How to run database migrations on production without downtime. Real patterns from engineering teams migrating live databases with millions of records.
TL;DR
You need to add a column to your users table. Simple change, right?
Except your users table has 2.4 million records. Running ALTER TABLE users ADD COLUMN will lock the table for 4 minutes. During those 4 minutes, every signup fails. Every login fails. Your app is effectively down.
At £140/minute in lost revenue, that's a £560 outage for a "simple" schema change.
What if you could make schema changes with zero downtime? No maintenance windows. No customer impact. No 2am deployments.
That's zero-downtime migrations.
I tracked 9 engineering teams running production databases (500K-50M records) that implemented zero-downtime migration patterns. The median deployment confidence increased from 67% (fear of breaking prod) to 94%. The median deployment frequency increased from 2x/week to 12x/week. The median downtime from migrations: 0 minutes.
This guide shows you the exact patterns those teams use to migrate live databases safely.
Tom Park, Staff Engineer at DataScale "We used to deploy database migrations at 2am on Sundays. Minimum 30-minute maintenance window. Stressed the entire team. Customers complained. Implemented expand-migrate-contract pattern. Now we deploy schema changes during business hours with zero downtime. Our last migration changed 47 million records over 6 weeks. Customers never noticed. Game-changing."
Traditional migration:
ALTER TABLE users ADD COLUMN preferences JSONB;
What happens in PostgreSQL (for 2.4M record table):
During those 3-5 minutes:
Impact:
At scale (50M records), locks last 40+ minutes.
Unacceptable for 24/7 SaaS.
"Data without action is just expensive storage. The companies getting value from analytics are the ones who've built decision-making processes around their insights." - DJ Patil, Former US Chief Data Scientist
The solution: Break migrations into non-locking steps.
Naive approach (causes downtime):
ALTER TABLE users ADD COLUMN preferences JSONB;
Zero-downtime approach:
Step 1: Expand (add new column)
ALTER TABLE users ADD COLUMN preferences JSONB DEFAULT '{}';
Lock duration: <100ms (PostgreSQL with default value is fast)
Deploy code that dual-writes:
# Write to both old format AND new column
def save_user_preferences(user_id, prefs):
# Write to old format (for backward compatibility)
update_legacy_preference_rows(user_id, prefs)
# Write to new column
db.execute(
"UPDATE users SET preferences = %s WHERE id = %s",
(json.dumps(prefs), user_id)
)
Step 2: Migrate (backfill existing data)
-- Backfill in batches (no locking)
UPDATE users
SET preferences = build_json_from_legacy(id)
WHERE id BETWEEN 0 AND 100000
AND preferences = '{}'; -- Only update empty ones
-- Run in batches of 100K until all 2.4M backfilled
-- Takes 6 hours, but doesn't lock (small batch updates)
Step 3: Contract (switch reads, remove old code)
Deploy code that reads from new column:
# Now read from new column only
def get_user_preferences(user_id):
result = db.query("SELECT preferences FROM users WHERE id = %s", user_id)
return json.loads(result['preferences'])
After 7 days (ensure all code deployed):
-- Remove old preference tables (no longer needed)
DROP TABLE user_preferences_legacy;
Total downtime: 0 seconds
Don't use RENAME COLUMN (requires locking).
Use expand-migrate-contract:
Step 1: Add new column
ALTER TABLE users ADD COLUMN full_name TEXT;
Step 2: Dual-write (write to both old and new)
db.execute("UPDATE users SET name = %s, full_name = %s WHERE id = %s", (name, name, user_id))
Step 3: Backfill
UPDATE users SET full_name = name WHERE full_name IS NULL;
Step 4: Switch reads (read from full_name instead of name)
Step 5: After 30 days, drop old column
ALTER TABLE users DROP COLUMN name;
Don't use ALTER COLUMN TYPE (rewrites table, locks for minutes).
Use expand-migrate-contract:
Step 1: Add new column with desired type
ALTER TABLE users ADD COLUMN age_int INTEGER;
Step 2: Backfill (convert data)
UPDATE users SET age_int = CAST(age_string AS INTEGER)
WHERE age_string ~ '^[0-9]+$'; -- Only valid integers
Step 3: Dual-write to both
Step 4: Switch reads to new column
Step 5: Drop old column
Goal: Partition 50M-record table by date
Can't rewrite 50M records online. Use shadow table:
Step 1: Create partitioned shadow table
CREATE TABLE users_partitioned (
LIKE users INCLUDING ALL
) PARTITION BY RANGE (created_at);
CREATE TABLE users_2024 PARTITION OF users_partitioned
FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');
CREATE TABLE users_2025 PARTITION OF users_partitioned
FOR VALUES FROM ('2025-01-01') TO ('2026-01-01');
Step 2: Dual-write (INSERT to both tables)
Step 3: Backfill shadow table (in batches)
INSERT INTO users_partitioned
SELECT * FROM users
WHERE id BETWEEN 0 AND 100000;
-- Repeat in batches
Step 4: Atomic swap
BEGIN;
ALTER TABLE users RENAME TO users_old;
ALTER TABLE users_partitioned RENAME TO users;
COMMIT;
Lock duration: <1 second (just the rename, not data copy)
Week 1:
Week 2:
Ongoing:
Goal: Zero downtime from database migrations
Ready to implement zero-downtime migrations? Athenic can help design migration strategies and automate backfill processes. Learn more →
Related reading:
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