Start by clarifying requirements like data volume, consistency needs, and downtime tolerance, then outline a two-phase migration: initial consistent snapshot followed by continuous CDC from the WAL. Explain how to handle schema mapping, ordering, idempotency, and cutover to ensure correctness.
Pro tip: Emphasize the importance of a well-defined cutover strategy with dual-writes or a brief read-only window to avoid data loss, and mention monitoring lag and reconciliation checks to build trust in the migration.
Ask about data size, write throughput, acceptable downtime, consistency requirements, and target NoSQL store characteristics to tailor the design.
Explain how to take a consistent snapshot (e.g., using database-native snapshot or a transaction with repeatable read) and efficiently bulk-load it into the key-value store, handling schema mapping and data transformation.
Describe reading the write-ahead log (e.g., via logical replication or CDC tools) to capture ongoing changes, and apply them to the key-value store in order with idempotency to handle duplicates.
Discuss mechanisms for exactly-once or at-least-once processing, checkpointing, and reconciliation to detect and repair inconsistencies between source and target.
Outline a cutover strategy (e.g., dual-writes, read-only window) to switch traffic to the new store, with monitoring and a rollback plan if issues arise.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The tricky part is that the snapshot takes time, so rows written during the snapshot could show up in both the snapshot and the WAL.
Start by defining snapshot consistency in the context of CDC: a consistent point-in-time view of the database. Then explain how to establish a snapshot boundary using techniques like consistent snapshots (e.g., via database-native snapshot isolation or locking) and how to align the CDC stream start position (e.g., using log sequence numbers or binlog coordinates) to avoid gaps or duplicates. Finally, discuss trade-offs and handling of edge cases like schema changes or long-running transactions.
Pro tip: Emphasize idempotency and exactly-once semantics: even with a perfect boundary, failures can cause duplicates, so design the pipeline to be idempotent and use deduplication strategies. This shows you think beyond the happy path.
Clarify what 'snapshot consistency' means for the use case: point-in-time consistency across tables, transactional consistency, or eventual consistency. Discuss the impact of isolation levels and read phenomena.
Explain how to take a consistent snapshot using database features like snapshot isolation (e.g., PostgreSQL's exported snapshots, MySQL's consistent snapshot with --single-transaction) or by briefly quiescing writes. Mention the trade-offs of locking vs. MVCC.
Describe how to record the exact log position (e.g., LSN, binlog file/offset, SCN) at the time the snapshot is taken, ensuring the CDC stream starts from that point. Highlight the need for atomicity between snapshot and position capture.
Discuss techniques to avoid gaps or duplicates: e.g., start CDC from the snapshot's log position and filter out events already included in the snapshot, or use a two-phase approach with a consistent snapshot and then replay from the recorded position. Mention idempotent application of changes.
Cover scenarios like long-running transactions spanning the boundary, schema changes during snapshot, and failures during snapshot or CDC. Discuss trade-offs between consistency, latency, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system context (e.g., stream processing, batch, or RPC) and define what 'exactly-once' means in that context. Then explain the core mechanisms: idempotency, deduplication, transactional writes, and checkpointing, and how they combine to handle replays and failures. Finally, discuss trade-offs and how you would validate correctness.
Pro tip: Emphasize that exactly-once is achieved through a combination of at-least-once delivery plus idempotent processing or deduplication, and that end-to-end exactly-once requires coordination across all components. Mentioning real-world systems like Kafka or Flink shows practical depth.
Ask questions to understand the system: is it a stream processor, a batch job, or a service? What are the sources and sinks? Define what 'exactly-once' means for the user (e.g., no duplicates in output, no lost updates).
Explain that exactly-once is typically built on at-least-once delivery with deduplication or idempotent operations. Discuss options like transactional writes, idempotent producers, and unique message IDs.
Describe how to handle replays: use checkpointing to track progress, store state durably, and ensure operations are idempotent or deduplicated. For failures, discuss recovery from checkpoints and handling partial writes with transactions or two-phase commit.
Discuss trade-offs: performance overhead of deduplication, latency of transactions, and complexity of coordination. Mention edge cases like network partitions, duplicate detection window limits, and exactly-once across heterogeneous systems.
Explain how you would test and monitor exactly-once semantics: end-to-end tests with failure injection, metrics for duplicates/loss, and auditing mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the migration context (e.g., database migration, service migration) and then outline a strategy that covers both during and after migration. Emphasize backward/forward compatibility, versioning, and a phased rollout with monitoring and rollback plans.
Pro tip: Highlight the importance of decoupling schema changes from code deployments and using techniques like expand-contract to minimize downtime and risk. Mention how you'd handle data backfills and dual writes to ensure consistency.
Ask questions to understand the migration type, data volume, uptime requirements, and existing schema. This shows you tailor your approach to the specific situation.
Plan schema changes to be backward and forward compatible, using versioning and additive changes first. Avoid breaking changes by using techniques like expand-contract.
Roll out changes in phases: first add new schema, then dual-write/backfill, then switch reads, and finally remove old schema. Monitor each phase and have rollback plans.
Use checksums, reconciliation jobs, and validation queries to verify data consistency between old and new schemas during and after migration.
After migration, remove deprecated schema, update documentation, and set up monitoring to detect any issues. Conduct a post-mortem to capture lessons learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioning by primary key range or hash made sense for parallelism.
Start by clarifying the scope and constraints (data size, consistency requirements, downtime tolerance), then propose a partitioning strategy that aligns with access patterns and a queue-based migration approach that ensures idempotency and progress tracking. Finally, describe a monitoring plan that covers both data integrity and operational health, with clear rollback and validation steps.
Pro tip: Emphasize idempotency and checkpointing in your queue design—this shows you've thought about failure recovery and exactly-once semantics, which is critical at Google's scale.
Ask about data volume, consistency needs, acceptable downtime, and migration timeline to tailor your partitioning and queuing strategy.
Choose a partition key (e.g., user ID, time range) that balances load and minimizes cross-partition dependencies, and explain how you'd handle hot partitions.
Describe a queue-based system (e.g., using Pub/Sub or Kafka) with idempotent workers, checkpointing, and retry logic to process partitions in parallel while tracking progress.
Outline how you'd validate migrated data (checksums, row counts, sampling) and implement a rollback strategy if issues arise.
Specify metrics (throughput, error rates, latency, data integrity checks) and alerts to detect failures or slowdowns, plus dashboards for visibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.