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Partitioning strategies: 1) Range-based: e.g., user IDs 1–1000, 1001–2000. Ideal for ranges, prone to hotspots. 2) Hash-based: hash(key) % N. Uniform, avoids hotspots, but disrupts range queries. 3) Directory/Lookup-based: mapping service directs data location. 4) List-based:

Trade-offs: 1) Routing: System must identify data partition. 2) Queries: Range queries efficient (range sharding) or poor (hash sharding). 3) Hotspots: Poor partitioning causes skewed load. 4) Rebalancing: Moving partitions when adding/removing nodes.

Why: 1) Capacity scaling: A single machine cannot hold or process all data. 2) Performance scaling: Distribute reads/writes across nodes for parallelism. 3) Fault isolation: One shard/node failure does not affect the entire dataset.

Q10: What is partitioning (or sharding), and why is it used? 👇 Definition: Dividing a dataset into smaller, disjoint chunks (partitions/shards) and distributing them across multiple nodes. #SystemDesign #Interview #Coding #CodingJourney

2. Apply Lag (Replay Lag): A replica may apply logs slowly due to CPU/IO bottlenecks. Track the difference between the leader’s current offset and the replica’s applied offset.

1. Commit/Replication Lag: Each write has a log sequence number or offset. The leader tracks the offset each follower acknowledges.

Q8: In a leader–follower setup, what metric would you track to detect if a replica is lagging behind the leader? 👇 #SystemDesign #INTERVIEW #programminghelp #Coding

4) Async replicas serve stale data, causing clients to see anomalies (dirty reads, lost updates). 5) More replicas increase storage, network, and operational overhead; replication wastes resources if the workload doesn’t require it.

2) In synchronous setups, if replicas are slow or unavailable, the leader stalls, making the system appear "down" despite intact data. 3) The leader consumes excessive CPU/network resources pushing logs, reducing throughput.

Q9: When can replication harm a system? 👇 1) Misconfiguration Assuming data is safe, but async or poorly tuned replication → leader crash = data loss #SystemDesign #interview #systemsdesign

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Replica Unavailability Writes fail to reach all replicas, or reads return stale data. Mitigation: Fail requests requiring strong consistency. Use hinted handoff (temporarily store writes at another replica) Use Anti-entropy mechanisms like Merkle trees for later reconciliation.

Unclear write acknowledgment (when is a write "successful"?) Client assumes write succeeded, but replicas haven't applied it (data loss risk). Mitigation: Use quorum rules (R+W > N) or synchronous/semi-synchronous replication for stronger guarantees.

Q7: Name two common failures in replication 👇 #SystemDesign #Interview #interviewtips #Byte

Reads/Writes: Writes succeed after W replicas acknowledge. Reads succeed after R replicas respond, reconciling values if needed. Consistency: Tunable—adjusting R and W trades availability for consistency.

Flow: Any replica can accept reads or writes. The system uses quorum rules (R + W > N) for consistency. Replication: The coordinator node forwards writes to all relevant replicas (based on partitioning/sharding). Even if not a replica owner, it can hand off requests.