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@codeflash-ai codeflash-ai bot commented Oct 30, 2025

📄 18% (0.18x) speedup for derive_keys in electrum/plugins/digitalbitbox/digitalbitbox.py

⏱️ Runtime : 7.73 milliseconds 6.54 milliseconds (best of 41 runs)

📝 Explanation and details

The optimization focuses on eliminating unnecessary function call overhead and object creation in the sha256d function, which performs double SHA256 hashing.

Key Changes:

  • Direct hashlib usage: Instead of calling the wrapped sha256() function twice with bytes(sha256(sha256(x))), the optimized version uses hashlib.sha256() directly and chains the operations as hashlib.sha256(first).digest().
  • Eliminated intermediate bytes conversion: The original code wrapped the result in bytes() unnecessarily, as hashlib.sha256().digest() already returns bytes.
  • Added hashlib import: Added direct import of hashlib to avoid module lookup overhead.

Why This Is Faster:

  1. Reduced function call overhead: Eliminates two calls to the wrapper sha256() function, replacing them with direct hashlib.sha256() calls
  2. Eliminated unnecessary object creation: Removes the redundant bytes() wrapper around the final result
  3. Cleaner execution path: The double hash operation now has a more direct execution flow without intermediate function dispatching

Performance Results:
The line profiler shows the critical sha256d function improved from 15.7ms to 8.9ms (43% faster), with the main bottleneck line going from 11.4ms to 5.4ms. Test results consistently show 13-27% improvements across various input types, with particularly strong gains on basic operations and repeated calls. The optimization is most effective for workloads with frequent hashing operations, as evidenced by the 18-19% improvements in batch processing tests.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 4129 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import hashlib

# imports
import pytest  # used for our unit tests
from electrum.plugins.digitalbitbox.digitalbitbox import derive_keys

# unit tests

# ========== Basic Test Cases ==========

def test_derive_keys_basic_bytes():
    # Test with basic bytes input
    input_bytes = b"hello world"
    k1, k2 = derive_keys(input_bytes) # 7.12μs -> 5.60μs (27.2% faster)
    # Check determinism
    k1b, k2b = derive_keys(input_bytes) # 2.23μs -> 1.86μs (19.6% faster)

def test_derive_keys_basic_str():
    # Test with basic string input
    input_str = "hello world"
    k1, k2 = derive_keys(input_str) # 5.09μs -> 4.32μs (17.9% faster)
    # Check determinism
    k1b, k2b = derive_keys(input_str) # 2.37μs -> 1.95μs (21.5% faster)

def test_derive_keys_diff_inputs():
    # Test that different inputs produce different outputs
    k1a, k2a = derive_keys("foo") # 4.98μs -> 4.33μs (15.1% faster)
    k1b, k2b = derive_keys("bar") # 2.43μs -> 2.00μs (21.5% faster)

def test_derive_keys_unicode():
    # Test with unicode string input
    input_str = "héllö wørld"
    k1, k2 = derive_keys(input_str) # 5.42μs -> 4.78μs (13.3% faster)



def test_derive_keys_long_string():
    # Test with a long string input (edge case)
    input_str = "a" * 1000
    k1, k2 = derive_keys(input_str) # 6.11μs -> 5.40μs (13.2% faster)

def test_derive_keys_long_bytes():
    # Test with a long bytes input (edge case)
    input_bytes = b"\xff" * 1000
    k1, k2 = derive_keys(input_bytes) # 5.52μs -> 4.79μs (15.2% faster)

def test_derive_keys_non_ascii_bytes():
    # Test with non-ASCII bytes input
    input_bytes = b"\x00\xff\x10\x80"
    k1, k2 = derive_keys(input_bytes) # 5.14μs -> 4.41μs (16.7% faster)

def test_derive_keys_null_bytes():
    # Test with null bytes input
    input_bytes = b"\x00" * 32
    k1, k2 = derive_keys(input_bytes) # 4.74μs -> 4.47μs (6.05% faster)




def test_derive_keys_memoryview_input():
    # Test with memoryview input (should work if underlying is bytes)
    mv = memoryview(b"hello world")
    k1, k2 = derive_keys(mv.tobytes()) # 10.8μs -> 9.57μs (12.7% faster)

# ========== Large Scale Test Cases ==========

def test_derive_keys_many_unique_inputs():
    # Test performance and determinism for many unique inputs
    results = set()
    for i in range(1000):
        k1, k2 = derive_keys(str(i)) # 1.86ms -> 1.58ms (18.0% faster)
        results.add((k1, k2))

def test_derive_keys_large_data():
    # Test with near-limit large data input
    input_bytes = b"a" * 999
    k1, k2 = derive_keys(input_bytes) # 8.95μs -> 8.51μs (5.21% faster)


def test_derive_keys_collision_resistance():
    # Test that similar inputs do not collide
    base = "base_input"
    results = set()
    for i in range(1000):
        k1, k2 = derive_keys(base + str(i)) # 1.87ms -> 1.58ms (18.3% faster)
        results.add((k1, k2))
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import hashlib

# imports
import pytest  # used for our unit tests
from electrum.plugins.digitalbitbox.digitalbitbox import derive_keys

# unit tests

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

def test_derive_keys_basic_str():
    # Test with a simple string input
    key1, key2 = derive_keys("hello") # 8.96μs -> 8.05μs (11.3% faster)

def test_derive_keys_basic_bytes():
    # Test with a simple bytes input
    key1, key2 = derive_keys(b"hello") # 5.07μs -> 4.47μs (13.4% faster)

def test_derive_keys_consistency():
    # The same input should always yield the same output
    inp = "testinput"
    k1a, k2a = derive_keys(inp) # 5.39μs -> 4.69μs (14.9% faster)
    k1b, k2b = derive_keys(inp) # 2.42μs -> 1.95μs (23.6% faster)

def test_derive_keys_difference():
    # Different inputs should yield different outputs
    k1, k2 = derive_keys("input1") # 5.18μs -> 4.40μs (17.8% faster)
    l1, l2 = derive_keys("input2") # 2.35μs -> 2.04μs (15.0% faster)

# ---------------- EDGE TEST CASES ----------------



def test_derive_keys_unicode():
    # Test with unicode characters
    inp = "你好世界"
    key1, key2 = derive_keys(inp) # 10.7μs -> 9.88μs (8.14% faster)

def test_derive_keys_long_string():
    # Test with a long string (256 chars)
    inp = "a" * 256
    key1, key2 = derive_keys(inp) # 6.21μs -> 5.47μs (13.4% faster)

def test_derive_keys_long_bytes():
    # Test with a long bytes object (256 bytes)
    inp = b"\xff" * 256
    key1, key2 = derive_keys(inp) # 5.91μs -> 4.95μs (19.3% faster)

def test_derive_keys_non_ascii_bytes():
    # Test with non-ASCII bytes
    inp = b"\x00\x01\x02\xfe\xff"
    key1, key2 = derive_keys(inp) # 5.45μs -> 4.49μs (21.4% faster)



def test_derive_keys_mutation_sensitivity():
    # Changing one byte should change the output
    inp1 = b"abcdef"
    inp2 = b"abcdeg"
    k1a, k2a = derive_keys(inp1) # 11.0μs -> 9.74μs (12.4% faster)
    k1b, k2b = derive_keys(inp2) # 2.45μs -> 2.07μs (18.2% faster)


def test_derive_keys_large_string():
    # Test with a large string (1000 chars)
    inp = "z" * 1000
    key1, key2 = derive_keys(inp) # 11.8μs -> 10.8μs (9.77% faster)

def test_derive_keys_large_bytes():
    # Test with a large bytes object (1000 bytes)
    inp = b"\x01" * 1000
    key1, key2 = derive_keys(inp) # 6.38μs -> 5.65μs (12.8% faster)

def test_derive_keys_batch_unique():
    # Test that keys are unique for 1000 distinct inputs
    keys = set()
    for i in range(1000):
        k1, k2 = derive_keys(str(i)) # 1.86ms -> 1.57ms (18.1% faster)
        keys.add((k1, k2))

def test_derive_keys_batch_performance():
    # Test performance for 1000 calls (should not timeout)
    for i in range(1000):
        derive_keys(b"x" + bytes([i % 256])) # 1.78ms -> 1.50ms (19.1% faster)
    # If we reach here, performance is acceptable

def test_derive_keys_collision_resistance():
    # Test that similar inputs do not collide
    inp_base = "baseinput"
    outputs = set()
    for i in range(100):
        inp = inp_base + str(i)
        outputs.add(derive_keys(inp)) # 195μs -> 165μs (18.1% 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-derive_keys-mhctiwbk and push.

Codeflash Static Badge

The optimization focuses on eliminating unnecessary function call overhead and object creation in the `sha256d` function, which performs double SHA256 hashing.

**Key Changes:**
- **Direct hashlib usage**: Instead of calling the wrapped `sha256()` function twice with `bytes(sha256(sha256(x)))`, the optimized version uses `hashlib.sha256()` directly and chains the operations as `hashlib.sha256(first).digest()`.
- **Eliminated intermediate bytes conversion**: The original code wrapped the result in `bytes()` unnecessarily, as `hashlib.sha256().digest()` already returns bytes.
- **Added hashlib import**: Added direct import of `hashlib` to avoid module lookup overhead.

**Why This Is Faster:**
1. **Reduced function call overhead**: Eliminates two calls to the wrapper `sha256()` function, replacing them with direct `hashlib.sha256()` calls
2. **Eliminated unnecessary object creation**: Removes the redundant `bytes()` wrapper around the final result
3. **Cleaner execution path**: The double hash operation now has a more direct execution flow without intermediate function dispatching

**Performance Results:**
The line profiler shows the critical `sha256d` function improved from 15.7ms to 8.9ms (43% faster), with the main bottleneck line going from 11.4ms to 5.4ms. Test results consistently show 13-27% improvements across various input types, with particularly strong gains on basic operations and repeated calls. The optimization is most effective for workloads with frequent hashing operations, as evidenced by the 18-19% improvements in batch processing tests.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 30, 2025 02:41
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 30, 2025
codeflash-ai bot added a commit that referenced this pull request Nov 13, 2025
The optimization replaces a conditional check-then-access pattern with a try-except approach, achieving a **24% speedup** by eliminating redundant dictionary lookups.

**Key Changes:**
- **Original**: Uses `if key in self:` followed by `self[key]` - this performs **two** dictionary lookups for existing keys
- **Optimized**: Uses `try: self.__data[key]` with exception handling - this performs only **one** dictionary lookup

**Why It's Faster:**
1. **Eliminates redundant lookups**: The original code does `key in self` (lookup #1) then `self[key]` (lookup #2) for existing keys. The optimized version accesses `self.__data[key]` directly (single lookup).
2. **Avoids method call overhead**: Direct dictionary access (`self.__data[key]`) is faster than the `__getitem__` method call (`self[key]`).
3. **Leverages EAFP principle**: "Easier to Ask for Forgiveness than Permission" - Python's exception handling is optimized for the common case where exceptions don't occur.

**Performance Impact by Test Case:**
- **Existing keys** (most common): 31-67% faster due to eliminating the redundant lookup
- **Missing keys with defaults**: 1-13% slower due to exception overhead, but this is the less common path
- **Large-scale operations**: 22-28% faster, showing consistent benefits under load

The line profiler confirms this: the original `if key in self:` line took 22.8% of total time and `value = self[key]` took 27%, totaling ~50% for two lookups. The optimized version reduces this to just 16.2% for the single `self.__data[key]` access.

This optimization follows the common Python pattern of optimizing for the success case while handling failures through exceptions, making it particularly effective for cache implementations where successful lookups are the dominant operation.
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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