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This guide lays out the architecture and implementation order, then shows a runnable queue-and-worker baseline. Treat it as a foundation: for a production crawl, make the frontier and result writes recoverable, apply robots.txt rules, and coordinate request timing across every worker.
What “distributed” means for a crawler
A crawler has at least four separate jobs: deciding which URLs belong in the crawl, scheduling them, fetching and parsing pages, and recording what happened. A distributed design separates those concerns so that more than one worker can fetch at once without losing track of scope or progress.
BullMQ provides a Redis-backed queue and Node.js Queue and Worker roles. Workers can run in one process, separate processes, or separate machines while consuming from the same queue. Its retries and recovery mechanisms help keep work moving after failures, but they do not turn the entire crawl into a single exactly-once transaction. A worker may fetch a page and crash before recording the result; the job may be retried, so result handling must tolerate repeated work.
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- Frontier: durable records of URLs waiting to be fetched, plus their crawl state.
- Queue: distributes fetch jobs to available workers.
- Workers: apply crawl policy, fetch pages, classify responses, extract links, and write results.
- Shared coordination: prevents duplicate scheduling and controls request rates across workers, especially for the same origin.
A queue is a work-distribution mechanism, not a complete crawler. In particular, a Redis queue alone does not define URL canonicalization, robots handling, a crawl boundary, or durable result semantics.
Set crawl scope and URL identity first
Before starting workers, write down the rules that determine whether a discovered link is eligible. Keep these decisions in application code or configuration rather than letting whichever worker encounters a URL decide ad hoc.
Define the boundary
- Allow only the schemes you intend to fetch, typically HTTP and HTTPS; reject other schemes such as
javascript:andmailto:. - Specify allowed hosts and whether subdomains are included. Compare parsed hostnames, not string suffixes that could accidentally admit unrelated domains.
- Set a maximum link depth and any page-count, time, or storage limits appropriate to the crawl.
- Decide how to treat query strings, fragments, trailing slashes, default ports, and redirects. Different query strings can represent distinct pages, while tracking parameters may lead to near-infinite URL variations.
- Set an explicit stop condition. A crawl should end because its scope is exhausted or its budget is reached, not because the queue happened to look empty during a momentary lull.
Normalize without merging pages incorrectly
Parse each link relative to the page URL that contained it, discard fragments for fetch identity, and normalize the URL consistently before deduplication. Do not blindly remove all query parameters or lowercase a path: either change can merge URLs that a server treats as different resources. Keep both the canonical identity used for scheduling and the original discovered URL if you need provenance.
A stable identity can be a hash of the normalized URL. Store the full URL with the hash so you can inspect jobs and diagnose collisions or normalization mistakes. Use the same normalization function when seeding, extracting links, retrying, and checking state.
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Design a durable frontier and worker flow
Seed and enqueue
Store each seed as a frontier record, then create a queue job that references that record or its stable URL identity. BullMQ’s Queue can enqueue and manage jobs, but crawler-specific uniqueness remains your responsibility. A common approach is a database uniqueness constraint on normalized URL identity, or a Redis set when Redis is the deliberately chosen crawl-state store. Avoid treating an in-memory JavaScript set as global deduplication: each process has its own memory.
Think through the gap between “marked seen” and “job successfully enqueued.” If a process crashes between those actions, the URL can be stranded. For a robust frontier, record discovery and an outbox event in one database transaction, then have a dispatcher deliver pending events to the queue and mark them delivered. Make dispatch retryable. Alternatively, use a design with atomic state transitions and a reconciliation process that finds eligible frontier rows with no active job.
Claim, fetch, and classify
A worker should load the frontier record, verify that it remains eligible, apply the robots policy, coordinate its origin’s request slot, and only then fetch. Record a result category rather than collapsing every outcome into “success” or “failure”: useful categories include fetched, redirected, not found, disallowed by policy, transient network failure, timeout, and parse failure. Save the response status and the final URL after redirects when relevant to your application.
Extract links only from content types your crawler intends to parse. Resolve relative links against the final page URL, apply the same scope and normalization rules, and insert newly eligible URLs through the same deduplication path as seeds. This makes parallel discovery safe: several workers may find the same link at nearly the same time.
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Persist results idempotently
Use the normalized URL identity as a stable key for crawl state and results. An upsert keyed by that identity can safely replace or update an earlier attempt. Keep attempt timestamps, status, and error details so a retry does not erase the history needed to explain a failure. Where storing page bodies is costly, decide separately whether to retain the full response, extracted metadata, or just a status record.
Queue retry settings help recover transient failures, but they do not decide which errors are transient. Do not retry permanent policy denials as if they were network errors. For failures you do retry, bound the attempts and use a delay strategy; after exhaustion, persist a terminal state or a dead-letter workflow that an operator can inspect.
Build host-level politeness into shared coordination
A delay inside one worker process is not a distributed rate limit. If four machines each wait before requesting the same host, they can still send requests much closer together than intended. Coordinate by origin—scheme, hostname, and port—using shared state such as a database or Redis-backed limiter. Reserve the next allowed request time atomically, then have the worker wait until its reserved slot.
Robots.txt is a separate rule from request pacing. RFC 9309, published by the IETF in September 2022, defines the Robots Exclusion Protocol and says crawlers are requested to honor its rules. It also explicitly states: “These rules are not a form of access authorization.” Robots rules are not permission to access protected content. On a successful robots.txt retrieval, follow the parseable rules; the RFC also defines how to handle unavailable and unreachable responses. It does not set a universal per-origin request interval, so choose and document a rate policy for your workload.
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Cache robots policy according to your implementation’s rules, account for retrieval failures distinctly, and make the same policy apply across every worker. If policy state is unavailable, decide deliberately whether the safe behavior is to pause that origin rather than let individual workers improvise.
Runnable BullMQ queue-and-worker baseline
The following compact example demonstrates the queue boundary and worker deployment. It uses BullMQ, Redis through BullMQ’s connection option, and Cheerio for link extraction. Install dependencies with npm install bullmq cheerio and provide a reachable Redis instance through REDIS_URL. Save it as crawler.mjs. Run one process with node crawler.mjs seed https://example.com, then run one or more worker processes with node crawler.mjs worker.
This is an executable learning baseline, not a production frontier: its seen set is in Redis and its result writes are logs. Before a long or valuable crawl, replace those pieces with durable state, recover the enqueue gap, add robots enforcement and a shared origin limiter, and make result persistence idempotent.
import { Queue, Worker } from 'bullmq';
import { load } from 'cheerio';
import { createHash } from 'node:crypto';
const connection = {
url: process.env.REDIS_URL ?? 'redis://127.0.0.1:6379',
maxRetriesPerRequest: null
};
const queueName = 'crawl-frontier';
const queue = new Queue(queueName, { connection });
const redis = queue.client;
const allowedHosts = new Set(
(process.env.ALLOWED_HOSTS ?? 'example.com')
.split(',').map(host => host.trim().toLowerCase())
);
const maxDepth = Number(process.env.MAX_DEPTH ?? 2);
function normalize(raw, base) {
let u;
try { u = new URL(raw, base); } catch { return null; }
if (!['http:', 'https:'].includes(u.protocol)) return null;
u.hash = '';
return u;
}
function inScope(u) {
return allowedHosts.has(u.hostname.toLowerCase());
}
function idFor(url) {
return createHash('sha256').update(url).digest('hex');
}
async function enqueue(url, depth) {
const id = idFor(url);
const inserted = await redis.sadd('crawler:seen', id);
if (!inserted) return false;
try {
await queue.add('fetch', { url, depth, id }, { jobId: id });
return true;
} catch (error) {
// This rollback handles ordinary enqueue errors, not a process crash
// between SADD and queue.add. Production systems need reconciliation.
await redis.srem('crawler:seen', id);
throw error;
}
}
if (process.argv[2] === 'seed') {
const start = normalize(process.argv[3]);
if (!start || !inScope(start)) throw new Error('Seed URL is invalid or out of scope');
await enqueue(start.href, 0);
await queue.close();
process.exit(0);
}
if (process.argv[2] !== 'worker') {
throw new Error('Usage: node crawler.mjs seed URL | worker');
}
const worker = new Worker(queueName, async job => {
const page = normalize(job.data.url);
if (!page || !inScope(page)) return { verdict: 'out-of-scope' };
// Production requirement: retrieve/apply cached robots policy and reserve
// a shared per-origin request slot before making this request.
const response = await fetch(page, {
signal: AbortSignal.timeout(20000),
headers: { 'user-agent': 'ExampleResearchCrawler/1.0' }
});
const body = await response.text();
const finalURL = normalize(response.url);
const result = {
url: page.href,
finalURL: finalURL?.href ?? response.url,
status: response.status,
contentType: response.headers.get('content-type') ?? '',
fetchedAt: new Date().toISOString()
};
if (!response.ok || !result.contentType.includes('text/html')) {
console.log(JSON.stringify({ ...result, verdict: 'not-parsed' }));
return result;
}
const $ = load(body);
let discovered = 0;
if (job.data.depth < maxDepth) {
for (const href of $('a[href]').map((_, a) => $(a).attr('href')).get()) {
const link = normalize(href, finalURL?.href ?? page.href);
if (link && inScope(link)) {
if (await enqueue(link.href, job.data.depth + 1)) discovered++;
}
}
}
console.log(JSON.stringify({ ...result, verdict: 'fetched', discovered }));
return { ...result, discovered };
}, { connection, concurrency: Number(process.env.CONCURRENCY ?? 4) });
worker.on('failed', (job, error) => {
console.error(JSON.stringify({
jobId: job?.id, url: job?.data?.url, error: error.message
}));
});
async function shutdown() {
await worker.close();
await queue.close();
}
process.once('SIGINT', shutdown);
process.once('SIGTERM', shutdown);The code deliberately limits scope to configured hostnames and caps link depth. It does not make a per-process delay look like distributed politeness, and its comment marks the point where robots and origin coordination must be inserted. Its Redis seen-set key is shared by the participating processes, but that alone is not a transactional frontier: a crash after adding an ID and before adding its queue job can strand work. The rollback handles a returned queue error, not a process crash. A production implementation needs a dispatcher/reconciliation design as described above.
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Operate Redis and workers for recovery
BullMQ’s production guidance treats Redis configuration and worker lifecycle as part of operating the queue. Enable Redis persistence, configure maxmemory-policy as noeviction for the queue backend, design automatic reconnection behavior, log errors, and close workers gracefully during shutdown. If Redis evicts queue data or loses unpersisted state, a crawler can lose scheduling or coordination state even if its page fetch code is correct.
Start with modest worker concurrency and increase it only when the origin limiter, network capacity, Redis, and storage can keep up. Concurrency is not a throughput guarantee: actual rate depends on response time, content size, parsing, and persistence work. No benchmark or universal request rate is established here, so measure your own workload and retain enough headroom to avoid overwhelming either your service or the sites you fetch.
Track queue waiting and active counts alongside frontier counts, oldest pending age, retries, terminal failures, response classes, per-origin request timing, and storage errors. These measurements distinguish a quiet crawl from a stuck dispatcher, slow origin, Redis problem, or worker crash. On shutdown, stop claiming new work and let active tasks close cleanly; persist enough state that a restart can resume instead of reseeding blindly.
Troubleshooting common failure modes
- Workers start but receive no work: confirm producer and workers use the same Redis URL and queue name, verify the seed was accepted, and check for URLs rejected by host or scheme scope.
- The same URL appears repeatedly: compare normalization results from seed and link paths, ensure every process uses shared deduplication state, and check whether meaningful query parameters are being changed inconsistently.
- Some URLs never run: inspect the gap between marking an identity seen and queue insertion. Add a durable outbox or reconciliation process instead of relying only on a worker’s local retry.
- Requests to a host bunch together: replace per-process sleeps with an atomic limiter keyed by origin and shared among all workers.
- A job keeps retrying a permanent outcome: classify policy denials and other non-transient results separately; reserve retries for failures that may recover.
- Jobs disappear or queue behavior becomes inconsistent after Redis pressure: check Redis persistence and eviction configuration, reconnection handling, and logs before increasing worker count.
- A crawl stops on a site with redirects or large query spaces: check whether the final URL is normalized and in scope, and whether query policy, depth limits, or URL variants are expanding the frontier unexpectedly.
When a screenshot API belongs beside a crawler
A crawler fetches and analyzes URLs; a screenshot API captures a rendered visual result. They solve different tasks, so a screenshot call is not a replacement for the queue, frontier, or crawl policy. If a downstream workflow needs page screenshots—for example, to attach visual evidence to selected crawl results—you can call ScreenshotNeo separately. Its one-request API returns PNG, JPEG, WebP, or PDF output; its consent-banner and widget cleanup options are relevant when a clean visitor-style capture is needed.
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