Start by clarifying the requirements and constraints: what 'merge' means for identity, accounts, and transactions; whether accounts should be re-parented or kept separate; and how to handle idempotency and concurrency. Then propose a data model that separates customer identity from accounts and transactions, and design the merge as an atomic operation that updates a customer alias mapping and re-points references without altering balances or transaction records. Finally, discuss trade-offs around consistency, performance, and rollback.
Pro tip: Emphasize that the merge should be idempotent and reversible (via an audit log or soft merge) to handle retries and potential errors, and mention that you would use a distributed transaction or saga pattern to ensure atomicity across services.
Ask questions to understand what 'merge' entails: should the old customer ID be retired or aliased? Should accounts be re-assigned to the new customer or remain linked to their original IDs? What are the SLAs for consistency and availability? Are there regulatory requirements for audit trails?
Propose a schema where customers, accounts, and transactions are separate entities. Accounts reference a customer ID, and transactions reference an account ID. Consider adding a customer alias table or a merged_into field to track identity merges without altering existing references.
Outline the steps: validate both customers exist and are not already merged; create an alias from oldId to newId; update any customer-level metadata (e.g., preferences) to the new customer; ensure accounts remain attached to their original IDs but are now accessible via the new customer identity. Use a transaction or saga to guarantee atomicity.
Discuss how to handle concurrent merges or operations on the same customers. Use optimistic locking or distributed locks. Ensure idempotency by checking if the merge already occurred. Plan for rollback by keeping an audit log or soft-deleting the alias.
Compare hard merge (updating all references) vs. soft merge (alias table). Discuss performance impact on reads (e.g., resolving aliases) and writes. Consider sharding by customer ID and how merges across shards would work. Mention monitoring and alerting for merge failures.
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I went with an alias/redirect table pretty quickly.
Start by clarifying requirements: the merge must be atomic, both IDs must resolve to the same canonical identity for all reads and future writes, and the system must handle high scale and consistency. Then propose a design that uses an identity mapping service with a canonical ID, a write path that redirects writes to the canonical ID, and a read path that resolves any ID to the canonical one, ensuring atomicity via a transaction or a two-phase commit.
Pro tip: Emphasize idempotency and backward compatibility: the merge operation should be idempotent so retries are safe, and old IDs must continue to work indefinitely to avoid breaking existing clients or data references.
Ask about scale (QPS, data size), consistency requirements (strong vs eventual), latency SLAs, and whether the merge can be asynchronous. Confirm that both IDs must resolve to the same canonical identity for all reads and writes immediately after commit.
Introduce a canonical ID (e.g., the surviving ID or a new UUID) and maintain a mapping from any ID to the canonical ID. Store this mapping in a highly available, low-latency store (e.g., a distributed KV store) with strong consistency for writes.
Use a transaction or a consensus protocol (e.g., Paxos/Raft) to atomically update the mapping and redirect future writes. Ensure that after commit, any write to either ID is redirected to the canonical ID, and the operation is idempotent.
For reads, resolve the ID to the canonical ID via the mapping service. Use caching with appropriate invalidation (e.g., write-through or TTL) to reduce latency, but ensure cache coherence so that after merge, stale mappings are not served.
Discuss handling partial failures (e.g., mapping update succeeds but write redirection fails), and trade-offs between consistency and availability (e.g., using a global lock vs. eventual consistency). Mention monitoring and rollback strategies.
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The key insight I landed on was separating ownership metadata from the ledger records.
Start by clarifying that the ledger is immutable and should never be rewritten; instead, ownership is a mutable reference layer. Propose a design where ownership is stored as a separate mapping (e.g., customer_id -> owner_id) or as a versioned pointer, and all entities resolve ownership through that layer, enabling atomic reassignment without touching ledger entries.
Pro tip: Emphasize idempotency and auditability: reassignment should be a single atomic operation that emits an event, and all downstream systems should eventually converge. Also mention that you'd use a distributed transaction or saga pattern to ensure consistency across services.
Ask about scale, consistency needs, and whether the ledger is truly immutable. Confirm that ownership is metadata, not part of the ledger's core financial records.
Introduce a separate ownership service or table that maps entity IDs to owner IDs. All entities reference this layer instead of storing owner directly.
Use a transaction or saga to update the ownership mapping and emit an event. Ensure idempotency and handle partial failures with compensating actions.
Decide on read-your-writes consistency for the reassignment. Invalidate caches and propagate changes to read replicas or materialized views.
Log all reassignments with before/after states. Provide a way to revert if needed, without altering ledger entries.
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Start by clarifying the merge scenario and data model, then outline a reconciliation strategy that ensures idempotency and consistency. Emphasize using unique identifiers, transaction logs, and a state machine to track payment and transfer lifecycles, and discuss how to handle recurring/scheduled items with deduplication and conflict resolution.
Pro tip: Proactively mention the need for a rollback plan and monitoring during the merge to catch anomalies early, showing you think about production safety beyond just the happy path.
Ask questions to understand the merge context: which systems are merging, what data volumes are involved, and what the acceptable downtime or consistency guarantees are. Identify all payment types (one-time, recurring, scheduled) and their current storage.
Propose a canonical schema that maps old records to new ones using stable unique IDs (e.g., payment ID, transfer ID) and includes metadata like source system and merge timestamp. Ensure the model supports idempotent operations and audit trails.
Use deterministic keys (e.g., hash of user ID + amount + timestamp) to detect duplicates. For recurring/scheduled items, store a unique schedule ID and use a distributed lock or transaction to prevent double execution during the merge window.
For payments/transfers in progress, pause execution, reconcile state, and resume with a consistent state machine. For future scheduled items, re-register them in the new system with deduplication checks and adjust timing if needed.
Run reconciliation reports comparing counts and sums between old and new systems. Set up alerts for anomalies (e.g., duplicate executions, missing items) and have a rollback plan if issues arise.
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My answer was basically: in-flight items carry the account ID not the customer ID, so they settle fine as long as account ownership is still resolvable.
Start by clarifying the scope: what systems are merging (e.g., payment processors, ledgers) and what 'settle correctly' means (consistency, exactly-once). Then outline a strategy that ensures in-flight transactions are tracked, reconciled, and completed across the merge boundary, using idempotency, durable queues, and a reconciliation process.
Pro tip: Emphasize that the hardest part is not the happy path but handling failures and partial states during the merge; propose a reconciliation mechanism that runs continuously until all in-flight transactions are resolved, and highlight the need for observability to detect anomalies.
Ask questions to understand the systems involved, the definition of 'settle correctly', and any constraints (e.g., downtime, data consistency guarantees).
Enumerate pending transactions, authorizations, and transfers, and determine their lifecycle states and where they are stored.
Ensure each transaction has a unique ID and is processed exactly once, using durable queues or logs to persist state across the merge.
Create a reconciliation process that compares source and target systems, resolves discrepancies, and alerts on failures.
Define rollback procedures and handle edge cases like timeouts, partial failures, and duplicate submissions.
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This was the part I felt least prepared for.
Start by framing the problem as a deterministic merge of two data sources with conflicting constraints, then systematically address each conflict type with a clear resolution policy. Emphasize that your policies are idempotent, order-independent, and auditable, and explain how they preserve data integrity and user trust.
Pro tip: Propose a two-phase approach: first, detect and log all conflicts with a unique conflict ID, then apply deterministic resolution rules that are versioned and reversible. This shows you prioritize observability and rollback safety, which is critical at Meta's scale.
Enumerate each conflict type (duplicate payment methods, identical scheduled items, beneficiary collisions, single-primary constraints, unique nickname conflicts) and specify the invariants that must hold after merge (e.g., at most one primary payment method per token, unique nickname per customer).
For each conflict, define a total order or priority rule (e.g., latest timestamp wins, source system priority, lexicographic ID) that guarantees the same outcome regardless of merge order. Ensure policies are idempotent and commutative where possible.
Build a mechanism to detect conflicts during merge, assign a unique conflict ID, and log all relevant data (source records, resolution applied, timestamp). This enables auditing, debugging, and potential rollback.
Execute resolutions in a transaction or with idempotent operations to avoid partial merges. Use versioning or optimistic concurrency to handle concurrent merges safely.
After merge, run validation checks to ensure all invariants hold. Set up monitoring and alerts for conflict rates and resolution failures to catch issues early.
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Start by clarifying the system's consistency requirements and scale, then propose a hybrid approach that combines incremental updates for real-time correctness with periodic batch recomputation for resilience. Emphasize idempotency, versioning, and fallback mechanisms to handle failures and ensure aggregates remain accurate.
Pro tip: Mention that you would use a write-ahead log or change data capture to replay updates and rebuild caches/indexes, and highlight the importance of monitoring staleness metrics to detect and alert on inconsistencies.
Ask about consistency needs (e.g., immediate vs. eventual), data volume, update frequency, and latency SLAs to tailor the solution.
Propose a system where each merge triggers events that update affected aggregates (e.g., top-N leaderboards) in real-time, using idempotent operations and versioning to handle out-of-order updates.
Use a cache-aside pattern with short TTLs for leaderboards, and invalidate or update cache entries upon merge events; consider write-through for critical aggregates.
Schedule periodic batch jobs to recompute aggregates from source data, compare with incremental results, and correct any drift, ensuring eventual consistency.
Instrument the system to track update latency and cache hit rates, and set up alerts for inconsistencies or delays in leaderboard updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I covered the no-double-counting angle by pointing to the alias layer: both IDs resolve to the same canonical, so any aggregation query runs once against the canonical.
Start by clarifying the merge semantics and consistency requirements, then propose a design that uses a stable canonical ID with an alias mapping to resolve both old and new IDs to the same entity. Ensure all reads and writes go through this resolution layer, and use idempotent operations and transactional guarantees to prevent loss or double-counting.
Pro tip: Emphasize that correctness must be maintained during the merge itself, not just after; suggest a phased approach with dual-write and backfill, and highlight the importance of idempotency keys and monotonic versioning to handle concurrent queries.
Ask about the expected consistency model (strong vs eventual), the scale of data, and whether the merge is online or offline. Confirm that both old and new IDs must return identical results.
Propose a canonical customer ID with an alias table that maps old and new IDs to the canonical ID. Ensure this mapping is consistent and durable, and that all queries resolve IDs before accessing data.
Use transactions or idempotent writes with unique constraints to prevent double-counting. For aggregates, maintain a single source of truth and update it atomically during the merge.
Implement a phased migration: dual-write to both old and new structures, backfill historical data, then switch reads. Use versioning or timestamps to avoid stale reads and ensure no loss.
Set up reconciliation jobs to compare balances and aggregates before and after merge, and monitor for discrepancies. Provide a rollback plan if issues arise.
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Start by clarifying the merge operation's context (e.g., merging user accounts, data records) and define the atomicity and consistency guarantees expected. Then explain how you achieve idempotency through unique merge identifiers and versioning, and describe concurrency control mechanisms like locking or optimistic concurrency to handle simultaneous merges and chain scenarios.
Pro tip: Emphasize that idempotency and concurrency control must be designed together; for example, using a merge ID and a state machine ensures that even if a merge is retried or concurrent, the system converges to a consistent state without duplication.
Ask questions to understand the data model, expected guarantees, and failure scenarios. Define what atomicity and consistency mean for this specific merge operation.
Use a unique merge identifier and a version or timestamp to detect and ignore duplicate merge requests. Ensure the merge operation is deterministic and can be safely retried.
Use transactions or atomic compare-and-swap operations to update all affected records. Ensure that either all changes are applied or none, and that the system remains consistent.
Employ locking (pessimistic or optimistic) to serialize merges on the same entities. For chain scenarios, use a merge graph or union-find structure to resolve the final target and prevent cycles.
Compare approaches (e.g., locking vs. optimistic concurrency) and address potential issues like deadlocks, partial failures, and scalability.
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Merge is O(E) where E is the number of entity references to remap, plus O(log N) for leaderboard updates.
First, clarify the context by defining the data structures and operations involved in the merge and post-merge read path. Then, analyze the time and space complexity for each phase, considering best, average, and worst cases, and discuss trade-offs and optimizations.
Pro tip: Always relate complexity to real-world constraints like memory hierarchy and concurrency, and mention how you would measure or monitor performance in production.
Ask or state the data structures (e.g., arrays, linked lists, LSM trees) and the specific merge operation (e.g., merging two sorted lists, merging in a merge sort, or merging in a database).
Derive time and space complexity for the merge operation, considering input sizes and auxiliary space. Discuss if it's in-place or not.
Describe how reads occur after the merge (e.g., binary search, sequential scan, index lookup) and derive their time and space complexity.
Mention how different data structures or algorithms affect complexity, and potential optimizations like caching, parallelism, or lazy merging.
Provide a clear summary of the complexities and their implications for system design and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.