I started with org and account and felt okay, but the moment they asked me to wire in frequency cap policy as its own entity I kind of froze.
Start by clarifying the scope and key requirements of the advertiser intake system, then identify the core entities and their relationships. Propose an ERD-level schema with tables, primary keys, and foreign keys, and explain how the design supports scalability, data integrity, and business workflows.
Pro tip: Emphasize normalization to reduce redundancy, but also discuss strategic denormalization for performance-critical queries, showing you understand trade-offs in real-world systems.
Ask questions to understand the system's purpose, scale, and key workflows (e.g., how advertisers onboard, how campaigns are managed). This ensures your design meets actual needs.
List the main entities (Organization, Account, User, Campaign, AdGroup, Creative, Budget, Targeting, FrequencyCap, Billing, Approval) and define their relationships (one-to-many, many-to-many).
For each entity, specify the table structure: primary key, foreign keys, and essential attributes. Ensure referential integrity and appropriate data types.
Discuss how to handle hierarchical relationships (e.g., Organization to Account), many-to-many relationships (e.g., Campaign to Targeting), and audit trails for approvals.
Summarize the design, highlighting scalability, performance (indexes), and flexibility for future changes. Mention any trade-offs made.
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Talked through keeping targeting rules normalized to avoid duplication across campaigns, but then they pushed on query performance and I started hedging.
Start by clarifying the schema's access patterns and performance goals, then explain how normalization reduces redundancy and anomalies while denormalization improves read performance. Discuss how multi-tenancy with row-level security (RLS) adds constraints like tenant isolation and query filtering, which can shift the trade-off toward denormalization for read-heavy, tenant-scoped queries, but requires careful handling of data duplication and security.
Pro tip: Emphasize that RLS predicates can act as implicit filters that benefit from denormalized structures like covering indexes or materialized views per tenant, but warn against over-denormalizing because it complicates tenant data deletion and increases storage costs. Mention that Netflix often uses a polyglot persistence approach, so the decision may vary by service.
Identify the main queries, read/write ratios, latency SLAs, and tenant isolation needs. Ask if the schema is for OLTP, analytics, or both.
Discuss how normalization reduces data redundancy, ensures consistency, and simplifies updates, but may require complex joins that hurt read performance.
Explain how denormalization can speed up reads by pre-joining data, but increases storage, risks inconsistency, and complicates writes and tenant data management.
Analyze how RLS adds a tenant filter to every query, which can make denormalized structures (e.g., tenant-specific materialized views) more attractive, but also requires that all copies include tenant_id and are secured.
Recommend a hybrid approach: normalize core entities, denormalize for specific high-read, tenant-scoped use cases, and use RLS-aware indexing or partitioning. Justify with expected performance gains and operational costs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to an event log table and a versioned config pattern.
Start by clarifying that versioning and auditability are first-class concerns in a mutable configuration system, then propose an append-only, immutable versioned schema with effective timestamps and actor tracking. Walk through how entities like targeting rules and budget flights are represented as versioned records, and explain how reads resolve to the correct version at a point in time.
Pro tip: Emphasize that auditability is not just logging—it's a queryable, immutable history that supports rollbacks and compliance. Mention that you'd design the schema to make the common case (reading current config) fast while keeping the audit trail cheap to store and query.
Ask about read/write patterns, latency requirements, retention policies, and whether point-in-time queries or rollbacks are needed. This shows you don't over-engineer and tailor the schema to real needs.
Propose that each configurable entity (e.g., targeting rule, budget flight) has a stable ID and a series of immutable versions, each with a version number, effective timestamp, and status (draft/active/archived).
Include fields like created_by, created_at, change_reason, and a reference to the previous version. Optionally, store a diff or full snapshot to balance storage and query efficiency.
Explain how writes create new versions without mutating existing ones, and how reads resolve the correct version based on time or explicit version. Mention indexing strategies for fast current-version lookups.
Describe how to roll back by activating a previous version, how to archive old versions, and how to enforce immutability for audit purposes. Mention any regulatory or internal policy considerations.
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Classic question but the FK integrity angle is where people slip up.
Start by defining soft deletes as adding a deleted_at timestamp column and filtering queries to exclude deleted rows. Then explain how this breaks unique constraints (e.g., duplicate active rows) and foreign key integrity (e.g., references to deleted rows), and propose solutions like partial unique indexes and ON DELETE SET NULL or application-level checks. Emphasize trade-offs between data retention, query complexity, and performance.
Pro tip: Mention that soft deletes can be implemented with a deleted_at column and partial indexes, but be aware that they complicate cascading deletes and can lead to orphaned records if not handled carefully. Also, consider using a separate archive table for compliance and performance.
Add a nullable deleted_at timestamp column to relevant tables. Queries must filter out rows where deleted_at is not null, either via views or application logic.
Unique constraints on columns like email will fail if a deleted row exists with the same value. Use partial unique indexes (e.g., CREATE UNIQUE INDEX ... WHERE deleted_at IS NULL) to enforce uniqueness only among active rows.
Foreign keys referencing soft-deleted rows can cause issues: either allow references to deleted rows (and handle in application), or use ON DELETE SET NULL/CASCADE. Alternatively, implement triggers to enforce referential integrity based on deleted_at.
Soft deletes increase storage and query complexity, and can degrade performance without proper indexing. Consider archiving to a separate table or using event sourcing for auditability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second and said deduplication key on the import job table.
Start by defining idempotency in the context of imports: the same logical submission should result in the same stored state, regardless of retries. Then propose a schema that includes a client-generated idempotency key (or content hash) with a unique constraint, and describe how to handle conflicts gracefully. Finally, discuss how this scales for bulk uploads and how to manage partial failures.
Pro tip: Mention that idempotency keys should be scoped to the advertiser and operation, and that you'd store the key with a TTL or archive old keys to avoid unbounded growth. Also, highlight the importance of logging and monitoring duplicate attempts to detect client bugs.
Ask clarifying questions: What defines a duplicate? Is it the entire form or specific fields? What's the expected volume and retry behavior? This ensures you design for the right granularity.
Decide between client-generated keys (e.g., UUID) or server-generated content hashes. Client keys are more reliable for retries; content hashes work when clients can't change. Explain trade-offs.
Add a column for the idempotency key and create a unique index, possibly composite with advertiser_id and operation_type. This enforces idempotency at the database level.
On duplicate key violation, return the existing record (or a success response) instead of erroring. For bulk uploads, process in batches and track per-item status to allow partial success.
Discuss partitioning or archiving old idempotency keys to prevent table bloat. Consider using a separate table for idempotency records with TTL, and ensure the design supports high throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Framed it as a draft state on each entity with a completeness score computed from required fields.
Start by framing the problem as a state machine for onboarding, where each session can advance the advertiser's progress. Then describe how your schema separates draft data from validated data, using nullable fields, versioning, and validation rules that can be applied incrementally. Emphasize how this design supports partial submissions without compromising data integrity.
Pro tip: Show awareness of the trade-off between strict schema validation and flexibility for partial data—mention techniques like JSON Schema with optional fields or a staging table, and how you'd handle validation errors gracefully to guide the user.
Explain that onboarding is a multi-step process with states like 'incomplete', 'pending validation', and 'complete'. This helps clarify how data is collected and validated over time.
Describe how you allow nullable fields or use a separate draft table to store incomplete submissions. Mention that you avoid enforcing all constraints upfront to support incremental data entry.
Detail how validation is applied conditionally—e.g., only validate fields that are present, or use a rules engine that checks completeness and correctness at each step. Mention error handling and feedback to the user.
Explain how you persist partial data across sessions, perhaps using a session ID or user ID, and how you merge new data with existing drafts without overwriting valid entries.
Describe how you perform a final validation before promoting the draft to a complete, active advertiser record, ensuring all required fields are present and valid.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.