Scaling Writes hard
The CAP theorem discusses partitions (P). The PACELC theorem extends this by asking: 'Else (E), when the system is running normally (Latency vs Consistency)...'
- A Do you choose Efficiency or Cost?
- B Do you choose Partition or Availability?
- C Do you choose Latency or Consistency? (e.g., do you wait for replication to finish to guarantee consistency, or return early for low latency?)
- D Do you choose Logging or Caching?
Answer: C. Do you choose Latency or Consistency? (e.g., do you wait for replication to finish to guarantee consistency, or return early for low latency?)
Even without partitions (which is 99% of the time), you have a trade-off. Synchronous replication = Consistency but High Latency. Asynchronous = Low Latency but Eventual Consistency. PACELC formalizes this.
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?