This was one question but it basically ate the entire session.
Start by clarifying requirements and scale, then design a dual-path architecture: a batch pipeline for offline training and a low-latency serving layer for online inference, ensuring consistency between them. Walk through ingestion, storage, and serving, emphasizing trade-offs and Reddit-specific use cases like feed ranking and user embeddings.
Pro tip: Highlight the importance of point-in-time correctness for offline training to prevent data leakage, and propose a unified transformation layer (e.g., using Spark or Flink) to ensure feature consistency between offline and online stores.
Ask about data volume, latency SLAs, feature freshness, and key use cases (e.g., ranking, recommendations). Establish non-functional requirements like consistency, scalability, and cost.
Outline batch and streaming ingestion from sources like Kafka, databases, and logs. Describe transformation pipelines (e.g., Spark for batch, Flink for streaming) to compute features and handle backfills.
For offline: use a data lake (S3/HDFS) with Parquet/ORC and a query engine (Presto/Spark). For online: use a low-latency store (Redis, Cassandra, DynamoDB) with appropriate indexing and TTL.
Design APIs for online feature retrieval (gRPC/REST) with caching. Ensure offline-online consistency via a unified transformation layer and point-in-time joins for training data generation.
Discuss monitoring for feature drift, latency, and freshness. Cover versioning, backfilling, and how to handle schema evolution and A/B testing of features.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew the concept but struggled to articulate it cleanly under pressure.
Start by defining point-in-time correctness as ensuring that features used for training a model at time T only use data that was available before T, preventing label leakage. Then describe a system design that includes a feature store with time-travel capabilities, using event timestamps and versioning, and explain how to handle late-arriving data and backfills. Finally, discuss validation techniques like temporal splits and monitoring for data leakage.
Pro tip: Emphasize the importance of aligning offline and online feature computation to avoid training-serving skew, and mention that point-in-time correctness is not just about timestamps but also about ensuring the same transformation logic is applied consistently.
Explain that it means using only data available up to the prediction time for each training example, avoiding future data leakage. Highlight that this is crucial for realistic model evaluation and production performance.
Describe a feature store that stores feature values with event timestamps and versioning, allowing retrieval of feature values as of a specific point in time. Mention using a temporal join or as-of join to fetch the correct feature values for each training example.
Discuss strategies for handling late-arriving data, such as watermarks or grace periods, and how to backfill features without introducing leakage. Emphasize the need for idempotent and reproducible feature computation.
Explain how to use the same feature transformation code for both offline training and online serving to prevent training-serving skew. Mention logging online features for monitoring and retraining.
Describe validation techniques like temporal cross-validation and monitoring for data leakage, such as checking feature distributions over time. Mention the importance of automated tests for point-in-time correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with LRU as a starting point and they immediately asked what happens when feature freshness requirements conflict with cache hit rates.
Start by clarifying the requirements: feature serving involves low-latency reads of precomputed features, so a read-through cache with a short TTL is a good default. Then discuss eviction policies (e.g., LRU, LFU) and how to handle stale data, emphasizing trade-offs between freshness and performance. Finally, tie your answer to Reddit's scale and real-time needs, mentioning monitoring and fallback strategies.
Pro tip: Show that you understand the difference between caching for online serving (where latency matters) and offline training (where consistency matters), and mention that you'd measure cache hit rate and adjust TTLs based on business impact.
Ask about read/write patterns, latency SLAs, data freshness requirements, and scale (QPS, feature size). This shows you don't jump to solutions.
Propose a read-through cache with a short TTL (e.g., 1-5 minutes) for feature serving, possibly with a local in-memory cache (e.g., Caffeine) in front of a distributed cache (e.g., Redis) for hot features.
Discuss LRU or LFU for general use, but consider TTL-based eviction for freshness. For Reddit, where some features are more popular, LFU might be better to keep hot items.
Explain how to invalidate on feature updates (e.g., pub/sub, versioning) and how to handle stale reads (e.g., serve stale while revalidating, or fallback to source).
Mention tracking cache hit rate, latency, and eviction rates, and using that data to tune TTLs and eviction policies. Also discuss fallback to the feature store if cache misses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about read path vs write path failures separately, which I think was the right framing.
Start by clarifying the feature store's role and criticality, then discuss reliability strategies like redundancy, replication, and graceful degradation. Define SLOs for availability, latency, and data freshness, and explain how they tie to fault tolerance mechanisms.
Pro tip: Tie SLOs to business impact—e.g., '99.9% availability for online serving'—and mention error budgets to balance reliability with feature velocity. Show you understand trade-offs between consistency and availability for different feature types.
Ask about the feature store's use cases (online vs offline), criticality, and expected scale. This determines the reliability and fault tolerance needs.
Propose redundancy at multiple levels: replicated storage, multi-AZ deployment, and fallback to default or stale features. Discuss trade-offs between consistency and availability.
Specify SLOs for availability (e.g., 99.9% for online serving), latency (e.g., p99 < 100ms), and data freshness (e.g., < 5 min lag). Align with business needs.
Set up metrics for SLO compliance, error budgets, and anomaly detection. Use alerts to trigger automated failover or degradation.
Define runbooks for common failures, conduct chaos testing, and ensure graceful degradation. Review post-mortems to improve resilience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements: multiple teams, data sensitivity, and compliance needs. Then propose a layered architecture with centralized governance policies, role-based access control (RBAC) or attribute-based access control (ABAC), and automated lineage tracking. Emphasize cross-team collaboration through federated ownership and self-service tooling.
Pro tip: Highlight the trade-off between centralization and team autonomy: a central governance body sets policies, but teams manage their own feature namespaces with delegated permissions. This shows you understand organizational dynamics, not just technology.
Ask about the number of teams, data sensitivity levels, compliance regulations (e.g., GDPR, CCPA), and existing infrastructure. This ensures your solution is tailored to Reddit's scale and needs.
Propose a federated governance model with a central platform team defining policies and standards, while domain teams own their feature definitions and metadata. Include a review process for new features and regular audits.
Use RBAC for coarse-grained access (e.g., team-level) and ABAC for fine-grained control (e.g., based on data classification, user role, or purpose). Integrate with existing identity providers (e.g., LDAP, OAuth) and enforce least privilege.
Automatically capture lineage from data sources to features to models by instrumenting pipelines and storing metadata in a central catalog. Provide a UI for teams to explore dependencies and impact analysis.
Establish clear ownership, documentation, and communication channels. Use a feature registry with search and discovery, and encourage reuse through incentives and shared best practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around the testing pyramid, explaining how each layer (unit, integration, end-to-end) applies to feature pipelines, and emphasize data validation as a cross-cutting concern. Use a concrete example, such as a feature pipeline that computes user engagement metrics, to illustrate your approach and trade-offs.
Pro tip: Highlight the importance of testing data quality and pipeline idempotency, and mention how you'd use tools like Great Expectations or dbt tests for data validation. Also, discuss how you balance test coverage with execution speed in CI/CD.
Ask clarifying questions about the pipeline's purpose, data sources, expected scale, and SLAs to tailor your testing strategy. This shows you understand the context before diving into specifics.
Test individual functions and transformations in isolation, mocking external dependencies. Focus on edge cases, null handling, and correctness of business logic.
Verify that components work together, including data ingestion, transformation, and storage. Use test data that mimics production and validate schema compatibility and error handling.
Run the entire pipeline from source to sink in a staging environment, asserting on final outputs and monitoring for failures. Include tests for idempotency and recovery from failures.
Implement checks for data quality (e.g., completeness, uniqueness, distribution) at each stage. Use automated tools and define thresholds for alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.