Design Web Crawler — System Design Interview Practice

Interview prompt

Design polite distributed discovery, fetching, deduplication, and indexing so users can discover and fetch a URL reliably at scale.

• Define the source of truth for frontier state and fetch history and make retries idempotent.
• Use bounded, partitioned state to meet 10B URLs crawled per day across millions of hosts and frontier enqueue <=100ms.
• Separate the critical request path from fetching, parsing, canonicalization, and indexing.
• Explain consistency, failure recovery, authorization, observability, and a degraded mode.

Requirements and scale assumptions

• Support the core workflow to discover and fetch a URL.
• Expose status, results, and freshness appropriate to polite distributed discovery, fetching, deduplication, and indexing.
• Support authorization, validation, updates, deletion, and recovery semantics.
• Meet frontier enqueue <=100ms under normal load.
• Scale to 10B URLs crawled per day across millions of hosts without a single hot key or unbounded synchronous work.
• Do not lose committed state; make retries and duplicate events safe.
• Degrade safely when downstream workers, caches, or external dependencies fail.
• 10B URLs crawled per day across millions of hosts
• Partition by the primary tenant, user, item, or geographic key and isolate hot partitions.
• Keep serving state bounded; retain raw events or durable records for replay and auditing.

• Peak scale: 10B URLs crawled per day across millions of hosts — Capacity assumption that drives partitioning and backpressure.
• Latency target: frontier enqueue <=100ms — User-facing budget for the primary request or read path.
• Durable boundary: Committed before async — The source of truth is frontier state and fetch history.
• Async boundary: At-least-once workers — Keep fetching, parsing, canonicalization, and indexing off the synchronous path.

Key entities

• DocumentVersiondocumentId, sourceVersion, contentHash, aclVersion, language, updatedAt

  Canonical web crawler content and access-policy version used for indexing.

• IndexGenerationgenerationId, sourceWatermark, schemaVersion, status, alias, createdAt

  Rebuildable web crawler index generation that can be validated before an atomic alias swap.

• QuerySessionqueryId, tenantId, normalizedQuery, filters, generationId, nextCursor

  Auditable web crawler query context with filters, cursor, and the generation used to answer it.

• RankingFeedbackqueryId, documentId, position, action, modelVersion, occurredAt

  Privacy-scoped web crawler relevance signal for offline evaluation and ranking improvement.

Data flow

1. 1. Accept and authorize source changesThe web crawler ingestion boundary validates content, tenant ownership, ACLs, versions, and idempotency before publishing a document change.
2. 2. Retrieve and rank candidatesThe query service applies authorization filters, retrieves from the active web crawler generation, ranks within the latency budget, and returns generation freshness.
3. 3. Build a safe index generationPartitioned workers transform web crawler documents, checkpoint progress, validate counts and ACL parity, then atomically swap the serving alias.
4. 4. Handle freshness and deletesTombstones and ACL changes propagate through the same pipeline so deleted or newly restricted web crawler content is not left searchable.
5. 5. Measure relevance and recoverFeedback, query traces, lag, and failed partitions drive web crawler ranking evaluation, replay, and bounded degraded behavior.

Deep dives and trade-offs

• ACL correctness and index generationsFilter web crawler results by tenant and effective ACL, or prove the active generation contains the same policy snapshot. Build shadow generations and swap aliases atomically so partial reindexes are never visible. Keep source versions and ACL snapshots for replay when permissions or content change.
• Latency, cursors, and graceful degradationUse bounded candidate retrieval, stable sort keys, and generation-aware cursors for web crawler pagination. Serve the last healthy generation when a new build is incomplete, but expose freshness and avoid silently violating authorization. Protect the query path with timeouts, circuit breakers, and per-tenant quotas.
• Relevance feedback without leakageSeparate web crawler click or conversion signals from personally identifying data and honor retention or deletion requests. Evaluate ranking by query class and tail latency, not only aggregate click-through. Use replayable query sets and staged model or synonym changes before production rollout.
• Synchronous indexing versus queued indexingCommit the source version synchronously and index asynchronously with a visible freshness contract. Waiting for index mutation makes writes fragile and cannot guarantee immediate consistency at scale.
• Denormalized ACL fields versus filter-time checksDenormalize safe, versioned authorization facts when it meets the policy model, while retaining a source-of-truth check for sensitive results. Stale permissions can become a data-leak path if index updates are treated as authoritative.
• Lexical, vector, or hybrid retrievalStart with the retrieval method that matches the corpus and latency budget, then add hybrid ranking behind an experiment and rollback boundary. Adding embeddings without freshness, explainability, or access-control design increases cost without improving user trust.