On My First Day at Work, a Data Sync Nearly Triggered a P0-Level Incident

Xiao Aba’s Data Synchronization Journey

You are Xiao Aba, fresh into your first job at an e-commerce company.

On day one, your boss hands you a mission: regularly sync order data to the analytics warehouse.

You immediately recall your brother Yupi’s tale — a failed sync on Singles’ Day cost over 100 million yuan in missed sales, and his job.

Determined not to fail, you clench your fists and promise to get it right.

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1. Full Synchronization

Data synchronization is like copying photos from one phone to another — regularly replicating data from one database to another.

Your first idea: every day, fetch the entire `orders` table and dump it into the warehouse.

Full synchronization copies all records whether they have changed or not.

# Fetch all orders
orders = db.query("SELECT * FROM orders")
# Clear old data
warehouse.execute("DELETE FROM orders")
# Insert new values
warehouse.insert(order)

The first run takes 3 hours for 10,000 orders — success! The boss compliments your speed.

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2. Basic Alerts

The next day, sync fails without warning, leaving the boss blind in a meeting.

Lesson: No alerts = bad idea.

You add error logging, email notifications to admins, and rollback on failure.

Now, if something breaks, you know immediately and can rerun the task.

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3. Incremental Synchronization

With 100,000 orders, full sync takes 30 hours.

Scaling to millions becomes impossible.

Solution: Sync only new or updated orders since the last run — incremental synchronization.

Initially, you use `creation_time`, but status changes (like refunds) on old orders aren’t captured.

You switch to `updated_time`, which changes whenever data is modified.

Problem solved — newly created and modified orders are included.

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4. Batch Processing

During a big promo, your midnight task crashes the server — Out Of Memory (OOM) — due to loading too many rows at once.

Fix: batch queries in small chunks (e.g., 100 rows), processing pages sequentially.

Benefits:

• Lower memory usage.
• Easier recovery from batch failures.

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5. Cursor Mechanism

Paginated queries using `OFFSET` lose data when new updates occur mid-sync.

Example:

• Page 1: Orders A, B
• Before Page 2 runs, B’s `update_time` changes
• Page 2 skips C entirely — data loss

Fix: use a cursor (e.g., record `id` of last synced row).

Next query: `WHERE id > cursor`.

Advantages:

• No deep `OFFSET` performance hit.
• Acts as a progress checkpoint for restarts.

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6. Performance Optimization

Orders reach millions per day; boss wants sync every 2 hours.

Optimizations:

• Bulk Inserts: insert multiple orders in one statement.
• Parallel Batches: process batches in multiple threads.

Result: sync runs much faster — and the boss promises promotion.

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7. Real-Time Synchronization

Boss demands instant updates for investor dashboards.

Solution:

Use CDC (Change Data Capture) + Message Queue.

• CDC: monitors the database for any changes.
• Message Queue: acts as a transfer station — stores changes until processed.

Now, new orders appear on dashboards within 100 ms.

Queue monitoring triggers alerts on backlog.

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8. Refinement Challenges

During Double 11:

• Duplicate messages
• Out-of-order updates
• Backlogs slowing processing

Intern Akun suggests:

• Idempotency: track processed message IDs to avoid duplicates.
• Partitioning: group related messages for sequential processing.
• Scaling: use clustering to handle peak loads.

Then asks:

"Why write all this from scratch? Use tools like DataX, Canal, or Debezium!"

Also: "Do periodic data reconciliation to check integrity."

Boss promotes Akun — and sends you to reflect.

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Key Takeaways

When designing high-volume, real-time data sync:

• Use incremental + cursor-based logic to avoid waste & loss.
• Apply batch + parallel processing for speed.
• Ensure idempotency, ordering guarantees, and scaling with message queues.
• Consider enterprise tools instead of reinventing.
• Perform data reconciliation regularly.

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Author: Programmer Yupi

Source: WeChat Official Account – Programmer Yupi (`coder_yupi`)

Submission Email: editor@dbaplus.cn