Start by clarifying requirements and scale, then propose a streaming architecture with a message queue, stream processor, and a multi-tier storage layer for real-time and batch aggregations. Emphasize deduplication via idempotent processing and late-event handling using watermarks and allowed lateness. Discuss trade-offs between latency, accuracy, and cost.
Pro tip: Meta values data quality and freshness; explicitly discuss how you'd monitor and alert on deduplication rates and late-event volumes, and how you'd backfill or reconcile with batch pipelines to ensure consistency.
Ask about expected QPS, number of ads/advertisers/geos, acceptable latency for each window, and consistency requirements. This shapes technology choices and partitioning strategy.
Propose a scalable message queue (e.g., Kafka) for click events. Implement deduplication using a unique click ID and a fast lookup store (e.g., Redis or RocksDB) with TTL, or leverage exactly-once semantics in the stream processor.
Use a stream processor (e.g., Flink) to compute aggregations over tumbling or sliding windows. Handle late events with watermarks and allowed lateness, emitting early results and updating them as late data arrives.
Store real-time aggregates in a low-latency store (e.g., Redis, Cassandra) for serving. For longer windows (1 day), use a scalable OLAP store (e.g., Druid, ClickHouse) or batch processing with periodic updates.
Discuss trade-offs: exactly-once vs at-least-once, latency vs accuracy, cost of stateful processing. Explain partitioning by ad/advertiser/geo to scale horizontally and how to handle hotspots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with at-least-once plus idempotent aggregation rather than exactly-once because exactly-once in distributed stream processing has real overhead and I didn't think the use case needed it.
Start by clarifying the scale and constraints, then systematically walk through each non-functional requirement (latency, throughput, consistency, availability, durability, cost) and how they interact. For millions of QPS with low latency, describe a layered architecture with caching, sharding, and async processing, and explicitly state the consistency model (e.g., eventual consistency with read-your-writes) and trade-offs. Conclude by tying choices back to business impact and how you'd measure and iterate.
Pro tip: Quantify everything: give concrete numbers for latency targets (e.g., p99 < 100ms), QPS per node, and consistency SLAs. This shows you think in terms of measurable SLOs, not vague ideals—exactly what Meta expects.
Ask questions to understand the workload: read/write ratio, data size, geographic distribution, and what 'low latency' means (p50 vs p99). Confirm the QPS target and any consistency requirements from the business.
List the key NFRs: latency, throughput, consistency, availability, durability, scalability, and cost. Explain how they conflict (e.g., strong consistency vs low latency) and which are most critical for this system.
Describe techniques: horizontal scaling with sharding, caching (CDN, in-memory), async processing, batching, and load balancing. Mention specific technologies (e.g., Memcached, Kafka, Thrift) and how they reduce latency.
State the consistency guarantees you target (e.g., eventual consistency, read-your-writes, monotonic reads) and justify why they meet business needs. Explain how you'd implement them (e.g., quorum reads/writes, versioning).
Explain how you'd track SLOs with metrics (p99 latency, error rates, QPS) and use them to drive improvements. Mention capacity planning and failure testing to ensure resilience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CDN or edge collection, then into Kafka with partitioning by ad ID.
Start by clarifying requirements (scale, latency, reliability) and then walk through the end-to-end pipeline: client-side event capture, transport to the backend, ingestion layer, and finally the stream processor. Emphasize trade-offs at each stage, such as batching vs. low latency, and how you ensure data integrity and exactly-once processing.
Pro tip: Show awareness of Meta's scale by discussing how to handle millions of events per second and the importance of backpressure and idempotency to avoid data loss or duplication.
Ask about expected event volume, latency requirements, data loss tolerance, and processing guarantees (at-least-once vs. exactly-once). This sets the stage for design decisions.
Describe how the click event is captured on the client (e.g., JavaScript SDK), enriched with metadata, and batched to reduce network overhead. Mention using sendBeacon or WebSocket for reliable delivery.
Explain how events are sent to an ingestion endpoint (e.g., HTTP API, load balancer) and then to a durable, scalable message queue like Kafka or Meta's equivalent (e.g., Scribe). Discuss partitioning and replication for fault tolerance.
Detail how the stream processor (e.g., Flink, Spark Streaming) consumes from the queue, performs transformations, windowing, and aggregations, and writes to sinks (e.g., data warehouse, real-time dashboards).
Cover mechanisms for exactly-once processing (idempotent writes, checkpoints), backpressure handling, and monitoring (latency, throughput, error rates). Discuss auto-scaling of ingestion and processing layers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with Flink and talked through tumbling windows for the 1m/1h/1d aggregates and briefly mentioned sliding windows for smoother rollups.
Start by clarifying the use case and requirements (e.g., event time vs processing time, latency, accuracy, scale). Then propose a stream processing architecture (e.g., using Flink, Kafka Streams, or Beam) and detail windowing strategies (tumbling, sliding, session) and watermark mechanisms (periodic, punctuated, allowed lateness). Finally, discuss trade-offs and how you'd handle late data and ensure correctness.
Pro tip: Emphasize that watermarks are a heuristic for event-time completeness, not a guarantee; always pair them with allowed lateness and a side output for late data to balance correctness and latency.
Ask about data characteristics (event time vs processing time, out-of-order events), latency/accuracy trade-offs, and scale. This shapes windowing and watermark choices.
Select appropriate window types (tumbling, sliding, session) based on the use case. Explain how each handles event grouping and triggers.
Describe how watermarks are generated (periodic or punctuated) and propagated. Discuss how they trigger window evaluation and handle late data.
Explain mechanisms for late data: allowed lateness, side outputs, and reprocessing. Discuss how to maintain correctness (e.g., exactly-once semantics).
Compare latency vs completeness, watermark heuristics, and resource usage. Mention optimizations like incremental aggregation and state management.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said an OLAP store, something like Druid or ClickHouse, for the ad-hoc query path.
Start by clarifying the requirements: query patterns, data volume, latency expectations, and cost constraints. Then propose a columnar OLAP storage layer (e.g., Presto/Trino on Hive/Parquet, or a specialized engine like Druid/ClickHouse) and justify it by contrasting with row-based OLTP stores. Finally, discuss trade-offs around performance, cost, and operational complexity, and how it integrates with Meta's data ecosystem.
Pro tip: Mention that ad-hoc analytics often benefit from a two-tier approach: a hot layer (e.g., Druid) for sub-second dashboards and a warm layer (e.g., Presto on Parquet) for flexible, deep queries, balancing cost and performance.
Ask about query complexity, concurrency, data freshness, and SLA to understand if the workload is interactive or batch-oriented.
Compare columnar stores (Parquet/ORC on HDFS/S3), OLAP engines (ClickHouse, Druid), and data warehouses (Snowflake, BigQuery) based on scan efficiency, compression, and indexing.
Assess how the storage layer fits with existing data pipelines, metadata catalogs, and query engines like Presto/Spark at Meta.
Weigh performance vs. cost, flexibility vs. optimization, and operational overhead vs. managed services.
Recommend a specific storage layer (e.g., Presto on Parquet) and explain why it meets the requirements, mentioning alternatives if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the use case and requirements (latency, accuracy, consistency) to justify the need for dual paths. Then describe the architecture: a hot path for low-latency approximate results and a cold path for accurate batch processing, and explain how you reconcile them, including when to fall back to batch results. Emphasize trade-offs and monitoring.
Pro tip: Mention that the hot path should be designed to degrade gracefully and that batch results serve as the source of truth for reconciliation and backfilling, not just as a fallback. This shows you understand both real-time and batch systems deeply.
Ask about latency, accuracy, consistency, and scale requirements to determine if a dual-path approach is necessary and what trade-offs are acceptable.
Describe a low-latency, approximate real-time pipeline (e.g., stream processing with Kafka, Flink) that provides quick but potentially less accurate results.
Outline a batch processing system (e.g., Hadoop, Spark) that computes accurate results over historical data, often with higher latency.
Explain how to reconcile discrepancies between hot and cold paths, and when to revert to batch results (e.g., when hot path confidence is low, on system failures, or for critical decisions).
Discuss monitoring metrics (latency, accuracy, drift) and how to iterate on the system to improve both paths over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I put fraud filtering early in the pipeline, right after ingestion, using a combination of rule-based filters (same IP clicking the same ad repeatedly within a window) and a flag from an async ML scoring service.
Start by clarifying the pipeline context and requirements, then propose a multi-layered filtering approach combining real-time and batch processing. Discuss specific techniques like rule-based filters, ML models, and graph analysis, and explain how to balance accuracy, latency, and scalability.
Pro tip: Emphasize that fraud detection is an adversarial problem, so you need continuous monitoring and adaptation. Mention the importance of feedback loops and human review for edge cases.
Ask about the pipeline's purpose, data volume, latency requirements, and what constitutes a fraudulent click. Understand the scale and business impact.
Propose a combination of real-time and batch processing layers: simple rules for immediate filtering, ML models for probabilistic scoring, and graph-based analysis for coordinated fraud.
Explain rule-based filters (e.g., IP blacklists, click frequency), ML models (e.g., logistic regression, random forests, neural networks), and graph algorithms (e.g., connected components, PageRank) to detect anomalies.
Discuss trade-offs between precision and recall, latency vs. accuracy, and how to scale using distributed systems (e.g., Kafka, Flink, Spark). Mention monitoring and alerting.
Describe how to incorporate feedback from human reviewers and adapt models over time to counter evolving fraud tactics. Suggest A/B testing and continuous evaluation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Schema registry with backward compatibility enforcement.
Start by acknowledging that schema evolution is inevitable in event-driven systems and that the approach depends on compatibility requirements and consumer needs. Then outline a strategy that balances backward/forward compatibility, schema registry usage, and versioning, while addressing migration and deprecation. Conclude with trade-offs and how you'd handle breaking changes.
Pro tip: Emphasize that you design for evolution from day one by using a schema registry and enforcing compatibility checks in CI/CD, and mention that you always consider the consumer's ability to handle unknown fields (e.g., via tolerant readers).
Ask about the event streaming platform (Kafka, Pulsar, etc.), consumer types (real-time, batch), and compatibility guarantees needed (backward, forward, full).
Discuss using a schema registry (e.g., Confluent Schema Registry) with Avro, Protobuf, or JSON Schema, and define compatibility rules (e.g., backward compatible by default).
Explain techniques like adding optional fields with defaults, avoiding deletions/renames, using union types for new variants, and versioning via subject name strategies.
Describe how to manage breaking changes: dual-write to new topic, use a new schema version with a migration period, and coordinate consumer upgrades.
Mention setting up CI/CD checks for schema compatibility, monitoring consumer lag/errors, and having a deprecation policy for old schemas.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second on the exact numbers and kind of worked backwards from 'let's say 5 million clicks per second' which is probably high but I wanted to stress-test the design.
Start by clarifying the system's scope and assumptions (e.g., read/write ratio, payload size, storage duration). Then break down the QPS into components like requests per second, bandwidth, storage, and compute, using round numbers and powers of ten for quick mental math. Finally, validate the estimates against known benchmarks and discuss trade-offs or bottlenecks.
Pro tip: Always state your assumptions explicitly and use powers of ten to simplify calculations; interviewers care more about your reasoning process than exact numbers.
Ask questions to understand the system's functionality, expected read/write ratio, average request/response size, data retention period, and any peak-to-average traffic ratio.
Given 'millions of QPS', assume a specific number (e.g., 5 million QPS) and break it down into reads and writes based on the ratio. Calculate peak QPS by applying a peak factor (e.g., 2-3x).
Multiply QPS by average request/response size to get bandwidth (e.g., Mbps/Gbps). For storage, multiply write QPS by average data size and retention period to estimate total storage needed.
Determine the number of servers needed based on per-server capacity (e.g., 10k QPS per server). Estimate memory requirements for caching (e.g., 20% of daily reads) and database working set.
Sanity-check numbers against known systems (e.g., Google, Facebook). Discuss bottlenecks (e.g., database, network) and potential optimizations like sharding, caching, or CDN.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.