The Tool Desk
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What reliability means in web data collection
Reliability has several dimensions that can conflict unless you define them explicitly:
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- Policy compliance: requests follow the target host’s robots.txt rules, published limits, contractual terms, and applicable privacy and data-protection obligations.
- Completeness: the system finds the URLs that are in scope and does not silently lose pages in queues, retries, redirects, or parser exceptions.
- Freshness: records are updated on a schedule appropriate to the source, with stale or delayed origins visible to operators.
- Correctness: extraction is validated against a versioned schema rather than accepting any HTTP 200 response as good data.
- Recoverability: a worker, network link, parser, or deployment can fail without forcing a full restart.
- Traceability: each published value can be tied to its source URL, retrieval time, response status, parser version, and raw evidence or content hash.
Set service objectives before choosing infrastructure. For example, one collection may prioritize hourly freshness while another prioritizes a complete historical snapshot. The scheduler, retry budget, storage retention, and compute model should follow that objective instead of an arbitrary request-per-second target.
The reference architecture
Keep the stages loosely coupled so a policy decision or parser change does not require rewriting the entire crawler. A durable queue or equivalent checkpointed state connects each stage.
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| Stage | Responsibilities | Durability and controls |
|---|---|---|
| Discovery | Accept seeds, parse sitemaps, follow in-scope links, canonicalize URLs, and deduplicate. | Store discovered URLs and their scope decision before scheduling. |
| Policy gate | Fetch and parse the exact host’s top-level robots.txt; select the matching user-agent group. | Cache policy with retrieval time, parser result, and an allow, deny, or defer decision. |
| Scheduler | Maintain per-origin queues, token buckets, concurrency caps, retry budgets, and jitter. | Persist leases and attempt counts so abandoned work can be reclaimed safely. |
| Fetcher | Make identified HTTP requests, enforce timeouts and response limits, and handle redirects and conditional requests. | Record request and response metadata before handing content to parsing. |
| Parser and validator | Extract fields, normalize values, validate schemas, and isolate malformed pages. | Version extraction code; retain raw responses or hashes for replay. |
| Storage | Write immutable observations and publish curated records separately. | Include source URL, retrieval timestamp, status, parser version, and provenance. |
| Operations | Measure freshness, success, latency, queue depth, bytes, retries, policy denials, and parse failures. | Alert on trends and runbooks, not only on process uptime. |
A worker should be replaceable. If a process dies after downloading a page but before acknowledging the queue item, the lease should expire and the item should be retried or sent to review. Idempotent writes, content hashes, and deduplication prevent that recovery path from creating duplicate published records.
Step 1: Define scope, identity, and governance
Write an explicit crawl contract
For every project, record the target hosts, allowed protocols and ports, URL path scope, content types, collection purpose, fields to retain, retention period, and refresh schedule. Decide whether query parameters represent separate resources or tracking noise. A scope rule should be machine-testable: an out-of-scope URL is rejected before it reaches a fetch worker.
Identify the crawler
Use a stable, descriptive User-Agent that names the crawler and provides an operator contact or information page when appropriate. Do not impersonate a browser or rotate identities to evade a site’s controls. The identity used for HTTP requests should be the same identity considered when selecting robots.txt rules.
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Review terms of service, privacy notices, copyright and licensing conditions, data-protection requirements, and sector-specific rules. Robots.txt is a technical preference mechanism, not permission to access protected information. It does not replace authentication, authorization, a data-processing agreement, or a legal review. The applicable rules vary by jurisdiction and dataset, so document the decision and an escalation owner.
Step 2: Discover and canonicalize URLs
Use several discovery sources
- Begin with a maintained seed list whose owner and scope are recorded.
- Parse XML sitemaps and sitemap indexes when they are available.
- Extract links from fetched pages only when the destination remains in scope.
- Accept explicit URL submissions through a controlled intake rather than writing directly to a worker queue.
Canonicalize before deduplication
Normalize scheme and host casing, remove fragments, apply a documented trailing-slash rule, and resolve relative links against the response URL. Treat query parameters conservatively: removing a parameter can merge distinct resources, while retaining every tracking parameter can create an unbounded crawl. Maintain both the original URL and the canonical key so an operator can explain why two links were considered the same.
Control expansion
Enforce host, path, depth, and maximum-URL limits at discovery time. Reject private-network destinations and unexpected schemes such as local file handlers. A scope decision should carry a reason such as seed, sitemap, in-scope link, duplicate, or blocked by rule; that makes a missing page distinguishable from a deliberate exclusion.
Step 3: Build a conservative robots.txt policy gate
Fetch the correct file
Robots rules live at the top-level /robots.txt path and are UTF-8 plain text. Rules apply only to the exact combination of host, protocol, and port that served the file. A policy service should therefore key its cache by that origin, not merely by a registrable domain.
RFC 9309 (Internet Engineering Task Force, 2022) requires parsers to support at least 500 KiB of robots.txt content and says crawlers should follow at least five consecutive redirects while fetching it. Follow redirects within the same policy-fetch procedure, record the final URL, and cap additional redirects defensively.
Select and apply the matching group
User-agent selection is case-insensitive. After choosing the applicable group, path matching is generally case-sensitive and the most specific matching rule wins. A successful download means the crawler must follow the parseable rules. Parse errors should be logged with the affected line and policy version rather than silently treated as permission.
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Handle availability and caching correctly
RFC 9309 says a crawler should not use a cached robots.txt version for more than 24 hours unless the file is unreachable. Cache the body, retrieval time, response status, parser version, and resulting rules. If a server or network error makes the file unreachable, the standard requires complete disallow; deny or defer content requests until a usable policy is available. Distinguish that conservative standard behavior from any product-specific handling of 2xx, 3xx, 4xx, 429, and 5xx responses, and document the choice.
Do not treat a robots response as authorization. A site can permit crawling of a path that still requires a login, contains personal data, or is restricted by contract. Conversely, a policy denial should not be bypassed by changing a path spelling, hostname, or User-Agent.
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Use token buckets and concurrency caps
Maintain independent scheduling state for each origin. A token bucket controls average request rate; a concurrency cap limits simultaneous connections; a small random jitter prevents synchronized bursts. Separate queues stop a slow or hostile origin from consuming all workers. Apply limits to redirects and retries as well as first attempts.
AWS Prescriptive Guidance gives illustrative starting points of one request every 10–15 seconds for small or medium sites and 1–2 requests per second for larger sites or sites with explicit crawl permissions. These are not universal entitlements: start below the limit, watch response behavior, and tune for each origin’s capacity and published instructions.
Adapt to feedback
Increase delay when latency, 429 responses, connection failures, or server errors rise. Honor a server-supplied Retry-After value when present. Reduce concurrency before increasing it, and make the change per origin so a problem at one host does not slow every source.
Budget retries
Define maximum attempts and a time window for each failure class. Exponential backoff with full jitter prevents a retry storm. Once the budget is exhausted, move the URL to a review queue with its last status, error, and attempt history. Never retry a policy denial blindly; it requires a policy change or an explicit operator decision.
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Set bounded request behavior
- Set connection, read, and total request timeouts.
- Enforce a maximum response size before buffering or decompressing content.
- Accept only required content types and reject unexpected binary or executable payloads.
- Limit redirect count and reapply scope and origin checks after every redirect.
- Use conditional requests such as validators when the origin supports them, reducing bandwidth and load.
- Record request start and end times, final URL, status, headers needed for diagnosis, byte count, and a content hash.
Handle status codes deliberately
| Response | Default action | Why |
|---|---|---|
| 2xx | Pass to content-type and size checks, then parse. | A successful transport response can still contain an error page or invalid schema. |
| 3xx | Follow only within the redirect limit and scope; otherwise record a redirect failure. | Redirect chains can leave the approved origin or create loops. |
| 403 | Record a policy or access denial and stop automatic retries unless an operator changes the decision. | Repeated requests can worsen blocking and do not solve authorization. |
| 429 | Honor Retry-After, reduce the origin’s rate, and retry within a bounded budget. |
The origin is explicitly signaling throttling. |
| 5xx | Retry with backoff and jitter, then send exhausted work to review. | These are often transient server failures, but unlimited retries create load. |
| Timeout or network error | Retry within a deadline while preserving the original attempt record. | Connectivity can recover, but the URL must not disappear from accounting. |
A 200 response does not prove that the intended page was received. Check content type, expected markers, minimum or maximum size, and schema validity. Store a small diagnostic sample or hash for blocked, truncated, and malformed responses, subject to your retention and privacy rules.
Step 6: Parse, validate, and version extraction
Separate transport from extraction
Keep raw-response handling independent from HTML or structured-data parsing. A parser crash should mark one observation as a parse failure, not terminate the worker or acknowledge the queue item as successful. Capture parser exceptions, selector names, and the parser version.
Validate at the boundary
Define required and optional fields, types, ranges, allowed enumerations, and normalization rules. Reject or quarantine records that violate required constraints. Track field-level completeness so a page that returns only a title is not counted as a fully successful extraction.
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Make replays possible
Retain raw responses where lawful and practical, or retain a content-addressed copy or hash plus enough metadata to retrieve an approved archive. When extraction logic changes, replay the stored observation through the new parser instead of re-crawling every origin. Store both the immutable observation and the current curated record; never overwrite the evidence needed to explain a later correction.
Step 7: Store provenance and publish separately
An observation record should include at least:
- canonical and original source URL;
- origin and retrieval timestamp in a consistent time zone;
- HTTP status, final URL, content type, byte count, and content hash;
- robots-policy version and allow or deny decision;
- parser and schema versions;
- validation outcome and error details;
- crawl job, attempt, and worker identifiers.
Write these observations append-only. A separate publishing layer can deduplicate, merge, redact, and expose curated records to applications. This separation lets you correct a parser or policy interpretation without erasing what was actually received.
Step 8: Make batches resumable
Checkpoint every unit of useful work
Persist queue state before dispatch, lease ownership while processing, and completion or failure after the result is durable. Checkpoint at page or small-batch boundaries rather than only at the end of a crawl. On restart, reclaim expired leases and continue from the last durable state.
Use idempotent operations
Derive an observation key from canonical URL, retrieval time or snapshot identifier, and content hash as appropriate. Upserts or deduplicated writes prevent duplicate records when a worker times out after the server has already responded. Side effects such as notifications should have their own idempotency key.
Choose a compute model deliberately
Long-running workers suit large, continuously scheduled crawls because they keep queues and connection pools warm. Serverless execution can suit short-lived, event-driven batches when each invocation has bounded duration, memory, response size, and retry behavior. In either model, the durable queue and storage—not the process memory—must be the source of truth.
Scaling choices and their trade-offs
| Approach | Best fit | Advantages | Risks to control |
|---|---|---|---|
| Single scheduled worker | Small, stable scope with modest freshness needs. | Simple deployment and easy debugging. | One failure domain; limited isolation between origins. |
| Partitioned workers by origin | Many hosts with different policies and rates. | Independent throttling and failure recovery. | More queue, lease, and capacity management. |
| Event-driven serverless batches | Short, bursty jobs with a durable queue. | Automatic burst capacity and no idle worker fleet. | Invocation limits, cold starts, duplicate delivery, and harder local replay. |
| Managed crawling service | Teams that need packaged scheduling and operations. | Less infrastructure to operate. | Verify policy controls, provenance, data residency, and failure semantics before adoption. |
Compare options on politeness, freshness, completeness, cost, and operational complexity. More parallelism improves throughput only until an origin’s rate or your parser and storage capacity becomes the bottleneck.
Observability that finds silent failures
Track service and data metrics
- Freshness age by origin and dataset.
- Discovery-to-fetch conversion and queue depth.
- Request success rate, latency percentiles, bytes, and timeout rate.
- 429, 403, 5xx, redirect, and policy-denial counts by origin.
- Retry attempts, exhausted budgets, and review-queue size.
- Parse failure rate, required-field completeness, and schema-version distribution.
- Duplicate rate, storage lag, and publication lag.
Alert on symptoms, not only uptime
A process can be healthy while returning empty pages, receiving a new block page, or silently skipping an entire sitemap. Alert when freshness exceeds its objective, a required field collapses, status-code composition changes sharply, or queue depth grows without corresponding throughput. Include the origin, policy version, parser version, and a representative diagnostic hash in the alert so an operator can act without reproducing the whole crawl.
Keep an operational history
Log structured events for policy fetches, scope decisions, leases, retries, parser exceptions, and publication changes. Retain enough history to answer which rule allowed a request, which parser produced a value, and why a URL was deferred.
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Isolate fetchers
Run fetchers in isolated environments with outbound-only network access where practical. Block access to private, link-local, and metadata-service addresses; recheck the destination after DNS resolution and redirects. Use separate credentials and least-privilege roles for queue, raw storage, curated storage, and monitoring.
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Treat downloaded material as untrusted
Robots.txt, HTML, headers, compressed bodies, and embedded scripts are input, not instructions. Parse with resource limits, sanitize content before displaying it, and never execute downloaded code. Enforce decompression and nesting limits to prevent archive or parser bombs.
Protect sensitive data
Do not place secrets in URLs or logs. Redact personal data where it is not required, encrypt stored observations, define retention and deletion procedures, and restrict raw-response access. Authentication for a protected source must be explicitly authorized; robots.txt permission does not grant it.
A practical build sequence
- Document scope and governance: list origins, paths, fields, identity, retention, legal review, and freshness objectives.
- Create the URL ledger: store seeds, canonical keys, scope decisions, and discovery provenance.
- Deploy the policy service: fetch exact-origin robots.txt, parse the matching group, cache for no more than 24 hours unless unreachable, and fail closed on unavailable policy.
- Add per-origin scheduling: token buckets, concurrency caps, jitter, Retry-After support, and bounded retry budgets.
- Harden fetching: timeouts, size and content-type limits, redirect checks, conditional requests, and complete status recording.
- Version parsing: validate schemas, isolate exceptions, and retain raw evidence or hashes for replay.
- Make writes idempotent: checkpoint leases, deduplicate observations, and separate immutable evidence from curated output.
- Instrument operations: dashboards and alerts for freshness, policy denials, response classes, retries, parse quality, and queue health.
- Run a controlled pilot: begin with a small scope and conservative rate, inspect logs and server responses, then expand origin by origin.
Troubleshooting common failure patterns
The crawl suddenly receives many 403 or 429 responses
Pause or slow that origin, verify the User-Agent and robots decision, honor Retry-After, and inspect whether redirects or retries multiplied request volume. Do not switch identities or bypass a denial. Resume only after the origin’s policy and rate are clear.
Robots.txt cannot be fetched
Record the network or server error, mark the policy unreachable, and defer content requests under the conservative RFC 9309 behavior. Retry the policy fetch on its own schedule; do not use an expired cache indefinitely.
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Queue depth grows while workers appear healthy
Check per-origin token buckets, lease expiry, retry storms, response-size limits, and parser latency. A single slow origin may be consuming workers if partitioning is missing. Move exhausted items to review so transient failures do not block new work.
Extraction quality drops after a site redesign
Compare parser versions, field-level completeness, content hashes, and representative raw responses. Keep the old curated output, quarantine new failures, update selectors with tests, and replay retained observations before returning the new parser to production.
Records are duplicated after a restart
Inspect acknowledgement timing and write keys. Make the observation write idempotent, tie side effects to an idempotency key, and let expired leases be safely replayed.
Operating checklist
- Exact host, protocol, and port are used for every robots policy.
- Robots rules are parsed as UTF-8, with at least 500 KiB supported and five redirects handled.
- Policy retrieval time, parser result, and cache expiry are stored.
- Unavailable policy causes deny or defer rather than permissive crawling.
- Each origin has its own rate, concurrency, jitter, and retry budget.
- Requests identify the crawler and enforce timeout, size, content-type, and redirect limits.
- 429 responses honor Retry-After; 403 and policy denials are not blindly retried.
- Queue leases, checkpoints, idempotent writes, and review queues are tested with failure injection.
- Raw evidence or hashes, parser versions, and provenance support replay and audits.
- Fetchers are isolated, credentials are protected, and downloaded content is sanitized.
- Freshness, completeness, status mix, parse quality, and policy denials have dashboards and alerts.
Frequently Asked Questions
How often should a crawler request a page?
There is no universal rate. Start conservatively per origin; AWS gives illustrative guidance of one request every 10–15 seconds for small or medium sites and 1–2 requests per second for larger sites or sites with explicit permission, then tune using the origin’s responses and instructions.
What should happen when robots.txt is unavailable?
Under RFC 9309’s conservative behavior, a server or network error makes the file unreachable, so defer or deny content requests until a usable policy is retrieved rather than relying on an expired cache indefinitely.
Can robots.txt authorize access to private data?
No. Robots rules govern crawler preferences and are not access authorization. Protected data still requires explicit authentication, authorization, and appropriate legal and privacy controls.
How can a crawl resume after a worker fails?
Use durable queue leases, page-level or small-batch checkpoints, idempotent observation writes, and a review queue for exhausted retries. Expired leases can then be reclaimed without restarting the entire crawl.
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