+  );
+
+  // Average across the distribution
+  return selectivities.reduce((a, b) => a + b, 0) / selectivities.length;
+}
+```
+
+**Challenge**: `stat4` only samples rows that exist in the index. Parents with 0 children don't appear. Need to adjust for missing parents.
+
+## References
+
+### PostgreSQL Documentation
+
+- [Semi-Join Planning](https://postgrespro.com/blog/pgsql/5969618)
+- [compute_semi_anti_join_factors](https://doxygen.postgresql.org/costsize_8c.html)
+  - Calculates `outer_match_frac`: fraction of outer rows that have matches
+  - Uses NDV (number of distinct values) from statistics
+
+### SQLite Documentation
+
+- [sqlite_stat1 Format](https://sqlite.org/fileformat2.html#stat1tab)
+- [Query Planning Overview](https://sqlite.org/optoverview.html)
+- [ANALYZE Command](https://sqlite.org/lang_analyze.html)
+
+### Academic References
+
+- "Access Path Selection in a Relational Database Management System" (Selinger et al., 1979)
+  - Original paper on cost-based optimization
+  - Discusses selectivity estimation for joins
+
+## Open Questions
+
+1. **Should we fix zero selectivity edge case in same PR?**
+   - Pro: Related to selectivity handling
+   - Con: Separate concern, could be separate PR
+
+2. **What's the right default fan-out?**
+   - 1: Current behavior (conservative for FK, wrong for one-to-many)
+   - 3: Moderate (recommended)
+   - 10: SQLite's default (optimistic)
+
+3. **Should we add warnings/logging when falling back to default?**
+   - Could help users identify missing ANALYZE or indexes
+   - Might be noisy for intentionally simple cost models