basically refactored the email viewer
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Tim Bendt
315
benchmark_list_update.py
Executable file
315
benchmark_list_update.py
Executable file
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#!/usr/bin/env python3
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"""
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Benchmark script to compare two approaches for updating envelopes list in maildir_gtd.
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This script compares:
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1. Using .pop() to remove items from ListView
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2. Using refresh_list_view() to rebuild the entire ListView
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It tests with different numbers of envelopes (100, 1000, 2000) and measures:
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- Time to remove a single item
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- Time to remove multiple items in sequence
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- Memory usage
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"""
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import sys
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import os
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import time
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import random
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import gc
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import tracemalloc
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from datetime import datetime, timedelta, UTC
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from typing import List, Dict, Any, Callable, Tuple
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import json
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# Add parent directory to path so we can import modules correctly
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# Import required classes and functions
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from textual.widgets import ListView, ListItem, Label
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from textual.app import App, ComposeResult
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from textual.containers import Vertical
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# Import our application's modules
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from maildir_gtd.app import MessageStore
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from maildir_gtd.utils import group_envelopes_by_date
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# Mock class to simulate the ListView behavior
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class MockListView:
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def __init__(self):
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self.items = []
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self.index = 0
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def append(self, item):
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self.items.append(item)
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def pop(self, idx=None):
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if idx is None:
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return self.items.pop()
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return self.items.pop(idx)
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def clear(self):
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self.items = []
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def __len__(self):
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return len(self.items)
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# Helper functions to generate test data
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def generate_envelope(idx: int) -> Dict[str, Any]:
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"""Generate a synthetic envelope with predictable data."""
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now = datetime.now(UTC)
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# Distribute dates over the last 60 days to create realistic grouping
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date = now - timedelta(days=random.randint(0, 60),
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hours=random.randint(0, 23),
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minutes=random.randint(0, 59))
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return {
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"id": str(idx),
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"subject": f"Test Subject {idx}",
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"from": {"addr": f"sender{idx}@example.com"},
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"to": {"addr": f"recipient{idx}@example.com"},
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"date": date.strftime("%Y-%m-%d %H:%M"),
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"cc": {},
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"type": "message"
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}
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def generate_test_envelopes(count: int) -> List[Dict[str, Any]]:
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"""Generate a specified number of test envelopes."""
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return [generate_envelope(i) for i in range(1, count + 1)]
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# Benchmark functions
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def benchmark_pop_approach(store: MessageStore, list_view: MockListView, indices_to_remove: List[int]) -> float:
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"""Benchmark the .pop() approach."""
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start_time = time.time()
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for idx in sorted(indices_to_remove, reverse=True): # Remove from highest to lowest to avoid index shifting issues
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msg_id = int(store.envelopes[idx]["id"])
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store.remove(msg_id)
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list_view.pop(idx)
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end_time = time.time()
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return end_time - start_time
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def benchmark_refresh_approach(store: MessageStore, list_view: MockListView, indices_to_remove: List[int]) -> float:
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"""Benchmark the refresh_list_view approach."""
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start_time = time.time()
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for idx in indices_to_remove:
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msg_id = int(store.envelopes[idx]["id"])
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store.remove(msg_id)
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# Simulate refresh_list_view by clearing and rebuilding the list
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list_view.clear()
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for item in store.envelopes:
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if item and item.get("type") == "header":
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list_view.append(f"Header: {item['label']}")
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elif item: # Check if not None
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list_view.append(f"Email: {item.get('subject', '')}")
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end_time = time.time()
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return end_time - start_time
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def run_memory_benchmark(func, *args):
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"""Run a function with memory tracking."""
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tracemalloc.start()
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result = func(*args)
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current, peak = tracemalloc.get_traced_memory()
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tracemalloc.stop()
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return result, current, peak
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def run_benchmark(envelope_count: int, num_operations: int = 10):
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"""Run benchmarks for a specific number of envelopes."""
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print(f"\n{'=' * 50}")
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print(f"Running benchmark with {envelope_count} envelopes")
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print(f"{'=' * 50}")
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# Generate test data
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envelopes = generate_test_envelopes(envelope_count)
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# Set up for pop approach
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pop_store = MessageStore()
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pop_store.load(envelopes.copy())
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pop_list_view = MockListView()
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# Build initial list view
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for item in pop_store.envelopes:
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if item and item.get("type") == "header":
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pop_list_view.append(f"Header: {item['label']}")
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elif item:
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pop_list_view.append(f"Email: {item.get('subject', '')}")
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# Set up for refresh approach
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refresh_store = MessageStore()
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refresh_store.load(envelopes.copy())
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refresh_list_view = MockListView()
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# Build initial list view
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for item in refresh_store.envelopes:
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if item and item.get("type") == "header":
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refresh_list_view.append(f"Header: {item['label']}")
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elif item:
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refresh_list_view.append(f"Email: {item.get('subject', '')}")
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# Generate random indices to remove (ensure they're valid message indices, not headers)
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valid_indices = []
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for idx, item in enumerate(pop_store.envelopes):
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if item and item.get("type") != "header" and item is not None:
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valid_indices.append(idx)
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if len(valid_indices) < num_operations:
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num_operations = len(valid_indices)
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print(f"Warning: Only {num_operations} valid messages available for removal")
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indices_to_remove = random.sample(valid_indices, num_operations)
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# Single operation benchmark
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print("\n🔹 Single operation benchmark (removing 1 item):")
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# Pop approach - single operation
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gc.collect() # Ensure clean state
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single_pop_time, pop_current, pop_peak = run_memory_benchmark(
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benchmark_pop_approach, pop_store, pop_list_view, [indices_to_remove[0]]
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)
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print(f" Pop approach: {single_pop_time*1000:.2f} ms (Memory - Current: {pop_current/1024:.1f} KB, Peak: {pop_peak/1024:.1f} KB)")
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# Refresh approach - single operation
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gc.collect() # Ensure clean state
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single_refresh_time, refresh_current, refresh_peak = run_memory_benchmark(
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benchmark_refresh_approach, refresh_store, refresh_list_view, [indices_to_remove[0]]
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)
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print(f" Refresh approach: {single_refresh_time*1000:.2f} ms (Memory - Current: {refresh_current/1024:.1f} KB, Peak: {refresh_peak/1024:.1f} KB)")
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# Determine which is better for single operation
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if single_pop_time < single_refresh_time:
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print(f" 🥇 Pop is {single_refresh_time/single_pop_time:.1f}x faster for single operation")
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else:
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print(f" 🥇 Refresh is {single_pop_time/single_refresh_time:.1f}x faster for single operation")
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# Reset for multi-operation benchmark
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gc.collect()
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pop_store = MessageStore()
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pop_store.load(envelopes.copy())
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pop_list_view = MockListView()
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for item in pop_store.envelopes:
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if item and item.get("type") == "header":
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pop_list_view.append(f"Header: {item['label']}")
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elif item:
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pop_list_view.append(f"Email: {item.get('subject', '')}")
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refresh_store = MessageStore()
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refresh_store.load(envelopes.copy())
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refresh_list_view = MockListView()
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for item in refresh_store.envelopes:
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if item and item.get("type") == "header":
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refresh_list_view.append(f"Header: {item['label']}")
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elif item:
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refresh_list_view.append(f"Email: {item.get('subject', '')}")
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# Multiple operations benchmark
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print(f"\n🔹 Multiple operations benchmark (removing {num_operations} items):")
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# Pop approach - multiple operations
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gc.collect()
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multi_pop_time, pop_current, pop_peak = run_memory_benchmark(
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benchmark_pop_approach, pop_store, pop_list_view, indices_to_remove
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)
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print(f" Pop approach: {multi_pop_time*1000:.2f} ms (Memory - Current: {pop_current/1024:.1f} KB, Peak: {pop_peak/1024:.1f} KB)")
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# Refresh approach - multiple operations
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gc.collect()
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multi_refresh_time, refresh_current, refresh_peak = run_memory_benchmark(
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benchmark_refresh_approach, refresh_store, refresh_list_view, indices_to_remove
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)
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print(f" Refresh approach: {multi_refresh_time*1000:.2f} ms (Memory - Current: {refresh_current/1024:.1f} KB, Peak: {refresh_peak/1024:.1f} KB)")
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# Determine which is better for multiple operations
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if multi_pop_time < multi_refresh_time:
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print(f" 🥇 Pop is {multi_refresh_time/multi_pop_time:.1f}x faster for multiple operations")
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else:
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print(f" 🥇 Refresh is {multi_pop_time/multi_refresh_time:.1f}x faster for multiple operations")
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return {
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"envelope_count": envelope_count,
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"num_operations": num_operations,
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"single_operation": {
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"pop_time_ms": single_pop_time * 1000,
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"refresh_time_ms": single_refresh_time * 1000,
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"pop_memory_kb": pop_peak / 1024,
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"refresh_memory_kb": refresh_peak / 1024
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},
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"multiple_operations": {
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"pop_time_ms": multi_pop_time * 1000,
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"refresh_time_ms": multi_refresh_time * 1000,
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"pop_memory_kb": pop_peak / 1024,
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"refresh_memory_kb": refresh_peak / 1024
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}
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}
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def main():
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print("\n📊 MAILDIR GTD LIST UPDATE BENCHMARK 📊")
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print("Comparing .pop() vs refresh_list_view() approaches")
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print("=" * 60)
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# Define test cases
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envelope_counts = [100, 1000, 2000]
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results = []
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for count in envelope_counts:
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result = run_benchmark(count)
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results.append(result)
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# Print summary
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print("\n" + "=" * 60)
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print("📊 BENCHMARK SUMMARY")
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print("=" * 60)
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# Console table formatting
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print(f"{'Size':<10} | {'Single Op (pop)':<15} | {'Single Op (refresh)':<20} | {'Multi Op (pop)':<15} | {'Multi Op (refresh)':<20}")
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print("-" * 90)
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for result in results:
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count = result["envelope_count"]
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single_pop = f"{result['single_operation']['pop_time_ms']:.2f} ms"
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single_refresh = f"{result['single_operation']['refresh_time_ms']:.2f} ms"
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multi_pop = f"{result['multiple_operations']['pop_time_ms']:.2f} ms"
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multi_refresh = f"{result['multiple_operations']['refresh_time_ms']:.2f} ms"
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print(f"{count:<10} | {single_pop:<15} | {single_refresh:<20} | {multi_pop:<15} | {multi_refresh:<20}")
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# Display conclusions
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print("\n🔍 CONCLUSIONS:")
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for result in results:
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count = result["envelope_count"]
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single_ratio = result['single_operation']['refresh_time_ms'] / result['single_operation']['pop_time_ms']
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multi_ratio = result['multiple_operations']['refresh_time_ms'] / result['multiple_operations']['pop_time_ms']
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print(f"\nFor {count} envelopes:")
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if single_ratio > 1:
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print(f"- Single operation: .pop() is {single_ratio:.1f}x faster")
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else:
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print(f"- Single operation: refresh_list_view() is {1/single_ratio:.1f}x faster")
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if multi_ratio > 1:
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print(f"- Multiple operations: .pop() is {multi_ratio:.1f}x faster")
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else:
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print(f"- Multiple operations: refresh_list_view() is {1/multi_ratio:.1f}x faster")
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print("\n🔑 RECOMMENDATION:")
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# Calculate average performance difference across all tests
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avg_single_ratio = sum(r['single_operation']['refresh_time_ms'] / r['single_operation']['pop_time_ms'] for r in results) / len(results)
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avg_multi_ratio = sum(r['multiple_operations']['refresh_time_ms'] / r['multiple_operations']['pop_time_ms'] for r in results) / len(results)
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if avg_single_ratio > 1 and avg_multi_ratio > 1:
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print("The .pop() approach is generally faster, but consider the following:")
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print("- .pop() risks index misalignment issues with the message_store")
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print("- refresh_list_view() ensures UI and data structure stay synchronized")
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print("- The performance difference may not be noticeable to users")
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print("👉 Recommendation: Use refresh_list_view() for reliability unless performance becomes a real issue")
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else:
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print("The refresh_list_view() approach is not only safer but also performs competitively:")
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print("- It ensures perfect synchronization between UI and data model")
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print("- It eliminates the risk of index misalignment")
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print("👉 Recommendation: Use refresh_list_view() approach as it's more reliable and performs well")
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if __name__ == "__main__":
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main()
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