This is the core question and it's deceptively broad.
Start by clarifying requirements and scale (billions of URLs/day, politeness, freshness, deduplication). Then propose a high-level architecture with key components (URL frontier, fetchers, parsers, storage, dedup) and dive into trade-offs for scalability, fault tolerance, and efficiency.
Pro tip: Emphasize the importance of a distributed URL frontier with per-host politeness and prioritization, as it's often the trickiest part at scale. Also, mention using a consistent hashing scheme to partition hosts across workers to avoid overloading any single host.
Ask about scale (billions/day), politeness (robots.txt, crawl delay), freshness, content types, and deduplication needs. Estimate resources (bandwidth, storage, number of machines).
Outline components: URL frontier (scheduler), fetchers, parsers, content storage, dedup (URL and content), and monitoring. Explain data flow from seed URLs to storage.
Detail the URL frontier: distributed queues, per-host politeness, prioritization. Discuss fetcher design: async I/O, rate limiting, retries, and handling failures. Explain dedup using Bloom filters or hashing.
Describe partitioning (e.g., by host), replication, and load balancing. Discuss how to handle failures (checkpointing, retries) and ensure exactly-once processing where needed.
Discuss trade-offs: push vs pull, batch vs stream, storage choices (blob store vs DB). Mention optimizations like DNS caching, connection pooling, and compression.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Content hashing on normalized HTML plus URL canonicalization.
Start by clarifying requirements: scale (billions of pages), definition of duplicate (exact vs near-duplicate), and acceptable false positive/negative rates. Then propose a multi-stage pipeline: exact dedup via hashing, near-duplicate detection via shingling and MinHash/LSH, and finally a scalable distributed architecture using MapReduce/Spark with efficient storage and indexing.
Pro tip: Emphasize trade-offs between precision and recall, and how you'd handle incremental updates and deletions in a petabyte-scale system. Mention that you'd measure success with metrics like duplicate reduction rate and false positive rate, and iterate based on business impact.
Ask about scale (number of pages, growth rate), definition of duplicate (exact, near-duplicate, semantic), and acceptable false positive/negative rates. Also consider latency, cost, and update frequency.
Use cryptographic hashes (e.g., SHA-256) of page content to identify exact duplicates. Store hashes in a distributed key-value store (e.g., Bigtable, Cassandra) or use a Bloom filter for efficient membership checks.
Apply shingling (n-grams) to convert pages into sets of shingles, then use MinHash to create compact signatures. Use Locality-Sensitive Hashing (LSH) to bucket similar signatures and compare candidates within buckets.
Implement the pipeline using MapReduce or Spark: map phase computes signatures, reduce phase groups and compares. Use partitioning and parallelism to handle billions of pages efficiently.
Discuss incremental updates (new pages, deletions), storage optimization (e.g., storing only signatures for non-duplicates), and monitoring (e.g., duplicate rate, false positives). Consider using a graph of duplicates for analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a wide-column store for the raw HTML and a relational DB for metadata, which led to a pretty pointed question about write amplification.
Start by clarifying the scale and access patterns (e.g., billions of pages, low-latency random reads for serving, batch analytics for ranking). Then propose a hybrid storage architecture: object storage for raw HTML, a distributed key-value store for metadata and inverted indexes, and a columnar store for analytics. Justify each choice with trade-offs around cost, latency, durability, and query flexibility.
Pro tip: Emphasize that storage choices must align with the lifecycle of the data—hot metadata needs low-latency access, while cold raw HTML can be archived in cheaper object storage with compression. Also mention that you'd measure and iterate based on real access patterns rather than over-engineering upfront.
Ask about data volume, read/write patterns, latency SLAs, and durability needs. This ensures your storage choices are grounded in actual constraints.
Store raw HTML in object storage (e.g., S3) due to large size and infrequent access, and metadata in a low-latency KV store (e.g., RocksDB, Cassandra) for fast lookups.
Use an inverted index (e.g., Elasticsearch) for full-text search and a columnar store (e.g., Parquet on HDFS) for analytics. Explain how each serves different queries.
Discuss compression, tiered storage, caching, and sharding/replication to balance cost, performance, and durability.
Conclude with a coherent architecture diagram in words and note that you'd monitor and adjust based on evolving access patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the limitations of static crawling and the need for headless browsers. Then outline a hybrid architecture that uses headless browsers for JavaScript-heavy pages while optimizing for scale and cost. Finally, discuss trade-offs and mitigation strategies.
Pro tip: Emphasize that you would only use headless browsers when necessary, as they are resource-intensive, and propose a detection mechanism to decide when to render JavaScript. This shows cost-awareness and scalability thinking.
Explain how to detect if a page requires JavaScript rendering, e.g., by comparing initial HTML with rendered DOM or using heuristics like presence of certain frameworks.
Describe using tools like Puppeteer or Playwright to render pages, execute JavaScript, and extract the fully rendered DOM.
Discuss strategies to handle scale, such as browser pooling, caching rendered results, and using a queue system to manage concurrency.
Acknowledge increased resource usage, slower crawl rates, and potential blocking; propose solutions like rate limiting, rotating proxies, and fallback to static crawling.
Suggest monitoring for changes in page rendering and dynamically adjusting the crawling strategy, e.g., switching between static and dynamic rendering based on performance metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came late in the round and I was a bit fried.
Start by defining what a hot shard is in a distributed crawler context—a shard receiving disproportionate traffic due to skewed URL distribution or domain popularity. Then walk through a layered mitigation strategy: detection, dynamic rebalancing, and architectural changes to prevent recurrence, emphasizing trade-offs and practical implementation at scale.
Pro tip: Mention that hot shards are often a symptom of poor partitioning logic, not just load spikes—proactively discuss how you'd redesign the sharding key (e.g., using consistent hashing with virtual nodes or domain-based partitioning) to avoid hotspots altogether.
Explain what constitutes a hot shard in your crawler (e.g., a shard handling >X% of total requests or with queue depth growing unbounded). Describe monitoring metrics like per-shard QPS, queue length, and latency to detect them early.
Outline short-term fixes: dynamic splitting of the hot shard, throttling or rate-limiting requests to that shard, or temporarily routing some traffic to underutilized shards via a load balancer.
Discuss rebalancing strategies such as consistent hashing with virtual nodes, range-based partitioning with dynamic splits, or using a distributed queue (e.g., Kafka) with multiple partitions and consumer groups to smooth load.
Propose architectural changes to avoid future hotspots: shard by domain or host to localize load, use a two-level sharding scheme (e.g., by domain then by URL hash), or implement a feedback loop that adjusts shard assignments based on real-time load.
Acknowledge trade-offs: rebalancing adds complexity and latency, throttling may delay crawling, and over-sharding increases overhead. Suggest metrics to evaluate effectiveness (e.g., 99th percentile latency, shard load variance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.