# Glassdoor Scraper - Jobs, Companies & Salaries (`makework36/glassdoor-scraper`) Actor Scrape Glassdoor job listings: title, company, location, salary parsed, employment type, description. 25+ structured fields per job via JSON-LD. HTTP-only, low cost. - **URL**: https://apify.com/makework36/glassdoor-scraper.md - **Developed by:** [deusex machine](https://apify.com/makework36) (community) - **Categories:** Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Glassdoor Scraper API — Jobs, Companies, Salaries & Reviews Data A high-performance **Glassdoor scraper** that extracts complete job listings from [Glassdoor.com](https://www.glassdoor.com), one of the most trusted job & employer review platforms with 80M+ reviews and millions of active job postings across the US, UK, Canada, India, Australia, Germany and France. Pull 25+ structured fields per job — title, company name, location parsed to city/state/country, salary range with currency and unit, full description, employment type, posting date, and education/experience requirements — without writing a single line of HTML scraping logic. Built for recruiters tracking US, UK and EMEA labor supply, market analysts comparing pay across [LinkedIn Jobs](https://www.linkedin.com/jobs), [Indeed](https://www.indeed.com), and [BLS](https://www.bls.gov) data, salary benchmarking firms, founders monitoring competitor hiring patterns, and ML teams training resume-to-job matching pipelines against the [schema.org JobPosting](https://schema.org/JobPosting) schema. Output is clean JSON, ready for [Google BigQuery](https://cloud.google.com/bigquery), Snowflake, Postgres, or [Google Sheets](https://workspace.google.com/products/sheets/). ### What this Glassdoor scraper does This actor automates browsing **glassdoor.com** the way a real visitor does. It hits the public job search pages, walks through pagination, parses the embedded [schema.org JobPosting](https://schema.org/JobPosting) JSON-LD blocks served alongside each search result, and visits every individual job detail page to extract the full structured posting — title, company, salary, location, employment type, description, education and experience requirements. The pricing logic is automatic: Glassdoor's JSON-LD baseSalary block becomes `salaryMin: 143913`, `salaryMax: 197200`, `salaryCurrency: "USD"`, `salaryUnit: "YEAR"`, plus a human-readable `salaryText: "$143,913 - $197,200 USD/year"`. Locations are split into city, state/region, country, postal code and street address, so downstream filtering by city or state works out of the box. The scraper is HTTP-only — no headless browser, no captcha solver. Memory footprint is 512 MB, dramatically lower than the 4 GB needed by browser-based competitors. It runs on Apify's residential proxy network out of the box. ### Data fields the Glassdoor scraper extracts Every record contains these fields when present in Glassdoor's structured data: #### Identity & links - `title` — Position title - `jobUrl` — Direct link to the Glassdoor listing - `position` — Position in the search results ranking #### Company - `companyName` — Hiring company - `companyUrl` — Company's external URL (sameAs from JSON-LD) - `companyLogoUrl` — Logo image URL - `industry` — Industry classification #### Location - `location` — Concatenated "City, State, Country" string - `addressLocality` — City - `addressRegion` — State or region - `addressCountry` — Country name - `postalCode` — Postal/ZIP code - `streetAddress` — Street address when published - `jobLocationType` — `"TELECOMMUTE"` when remote - `applicantLocationRequirements` — Geographic eligibility #### Salary - `salaryText` — Human-readable formatted string - `salaryMin` — Lower bound number - `salaryMax` — Upper bound number - `salaryValue` — Single value when no range is given - `salaryCurrency` — `"USD"`, `"EUR"`, `"GBP"`, etc. - `salaryUnit` — `"YEAR"`, `"HOUR"`, `"MONTH"`, `"WEEK"` #### Description & requirements - `jobDescription` — Full description text - `jobDescriptionHtml` — Original HTML markup of the description - `qualifications` — Required qualifications when separately published - `responsibilities` — Day-to-day responsibilities - `skills` — Skills list from JSON-LD when provided - `educationRequirements` — Education level required - `experienceRequirements` — Experience required #### Timestamps & employment type - `datePosted` — ISO 8601 posting date - `postedDate` — Alias of datePosted - `validThrough` — Listing expiration date - `employmentType` — Array of types (`"FULL_TIME"`, `"PART_TIME"`, `"CONTRACTOR"`, `"INTERN"`) - `directApply` — Boolean: can apply directly on Glassdoor? ### Use cases for this Glassdoor data API 1. **Recruitment talent radar** — Track jobs matching your hiring niche, filter by `addressRegion` for US states, by `employmentType` for full-time only, by `salaryMin` for compensation bands. 2. **Salary benchmarking** — Aggregate `salaryMin`/`salaryMax` by job title + location to produce reliable pay bands. The `salaryUnit` field lets you cleanly separate hourly vs annual data. 3. **Hiring intelligence** — Track which companies are scaling specific functions. Combine `companyName` + `industry` for clean B2B segmentation. 4. **ML training corpora** — Pair `title`, full `jobDescription`, `educationRequirements`, and `experienceRequirements` to train resume-to-job matching models. 5. **Lead generation** — Identify companies actively hiring in your niche (e.g. all US companies hiring a "VP of Engineering"). Use `companyName` + `addressLocality` for geographic prospecting. 6. **Compensation transparency reports** — Detect employers consistently posting salary ranges vs those who don't, useful for pay equity research. ### How to use this Glassdoor scraper #### Mode 1 — Search by keywords (easiest) ```json { "mode": "keywords", "keywords": ["python developer", "data engineer"], "country": "us", "maxJobs": 100, "fetchDetails": true } ```` #### Mode 2 — Multi-country pipeline ```json { "mode": "keywords", "keywords": ["software engineer"], "country": "uk", "maxJobs": 200, "fetchDetails": true } ``` #### Mode 3 — Pre-built search URLs (advanced) ```json { "mode": "urls", "searchUrls": [ { "url": "https://www.glassdoor.com/Job/remote-python-developer-jobs-SRCH_IL.0,6_IS11047_KO7,23.htm" } ], "maxJobs": 50, "fetchDetails": true } ``` ### Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `mode` | enum | `"keywords"` | `"keywords"` or `"urls"` | | `keywords` | array | `["python developer"]` | Job titles / keywords | | `country` | enum | `"us"` | Regional Glassdoor domain | | `searchUrls` | array | `[]` | Pre-built URLs (urls mode) | | `maxJobs` | integer | `50` | Cap per keyword/URL | | `fetchDetails` | boolean | `true` | Visit detail pages for full structured data | | `proxyConfiguration` | object | RESIDENTIAL | Apify proxy (residential recommended) | ### Output example (single record, JSON) ```json { "position": 1, "title": "Python Developer", "jobUrl": "https://www.glassdoor.com/job-listing/python-developer-social-security-administration-...", "companyName": "US Social Security Administration", "companyLogoUrl": "https://media.glassdoor.com/sql/...png", "companyUrl": "https://www.ssa.gov", "industry": "Government", "location": "Woodlawn, Maryland, United States", "addressLocality": "Woodlawn", "addressRegion": "Maryland", "addressCountry": "United States", "salaryText": "$143,913 - $197,200 USD/year", "salaryMin": 143913, "salaryMax": 197200, "salaryCurrency": "USD", "salaryUnit": "YEAR", "employmentType": ["FULL_TIME"], "datePosted": "2026-05-06T00:00:00", "validThrough": "2026-06-06T00:00:00", "jobDescription": "Summary IT Specialist (APPSW), Python Developer position(s) are being filled through the Office of Personnel Management...", "educationRequirements": "Bachelor's degree", "experienceRequirements": "5 years" } ``` ### How to call this Glassdoor scraper from your code #### From the Apify API (curl) ```bash curl -X POST "https://api.apify.com/v2/acts/makework36~glassdoor-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "mode": "keywords", "keywords": ["data engineer"], "country": "us", "maxJobs": 100, "fetchDetails": true }' ``` #### From Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("makework36/glassdoor-scraper").call(run_input={ "mode": "keywords", "keywords": ["python developer", "data scientist"], "country": "us", "maxJobs": 200, "fetchDetails": True, }) for job in client.dataset(run["defaultDatasetId"]).iterate_items(): print(job["title"], job["companyName"], job.get("salaryText")) ``` #### From Node.js (apify-client) ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' }); const run = await client.actor('makework36/glassdoor-scraper').call({ mode: 'keywords', keywords: ['software engineer'], country: 'us', maxJobs: 100, fetchDetails: true, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Got ${items.length} jobs`); ``` #### Export to CSV ```bash curl "https://api.apify.com/v2/acts/makework36~glassdoor-scraper/runs/last/dataset/items?format=csv&token=$APIFY_TOKEN" \ -o glassdoor_jobs.csv ``` ### Performance & cost | Mode | Time per job | 100 jobs ETA | |---|---:|---:| | Search only (titles + URLs) | ~0.5 s | ~50 s | | With `fetchDetails: true` | ~7–15 s | ~12–25 min | Detailed mode visits each individual job page to extract the full JSON-LD JobPosting. This is necessary for salary, full description, employment type and address. Search-only mode is much faster but returns only title + URL. ### Glassdoor scraper comparison | Feature | This actor | Browser-only with proxy | Headless + captcha solver | |---|:---:|:---:|:---:| | Output fields | 25+ structured | 15–20 | 18 | | Salary parsed to Min/Max/Currency/Unit | ✅ | partial | partial | | Location split (city/state/country) | ✅ | ❌ | ❌ | | Pure HTTP (no browser) | ✅ | ❌ | ❌ | | Memory footprint | 512 MB | 4 GB | 4 GB | | Captcha solver required | ❌ | ❌ | ✅ | | Pricing model | PAY\_PER\_EVENT tiered | flat | flat | ### Step-by-step tutorial — your first run in 2 minutes 1. Click **Try for free** at the top of this page. 2. Leave `mode` as `keywords` and `country` as `us`. 3. Change the default keyword (e.g. `"data analyst"`) and adjust `maxJobs` to `20` for a quick test. 4. Click **Start**. The run finishes in ~3–5 minutes (detailed mode). 5. Open the dataset; you'll see a table with titles, companies, locations, salaries. 6. Export as CSV, JSON or Excel from the **Export** button. 7. Once happy, scale `maxJobs`, schedule recurring runs, or hook into your data pipeline. ### Advanced usage patterns #### Pattern A — Daily talent radar by US region Schedule the actor daily with `keywords: ["your role"]` + `country: "us"`. Use the `addressRegion` field to slice results by state. #### Pattern B — Multi-country compensation atlas Run with the same keyword set across `country: "us"`, `"uk"`, `"de"`, `"in"`. Aggregate `salaryMin`/`salaryMax` by country + role. #### Pattern C — Remote-only feed Filter outputs where `jobLocationType == "TELECOMMUTE"` post-extract. Combine with `salaryMin >= 100000` to build a high-quality remote tech feed. #### Pattern D — Education requirement tracker Group by `educationRequirements` over time. Detect roles shifting from "Bachelor's" toward "Master's" requirements as competition intensifies. #### Pattern E — Competitor hiring tracker Use Glassdoor's URL filter for a specific company name (`mode: urls`). Track posting velocity over weeks to detect headcount growth. ### Troubleshooting **Some salary fields are null.** Not every Glassdoor posting includes salary. About 50–60% of US listings have salary in the JSON-LD; the rest return `null`. The structured fields are populated only when the employer publishes them. **Run takes longer than expected.** Detail mode adds ~7–15 s per job because each individual page is fetched and parsed. For 1,000 jobs in detailed mode, expect 2–4 hours. Run search-only mode first for a fast title-level scan, then re-run only on interesting jobs in detailed mode. **HTTP 403 errors in the logs.** Normal — Glassdoor rate-limits each IP. The actor rotates Apify residential proxy sessions and retries automatically. As long as the final job count is non-zero, the scraper recovered. **My run hit a proxy budget limit.** Each search page is ~500 KB and each detail page ~80 KB. For 1,000 jobs (~30 search pages + 1,000 detail pages) expect ~95 MB of proxy traffic. Check your Apify residential allowance. **Results show only `title` and `jobUrl`.** You ran with `fetchDetails: false`. Enable it to extract the full structured data from each job's individual page. **I want only jobs from one specific country.** Set `country` to `us`, `uk`, `ca`, `in`, `au`, `de`, or `fr`. The scraper uses the matching regional Glassdoor domain. ### Pricing This actor uses **PAY\_PER\_EVENT** pricing with tiered volume discounts. | Tier | Standard job (URL+title only) | Detailed job (full JSON-LD) | |:-----|---:|---:| | FREE | $1.50 / 1K | $7.50 / 1K | | BRONZE | $1.10 / 1K | $5.50 / 1K | | SILVER | $0.80 / 1K | $4.00 / 1K | | GOLD | $0.55 / 1K | $2.80 / 1K | | PLATINUM | $0.40 / 1K | $2.20 / 1K | | DIAMOND | $0.28 / 1K | $1.80 / 1K | Plus a one-time **$0.001 per actor run**. Detailed mode is the default because Glassdoor's value lives in the structured JobPosting data on each individual page. ### FAQ **Why does the scraper need Residential proxies?** Glassdoor blocks datacenter IPs aggressively. Residential proxies route traffic through real households, which Glassdoor accepts. Apify includes residential proxy access in most paid plans. **Is scraping Glassdoor legal?** This actor accesses publicly available job pages — no login, no paywall bypass, no PII extraction beyond what employers post publicly. Always check Glassdoor's terms of service and consult legal counsel for your jurisdiction. **Can I scrape Glassdoor company reviews / salaries?** No — this actor focuses on job listings. Glassdoor's reviews and aggregated salary reports require a different stack and have different terms-of-service implications. **Does this support Glassdoor UK / Canada / Germany / France?** Yes. Set `country` to `uk`, `ca`, `de`, `fr`, `in`, or `au` and the scraper hits the matching regional Glassdoor domain. **How fresh is the data?** The scraper fetches in real time at run start. Glassdoor updates listings continuously. **Can I scrape Glassdoor without writing code?** Yes — use the input form on this page, click Start, then download the dataset as CSV or Excel. **Why are `qualifications` and `responsibilities` fields sometimes empty?** Not all Glassdoor postings split these out as separate JSON-LD properties. When absent, the same information typically lives inside `jobDescription` as a paragraph. **Does the scraper deduplicate jobs?** Yes. Each `jobUrl` is tracked across pages within a run; duplicates are skipped. **Is there a rate limit on my side?** The actor uses jitter between pages and rotates proxy sessions automatically. For very large runs (>5,000 jobs), split across multiple Apify Tasks scheduled minutes apart. ### Changelog - **0.1** (2026-05) — Initial release. Keywords + URLs modes. JSON-LD parsing for search and detail. 25+ fields. Residential proxy default. ### Related scrapers from the same author - [`makework36/indeed-scraper`](https://apify.com/makework36/indeed-scraper) — Indeed.com US jobs - [`makework36/naukri-scraper`](https://apify.com/makework36/naukri-scraper) — Naukri.com India jobs - [`makework36/trustpilot-reviews-scraper`](https://apify.com/makework36/trustpilot-reviews-scraper) — Trustpilot reviews - [`makework36/google-maps-scraper-full`](https://apify.com/makework36/google-maps-scraper-full) — Google Maps places ### Legal & ethics note This Glassdoor scraper accesses publicly available information. It does not bypass authentication, paywalls, or extract personally identifiable information beyond what employers and Glassdoor display publicly. Users are responsible for complying with Glassdoor's [terms of service](https://www.glassdoor.com/about/terms.htm), the [Computer Fraud and Abuse Act](https://www.law.cornell.edu/uscode/text/18/1030), and any data protection regulations applicable in their jurisdiction (e.g. CCPA, GDPR). The author of this actor accepts no responsibility for misuse. # Actor input Schema ## `mode` (type: `string`): 'keywords' searches Glassdoor by keyword. 'urls' takes pre-built search URLs. ## `keywords` (type: `array`): Job titles or keywords to search (e.g. 'python developer', 'data scientist'). ## `searchUrls` (type: `array`): Pre-built Glassdoor search URLs. ## `country` (type: `string`): Glassdoor regional domain to search on. ## `maxJobs` (type: `integer`): Maximum jobs per keyword/URL. Glassdoor shows ~30 jobs per page. ## `fetchDetails` (type: `boolean`): Visit each job detail page for full description + JSON-LD JobPosting (salary, employment type, address). Slower but ~20 extra fields. ## `proxyConfiguration` (type: `object`): Apify proxy. Residential recommended to avoid Glassdoor rate-limiting. ## Actor input object example ```json { "mode": "keywords", "keywords": [ "python developer" ], "searchUrls": [], "country": "us", "maxJobs": 50, "fetchDetails": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("makework36/glassdoor-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], } } # Run the Actor and wait for it to finish run = client.actor("makework36/glassdoor-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call makework36/glassdoor-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=makework36/glassdoor-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Glassdoor Scraper - Jobs, Companies & Salaries", "description": "Scrape Glassdoor job listings: title, company, location, salary parsed, employment type, description. 25+ structured fields per job via JSON-LD. HTTP-only, low cost.", "version": "0.1", "x-build-id": "lKUyT3FnTFUqkcUjS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/makework36~glassdoor-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-makework36-glassdoor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/makework36~glassdoor-scraper/runs": { "post": { "operationId": "runs-sync-makework36-glassdoor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/makework36~glassdoor-scraper/run-sync": { "post": { "operationId": "run-sync-makework36-glassdoor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "keywords", "urls" ], "type": "string", "description": "'keywords' searches Glassdoor by keyword. 'urls' takes pre-built search URLs.", "default": "keywords" }, "keywords": { "title": "Keywords", "type": "array", "description": "Job titles or keywords to search (e.g. 'python developer', 'data scientist').", "default": [ "python developer" ], "items": { "type": "string" } }, "searchUrls": { "title": "Search URLs", "type": "array", "description": "Pre-built Glassdoor search URLs.", "default": [], "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "country": { "title": "Glassdoor country", "enum": [ "us", "uk", "ca", "in", "au", "de", "fr" ], "type": "string", "description": "Glassdoor regional domain to search on.", "default": "us" }, "maxJobs": { "title": "Max jobs", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum jobs per keyword/URL. Glassdoor shows ~30 jobs per page.", "default": 50 }, "fetchDetails": { "title": "Fetch detailed job info", "type": "boolean", "description": "Visit each job detail page for full description + JSON-LD JobPosting (salary, employment type, address). Slower but ~20 extra fields.", "default": true }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Apify proxy. Residential recommended to avoid Glassdoor rate-limiting.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```