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An effective web-scraper input schema is a clear, validated contract between the person or system starting a crawl and the scraper that runs it. Define only caller-controlled values, give routine behavior sensible defaults, validate real constraints before work starts, and make the generated form understandable. In Apify, an Actor input schema also drives validation, the Console form, API documentation, and integration examples; other frameworks use different syntax and may not generate a form at all.
Start with the scraper’s public contract
Before writing schema syntax, list what a caller must be able to change without editing scraper code. Typical inputs include a set of start URLs, a crawl limit, a site-specific search term, pagination settings, authentication data, or a choice of output mode. These are design examples, not a universal field list.
Keep implementation details private. A caller usually needs to say “crawl these URLs” and perhaps “stop after 500 pages”; they should not have to supply an internal queue name, CSS selector that never changes, or a browser flag that the scraper can choose automatically.
Separate required values, defaults and prefills
- Required: information without which a meaningful run cannot proceed. A start URL is required when the scraper has no other target source.
- Default: a behavior the scraper needs but most callers should not configure. For example, a conservative page limit can be applied whenever the caller omits
maxPages. In Apify, defaults are supplied for API, CLI, scheduler and Console starts. - Prefill: a UI example. Apify documents prefills as a way to show a test value when a field has no reasonable default; a prefill is UI-only and should not be mistaken for a value sent by API callers.
This distinction prevents a dangerous assumption: an example URL displayed in a form does not mean an automated launch will receive that URL.
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Model a small, typed root object
Use one root object and give every public property one accepted type. Apify’s documented input types are string, array, object, boolean and integer. Its schema resembles JSON Schema but adds extensions and differences, so validate with the platform’s validator rather than assuming every generic JSON Schema tool behaves identically.
The following conceptual schema shows a useful starting point for a hosted scraper. Field names are examples; adapt them to the actual crawler.
{
"schemaVersion": 1,
"title": "Catalog crawler input",
"type": "object",
"properties": {
"startUrls": {
"title": "Start URLs",
"type": "array",
"description": "Pages where crawling begins.",
"editor": "requestListSources",
"minItems": 1,
"maxItems": 100
},
"maxPages": {
"title": "Maximum pages",
"type": "integer",
"description": "Upper bound for pages fetched in this run.",
"default": 500,
"minimum": 1,
"maximum": 100000
},
"query": {
"title": "Catalog query",
"type": "string",
"description": "Optional site search term.",
"maxLength": 200
},
"followNext": {
"title": "Follow next-page links",
"type": "boolean",
"default": true
}
},
"required": ["startUrls"],
"additionalProperties": false
}
The exact editor names and available keywords depend on the framework. The important design is visible to any reader: one or more URLs are needed, the page count is bounded, the query is optional, and pagination has an explicit default.
Use constraints that express reality
A type check alone will accept many values your crawler cannot use. Add constraints for the actual requirement:
- Strings: minimum and maximum length, a pattern for a known identifier format, or an enumeration for a genuinely closed set.
- Integers: lower and upper bounds that protect the target site and your run budget.
- Arrays: minimum and maximum item counts, plus item schemas so every entry has the expected shape.
- Objects: nested properties, required nested keys and, where appropriate, their own
additionalPropertiespolicy.
Write validation messages in user language: “Enter at least one start URL” is more useful than “minItems violated.” Rejecting an impossible input before the Actor starts is cheaper and easier to explain than failing after requests have been made.
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Choose a form that matches each value
Schema-driven interfaces are only helpful when the control matches the data. In Apify’s documented input UI capabilities, use a URL-list editor for start URLs, a select control for a truly closed set of options, and a code editor when the caller must enter code-valued text. Put advanced settings in a separate section when the platform supports grouping, and use descriptions for practical examples, units and side effects.
Do not turn an open-ended value into a select merely to make the form look tidy. If a site accepts arbitrary CSS selectors, a text or code editor is more honest than a list containing a few examples. Conversely, if the scraper supports only html and json output, an enumeration prevents spelling variants from reaching execution.
Decide how strict the contract should be
Unknown fields are a compatibility decision, not a purely technical one. Apify documents permissive additionalProperties behavior by default at the root and nested-object levels. Set it to false when undeclared fields should fail early; this catches typos such as maxPage instead of maxPages. Before tightening a published interface, check existing API clients, schedules and saved inputs, because callers that send extra metadata may break.
A practical rollout is to document accepted fields first, observe callers, then enforce strictness in a versioned change. If compatibility matters more than typo detection, keep the object permissive but ignore unknown values deliberately and document that behavior.
Discover inputs for JavaScript-heavy targets
A schema cannot compensate for choosing the wrong data source. When a page fills its table after JavaScript runs, inspect the browser’s network activity and identify the request that returns the records. Scrapy’s workflow documentation (version 2.1.0) recommends reproducing that request, including its HTTP method and URL and, when needed, its body, headers and form parameters.
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Prefer a structured request when it works
If an XHR or fetch response already contains JSON, expose only the values that change that request: search text, page number, filters, or date range. This usually avoids a full browser and makes retries, rate limiting and validation simpler. Do not add a generic “render JavaScript” switch to every schema before checking whether a direct data request is available.
Use rendering when the artifact requires it
A headless browser remains appropriate when the target requires client-side execution, the data is assembled in the browser, or the requested output is a browser-visible artifact such as a screenshot. Rendering adds startup time and failure modes, so make it an execution decision with a sensible default rather than forcing every caller to understand it.
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- Start with the smallest valid object and confirm the scraper reaches its first request.
- Omit each optional field and verify that the documented default is actually applied.
- Submit empty strings, out-of-range numbers, empty arrays and malformed nested objects; each should produce a useful validation error.
- Try an undeclared property and confirm the result matches your intentional strictness policy.
- Launch through every supported path—Console, API, CLI and scheduler—because UI prefills and runtime defaults can appear different.
- Run a compatibility fixture containing inputs saved by existing callers before publishing a breaking schema change.
Apify states that input failing validation is rejected before the Actor starts. Treat that boundary as a testable guarantee: no queue should be created and no target request should be issued for invalid input.
Operational details that belong in the design
Protect cost and target sites
Bound crawl counts, pagination depth and batch sizes. Defaults should be conservative enough for a first run, while maximums should reflect the capacity you can support. If a caller can provide many URLs, cap the array and explain how to split larger jobs.
Make retries safe
Prefer deterministic inputs and expose a stable run identifier or resume option only if the implementation supports it. Avoid accepting a free-form “retry everything” flag that can multiply traffic unexpectedly. Document whether changing a filter or page limit creates a new run or resumes an old one.
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Keep secrets out of ordinary fields
Authentication values, cookies and authorization headers need platform-appropriate secret handling. Do not use a prefilled example token, include secrets in descriptions, or write them to ordinary run logs. The schema should name the credential purpose without exposing its value.
Respect access rules
A valid schema does not grant permission to crawl a site. Check the target’s terms, robots directives, authentication requirements and applicable law before running a scraper. Make rate limits and allowed domains explicit where those controls are part of your implementation.
Common failures and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| The form shows a value, but an API run has none | A UI prefill was mistaken for a default | Set a real default or require the property; test an API launch with the field omitted. |
| Valid-looking JSON is rejected | Framework-specific schema syntax or an unsupported editor | Validate with the framework’s own validator and remove assumptions imported from generic JSON Schema. |
| The crawler starts, then makes useless requests | Constraints are missing or applied only in scraper code | Add bounds, patterns, item schemas and cross-field checks before execution. |
| A new client breaks after a schema update | additionalProperties was tightened |
Review saved inputs, communicate the change and version the contract if necessary. |
| A rendered page has no records | The records came from a separate network request | Inspect the request and reproduce its method, URL, body, headers or form parameters; render only when required. |
| Runs are slow and expensive | Every job launches a browser for data available from an API response | Expose the structured request inputs and use direct retrieval where feasible. |
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Use the API examples in the ScreenshotNeo documentation. Replace the target URL as needed.
cURL
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webp
Python
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)
Node.js
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);
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Apify documents schema version 1 and a maximum Actor input-schema file size of 500 kB. Those are Apify platform facts, not universal limits for scraper frameworks. Keep schemas focused even below that ceiling: a smaller public contract is easier to review, migrate and present in generated documentation.
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Portability requires translation. An Apify Actor schema’s editors, defaults and validation messages may have no direct equivalent in Scrapy or a custom command-line tool. Preserve the contract—names, types, requiredness and bounds—while mapping the presentation layer to the target framework.
Frequently Asked Questions
Should every scraper input be required?
No. Require only values without which a meaningful run cannot proceed; provide defaults for routine behavior and optional fields for legitimate variations.
Is an Apify Actor schema ordinary JSON Schema?
It is similar, but Apify documents extensions and differences. Use Apify’s validator instead of assuming every generic JSON Schema tool will accept or enforce it identically.
When should I expose a browser-rendering option?
Only after checking whether the target’s data comes from a reproducible network request. Use rendering when client-side execution or a browser-visible artifact is genuinely required.
The Bottom Line
Design the schema as a small public contract: typed fields, explicit required/default/prefill semantics, constraints that match reality, intentional strictness and a data-retrieval strategy chosen after inspecting the target. Validate it through every launch path before you publish it.
Quick Recap
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