Start by clarifying requirements (scale, latency, ranking factors, freshness) and then outline a high-level architecture that separates ingestion, indexing, and query serving. Focus on the trade-offs between relevance and scalability, and propose a concrete design using inverted indexes, sharding, and caching.
Pro tip: Emphasize that relevance ranking is an iterative process: start with a simple TF-IDF or BM25 baseline, then layer on engagement signals and machine learning models, but always measure impact via A/B tests. Also, discuss how to handle real-time indexing and deletion of posts to keep results fresh.
Ask questions to understand scale (daily posts, queries per second), latency requirements, ranking criteria (recency, engagement, relevance), and consistency needs. Define what 'relevance' means for Facebook posts.
Propose a pipeline: ingestion (posts streamed to indexers), indexing (build inverted index with post metadata), and query serving (parse query, retrieve candidates, rank, return results). Mention components like Kafka, distributed file system, and query servers.
Design the inverted index: tokenization, stemming, stop words, and posting lists with doc IDs and term frequencies. Discuss sharding by document or term, replication for fault tolerance, and storage optimizations (compression, tiered storage).
Explain query flow: parse, rewrite, retrieve top-K candidates using index, then rank using a combination of textual relevance (BM25) and social signals (likes, comments, shares, recency). Mention machine learning models for ranking and how to train them.
Address scaling: horizontal scaling of index shards, caching frequent queries, using CDNs for static assets. Discuss trade-offs: consistency vs. latency, index freshness vs. throughput, and cost vs. performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a streaming pipeline that appends to an in-memory buffer, flushes to immutable segments periodically, then compacts in the background.
Start by clarifying the scale and requirements (e.g., QPS, latency SLAs, freshness). Then propose a decoupled architecture using a message queue and a separate indexing pipeline that writes to a new index version, with atomic alias swaps to avoid impacting read performance. Finally, discuss trade-offs and monitoring.
Pro tip: Emphasize that read performance is paramount and that you would use techniques like double-buffering (blue-green deployment) and incremental indexing to minimize disruption. Also, mention the importance of backpressure and dead-letter queues to handle ingestion spikes gracefully.
Ask about the scale (documents per second, total index size), read latency SLA, and freshness requirements (how soon must new posts be searchable).
Propose a message queue (e.g., Kafka) to buffer incoming posts, allowing the indexing pipeline to process at its own pace without affecting read traffic.
Build a new index version in the background, then atomically swap an alias to point to it, ensuring reads are never blocked and consistency is maintained.
Use techniques like segment merging, incremental indexing, and caching to minimize the impact on read operations. Consider read/write separation with dedicated replicas.
Implement monitoring for ingestion lag, read latency, and error rates. Use backpressure, retries, and dead-letter queues to handle failures without degrading read performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Started with TF-IDF, moved to BM25, then described layering a ranking model on top with features like engagement signals and personalization.
Start by outlining a multi-stage ranking pipeline: candidate generation, feature extraction, and final scoring with machine learning. Emphasize that you go beyond keyword matching by incorporating signals like user behavior, document quality, and semantic understanding. Conclude by discussing how you evaluate and iterate on the ranking model.
Pro tip: At Meta, ranking systems must handle massive scale and real-time updates, so highlight your experience with distributed systems and online experimentation. Mention that you balance relevance with other objectives like freshness, diversity, and business metrics.
Describe how you efficiently retrieve a set of potentially relevant documents from a large corpus, using inverted indices, embeddings, or graph-based methods. This stage prioritizes recall over precision.
Explain the features you extract for each query-document pair, such as textual similarity, click-through rates, user engagement history, and document authority. Include both handcrafted and learned features.
Detail the machine learning model used to score and order candidates, e.g., gradient boosted trees or neural networks. Discuss how you train it on labeled data (e.g., clicks, relevance judgments) and handle biases.
Explain how you measure ranking quality offline (e.g., NDCG, MRR) and online (A/B tests). Describe how you use feedback to refine features and model architecture.
Highlight techniques like semantic embeddings, query understanding, personalization, and contextual signals that capture intent beyond literal term matching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.