# Gitlore Performance Audit Report

**Date**: 2026-02-05
**Auditor**: Claude Code (Opus 4.5)
**Scope**: Core system performance - ingestion, embedding, search, and document regeneration

## Executive Summary

This audit identifies 12 high-impact optimization opportunities across the Gitlore codebase. The most significant findings center on:

1. **SQL query patterns** with N+1 issues and inefficient correlated subqueries
2. **Memory allocation patterns** in hot paths (embedding, chunking, ingestion)
3. **Change detection queries** using triple-EXISTS patterns instead of JOINs

**Estimated overall improvement potential**: 30-50% reduction in latency for filtered searches, 2-5x improvement in ingestion throughput for issues/MRs with many labels.

---

## Methodology

- **Codebase analysis**: Full read of all modules in `src/`
- **SQL pattern analysis**: All queries checked for N+1, missing indexes, unbounded results
- **Memory allocation analysis**: Clone patterns, unnecessary collections, missing capacity hints
- **Test baseline**: All tests pass (`cargo test --release`)

Note: Without access to a live GitLab instance or populated database, profiling is code-analysis based rather than runtime measured.

---

## Opportunity Matrix

| ID | Issue | Location | Impact | Confidence | Effort | ICE Score | Status |
|----|-------|----------|--------|------------|--------|-----------|--------|
| 1 | Triple-EXISTS change detection | `change_detector.rs:19-46` | HIGH | 95% | LOW | **9.5** | **DONE** |
| 2 | N+1 label/assignee inserts | `issues.rs:270-285`, `merge_requests.rs:242-272` | HIGH | 95% | MEDIUM | **9.0** | Pending |
| 3 | Clone in embedding batch loop | `pipeline.rs:165` | HIGH | 90% | LOW | **9.0** | Pending |
| 4 | Correlated GROUP_CONCAT in list | `list.rs:341-348` | HIGH | 90% | MEDIUM | **8.5** | Pending |
| 5 | Multiple EXISTS per label filter | `filters.rs:100-107` | HIGH | 85% | MEDIUM | **8.0** | **DONE** |
| 6 | String allocation in chunking | `chunking.rs:7-49` | MEDIUM | 95% | MEDIUM | **7.5** | Pending |
| 7 | Multiple COUNT queries | `count.rs:44-56` | MEDIUM | 95% | LOW | **7.0** | **DONE** |
| 8 | Collect-then-concat pattern | `truncation.rs:60-61` | MEDIUM | 90% | LOW | **7.0** | **DONE** |
| 9 | Box<dyn ToSql> allocations | `filters.rs:67-135` | MEDIUM | 80% | HIGH | **6.0** | Pending |
| 10 | Missing Vec::with_capacity | `pipeline.rs:106`, multiple | LOW | 95% | LOW | **5.5** | **DONE** |
| 11 | FTS token collect-join | `fts.rs:26-41` | LOW | 90% | LOW | **5.0** | **DONE** |
| 12 | Transformer string clones | `merge_request.rs:51-77` | MEDIUM | 85% | HIGH | **5.0** | Pending |

ICE Score = (Impact x Confidence) / Effort, scaled 1-10

---

## Detailed Findings

### 1. Triple-EXISTS Change Detection Query (ICE: 9.5)

**Location**: `src/embedding/change_detector.rs:19-46`

**Current Code**:
```sql
SELECT d.id, d.content_text, d.content_hash
FROM documents d
WHERE d.id > ?1
  AND (
    NOT EXISTS (SELECT 1 FROM embedding_metadata em WHERE em.document_id = d.id AND em.chunk_index = 0)
    OR EXISTS (SELECT 1 FROM embedding_metadata em WHERE em.document_id = d.id AND em.chunk_index = 0 AND em.document_hash != d.content_hash)
    OR EXISTS (SELECT 1 FROM embedding_metadata em WHERE em.document_id = d.id AND em.chunk_index = 0 AND (...))
  )
ORDER BY d.id
LIMIT ?2
```

**Problem**: Three separate EXISTS subqueries, each scanning `embedding_metadata`. SQLite cannot short-circuit across OR'd EXISTS efficiently.

**Proposed Fix**:
```sql
SELECT d.id, d.content_text, d.content_hash
FROM documents d
LEFT JOIN embedding_metadata em
  ON em.document_id = d.id AND em.chunk_index = 0
WHERE d.id > ?1
  AND (
    em.document_id IS NULL                      -- no embedding
    OR em.document_hash != d.content_hash       -- hash mismatch
    OR em.chunk_max_bytes IS NULL
    OR em.chunk_max_bytes != ?3
    OR em.model != ?4
    OR em.dims != ?5
  )
ORDER BY d.id
LIMIT ?2
```

**Isomorphism Proof**: Both queries return documents needing embedding when:
- No embedding exists for chunk_index=0 (NULL check)
- Hash changed (direct comparison)
- Config mismatch (model/dims/chunk_max_bytes)

The LEFT JOIN + NULL check is semantically identical to NOT EXISTS. The OR conditions inside WHERE match the EXISTS predicates exactly.

**Expected Impact**: 2-3x faster for large document sets. Single scan of embedding_metadata instead of three.

---

### 2. N+1 Label/Assignee Inserts (ICE: 9.0)

**Location**:
- `src/ingestion/issues.rs:270-285`
- `src/ingestion/merge_requests.rs:242-272`

**Current Code**:
```rust
for label_name in label_names {
    let label_id = upsert_label_tx(tx, project_id, label_name, &mut labels_created)?;
    link_issue_label_tx(tx, local_issue_id, label_id)?;
}
```

**Problem**: Each label triggers 2+ SQL statements. With 20 labels × 100 issues = 4000+ queries per batch.

**Proposed Fix**: Batch insert using prepared statements with multi-row VALUES:

```rust
// Build batch: INSERT INTO issue_labels VALUES (?, ?), (?, ?), ...
let mut values = String::new();
let mut params: Vec<Box<dyn ToSql>> = Vec::with_capacity(label_ids.len() * 2);
for (i, label_id) in label_ids.iter().enumerate() {
    if i > 0 { values.push_str(","); }
    values.push_str("(?,?)");
    params.push(Box::new(local_issue_id));
    params.push(Box::new(*label_id));
}
let sql = format!("INSERT OR IGNORE INTO issue_labels (issue_id, label_id) VALUES {}", values);
```

Or use `prepare_cached()` pattern from `events_db.rs`.

**Isomorphism Proof**: Both approaches insert identical rows. OR IGNORE handles duplicates identically.

**Expected Impact**: 5-10x faster ingestion for issues/MRs with many labels.

---

### 3. Clone in Embedding Batch Loop (ICE: 9.0)

**Location**: `src/embedding/pipeline.rs:165`

**Current Code**:
```rust
let texts: Vec<String> = batch.iter().map(|c| c.text.clone()).collect();
```

**Problem**: Every batch iteration clones all chunk texts. With BATCH_SIZE=32 and thousands of chunks, this doubles memory allocation in the hot path.

**Proposed Fix**: Transfer ownership instead of cloning:

```rust
// Option A: Drain chunks from all_chunks instead of iterating
let texts: Vec<String> = batch.into_iter().map(|c| c.text).collect();

// Option B: Store references in ChunkWork, clone only at API boundary
struct ChunkWork<'a> {
    text: &'a str,
    // ...
}
```

**Isomorphism Proof**: Same texts sent to Ollama, same embeddings returned. Order and content identical.

**Expected Impact**: 30-50% reduction in embedding pipeline memory allocation.

---

### 4. Correlated GROUP_CONCAT in List Queries (ICE: 8.5)

**Location**: `src/cli/commands/list.rs:341-348`

**Current Code**:
```sql
SELECT i.*,
       (SELECT GROUP_CONCAT(l.name, X'1F') FROM issue_labels il JOIN labels l ... WHERE il.issue_id = i.id) AS labels_csv,
       (SELECT COUNT(*) FROM discussions WHERE issue_id = i.id) as discussion_count
FROM issues i
```

**Problem**: Each correlated subquery executes per row. With LIMIT 50, that's 100+ subquery executions.

**Proposed Fix**: Use window functions or pre-aggregated CTEs:

```sql
WITH label_agg AS (
    SELECT il.issue_id, GROUP_CONCAT(l.name, X'1F') AS labels_csv
    FROM issue_labels il JOIN labels l ON il.label_id = l.id
    GROUP BY il.issue_id
),
discussion_agg AS (
    SELECT issue_id, COUNT(*) AS cnt
    FROM discussions WHERE issue_id IS NOT NULL
    GROUP BY issue_id
)
SELECT i.*, la.labels_csv, da.cnt
FROM issues i
LEFT JOIN label_agg la ON la.issue_id = i.id
LEFT JOIN discussion_agg da ON da.issue_id = i.id
WHERE ...
LIMIT 50
```

**Isomorphism Proof**: Same data returned - labels concatenated, discussion counts accurate. JOIN preserves NULL when no labels/discussions exist.

**Expected Impact**: 3-5x faster list queries with discussion/label data.

---

### 5. Multiple EXISTS Per Label Filter (ICE: 8.0)

**Location**: `src/search/filters.rs:100-107`

**Current Code**:
```sql
WHERE EXISTS (SELECT 1 ... AND label_name = ?)
  AND EXISTS (SELECT 1 ... AND label_name = ?)
  AND EXISTS (SELECT 1 ... AND label_name = ?)
```

**Problem**: Filtering by 3 labels generates 3 EXISTS subqueries. Each scans document_labels.

**Proposed Fix**: Single EXISTS with GROUP BY/HAVING:

```sql
WHERE EXISTS (
    SELECT 1 FROM document_labels dl
    WHERE dl.document_id = d.id
      AND dl.label_name IN (?, ?, ?)
    GROUP BY dl.document_id
    HAVING COUNT(DISTINCT dl.label_name) = 3
)
```

**Isomorphism Proof**: Both return documents with ALL specified labels. AND of EXISTS = document has label1 AND label2 AND label3. GROUP BY + HAVING COUNT(DISTINCT) = 3 is mathematically equivalent.

**Expected Impact**: 2-4x faster filtered search with multiple labels.

---

### 6. String Allocation in Chunking (ICE: 7.5)

**Location**: `src/embedding/chunking.rs:7-49`

**Current Code**:
```rust
chunks.push((chunk_index, remaining.to_string()));
```

**Problem**: Converts `&str` slices to owned `String` for every chunk. The input is already a `&str`.

**Proposed Fix**: Return borrowed slices or use `Cow`:

```rust
pub fn split_into_chunks(content: &str) -> Vec<(usize, &str)> {
    // Return slices into original content
}
```

Or if ownership is needed later:
```rust
pub fn split_into_chunks(content: &str) -> Vec<(usize, Cow<'_, str>)>
```

**Isomorphism Proof**: Same chunk boundaries, same text content. Only allocation behavior changes.

**Expected Impact**: Reduces allocations by ~50% in chunking hot path.

---

### 7. Multiple COUNT Queries (ICE: 7.0)

**Location**: `src/cli/commands/count.rs:44-56`

**Current Code**:
```rust
let count = conn.query_row("SELECT COUNT(*) FROM issues", ...)?;
let opened = conn.query_row("SELECT COUNT(*) FROM issues WHERE state = 'opened'", ...)?;
let closed = conn.query_row("SELECT COUNT(*) FROM issues WHERE state = 'closed'", ...)?;
```

**Problem**: 5 separate queries for MR state breakdown, 3 for issues.

**Proposed Fix**: Single query with CASE aggregation:

```sql
SELECT
    COUNT(*) AS total,
    SUM(CASE WHEN state = 'opened' THEN 1 ELSE 0 END) AS opened,
    SUM(CASE WHEN state = 'closed' THEN 1 ELSE 0 END) AS closed
FROM issues
```

**Isomorphism Proof**: Identical counts returned. CASE WHEN with SUM is standard SQL for conditional counting.

**Expected Impact**: 3-5x fewer round trips for count command.

---

### 8. Collect-then-Concat Pattern (ICE: 7.0)

**Location**: `src/documents/truncation.rs:60-61`

**Current Code**:
```rust
let formatted: Vec<String> = notes.iter().map(format_note).collect();
let total: String = formatted.concat();
```

**Problem**: Allocates intermediate Vec<String>, then allocates again for concat.

**Proposed Fix**: Use fold or format directly:

```rust
let total = notes.iter().fold(String::new(), |mut acc, note| {
    acc.push_str(&format_note(note));
    acc
});
```

Or with capacity hint:
```rust
let total_len: usize = notes.iter().map(|n| estimate_note_len(n)).sum();
let mut total = String::with_capacity(total_len);
for note in notes {
    total.push_str(&format_note(note));
}
```

**Isomorphism Proof**: Same concatenated string output. Order preserved.

**Expected Impact**: 50% reduction in allocations for document regeneration.

---

### 9. Box<dyn ToSql> Allocations (ICE: 6.0)

**Location**: `src/search/filters.rs:67-135`

**Current Code**:
```rust
let mut params: Vec<Box<dyn rusqlite::types::ToSql>> = vec![Box::new(ids_json)];
// ... more Box::new() calls
let param_refs: Vec<&dyn rusqlite::types::ToSql> = params.iter().map(|p| p.as_ref()).collect();
```

**Problem**: Boxing each parameter, then collecting references. Two allocations per parameter.

**Proposed Fix**: Use rusqlite's params! macro or typed parameter arrays:

```rust
// For known parameter counts, use arrays
let params: [&dyn ToSql; 4] = [&ids_json, &author, &state, &limit];

// Or build SQL with named parameters and use params! directly
```

**Expected Impact**: Eliminates ~15 allocations per filtered search.

---

### 10. Missing Vec::with_capacity (ICE: 5.5)

**Locations**:
- `src/embedding/pipeline.rs:106`
- `src/embedding/pipeline.rs:162`
- Multiple other locations

**Current Code**:
```rust
let mut all_chunks: Vec<ChunkWork> = Vec::new();
```

**Proposed Fix**:
```rust
// Estimate: average 3 chunks per document
let mut all_chunks = Vec::with_capacity(pending.len() * 3);
```

**Expected Impact**: Eliminates reallocation overhead during vector growth.

---

### 11. FTS Token Collect-Join (ICE: 5.0)

**Location**: `src/search/fts.rs:26-41`

**Current Code**:
```rust
let tokens: Vec<String> = trimmed.split_whitespace().map(...).collect();
tokens.join(" ")
```

**Proposed Fix**: Use itertools or avoid intermediate vec:

```rust
use itertools::Itertools;
trimmed.split_whitespace().map(...).join(" ")
```

**Expected Impact**: Minor - search queries are typically short.

---

### 12. Transformer String Clones (ICE: 5.0)

**Location**: `src/gitlab/transformers/merge_request.rs:51-77`

**Problem**: Multiple `.clone()` calls on String fields during transformation.

**Proposed Fix**: Use `std::mem::take()` where possible, or restructure to avoid cloning.

**Expected Impact**: Moderate - depends on MR volume.

---

## Regression Guardrails

For any optimization implemented:

1. **Test Coverage**: All existing tests must pass
2. **Output Equivalence**: For SQL changes, verify identical result sets with test data
3. **Benchmark Suite**: Add benchmarks for affected paths before/after

Suggested benchmark targets:
```rust
#[bench] fn bench_change_detection_1k_docs(b: &mut Bencher) { ... }
#[bench] fn bench_label_insert_50_labels(b: &mut Bencher) { ... }
#[bench] fn bench_hybrid_search_filtered(b: &mut Bencher) { ... }
```

---

## Implementation Priority

**Phase 1 (Quick Wins)** - COMPLETE:
1. ~~Change detection query rewrite (#1)~~ **DONE**
2. ~~Multiple COUNT consolidation (#7)~~ **DONE**
3. ~~Collect-concat pattern (#8)~~ **DONE**
4. ~~Vec::with_capacity hints (#10)~~ **DONE**
5. ~~FTS token collect-join (#11)~~ **DONE**
6. ~~Multiple EXISTS per label (#5)~~ **DONE**

**Phase 2 (Medium Effort)**:
5. Embedding batch clone removal (#3)
6. Label filter EXISTS consolidation (#5)
7. Chunking string allocation (#6)

**Phase 3 (Higher Effort)**:
8. N+1 batch inserts (#2)
9. List query CTEs (#4)
10. Parameter boxing (#9)

---

## Appendix: Test Baseline

```
cargo test --release
running 127 tests
test result: ok. 127 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out
```

All tests pass. Any optimization must maintain this baseline.
