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Use an extraction API when you need page content as predictable fields rather than raw HTML. The usual process is to define a schema, choose direct fetching, browser rendering, crawling or a page-type extractor, submit the URL and credentials, then validate the response and retain its source URL. Hosted services differ in how they discover pages, execute JavaScript, represent missing values and expose jobs, so test representative pages before building a production pipeline.
What “structured data” means
Structured output has named fields and values in a predictable representation such as JSON. Instead of asking your application to interpret an entire HTML document, you can request fields such as title, author, published_at, price and currency. A schema can also state types and whether a field may be absent.
For example, an article record might be:
{
"source_url": "https://example.com/story",
"title": "Example headline",
"author": "A. Writer",
"published_at": "2026-09-20",
"tags": ["technology"],
"image_url": null
}The source_url and retrieval time are useful provenance. Keep them with the extracted record so a person can revisit the page when a value looks wrong.
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Choose the extraction approach
| Approach | Use it when | Questions to answer first |
|---|---|---|
| Direct page extraction | One known URL is accessible without site-wide discovery. | Does the service read static HTML only, or can it render a browser? Can fields be named and typed? |
| Schema-driven extraction | Your application requires a stable, custom JSON shape. | How are absent fields represented? Is the source evidence or provenance returned? |
| Crawler or hosted scraper | Data is spread across many pages or must run in batches or on a schedule. | How are links discovered, limits enforced, jobs retried and datasets exported? |
| Page-type extractor | Pages fit a supported class such as article or product. | Which types are supported, and how are classification or extraction failures reported? |
Context.dev documents site crawling into a JSON Schema you define (documentation). Scrapy.io documents scraper discovery, synchronous and asynchronous jobs, polling, dataset export and recurring schedules (API documentation). Firecrawl describes extraction from one or multiple URLs with prompts and/or schemas (project documentation). Diffbot provides typed page extractors, including several page classes (Extract API documentation).
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A practical workflow
1. Define the contract
Write the fields your downstream code actually consumes. Specify string, number, Boolean, date or array types; permitted nulls; units and normalization rules. Decide whether a missing value should be null, an omitted key or a validation error. Do not request dozens of fields “just in case”: each additional field creates another opportunity for ambiguity.
2. Select representative URLs
Include normal pages, pages with missing fields, a recently changed page and a page whose content is loaded by JavaScript. Compare returned values with the rendered page before expanding the job. Documentation describes capabilities, not independent accuracy rates, so this sample is your quality check.
3. Decide whether JavaScript is required
Static HTML may contain the data you need. If the initial response is only an application shell, select a browser-rendered mode if the provider offers one. Monocrawl documents direct static fetching separately from an explicitly requested browser mode and notes that non-direct modes are deployment-gated and off by default; that behavior is vendor-specific, not a rule for every API (Monocrawl documentation).
4. Choose one page or a crawl
A single URL call is simpler and easier to retry. A multi-page task needs discovery as well as extraction: define allowed domains, path rules, maximum pages, pagination handling and whether links may leave the starting section. Context.dev says its crawler prioritizes relevant internal links; Scrapy.io documents discovery and run/job endpoints.
5. Submit the request
An extraction request normally contains an API credential, URL or crawl seed, schema or scraper selection, and crawl settings. Use the exact endpoint, authentication header and parameter names in the provider’s current documentation. Store credentials in environment variables or a secret manager, never in source control. Hosted APIs described here expose different contracts, so there is no single cross-provider request URL.
6. Validate before persistence
Parse the response as JSON, check required keys and types, validate dates and numeric ranges, and reject or quarantine records that fail. Record the provider’s status, any error or classification signal, the original URL and retrieval timestamp. Keep raw responses when your retention policy permits; they make debugging a changed selector or page layout possible.
7. Operate the job
For asynchronous crawls, submit once, poll the documented job status, then export the dataset. Apply bounded retries with backoff for transient transport errors, but do not endlessly retry an access denial or a deterministic schema failure. For recurring collection, record a run identifier and reconcile duplicates using a stable key such as canonical URL plus publication identifier.
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Getting website data as JSON
The response shape depends on the provider. A schema-oriented service may return your named fields directly; a scraper platform may return a dataset whose items contain those fields; a page-type extractor may add classification metadata. Normalize all three into an internal envelope:
{
"source_url": "…",
"retrieved_at": "…",
"status": "ok",
"data": { "title": "…" },
"errors": []
}Keep extraction errors separate from legitimate empty values. “No price shown” is different from “the extractor timed out.” If the service supplies evidence such as the source text or selector, retain it beside the field for review.
Crawling, batching and scheduling decisions
- Discovery: determine how links are found and whether robots, canonical links, sitemaps or explicit URL lists are used.
- Limits: set page, depth, request-rate and payload limits before a crawl can expand unexpectedly.
- Jobs: use asynchronous execution when a crawl may outlive an HTTP request; persist job IDs and poll status.
- Exports: verify whether the dataset is JSON, JSON Lines, CSV or another format and how pagination works.
- Schedules: define what counts as a new or changed record and how failed runs are surfaced.
Scrapy.io documents these job, dataset and recurring-schedule concepts. A crawler is not automatically better than a direct call: for a handful of known URLs, discovery adds latency and cost without helping.
Reliability, performance and cost
Do not infer comparative speed, accuracy or price from vendor feature lists. The available documentation does not provide independent head-to-head measurements or a common pricing comparison. Measure the fields that matter to your project: valid-record rate, missing-field rate, latency, browser-render percentage, retry rate and total request cost.
Improve throughput by sending only required fields, limiting crawl scope, reusing cached results where policy permits and separating discovery from extraction. Protect the target and your budget with concurrency limits. Browser rendering generally consumes more resources than static fetching, so enable it only for pages that need it. Cache by URL and relevant options, and invalidate when the page’s update cadence requires fresh data.
Common failures and fixes
Empty or null fields
Likely causes: the field is absent, content is JavaScript-generated, or the schema name does not match the page. Fix: inspect the page, confirm the exact field contract, try the provider’s browser mode, and distinguish a legitimate null from an extraction error.
HTML shell instead of content
Cause: data is rendered after load. Fix: use a documented browser-rendered mode, wait for the relevant selector or network idle condition if supported, and retest on a representative URL.
Crawl finds too many or too few pages
Cause: link rules, pagination or domain boundaries are wrong. Fix: constrain allowed paths and depth, define pagination behavior, set a page limit, and inspect discovered URLs before enabling a large run.
Job remains pending or fails
Cause: asynchronous work needs polling, a transient provider issue occurred, or credentials and limits are invalid. Fix: poll the documented status endpoint, use bounded backoff, capture the returned error, and verify credentials and quota before retrying.
Values changed after deployment
Cause: the site layout or content changed. Fix: retain provenance and sample responses, add schema validation and alerts for unusual null rates, then update the schema or scraper deliberately.
Access is denied
Review the site’s terms, access rules and applicable law for your jurisdiction and use case. The available documentation does not establish a universal legal rule. Do not attempt to bypass authentication, CAPTCHAs or technical restrictions without authorization.
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Further learning
Hands-On Web Scraping with Python includes a section on extraction with web APIs (PDF). Check the edition and availability yourself; the document does not establish a current print edition or marketplace listing.
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Should I use an API or write my own scraper?
Use an API when hosted rendering, crawling, retries or scheduling save more engineering effort than they cost. Build in-house when you need specialized control, already operate the browser and crawl infrastructure, or cannot send data to a hosted service.
How can I tell whether an extraction result is trustworthy?
Compare it with representative source pages, enforce types and required fields, retain provenance, and monitor missing-field and error rates over time.
What is the difference between extraction and crawling?
Extraction turns one or more known pages into fields. Crawling discovers pages and then extracts them, adding scope, limits and job-management concerns.
Quick Recap
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