Scaling Writes hard
When scaling a write-heavy cluster using Consistent Hashing, what happens when you add a new node?
- A All data is deleted.
- B Writes are blocked for 1 hour.
- C Only the keys that map to the new node's position on the ring are moved from its successor. This minimizes data transfer to `Total_Data / Node_Count`.
- D All keys are re-hashed.
Answer: C. Only the keys that map to the new node's position on the ring are moved from its successor. This minimizes data transfer to `Total_Data / Node_Count`.
In standard Modulo hashing, adding a node changes the mapping for almost all keys. Consistent hashing minimizes rebalancing traffic, which is critical when moving Terabytes of data under load.
One system design question a day, with spaced repetition so the ones you miss come back.
Get the appRelated questions
- What does the 'Atomicity' property in a database transaction guarantee regarding writes?
- Your database is struggling with write traffic. You decide to 'Scale Up' (Vertical Scaling) by buying a bigger server. What is the eventual hard limit of this strategy?
- A developer sets up a 'Fire-and-Forget' write pattern (sending data without waiting for a database confirmation) to speed up the app. What is the major risk?
- Why is inserting 1,000 rows in a single 'Batch' query faster than executing 1,000 separate `INSERT` queries?
- In the Raft consensus algorithm, what mechanism prevents a 'Split Vote' (where no leader gets a majority) from stalling the cluster indefinitely?
- Your 'Logs' table has 10 billion rows and writes are getting slow. You barely access logs older than 1 year. What is the simplest strategy to improve performance?