This is a meaty one and I underestimated how many sub-problems were hiding inside it.
Start by clarifying requirements and scale (QPS, data size, latency SLO), then propose a multi-tier architecture: an offline pipeline that builds a trie or prefix index with top-K suggestions per node, and an online serving layer with aggressive caching and sharding to meet the 100ms p99. Discuss how to blend popularity and recency (e.g., time-decayed scores) and how to update the index without full rebuilds.
Pro tip: Emphasize the read-heavy, latency-critical nature: precompute top-K per prefix offline and serve from memory, and mention that you'd validate the 100ms SLO with p99 measurements and load tests rather than averages.
Ask about QPS, number of distinct queries, update frequency, latency SLO (p99 vs average), and whether suggestions are personalized. Establish scale assumptions (e.g., 10M queries, 100K QPS) to guide design.
Describe aggregating query logs, computing popularity with time decay for recent trends, and building a trie or prefix hash index storing top-K suggestions per node. Explain batch rebuilds and incremental updates for new trending queries.
Propose a stateless service that looks up the prefix in an in-memory index (sharded across nodes), returns top 5, and uses caching (e.g., Redis or local LRU) for hot prefixes. Discuss load balancing and fallback behavior.
Explain how to combine popularity and recency (e.g., exponential decay or windowed counts) and how to merge offline scores with real-time signals if needed. Mention handling of typos or partial words.
Cover trade-offs: index size vs. latency, update frequency vs. freshness, and consistency vs. availability. Describe sharding by prefix, replication for read scalability, and monitoring for p99 latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, latency, ranking, personalization) and then present a high-level API contract before diving into the data model. Structure your answer around the query path and the underlying storage/indexing layers, emphasizing trade-offs at each decision point.
Pro tip: Mention that autocomplete is a latency-sensitive, read-heavy service where the index is built offline and served from memory—showing you understand the separation of concerns between ingestion and query serving impresses interviewers.
Ask about expected QPS, latency SLOs (e.g., p99 < 50ms), data sources (query logs, catalog), personalization needs, and whether results should be ranked or just prefix-matched.
Specify endpoints (e.g., GET /suggest?q=...&limit=...), request/response schemas, error handling, and optional parameters like user context or filters. Discuss versioning and rate limiting.
Describe how suggestions are stored (e.g., trie, inverted index, or key-value store with prefix keys), including fields like term, frequency, score, and metadata. Explain offline index building and incremental updates.
Walk through how a request is processed: normalization, prefix lookup, candidate retrieval, ranking (by popularity, personalization, or freshness), and response assembly with caching.
Cover sharding, replication, cache invalidation, cold-start, and how to handle typos or multi-language support. Highlight trade-offs between latency, freshness, and cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hot prefixes are genuinely tricky and I think I gave a decent answer about consistent hashing with a separate fast path for known-hot prefixes, but I fumbled when they asked how you detect a prefix is hot before it causes problems.
Start by clarifying requirements and scale, then explain how you would shard the autocomplete index (e.g., by prefix range or hash) and specifically address hot prefixes using techniques like prefix splitting, caching, or dedicated replicas. Emphasize trade-offs between consistency, latency, and operational complexity.
Pro tip: Mention that hot prefixes are often caused by real-world events (e.g., trending queries) and propose a dynamic rebalancing or adaptive sharding strategy to handle them gracefully.
Ask about query volume, latency targets, data size, and update frequency to ground your design in realistic constraints.
Explain sharding by prefix range (e.g., first few characters) or by hash, and discuss pros/cons like load distribution and query routing.
Describe how hot prefixes arise (e.g., popular searches) and propose solutions: prefix splitting, caching, dedicated shards, or adaptive rebalancing.
Discuss how to handle index updates (e.g., new terms) across shards, including trade-offs between consistency and availability.
Conclude by weighing latency, cost, complexity, and scalability of your chosen approach, and mention monitoring and rebalancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a two-tier approach: an in-process cache for the absolute hottest prefixes and a distributed cache layer behind that.
Start by clarifying the system's read/write patterns and latency requirements, then propose a multi-layer cache (client, CDN, application, database) with specific data types cached at each layer. Explain invalidation strategies (TTL, write-through, event-based) and justify trade-offs like consistency vs. latency, showing awareness of Moloco's ad-tech scale.
Pro tip: Emphasize that cache design is about trade-offs: explicitly state what you're optimizing for (e.g., low latency for ad serving) and acknowledge the cost of stale data, showing you understand business impact.
Ask about read/write ratio, data size, latency SLOs, consistency needs, and budget. This shapes cache layer choices and invalidation strategy.
List hot data (e.g., user profiles, ad creatives, model predictions) and map each to a layer: client-side, CDN, application-level (Redis/Memcached), or database query cache.
Select patterns like cache-aside, write-through, or write-behind for each data type, and justify eviction (LRU, LFU, TTL) based on access patterns.
Explain how to handle updates: TTL-based expiry, event-driven invalidation (e.g., pub/sub on data change), or versioning. Discuss trade-offs between staleness and complexity.
Cover cache stampede mitigation (e.g., locks, probabilistic early expiration), hot key handling, and metrics (hit rate, latency) to validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale, latency, freshness, and quality metrics. Then describe a lambda-style architecture with a batch pipeline for comprehensive model training and a streaming pipeline for real-time updates, explaining how they merge to serve fresh suggestions.
Pro tip: Emphasize how you'd handle cold-start and trending queries by blending real-time signals with batch-trained models, and discuss trade-offs like latency vs. freshness and cost vs. accuracy.
Ask about data volume, query latency SLAs, freshness requirements, and quality metrics to scope the design appropriately.
Describe how you'd periodically process historical search logs to build a comprehensive suggestion index, including data cleaning, aggregation, and model training.
Explain how you'd ingest real-time search logs, compute incremental updates (e.g., counts, trends), and update the serving layer with low latency.
Detail how the two pipelines converge—e.g., via a serving store that merges batch and real-time data—and how you'd handle consistency and fallback.
Discuss how you'd monitor pipeline health, measure suggestion quality, and iterate on models and thresholds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.