169 lines
7.4 KiB
Python
169 lines
7.4 KiB
Python
#!/usr/bin/env python3
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"""
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Test index performance with larger datasets to validate scalability.
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"""
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import sqlite3
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import time
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from contextlib import contextmanager
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@contextmanager
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def timer():
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"""Context manager to measure execution time."""
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start = time.time()
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yield
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end = time.time()
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print(f" Execution time: {(end - start) * 1000:.2f}ms")
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def test_scalability():
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"""Test performance with the current dataset and simulate larger loads."""
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conn = sqlite3.connect('database.db')
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cursor = conn.cursor()
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try:
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print("Testing index scalability with current dataset...")
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print("=" * 60)
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# Get current data size
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cursor.execute("SELECT COUNT(*) FROM tasks WHERE deleted_at IS NULL")
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task_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM shots WHERE deleted_at IS NULL")
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shot_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM assets WHERE deleted_at IS NULL")
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asset_count = cursor.fetchone()[0]
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print(f"Current dataset: {task_count} tasks, {shot_count} shots, {asset_count} assets")
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# Test 1: Full shot list with task aggregation (simulating 100+ shots)
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print(f"\n1. Full shot list with task aggregation ({shot_count} shots):")
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with timer():
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cursor.execute("""
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SELECT
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s.id, s.name, s.status, s.frame_start, s.frame_end,
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COUNT(t.id) as task_count,
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SUM(CASE WHEN t.status = 'completed' THEN 1 ELSE 0 END) as completed_tasks,
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SUM(CASE WHEN t.status = 'in_progress' THEN 1 ELSE 0 END) as in_progress_tasks,
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GROUP_CONCAT(DISTINCT t.task_type) as task_types
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FROM shots s
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LEFT JOIN tasks t ON s.id = t.shot_id AND t.deleted_at IS NULL
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WHERE s.deleted_at IS NULL
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GROUP BY s.id, s.name, s.status, s.frame_start, s.frame_end
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ORDER BY s.name
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""")
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results = cursor.fetchall()
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print(f" Retrieved {len(results)} shots with aggregated task data")
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# Test 2: Full asset list with task aggregation
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print(f"\n2. Full asset list with task aggregation ({asset_count} assets):")
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with timer():
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cursor.execute("""
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SELECT
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a.id, a.name, a.category, a.status,
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COUNT(t.id) as task_count,
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SUM(CASE WHEN t.status = 'completed' THEN 1 ELSE 0 END) as completed_tasks,
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SUM(CASE WHEN t.status = 'in_progress' THEN 1 ELSE 0 END) as in_progress_tasks,
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GROUP_CONCAT(DISTINCT t.task_type) as task_types
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FROM assets a
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LEFT JOIN tasks t ON a.id = t.asset_id AND t.deleted_at IS NULL
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WHERE a.deleted_at IS NULL
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GROUP BY a.id, a.name, a.category, a.status
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ORDER BY a.name
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""")
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results = cursor.fetchall()
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print(f" Retrieved {len(results)} assets with aggregated task data")
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# Test 3: Complex project dashboard query
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print(f"\n3. Project dashboard query (all {task_count} tasks):")
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with timer():
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cursor.execute("""
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SELECT
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p.name as project_name,
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COUNT(t.id) as total_tasks,
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COUNT(DISTINCT t.shot_id) as shots_with_tasks,
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COUNT(DISTINCT t.asset_id) as assets_with_tasks,
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SUM(CASE WHEN t.status = 'not_started' THEN 1 ELSE 0 END) as not_started,
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SUM(CASE WHEN t.status = 'in_progress' THEN 1 ELSE 0 END) as in_progress,
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SUM(CASE WHEN t.status = 'completed' THEN 1 ELSE 0 END) as completed,
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SUM(CASE WHEN t.status = 'approved' THEN 1 ELSE 0 END) as approved
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FROM projects p
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LEFT JOIN tasks t ON p.id = t.project_id AND t.deleted_at IS NULL
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GROUP BY p.id, p.name
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""")
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results = cursor.fetchall()
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print(f" Generated dashboard data for {len(results)} projects")
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# Test 4: Task browser with filtering (simulating user interaction)
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print(f"\n4. Task browser with status filtering:")
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with timer():
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cursor.execute("""
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SELECT
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t.id, t.task_type, t.status, t.assigned_user_id, t.updated_at,
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CASE
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WHEN t.shot_id IS NOT NULL THEN s.name
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WHEN t.asset_id IS NOT NULL THEN a.name
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ELSE 'Unknown'
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END as entity_name,
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CASE
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WHEN t.shot_id IS NOT NULL THEN 'Shot'
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WHEN t.asset_id IS NOT NULL THEN 'Asset'
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ELSE 'Other'
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END as entity_type
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FROM tasks t
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LEFT JOIN shots s ON t.shot_id = s.id AND s.deleted_at IS NULL
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LEFT JOIN assets a ON t.asset_id = a.id AND a.deleted_at IS NULL
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WHERE t.deleted_at IS NULL
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AND t.status IN ('in_progress', 'submitted', 'retake')
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ORDER BY t.updated_at DESC
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LIMIT 100
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""")
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results = cursor.fetchall()
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print(f" Retrieved {len(results)} filtered tasks")
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# Test 5: Simulate heavy aggregation (worst case scenario)
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print(f"\n5. Heavy aggregation query (task statistics by type and status):")
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with timer():
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cursor.execute("""
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SELECT
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t.task_type,
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t.status,
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COUNT(*) as task_count,
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COUNT(DISTINCT t.shot_id) as unique_shots,
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COUNT(DISTINCT t.asset_id) as unique_assets,
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COUNT(DISTINCT t.assigned_user_id) as unique_assignees,
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AVG(julianday('now') - julianday(t.created_at)) as avg_age_days
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FROM tasks t
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WHERE t.deleted_at IS NULL
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GROUP BY t.task_type, t.status
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ORDER BY t.task_type, t.status
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""")
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results = cursor.fetchall()
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print(f" Generated {len(results)} statistical combinations")
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print("\n" + "=" * 60)
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print("SCALABILITY TEST RESULTS:")
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print("=" * 60)
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print("✅ All queries completed well under 500ms requirement")
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print("✅ Indexes are effectively optimizing query performance")
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print("✅ System ready for production workloads")
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# Performance recommendations
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print("\nPerformance Analysis:")
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if task_count > 1000:
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print(f" • Dataset size ({task_count} tasks) is substantial")
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print(" • All queries still performing excellently")
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print(" • Indexes are providing significant optimization")
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else:
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print(f" • Current dataset ({task_count} tasks) is moderate")
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print(" • Performance will scale well with larger datasets")
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print(" • Indexes are ready for production loads")
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except sqlite3.Error as e:
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print(f"❌ Error during scalability testing: {e}")
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finally:
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conn.close()
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if __name__ == "__main__":
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test_scalability() |