Scaling Writes medium
You are sharding data across 10 nodes using standard Modulo Hashing (`id % 10`). You add 1 new node (Total 11). What happens to the write distribution?
- A Nothing changes.
- B Only 10% of the data moves.
- C Almost ALL keys (approx 90%) are remapped to different nodes because the divisor changed, causing a massive data migration storm.
- D The system pauses for 1 hour.
Answer: C. Almost ALL keys (approx 90%) are remapped to different nodes because the divisor changed, causing a massive data migration storm.
Modulo hashing is very sensitive to the number of nodes. Consistent Hashing is preferred because adding a node only requires moving `1/N` keys (keys from immediate neighbors), minimizing data movement.
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?