This is the core prompt and it's deceptively broad.
Start by clarifying requirements and constraints, then propose a high-level architecture that separates ingestion, aggregation, and query layers. Focus on how to handle high throughput with approximate algorithms and efficient data structures, and discuss trade-offs between accuracy, latency, and cost.
Pro tip: Emphasize that exact top-K over sliding windows at this scale is impractical; instead, propose approximate counting with error bounds and explain how to tune accuracy vs. resource usage. Also, mention the importance of idempotency and exactly-once processing in the ingestion pipeline.
Ask about event types, window sizes (e.g., 1 min, 5 min, 1 hour), query latency SLAs, and accuracy requirements. Confirm the need for per-restaurant, per-city, and global slices.
Propose a scalable ingestion layer using a distributed message queue (e.g., Kafka) to handle 1M+ events/sec. Discuss partitioning by restaurant/city to enable parallel processing.
Use stream processing (e.g., Flink, Spark Streaming) with approximate algorithms like Count-Min Sketch or Space-Saving for top-K. Maintain sliding windows via time-based buckets or exponential decay.
Store aggregated sketches in a low-latency store (e.g., Redis, Cassandra) keyed by slice and window. Serve queries by merging sketches or precomputing top-K per slice.
Discuss accuracy vs. memory, window granularity, and cost. Mention techniques like hierarchical aggregation (restaurant -> city -> global) and caching hot queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Minute-grain tumbling buckets plus a rolling sum over the last N buckets.
Start by clarifying the requirements: what is the sliding window (e.g., time-based, count-based), what queries are expected (e.g., count, sum, average), and the scale (events per second, window size). Then propose a pre-aggregation strategy: maintain incremental aggregates (e.g., counts, sums) in a data structure like a ring buffer or time-bucketed store, so queries can be answered in O(1) or O(log n) without scanning raw events. Finally, discuss trade-offs and optimizations for high throughput and low latency.
Pro tip: Emphasize that the key is to decouple event ingestion from query processing by maintaining a materialized view of the window, and mention how you would handle out-of-order events and late data to show depth.
Ask about the window type (time-based, count-based), query patterns (e.g., count, sum, distinct), expected throughput, latency requirements, and whether events can be out-of-order or late.
Decide on a data structure to maintain incremental aggregates, such as a ring buffer for fixed-size windows, time-bucketed counters for time-based windows, or a balanced tree for sliding windows with arbitrary ranges.
On each event, update the aggregate in O(1) or O(log n) by adding to the current bucket and removing expired data. For queries, combine pre-aggregated values (e.g., sum over buckets) without scanning raw events.
Address out-of-order events by using event time and watermarks, handle late data with allowed lateness or side outputs, and optimize for high concurrency with sharding or lock-free structures.
Compare with other approaches like streaming frameworks (Flink, Kafka Streams) or approximate algorithms (sketches) for high cardinality, and explain when to use each based on accuracy, memory, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Key-prefix sharding with a random suffix, then merge on the client side.
Start by clarifying the scenario: a hot key is a single item (e.g., a restaurant, event, or driver) receiving a massive, localized spike in traffic that overwhelms a single shard. Then propose a multi-layered mitigation strategy that includes immediate relief (caching, rate limiting) and longer-term architectural changes (sharding by key, replication, load shedding).
Pro tip: Emphasize that hot keys are a data distribution problem, not just a scaling problem—show you understand the trade-offs between consistency, latency, and cost when applying solutions like key splitting or local caching.
Ask clarifying questions to understand the scale, access pattern (read vs. write), and business impact. Define what 'overwhelms' means (e.g., CPU, memory, network) and the expected SLAs.
Propose short-term fixes like caching the hot item at the edge or in a local cache, rate limiting per user/IP, and load shedding to protect the shard. Mention using a CDN or in-memory cache like Redis.
Discuss longer-term strategies: splitting the hot key into sub-keys (e.g., key+random suffix) to distribute load, replicating the shard, or using a dedicated shard for hot items. Consider consistent hashing with virtual nodes.
Analyze trade-offs: caching may lead to stale data; splitting keys complicates reads/writes; replication increases cost. Discuss how to maintain consistency (e.g., write-through cache, eventual consistency) and monitor effectiveness.
Explain how you would detect hot keys proactively (e.g., via metrics, sampling) and iterate on the solution. Mention the importance of load testing and chaos engineering to validate resilience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Fetch more candidates than K from Redis, like 2x, then filter against a catalog availability cache before returning.
Start by clarifying requirements: read-heavy, low-latency, top-K by some ranking (e.g., popularity, distance, ETA). Then describe a layered design: a fast read path (cache or precomputed top-K) and a consistency mechanism to handle mid-window removals/out-of-stock, likely using versioning or tombstones with async reconciliation.
Pro tip: Emphasize that for top-K, approximate or slightly stale results are often acceptable, but you must guarantee that removed/out-of-stock items are never served. Use a 'soft delete' flag with a short TTL cache and a background invalidator to balance freshness and latency.
Ask about K, latency SLA, consistency needs (strong vs eventual), update frequency, and whether out-of-stock items should be filtered or ranked lower. Confirm read/write ratio and geographic distribution.
Propose a precomputed top-K per shard/region stored in a low-latency store (e.g., Redis, local cache). On read, merge results from shards, apply a lightweight filter for availability, and return top-K. Mention pagination or cursor if needed.
Use a versioned availability flag (e.g., item_status with timestamp) and a tombstone list for removals. On read, filter out items marked unavailable. For strong consistency, do a read-through to a primary store for the final K items; for eventual, use a short TTL cache and a pub/sub invalidation.
Discuss trade-offs: stale cache may serve removed items briefly; mitigate with short TTL, write-through invalidation, or a 'negative cache' for removed IDs. Handle partial failures by falling back to a secondary index or returning fewer results with a warning.
Suggest metrics: cache hit rate, staleness, filter rate, and latency percentiles. Consider adaptive TTL based on item churn. Mention A/B testing for ranking changes and canary deployments for new filter logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
You need an explicit merge policy and you have to say it out loud.
Start by acknowledging the core challenge: reconciling batch recomputation with real-time streaming updates in a shared store like Redis. Then propose a versioning or timestamp-based conflict resolution strategy, such as using monotonic version numbers or watermarks, and discuss how to handle late-arriving data and ensure atomicity.
Pro tip: Mention that you would use Redis transactions or Lua scripts to atomically compare-and-set only if the batch result is newer, and highlight the importance of monitoring and alerting on version conflicts to detect pipeline issues early.
Ask whether the system prioritizes freshness (streaming wins) or completeness (batch wins), and whether eventual consistency is acceptable. This determines the conflict resolution policy.
Propose attaching a monotonic version (e.g., batch execution timestamp or sequence number) to each write. The batch layer writes only if its version is greater than the current version in Redis.
Use Redis WATCH/MULTI/EXEC transactions or Lua scripts to atomically compare the version and update only if the batch version is newer, preventing race conditions.
Discuss how to reconcile late events that arrive after the batch write, possibly by allowing streaming to overwrite if its event time is newer, or by using a watermark-based merge.
Set up metrics for version mismatches and write rejections to detect anomalies, and consider fallback strategies like logging conflicts for manual review.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Dedup by event ID, filter known bot traffic, optionally throttle to one event per user per item per session so rankings can't be gamed.
Start by clarifying the requirements and scale (e.g., Uber's scale, real-time vs batch). Then propose a multi-stage pipeline: deduplication, bot filtering, and weighted engagement scoring, explaining each stage's techniques and trade-offs. Finally, discuss how to combine these into a popularity score and address challenges like data skew and latency.
Pro tip: Emphasize that deduplication and bot filtering should happen before aggregation to avoid skewing results, and consider using approximate algorithms (e.g., HyperLogLog) for scalability. Also, mention that weights should be configurable and A/B tested to align with business goals.
Ask about data volume, velocity, and latency requirements. Understand what 'popularity' means for Uber (e.g., restaurant or driver popularity) and the impact of bots.
Propose deduplication using unique identifiers (e.g., user ID + content ID) and techniques like Bloom filters or streaming dedup with windowing to handle duplicates in real-time.
Outline bot detection using heuristics (e.g., request rate, pattern analysis) and machine learning models. Discuss how to integrate filtering into the pipeline without adding significant latency.
Assign weights to different engagement types (e.g., likes, shares, comments) based on business value. Explain how to compute a weighted sum and normalize it to avoid bias.
Describe how to aggregate the cleaned and weighted signals into a popularity score. Discuss trade-offs between accuracy and performance, and how to handle data skew and ensure scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.