⚡️ Speed up method Response.json by 9%

[codeflash-ai]

📄 9% (0.09x) speedup for Response.json in marimo/_utils/requests.py

⏱️ Runtime : 917 microseconds → 839 microseconds (best of 182 runs)

📝 Explanation and details

The optimization achieves a 9% speedup by eliminating an unnecessary intermediate method call in the json() method and improving line ending normalization in the text() method.

Key optimizations:

1. Direct UTF-8 decoding in json(): The original code called self.text() which performed UTF-8 decoding plus line ending normalization, but json.loads() doesn't need normalized line endings. The optimized version calls self.content.decode("utf-8") directly, avoiding the overhead of the replace() operations.

2. More efficient line ending normalization in text(): Replaced two sequential replace() calls with splitlines() followed by join(). The splitlines() method handles all line ending variants (\r\n, \r, \n) in a single pass, which is more efficient than multiple string replacements.

Performance impact by test case:

• Small JSON objects: 8-18% faster (most common use case)
• Large strings: Up to 36% faster due to avoiding redundant line ending processing
• Large nested structures: 4-6% faster
• Error cases: 4-10% faster due to reduced overhead before exceptions

The optimization is particularly effective for JSON parsing workloads where line ending normalization is unnecessary, and for text processing where multiple line ending types need to be normalized. Since json() is likely called more frequently than text() in typical HTTP response processing, the direct decoding approach provides consistent performance gains across diverse JSON content types.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 31 Passed
🌀 Generated Regression Tests	✅ 108 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
_utils/test_utils_request.py::test_response_object	5.04μs	4.68μs	7.76%✅

🌀 Generated Regression Tests and Runtime

import json # used for generating JSON content for tests

imports

import pytest # used for our unit tests
from marimo._utils.requests import Response

unit tests

1. Basic Test Cases

def test_json_basic_dict():
# Test with a simple JSON object
data = {"a": 1, "b": "test"}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.13μs -> 4.34μs (18.0% faster)

def test_json_basic_list():
# Test with a simple JSON array
data = [1, 2, 3, "abc"]
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 4.49μs -> 3.98μs (12.9% faster)

def test_json_basic_string():
# Test with a JSON string
data = "hello world"
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 4.16μs -> 3.69μs (12.6% faster)

def test_json_basic_number():
# Test with a JSON number
data = 12345
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 4.00μs -> 3.68μs (8.61% faster)

def test_json_basic_boolean_null():
# Test with JSON booleans and null
for value in [True, False, None]:
content = json.dumps(value).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 7.11μs -> 6.47μs (9.88% faster)

2. Edge Test Cases

def test_json_empty_object():
# Test with empty JSON object
content = b"{}"
resp = Response(200, content, {})
codeflash_output = resp.json() # 3.94μs -> 3.65μs (7.92% faster)

def test_json_empty_array():
# Test with empty JSON array
content = b"[]"
resp = Response(200, content, {})
codeflash_output = resp.json() # 3.99μs -> 3.74μs (6.60% faster)

def test_json_empty_string():
# Test with empty JSON string
content = b'""'
resp = Response(200, content, {})
codeflash_output = resp.json() # 4.13μs -> 3.62μs (14.2% faster)

def test_json_whitespace_only():
# Test with whitespace before/after JSON
content = b" \n\t {"x": 1} \r\n"
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.55μs -> 4.50μs (23.3% faster)

def test_json_line_endings_normalization():
# Test with Windows and old Mac line endings in JSON
content = b'{\r\n"a"\r: 1\r\n}'
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.43μs -> 4.44μs (22.3% faster)

def test_json_utf8_characters():
# Test with non-ASCII UTF-8 characters
data = {"emoji": "😀", "accented": "café"}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.78μs -> 5.49μs (5.39% faster)

def test_json_invalid_json_raises():
# Test with invalid JSON should raise JSONDecodeError
content = b"{not valid json}"
resp = Response(200, content, {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 9.13μs -> 8.40μs (8.73% faster)

def test_json_invalid_utf8_raises():
# Test with invalid UTF-8 bytes should raise UnicodeDecodeError
content = b"\xff\xfe\xfa"
resp = Response(200, content, {})
with pytest.raises(UnicodeDecodeError):
resp.json() # 3.21μs -> 3.06μs (4.70% faster)

def test_json_with_bom():
# Test with UTF-8 BOM at start
bom = b"\xef\xbb\xbf"
data = {"bom": True}
content = bom + json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
# json.loads can handle BOM, but text() will include BOM
codeflash_output = resp.json()

def test_json_null_byte_in_content():
# Test with null byte in content (valid in JSON string)
data = {"x": "\u0000"}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 6.86μs -> 5.97μs (14.8% faster)

def test_json_content_with_trailing_comma_error():
# Test with trailing comma (invalid in JSON)
content = b'{"a": 1,}'
resp = Response(200, content, {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 10.1μs -> 9.45μs (6.76% faster)

def test_json_content_with_comments_error():
# Test with comments (invalid in JSON)
content = b'{ "a": 1 // comment }'
resp = Response(200, content, {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 8.89μs -> 8.25μs (7.81% faster)

def test_json_content_with_extra_data_error():
# Test with extra data after valid JSON
content = b'{"a": 1} extra'
resp = Response(200, content, {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 8.52μs -> 7.76μs (9.85% faster)

def test_json_content_with_leading_and_trailing_newlines():
# Test with newlines before and after JSON
content = b"\n\n{"a": 1}\n\n"
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.43μs -> 4.82μs (12.7% faster)

def test_json_content_with_tab_indentation():
# Test with tab-indented JSON
content = b'{\n\t"a": 1\n}'
resp = Response(200, content, {})
codeflash_output = resp.json() # 5.04μs -> 4.50μs (11.9% faster)

def test_json_content_with_large_numbers():
# Test with very large numbers
data = {"big": 2**60}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 4.91μs -> 4.38μs (12.1% faster)

def test_json_content_with_float_inf_nan_error():
# Test with float values not supported by JSON (inf, nan)
content = b'{"a": NaN, "b": Infinity}'
resp = Response(200, content, {})
with pytest.raises(json.JSONDecodeError):
resp.json()

3. Large Scale Test Cases

def test_json_large_list():
# Test with a large list of integers
data = list(range(1000))
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 37.9μs -> 35.8μs (6.06% faster)

def test_json_large_dict():
# Test with a large dict of integer keys and values
data = {str(i): i for i in range(1000)}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 111μs -> 109μs (1.87% faster)

def test_json_large_nested_structure():
# Test with a deeply nested structure
data = {"a": [{"b": [i for i in range(100)]}] * 10}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 40.5μs -> 38.6μs (4.89% faster)

def test_json_large_string():
# Test with a very large string
data = "x" * 10000
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 13.6μs -> 10.8μs (26.1% faster)

def test_json_large_mixed_types():
# Test with a large dict with mixed types
data = {
"ints": list(range(100)),
"strs": ["test"] * 100,
"bools": [True, False] * 50,
"dicts": [{"x": i} for i in range(100)],
"nested": {"y": [i for i in range(100)]}
}
content = json.dumps(data).encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 29.6μs -> 28.1μs (5.63% faster)

def test_json_large_content_with_line_endings():
# Test with large JSON content with Windows line endings
data = [{"x": i, "y": "val"} for i in range(500)]
json_str = json.dumps(data, indent=2)
# Replace all newlines with Windows style
content = json_str.replace("\n", "\r\n").encode("utf-8")
resp = Response(200, content, {})
codeflash_output = resp.json() # 115μs -> 85.0μs (36.3% faster)

def test_json_large_content_with_leading_trailing_whitespace():
# Test with large JSON content and lots of whitespace
data = [i for i in range(500)]
content = (b"\n" * 50) + json.dumps(data).encode("utf-8") + (b" " * 50)
resp = Response(200, content, {})
codeflash_output = resp.json() # 22.2μs -> 20.7μs (6.99% faster)

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

#------------------------------------------------
import json

imports

import pytest # used for our unit tests
from marimo._utils.requests import Response

function to test

(provided above, so not repeated here)

unit tests

--- Basic Test Cases ---

def test_json_with_simple_dict():
# Test parsing a simple JSON object
data = {"key": "value", "number": 123}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 5.58μs -> 5.10μs (9.47% faster)

def test_json_with_simple_list():
# Test parsing a simple JSON array
data = [1, 2, 3, "a", "b"]
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 4.93μs -> 4.35μs (13.2% faster)

def test_json_with_nested_structure():
# Test parsing nested JSON objects and arrays
data = {
"a": [1, {"b": 2}],
"c": {"d": [3, 4]}
}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 5.58μs -> 4.93μs (13.2% faster)

def test_json_with_boolean_and_null():
# Test parsing booleans and null values
data = {"ok": True, "fail": False, "none": None}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 4.81μs -> 4.39μs (9.50% faster)

def test_json_with_empty_object():
# Test parsing an empty JSON object
data = {}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 3.95μs -> 3.51μs (12.6% faster)

def test_json_with_empty_array():
# Test parsing an empty JSON array
data = []
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 3.90μs -> 3.54μs (10.2% faster)

--- Edge Test Cases ---

def test_json_with_non_utf8_bytes():
# Test bytes that are valid JSON but not UTF-8 encoded
# Should raise UnicodeDecodeError
data = {"key": "value"}
# Latin-1 encoding will break the decode step if non-ascii is present
bad_bytes = json.dumps({"key": "välue"}).encode("latin-1")
resp = Response(200, bad_bytes, {})
with pytest.raises(UnicodeDecodeError):
resp.json()

def test_json_with_invalid_json():
# Test invalid JSON content
invalid_json = b"{not: valid,}"
resp = Response(200, invalid_json, {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 11.7μs -> 10.6μs (9.87% faster)

def test_json_with_empty_content():
# Test empty content
resp = Response(200, b"", {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 8.19μs -> 7.84μs (4.44% faster)

def test_json_with_whitespace_content():
# Test content that is only whitespace
resp = Response(200, b" \n\t", {})
with pytest.raises(json.JSONDecodeError):
resp.json() # 7.86μs -> 7.35μs (6.90% faster)

def test_json_with_content_with_line_endings():
# Test content with Windows and Mac line endings
data = {"a": "line1\r\nline2\rline3\nline4"}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
# The text() function normalizes all line endings to \n
codeflash_output = resp.json(); parsed = codeflash_output # 6.07μs -> 5.60μs (8.36% faster)

def test_json_with_large_numbers():
# Test parsing large integers and floats
data = {"bigint": 2**62, "bigfloat": 1.79e308}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json(); result = codeflash_output # 7.40μs -> 6.88μs (7.62% faster)

def test_json_with_unicode_characters():
# Test parsing Unicode characters
data = {"emoji": "😊", "chinese": "汉字", "arabic": "مرحبا"}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 6.22μs -> 5.65μs (10.1% faster)

def test_json_with_escape_sequences():
# Test parsing JSON with escape sequences
data = {"quote": """, "backslash": "\", "newline": "\n"}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 5.25μs -> 4.68μs (12.4% faster)

def test_json_with_trailing_whitespace():
# Test JSON with trailing whitespace after the object
data = {"a": 1}
content = json.dumps(data) + " \n"
resp = Response(200, content.encode("utf-8"), {})
codeflash_output = resp.json() # 4.69μs -> 4.18μs (12.2% faster)

def test_json_with_non_ascii_keys():
# Test JSON with non-ASCII keys
data = {"ключ": "значение", "キー": "値"}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 5.74μs -> 5.05μs (13.6% faster)

def test_json_with_original_error_set():
# original_error should not affect json parsing
data = {"x": 1}
resp = Response(200, json.dumps(data).encode("utf-8"), {}, original_error=ValueError("fail"))
codeflash_output = resp.json() # 4.50μs -> 4.08μs (10.4% faster)

--- Large Scale Test Cases ---

def test_json_with_large_list():
# Test parsing a large list of integers
data = list(range(1000))
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 35.7μs -> 33.7μs (6.00% faster)

def test_json_with_large_dict():
# Test parsing a large dictionary
data = {str(i): i for i in range(1000)}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 111μs -> 108μs (2.64% faster)

def test_json_with_large_nested_structure():
# Test parsing a large nested structure
data = {"outer": [{"inner": list(range(100))} for _ in range(10)]}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 40.0μs -> 38.4μs (4.29% faster)

def test_json_with_large_string():
# Test parsing a large string value
large_str = "a" * 5000
data = {"text": large_str}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 9.39μs -> 7.70μs (22.1% faster)

def test_json_with_multiple_types_in_large_list():
# Test parsing a large list with mixed types
data = []
for i in range(500):
data.append(i)
data.append(str(i))
data.append({"num": i})
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json() # 94.0μs -> 89.8μs (4.61% faster)

def test_json_performance_large_scale():
# Test that parsing a large JSON structure completes within reasonable time
# (Not a strict performance test, but ensures no infinite loops)
data = {"data": [list(range(100)) for _ in range(10)]}
resp = Response(200, json.dumps(data).encode("utf-8"), {})
codeflash_output = resp.json(); result = codeflash_output # 38.4μs -> 36.6μs (4.70% faster)

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-Response.json-mhvi2vid and push.