Add comprehensive architectural documentation for the migration race condition fix, including: - ADR-022: Architectural decision record for the fix - migration-race-condition-answers.md: All 23 Q&A answered - migration-fix-quick-reference.md: Implementation checklist - migration-race-condition-fix-implementation.md: Detailed guide These documents guided the implementation in v1.0.0-rc.5. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Migration Race Condition Fix - Implementation Guide
Executive Summary
CRITICAL PRODUCTION ISSUE: Multiple gunicorn workers racing to apply migrations causes container startup failures.
Solution: Implement database-level advisory locking with retry logic in migrations.py.
Urgency: HIGH - This is a blocker for v1.0.0-rc.4 release.
Root Cause Analysis
The Problem Flow
- Container starts with
gunicorn --workers 4 - Each worker independently calls:
app.py → create_app() → init_db() → run_migrations() - All 4 workers simultaneously try to:
- INSERT into schema_migrations table
- Apply the same migrations
- SQLite's UNIQUE constraint on migration_name causes workers 2-4 to crash
- Container restarts, works on second attempt (migrations already applied)
Why This Happens
- No synchronization: Workers are independent processes
- No locking: Migration code doesn't prevent concurrent execution
- Immediate failure: UNIQUE constraint violation crashes the worker
- Gunicorn behavior: Worker crash triggers container restart
Immediate Fix Implementation
Step 1: Update migrations.py
Add these imports at the top of /home/phil/Projects/starpunk/starpunk/migrations.py:
import time
import random
Step 2: Replace run_migrations function
Replace the entire run_migrations function (lines 304-462) with:
def run_migrations(db_path, logger=None):
"""
Run all pending database migrations with concurrency protection
Uses database-level locking to prevent race conditions when multiple
workers start simultaneously. Only one worker will apply migrations;
others will wait and verify completion.
Args:
db_path: Path to SQLite database file
logger: Optional logger for output
Raises:
MigrationError: If any migration fails to apply or lock cannot be acquired
"""
if logger is None:
logger = logging.getLogger(__name__)
# Determine migrations directory
migrations_dir = Path(__file__).parent.parent / "migrations"
if not migrations_dir.exists():
logger.warning(f"Migrations directory not found: {migrations_dir}")
return
# Retry configuration for lock acquisition
max_retries = 10
retry_count = 0
base_delay = 0.1 # 100ms
while retry_count < max_retries:
conn = None
try:
# Connect with longer timeout for lock contention
conn = sqlite3.connect(db_path, timeout=30.0)
# Attempt to acquire exclusive lock for migrations
# BEGIN IMMEDIATE acquires RESERVED lock, preventing other writes
conn.execute("BEGIN IMMEDIATE")
try:
# Ensure migrations tracking table exists
create_migrations_table(conn)
# Quick check: have migrations already been applied by another worker?
cursor = conn.execute("SELECT COUNT(*) FROM schema_migrations")
migration_count = cursor.fetchone()[0]
# Discover migration files
migration_files = discover_migration_files(migrations_dir)
if not migration_files:
conn.commit()
logger.info("No migration files found")
return
# If migrations exist and we're not the first worker, verify and exit
if migration_count > 0:
# Check if all migrations are applied
applied = get_applied_migrations(conn)
pending = [m for m, _ in migration_files if m not in applied]
if not pending:
conn.commit()
logger.debug("All migrations already applied by another worker")
return
# If there are pending migrations, we continue to apply them
logger.info(f"Found {len(pending)} pending migrations to apply")
# Fresh database detection (original logic preserved)
if migration_count == 0:
if is_schema_current(conn):
# Schema is current - mark all migrations as applied
for migration_name, _ in migration_files:
conn.execute(
"INSERT INTO schema_migrations (migration_name) VALUES (?)",
(migration_name,)
)
conn.commit()
logger.info(
f"Fresh database detected: marked {len(migration_files)} "
f"migrations as applied (schema already current)"
)
return
else:
logger.info("Fresh database with partial schema: applying needed migrations")
# Get already-applied migrations
applied = get_applied_migrations(conn)
# Apply pending migrations (original logic preserved)
pending_count = 0
skipped_count = 0
for migration_name, migration_path in migration_files:
if migration_name not in applied:
# Check if migration is actually needed
should_check_needed = (
migration_count == 0 or
migration_name == "002_secure_tokens_and_authorization_codes.sql"
)
if should_check_needed and not is_migration_needed(conn, migration_name):
# Special handling for migration 002: if tables exist but indexes don't
if migration_name == "002_secure_tokens_and_authorization_codes.sql":
# Check if we need to create indexes
indexes_to_create = []
if not index_exists(conn, 'idx_tokens_hash'):
indexes_to_create.append("CREATE INDEX idx_tokens_hash ON tokens(token_hash)")
if not index_exists(conn, 'idx_tokens_me'):
indexes_to_create.append("CREATE INDEX idx_tokens_me ON tokens(me)")
if not index_exists(conn, 'idx_tokens_expires'):
indexes_to_create.append("CREATE INDEX idx_tokens_expires ON tokens(expires_at)")
if not index_exists(conn, 'idx_auth_codes_hash'):
indexes_to_create.append("CREATE INDEX idx_auth_codes_hash ON authorization_codes(code_hash)")
if not index_exists(conn, 'idx_auth_codes_expires'):
indexes_to_create.append("CREATE INDEX idx_auth_codes_expires ON authorization_codes(expires_at)")
if indexes_to_create:
for index_sql in indexes_to_create:
conn.execute(index_sql)
logger.info(f"Created {len(indexes_to_create)} missing indexes from migration 002")
# Mark as applied without executing full migration
conn.execute(
"INSERT INTO schema_migrations (migration_name) VALUES (?)",
(migration_name,)
)
skipped_count += 1
logger.debug(f"Skipped migration {migration_name} (already in SCHEMA_SQL)")
else:
# Apply the migration (within our transaction)
try:
# Read migration SQL
migration_sql = migration_path.read_text()
logger.debug(f"Applying migration: {migration_name}")
# Execute migration (already in transaction)
conn.executescript(migration_sql)
# Record migration as applied
conn.execute(
"INSERT INTO schema_migrations (migration_name) VALUES (?)",
(migration_name,)
)
logger.info(f"Applied migration: {migration_name}")
pending_count += 1
except Exception as e:
# Roll back the transaction
raise MigrationError(f"Migration {migration_name} failed: {e}")
# Commit all migrations atomically
conn.commit()
# Summary
total_count = len(migration_files)
if pending_count > 0 or skipped_count > 0:
if skipped_count > 0:
logger.info(
f"Migrations complete: {pending_count} applied, {skipped_count} skipped "
f"(already in SCHEMA_SQL), {total_count} total"
)
else:
logger.info(
f"Migrations complete: {pending_count} applied, "
f"{total_count} total"
)
else:
logger.info(f"All migrations up to date ({total_count} total)")
return # Success!
except MigrationError:
conn.rollback()
raise
except Exception as e:
conn.rollback()
raise MigrationError(f"Migration system error: {e}")
except sqlite3.OperationalError as e:
if "database is locked" in str(e).lower():
# Another worker has the lock, retry with exponential backoff
retry_count += 1
if retry_count < max_retries:
# Exponential backoff with jitter
delay = base_delay * (2 ** retry_count) + random.uniform(0, 0.1)
logger.debug(
f"Database locked by another worker, retry {retry_count}/{max_retries} "
f"in {delay:.2f}s"
)
time.sleep(delay)
continue
else:
raise MigrationError(
f"Failed to acquire migration lock after {max_retries} attempts. "
f"This may indicate a hung migration process."
)
else:
# Non-lock related database error
error_msg = f"Database error during migration: {e}"
logger.error(error_msg)
raise MigrationError(error_msg)
except Exception as e:
# Unexpected error
error_msg = f"Unexpected error during migration: {e}"
logger.error(error_msg)
raise MigrationError(error_msg)
finally:
if conn:
try:
conn.close()
except:
pass # Ignore errors during cleanup
# Should never reach here, but just in case
raise MigrationError("Migration retry loop exited unexpectedly")
Step 3: Testing the Fix
Create a test script to verify the fix works:
#!/usr/bin/env python3
"""Test migration race condition fix"""
import multiprocessing
import time
import sys
from pathlib import Path
# Add project to path
sys.path.insert(0, str(Path(__file__).parent))
def worker_init(worker_id):
"""Simulate a gunicorn worker starting"""
print(f"Worker {worker_id}: Starting...")
try:
from starpunk import create_app
app = create_app()
print(f"Worker {worker_id}: Successfully initialized")
return True
except Exception as e:
print(f"Worker {worker_id}: FAILED - {e}")
return False
if __name__ == "__main__":
# Test with 10 workers (more than production to stress test)
num_workers = 10
print(f"Starting {num_workers} workers simultaneously...")
with multiprocessing.Pool(num_workers) as pool:
results = pool.map(worker_init, range(num_workers))
success_count = sum(results)
print(f"\nResults: {success_count}/{num_workers} workers succeeded")
if success_count == num_workers:
print("SUCCESS: All workers initialized without race condition")
sys.exit(0)
else:
print("FAILURE: Race condition still present")
sys.exit(1)
Verification Steps
-
Local Testing:
# Test with multiple workers gunicorn --workers 4 --bind 0.0.0.0:8000 app:app # Check logs for retry messages # Should see "Database locked by another worker, retry..." messages -
Container Testing:
# Build container podman build -t starpunk:test -f Containerfile . # Run with fresh database podman run --rm -p 8000:8000 -v ./test-data:/data starpunk:test # Should start cleanly without restarts -
Log Verification: Look for these patterns:
- One worker: "Applied migration: XXX"
- Other workers: "Database locked by another worker, retry..."
- Final: "All migrations already applied by another worker"
Risk Assessment
Risk Level: LOW
The fix is safe because:
- Uses SQLite's native transaction mechanism
- Preserves all existing migration logic
- Only adds retry wrapper around existing code
- Fails safely with clear error messages
- No data loss possible (transactions ensure atomicity)
Rollback Plan
If issues occur:
- Revert to previous version
- Start container with single worker temporarily:
--workers 1 - Once migrations apply, scale back to 4 workers
Release Strategy
Option 1: Hotfix (Recommended)
- Release as v1.0.0-rc.3.1
- Immediate deployment to fix production issue
- Minimal testing required (focused fix)
Option 2: Include in rc.4
- Bundle with other rc.4 changes
- More testing time
- Risk: Production remains broken until rc.4
Recommendation: Deploy as hotfix v1.0.0-rc.3.1 immediately.
Alternative Workarounds (If Needed Urgently)
While the proper fix is implemented, these temporary workarounds can be used:
Workaround 1: Single Worker Startup
# In Containerfile, temporarily change:
CMD ["gunicorn", "--workers", "1", ...]
# After first successful start, rebuild with 4 workers
Workaround 2: Pre-migration Script
# Add entrypoint script that runs migrations before gunicorn
#!/bin/bash
python3 -c "from starpunk.database import init_db; init_db()"
exec gunicorn --workers 4 ...
Workaround 3: Delayed Worker Startup
# Stagger worker startup with --preload
gunicorn --preload --workers 4 ...
Summary
- Problem: Race condition when multiple workers apply migrations
- Solution: Database-level locking with retry logic
- Implementation: ~150 lines of code changes in migrations.py
- Testing: Verify with multi-worker startup
- Risk: LOW - Safe, atomic changes
- Urgency: HIGH - Blocks production deployment
- Recommendation: Deploy as hotfix v1.0.0-rc.3.1 immediately
Developer Questions Answered
All 23 architectural questions have been comprehensively answered in:
/home/phil/Projects/starpunk/docs/architecture/migration-race-condition-answers.md
Key Decisions:
- NEW connection per retry (not reused)
- BEGIN IMMEDIATE is correct (not EXCLUSIVE)
- Separate transactions for each operation
- Both multiprocessing.Pool AND gunicorn testing needed
- 30s timeout per attempt, 120s total maximum
- Graduated logging levels based on retry count
Implementation Status: READY TO PROCEED