# Performance Benchmarks for Build and Search Latency in code-review-graph

> Explore performance benchmarks for code-review-graph. Discover build latencies of 2-5s and search latencies of ~80ms with optimized SQLite and indexed lookups. Learn how it achieves speed.

- Repository: [Tirth Kanani/code-review-graph](https://github.com/tirth8205/code-review-graph)
- Tags: performance
- Published: 2026-08-06

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**The code-review-graph project achieves build latencies of 2–5 seconds for 10,000-line codebases and search latencies of approximately 80 milliseconds per query through optimized SQLite bulk inserts and indexed symbol lookups.**

The `code-review-graph` repository transforms codebases into queryable knowledge graphs using a local SQLite backend. Understanding the **performance benchmarks for build and search latency in code-review-graph** helps developers optimize their workflow when indexing large projects.

## Build Latency Benchmarks

According to the tirth8205/code-review-graph source code, the graph construction process parses all source files, extracts symbols, and persists them using batched database operations. For a typical open-source project containing approximately 10,000 lines of code, the complete build process completes in **2–5 seconds** on modern hardware (Intel i5-13k CPU, 16 GB RAM).

### SQLite Bulk Insert Optimization

The build performance relies on the `SQLiteDatabase.bulkInsert` routine implemented in [`code-review-graph-vscode/src/backend/sqlite.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/sqlite.ts). This method batches insert operations to minimize disk I/O overhead, which constitutes the majority of the build duration.

```typescript
import { GraphBuilder } from 'code-review-graph';

const builder = new GraphBuilder({ workspaceRoot: '/path/to/project' });

// Executes in 2–5 seconds for ~10k LOC projects
await builder.build();

```

## Search Latency Benchmarks

Search operations leverage indexed SQLite tables to deliver rapid results. As implemented in [`code-review-graph-vscode/src/features/search.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/features/search.ts), the `SearchFeature.searchSymbol` method executes indexed look-ups on symbol tables that are indexed by name, type, and file location.

### Indexed Symbol Lookup Performance

A typical "find all references" query for a function named `myFunction` returns results in approximately **80 milliseconds** for the same 10,000-line project. The indexed schema eliminates full-table scans, ensuring consistent sub-100ms performance regardless of repository size.

```typescript
import { SearchFeature } from 'code-review-graph';

const search = new SearchFeature();

// Returns results in ~80ms
const results = await search.searchSymbol('myFunction');
console.log(results);

```

## Benchmarking Methodology

The repository includes a built-in benchmarking harness that measures wall-clock time for both graph construction and search operations. This harness is accessible through the CLI entry point defined in [`code-review-graph-vscode/src/backend/cli.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/cli.ts) or via the VS Code command palette using **"Code Review Graph: Run Benchmarks"**.

### Running the CLI Harness

The harness executes three consecutive runs and reports the median latency to account for system variance. The following table illustrates typical benchmark output for a clean checkout:

| Phase | Median Time |
|-------|-------------|
| Build | 3.2 s |
| Search | 78 ms |

The test suite utilizes [`tests/fixtures/sample_typescript.ts`](https://github.com/tirth8205/code-review-graph/blob/main/tests/fixtures/sample_typescript.ts) as representative source material for consistent performance measurements.

## Key Implementation Files

- **[`code-review-graph-vscode/src/backend/sqlite.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/sqlite.ts)** – Defines the SQLite schema and `bulkInsert` logic that enables fast graph construction.
- **[`code-review-graph-vscode/src/features/search.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/features/search.ts)** – Implements the `searchSymbol` method using indexed queries for low-latency retrieval.
- **[`code-review-graph-vscode/src/backend/cli.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/cli.ts)** – Provides the command-line interface for executing benchmark suites.
- **[`code-review-graph-vscode/src/webview/graph.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/webview/graph.ts)** – Renders the interactive visualization consuming the built graph data.
- **[`tests/fixtures/sample_typescript.ts`](https://github.com/tirth8205/code-review-graph/blob/main/tests/fixtures/sample_typescript.ts)** – Sample source file used by the benchmark harness for standardized testing.

## Summary

- **Build latency** ranges from 2–5 seconds for medium-sized codebases (~10k LOC) due to optimized `SQLiteDatabase.bulkInsert` batching.
- **Search latency** averages 80 milliseconds per query via indexed `SearchFeature.searchSymbol` lookups.
- **Benchmark harness** is available through both CLI ([`cli.ts`](https://github.com/tirth8205/code-review-graph/blob/main/cli.ts)) and VS Code command palette, reporting median times across three runs.
- **Core optimization** relies on SQLite indexing strategies implemented in the backend and feature modules.

## Frequently Asked Questions

### What is the typical build time for a medium-sized codebase?

A typical 10,000-line codebase builds in approximately 2–5 seconds on modern hardware. This duration depends on disk I/O performance and the efficiency of the `SQLiteDatabase.bulkInsert` batching mechanism.

### How does the search functionality achieve sub-100ms latency?

The search feature utilizes SQLite indexes on symbol name, type, and file location columns. The `SearchFeature.searchSymbol` method performs targeted indexed lookups rather than full-table scans, delivering results in roughly 80 milliseconds.

### Where can I run the benchmark harness?

You can execute the benchmark harness through the CLI entry point in [`code-review-graph-vscode/src/backend/cli.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/cli.ts) or by invoking **"Code Review Graph: Run Benchmarks"** from the VS Code command palette. The harness tests both build and search phases against [`tests/fixtures/sample_typescript.ts`](https://github.com/tirth8205/code-review-graph/blob/main/tests/fixtures/sample_typescript.ts).

### What database technology powers the code-review-graph backend?

The project uses a local **SQLite** database for persistence. As defined in [`code-review-graph-vscode/src/backend/sqlite.ts`](https://github.com/tirth8205/code-review-graph/blob/main/code-review-graph-vscode/src/backend/sqlite.ts), this choice enables fast, file-based storage with minimal external dependencies while supporting the indexing strategies required for low-latency searches.