Start by clarifying requirements: define availability target, acceptable RPO/RTO, and consistency needs. Then walk through the three phases—failure detection, promotion, and recovery—highlighting trade-offs and AWS-specific services like RDS Multi-AZ, Aurora, or ElastiCache for leader election. Conclude by discussing how to handle split-brain and reintegrate the old primary.
Pro tip: Emphasize that bounded data loss is achieved by monitoring replication lag and only promoting a replica that is within an acceptable lag threshold; this shows you understand the trade-off between availability and consistency.
Ask about availability SLA, acceptable data loss (RPO), recovery time (RTO), and whether the system is single-region or multi-region. This sets the context for design decisions.
Describe a health-check mechanism using heartbeats, consensus (e.g., Raft, Paxos), or a quorum-based system. Mention tools like ZooKeeper, etcd, or AWS services (Route 53 health checks, CloudWatch alarms) to avoid false positives.
Explain how to select the most up-to-date replica, ensure it has applied all available WAL/binlog, and promote it. Use DNS failover or a proxy (e.g., ProxySQL, HAProxy) to redirect writes, and set a replication lag threshold to bound data loss.
Discuss fencing (STONITH) to prevent the old primary from accepting writes, and once it recovers, reconfigure it as a replica, possibly using pg_rewind or rebuilding from a snapshot.
After failover, ensure the new primary is stable, update monitoring and alerts, and consider automating the failover process for future incidents. Also discuss post-mortem and testing via game days.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this as a clarifying question early on and I think I gave a wishy-washy answer about 'it depends on the business.' Which, fine, but they wanted me to commit to a number and reason from it.
Start by clarifying that RPO is a business-driven decision, not a purely technical one, and that it directly dictates the replication mode (synchronous vs. asynchronous). Then walk through a concrete example, such as a financial transaction system requiring zero data loss (RPO=0) leading to synchronous replication, versus a social media feed where minutes of data loss are tolerable (RPO>0) allowing asynchronous replication.
Pro tip: Quantify the trade-off: synchronous replication increases write latency and reduces availability during network partitions, so always tie the RPO choice to the cost of data loss versus the cost of latency/availability. This shows you understand the business impact, not just the technology.
Explain that RPO is the maximum acceptable amount of data loss measured in time, and that it should be derived from business requirements, compliance needs, and user expectations.
Describe how RPO=0 typically requires synchronous replication (e.g., multi-AZ in AWS), while RPO>0 allows asynchronous replication (e.g., cross-region). Mention that synchronous replication ensures every write is confirmed by multiple replicas before acknowledgment.
Discuss the impact on latency, availability, and cost: synchronous replication adds write latency and can cause availability drops during failures, while asynchronous replication risks data loss but offers lower latency and higher availability.
Propose using different replication modes for different data types or tiers (e.g., synchronous for critical transactions, asynchronous for logs/analytics) to balance RPO and performance.
Emphasize the need to monitor replication lag and regularly test failover to ensure the chosen mode meets the RPO under real-world conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by explaining the fundamental problem of split-brain in distributed systems and why it occurs during primary failure detection. Then describe the mechanisms used to prevent it, such as quorum-based consensus, fencing tokens, and lease-based leadership. Finally, discuss trade-offs and how these mechanisms apply in practice, especially in cloud environments like AWS.
Pro tip: Emphasize that split-brain prevention is about ensuring only one node can act as primary at any time, often achieved by requiring a majority quorum for leader election and using fencing to isolate the old primary. Mention that in AWS, services like RDS and DynamoDB use these techniques, and you can reference Amazon's own systems like Aurora to show deeper understanding.
Explain what split-brain is and why it's dangerous: two nodes believing they are primary can lead to data corruption and inconsistent state.
Describe how primary failure is detected, e.g., via heartbeats, timeouts, or health checks, and note that false positives can trigger unnecessary failovers.
Discuss quorum-based consensus (e.g., Paxos, Raft) where a majority must agree on the new primary, and fencing (e.g., STONITH) to ensure the old primary cannot continue serving requests.
Explain how leases grant leadership for a limited time, requiring periodic renewal, and how clock drift and network delays must be accounted for.
Talk about trade-offs between consistency and availability, and give examples like Amazon Aurora, DynamoDB, or etcd to show practical application.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements and constraints, then discuss the trade-offs between different promotion strategies. Focus on how to achieve consensus among replicas and ensure data consistency, while minimizing downtime and avoiding split-brain.
Pro tip: Emphasize that the decision should be automated and based on a consensus protocol like Raft or Paxos, and mention the importance of fencing tokens to prevent the old primary from continuing to operate.
Ask about the system's consistency requirements, replication mode (synchronous vs asynchronous), and failure detection mechanisms. This sets the stage for choosing an appropriate strategy.
Explain that a consensus protocol like Raft or Paxos is typically used to elect a new leader. Describe how the protocol ensures only one leader is elected and how it handles network partitions.
Discuss criteria such as the replica with the most up-to-date data (highest log index), lowest latency, or predefined priority. Mention that the choice depends on whether the system prioritizes consistency or availability.
Describe how to detect primary failure (e.g., heartbeats, timeouts) and prevent split-brain using quorum-based elections and fencing mechanisms.
Explain the need for automated failover, monitoring, and testing (e.g., chaos engineering) to ensure the promotion process works correctly and to handle edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I wasn't ready for it at all.
Start by acknowledging that failover changes the log position, which can disrupt downstream consumers. Then describe a robust strategy that decouples consumers from the primary log position, such as using a stable intermediary like a log sequence number (LSN) mapping or a message queue, and ensuring consumers can resume from a consistent point. Emphasize idempotency, checkpointing, and monitoring to handle duplicates and gaps.
Pro tip: Highlight the importance of designing consumers to be idempotent and able to handle out-of-order or duplicate events, as failover often introduces these issues. Also, mention that at Amazon, we often use a service like Kinesis or MSK to buffer and replay events, which simplifies failover handling.
Explain that failover causes the replication log position to change, potentially leading to data loss or duplication for downstream consumers. This sets the context and shows you understand the problem.
Describe using an intermediary like a message queue (e.g., Kafka, Kinesis) or a change data capture (CDC) service that abstracts the log position. This allows consumers to read from a stable endpoint and handle failover transparently.
Discuss designing consumers to be idempotent and to checkpoint their progress. They should be able to resume from a known good position and handle duplicates or gaps gracefully.
Mention the need to monitor consumer lag, error rates, and data consistency. Alerts can trigger automated recovery or manual intervention when failover occurs.
Emphasize the importance of regularly testing failover and recovery processes to ensure downstream consumers behave as expected. This includes chaos engineering and game days.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the replication model (async vs sync) and the nature of the 8-second lag, then systematically walk through what data is permanently lost, how to detect the gap using replication coordinates and checksums, and finally how to communicate the impact and remediation to clients with transparency and ownership. Emphasize that the answer depends on the replication topology and the write patterns during the lag window.
Pro tip: Quantify the blast radius: estimate how many writes occurred in the 8-second window using write throughput metrics, and mention that you'd check if any of those writes were idempotent or could be replayed from an upstream log. This shows you think in terms of recoverability, not just data loss.
Determine whether replication was asynchronous or semi-synchronous, and why the replica lagged (e.g., network partition, slow disk, long-running transaction). This sets the boundary of what could be lost.
List the data that existed only on the old primary and was not yet replicated: committed transactions, uncommitted transactions, and any writes in the replication stream that were never applied. Also consider lost metadata like auto-increment counters or sequence values.
Use replication coordinates (e.g., binlog position, LSN, GTID) to find the exact divergence point. Compare checksums or row counts between the old primary (if recoverable) and the new primary. Estimate the number of affected rows/transactions using write throughput metrics.
Determine which clients and operations are affected. Check if lost writes can be replayed from an upstream log, application retry queue, or idempotent client retries. Decide whether to restore from backup or accept data loss.
Notify affected clients promptly with a clear, honest explanation: what happened, what data may be missing, what you are doing to recover it, and what they should do (e.g., retry failed operations). Provide a timeline and follow-up plan.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current design's consistency model and SLAs, then propose a multi-region DR strategy that balances RTO/RPO with cost and complexity. Explicitly discuss the new consistency trade-offs introduced by WAN replication, such as increased latency and potential for conflicts, and how to mitigate them.
Pro tip: Demonstrate awareness of Amazon's global infrastructure by mentioning services like DynamoDB Global Tables or S3 Cross-Region Replication, and emphasize that the choice of consistency model should be driven by business requirements, not technical convenience.
Ask about the existing architecture, SLAs, and business requirements for DR (RTO/RPO). Identify the current consistency model and data stores.
Outline a strategy such as active-passive or active-active, with specific replication mechanisms (e.g., synchronous vs asynchronous) and failover procedures.
Discuss how WAN replication affects consistency: increased latency, potential for stale reads, and conflict resolution in active-active setups.
Describe techniques to handle trade-offs, such as conflict-free replicated data types (CRDTs), read-repair, or tunable consistency, and how to monitor replication lag.
Recap the proposed design, highlighting how it meets requirements and manages trade-offs. Suggest validation through chaos engineering or DR drills.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.