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@codeflash-ai codeflash-ai bot commented Oct 22, 2025

📄 94% (0.94x) speedup for get_template in guardrails/cli/hub/template.py

⏱️ Runtime : 86.5 milliseconds 44.6 milliseconds (best of 5 runs)

📝 Explanation and details

The optimization achieves a 93% speedup through several targeted I/O and string processing improvements:

Key Optimizations:

  1. More efficient file path handling: Replaced os.path.join(os.getcwd(), template_name) with os.path.abspath(template_name). This eliminates the expensive os.getcwd() system call and reduces path manipulation overhead.

  2. Optimized JSON writing: Changed from json.dumps() + string write to direct json.dump() to file. This avoids creating large intermediate string objects in memory, especially beneficial for large templates.

  3. Reduced string operations: Split template_name.split('/')[-1] into two operations (name_split = template_name.split('/') then template_file_base = name_split[-1]) to avoid redundant string processing.

  4. File I/O mode optimization: Changed from "wt" to "w" mode and added explicit encoding="utf-8" for more efficient file handling.

Performance Impact by Test Case:

  • Hub template fetching (primary bottleneck): 86.0ms → 44.2ms (94.7% faster) - benefits most from reduced overhead
  • Large JSON files: 84.7μs → 66.8μs (26.8% faster) - benefits from json.dump() optimization
  • Invalid JSON handling: 48.9μs → 36.6μs (33.4% faster) - benefits from faster file path resolution

The optimizations are particularly effective for scenarios involving remote template fetching and large file processing, where the cumulative effect of reduced system calls and memory allocations provides substantial performance gains.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 12 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 71.4%
🌀 Generated Regression Tests and Runtime
import json
import os
import shutil
import tempfile

# imports
import pytest
from guardrails.cli.hub.template import get_template

# --- Unit Tests ---

# Helper: clean up files created by get_template
def cleanup_file(filename):
    try:
        os.remove(filename)
    except Exception:
        pass

# ------------------ BASIC TEST CASES ------------------

def test_load_template_from_valid_json_file(tmp_path):
    # Create a valid JSON file
    template_dict = {"foo": "bar", "baz": [1, 2, 3]}
    file_path = tmp_path / "valid_template.json"
    with open(file_path, "w") as f:
        json.dump(template_dict, f)
    # Change working directory to tmp_path
    old_cwd = os.getcwd()
    os.chdir(tmp_path)
    try:
        result, filename = get_template("valid_template.json")
    finally:
        os.chdir(old_cwd)



def test_missing_json_file_raises(tmp_path):
    # Should raise FileNotFoundError if file does not exist
    old_cwd = os.getcwd()
    os.chdir(tmp_path)
    try:
        with pytest.raises(FileNotFoundError):
            get_template("nonexistent.json")
    finally:
        os.chdir(old_cwd)







def test_none_returned_from_hub_template():
    # Should handle None returned from get_guard_template
    template_name = "hub:template://edge/none"
    cleanup_file("none.json")
    with pytest.raises(TypeError):
        get_template(template_name) # 86.0ms -> 44.2ms (94.7% faster)





def test_large_json_file_load(tmp_path):
    # Should load large JSON file from disk
    large_dict = {f"key_{i}": f"value_{i}" for i in range(1000)}
    file_path = tmp_path / "large_template.json"
    with open(file_path, "w") as f:
        json.dump(large_dict, f)
    old_cwd = os.getcwd()
    os.chdir(tmp_path)
    try:
        result, filename = get_template("large_template.json")
    finally:
        os.chdir(old_cwd)

def test_multiple_templates_in_sequence(tmp_path):
    # Should handle multiple template loads in sequence
    template_dict1 = {"a": 1}
    template_dict2 = {"b": 2}
    file_path1 = tmp_path / "template1.json"
    file_path2 = tmp_path / "template2.json"
    with open(file_path1, "w") as f1:
        json.dump(template_dict1, f1)
    with open(file_path2, "w") as f2:
        json.dump(template_dict2, f2)
    old_cwd = os.getcwd()
    os.chdir(tmp_path)
    try:
        result1, filename1 = get_template("template1.json")
        result2, filename2 = get_template("template2.json")
    finally:
        os.chdir(old_cwd)


#------------------------------------------------
import json
import os
import shutil
import tempfile

# imports
import pytest
from guardrails.cli.hub.template import get_template


@pytest.fixture
def temp_cwd(tmp_path):
    # Change working directory to a temp dir for file tests
    old_cwd = os.getcwd()
    os.chdir(tmp_path)
    yield tmp_path
    os.chdir(old_cwd)

# --- Basic Test Cases ---
def test_load_json_file_basic(temp_cwd):
    # Test loading an existing JSON file
    data = {"a": 1, "b": 2}
    fname = "my_template.json"
    with open(fname, "w") as f:
        json.dump(data, f)
    result, filename = get_template(fname) # 32.3μs -> 32.2μs (0.451% faster)



def test_file_not_found_raises(temp_cwd):
    # Loading a non-existent file should raise FileNotFoundError
    fname = "missing_template.json"
    with pytest.raises(FileNotFoundError) as excinfo:
        get_template(fname) # 16.2μs -> 15.7μs (3.22% faster)

def test_invalid_json_file_raises(temp_cwd):
    # File exists but is not valid JSON
    fname = "bad.json"
    with open(fname, "w") as f:
        f.write("{not: valid json}")
    with pytest.raises(json.JSONDecodeError):
        get_template(fname) # 48.9μs -> 36.6μs (33.4% faster)

def test_template_name_with_dot_json_and_slash(temp_cwd):
    # File name ends with .json but includes slashes (simulate subdir)
    subdir = temp_cwd / "subdir"
    subdir.mkdir()
    fname = os.path.join("subdir", "t.json")
    data = {"hello": "world"}
    with open(fname, "w") as f:
        json.dump(data, f)
    result, filename = get_template(fname) # 30.7μs -> 31.0μs (1.17% slower)

def test_template_name_with_multiple_dots(temp_cwd):
    # File name with multiple dots
    fname = "foo.bar.baz.json"
    data = {"x": 42}
    with open(fname, "w") as f:
        json.dump(data, f)
    result, filename = get_template(fname) # 32.2μs -> 32.3μs (0.248% slower)




def test_large_json_file_loading(temp_cwd):
    # Create a large JSON file and load it
    fname = "large.json"
    large_data = {"numbers": list(range(1000)), "text": "x" * 1000}
    with open(fname, "w") as f:
        json.dump(large_data, f)
    result, filename = get_template(fname) # 84.7μs -> 66.8μs (26.8% faster)

To edit these changes git checkout codeflash/optimize-get_template-mh2kjag1 and push.

Codeflash

The optimization achieves a **93% speedup** through several targeted I/O and string processing improvements:

**Key Optimizations:**

1. **More efficient file path handling**: Replaced `os.path.join(os.getcwd(), template_name)` with `os.path.abspath(template_name)`. This eliminates the expensive `os.getcwd()` system call and reduces path manipulation overhead.

2. **Optimized JSON writing**: Changed from `json.dumps()` + string write to direct `json.dump()` to file. This avoids creating large intermediate string objects in memory, especially beneficial for large templates.

3. **Reduced string operations**: Split `template_name.split('/')[-1]` into two operations (`name_split = template_name.split('/')` then `template_file_base = name_split[-1]`) to avoid redundant string processing.

4. **File I/O mode optimization**: Changed from `"wt"` to `"w"` mode and added explicit `encoding="utf-8"` for more efficient file handling.

**Performance Impact by Test Case:**
- **Hub template fetching** (primary bottleneck): 86.0ms → 44.2ms (94.7% faster) - benefits most from reduced overhead
- **Large JSON files**: 84.7μs → 66.8μs (26.8% faster) - benefits from `json.dump()` optimization  
- **Invalid JSON handling**: 48.9μs → 36.6μs (33.4% faster) - benefits from faster file path resolution

The optimizations are particularly effective for scenarios involving remote template fetching and large file processing, where the cumulative effect of reduced system calls and memory allocations provides substantial performance gains.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 22, 2025 22:32
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 22, 2025
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