# LinkedIn Jobs Scraper | Hiring & Job Listings Data (`b2b_leads/linkedin-jobs-scraper-hiring-job-listings-data`) Actor Scrape LinkedIn job postings at scale. Full search filters: keywords, title, company, location, remote/hybrid, experience & date posted. Structured JSON for recruiting, talent acquisition, job boards, hiring intelligence, salary research & LLM pipelines. 1,000 jobs/keyword. Real-time Apify export. - **URL**: https://apify.com/b2b\_leads/linkedin-jobs-scraper-hiring-job-listings-data.md - **Developed by:** [Chidubem Aneke](https://apify.com/b2b_leads) (community) - **Categories:** Jobs, MCP servers, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.97 / 1,000 jobs This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## LinkedIn Jobs Scraper **The fastest way to collect LinkedIn job postings on Apify.** Turn any job search into clean, structured JSON — ready for spreadsheets, dashboards, automations, and AI workflows. Results land in your dataset **in real time**, as each job is found. Built for recruiters, talent teams, job boards, market researchers, and builders who need **reliable hiring data at scale** — without slow setups or messy exports. --- ### Why this Actor | | LinkedIn Jobs Scraper | Typical job scraper | |---|----------------------|---------------------| | **Speed** | ~1–3 s per job | 5–15 s per job | | **Memory** | **256–512 MB** default | 2–4 GB+ | | **Setup** | Run in seconds — no login | Often requires accounts & maintenance | | **Cost** | Low compute, efficient runs | High | | **Output** | Structured JSON, LLM-ready | Often messy HTML | | **Scale** | Up to **1,000 jobs** per keyword | Often capped lower | | **Filters** | Full LinkedIn Jobs filters | Often keyword-only | --- ### What you get — every job, fully structured Each record is one job posting with `featureType: "job"` and `scrapedAt` so you can sort, filter, and join with your own data. | Field | What it tells you | |-------|-------------------| | `title`, `company`, `location`, `workplaceType` | Role, employer, place, remote/hybrid/onsite | | `jobUrl`, `applyUrl`, `companyUrl`, `companyLogo` | Direct links and company branding | | `descriptionText`, `descriptionHtml` | Full job description (when detail fetch is on) | | `seniorityLevel`, `employmentType`, `jobFunction`, `industries` | How LinkedIn classifies the role | | `salary`, `applicantCount`, `easyApply` | Compensation signals and competition | | `listedAtISO`, `postedTimeAgo` | How fresh the listing is | | `searchQuery`, `position` | Which search produced this row and its rank | Set `includeRaw: true` if you need the extended source payload for custom processing. --- ### Search like LinkedIn Jobs — get data like a database Use the **same filters recruiters use on LinkedIn**: - **Keywords** — one search per keyword (e.g. `software engineer`, `product manager fintech`) - **Title** — narrow to a specific role name - **Company** — jobs at Google, Stripe, or any employer - **Location** — United States, London, Remote, and more - **Experience level** — internship through executive - **Employment type** — full-time, contract, internship, volunteer, and more - **Workplace** — on-site, remote, hybrid - **Date posted** — any time, past month, week, or 24 hours - **Sort** — relevance or most recent - **Volume** — up to **1,000 jobs per keyword** - **Full details** — toggle rich descriptions and hiring criteria on or off --- ### Use cases — real outcomes - **Recruiting pipelines** — build candidate-ready job lists by role, location, and seniority - **Job boards & aggregators** — feed your product with fresh listings daily - **Market & salary research** — track who is hiring, where, and for what skills - **Competitive intelligence** — monitor competitor hiring by company and function - **Sales & BD** — spot companies in hiring mode for timely outreach - **AI assistants & agents** — summarize listings, score fit, draft outreach, or answer hiring questions - **Data warehouses & CRMs** — push JSON into Snowflake, BigQuery, HubSpot, Airtable, or your stack via API --- ### LLM & MCP integration Output is **structured JSON** — ideal for ChatGPT, Claude, Gemini, LangChain, LlamaIndex, CrewAI, and custom agents. No cleanup step. No HTML stripping. Just records your model can read. #### Recommended workflow 1. Run the Actor with your keywords and filters. 2. Pull dataset items via [Apify API](https://docs.apify.com/api/v2), webhook, or export JSON/CSV. 3. Pass records to your LLM, vector store, or automation — one job per row. #### Example: one job record for an LLM prompt ```json { "featureType": "job", "title": "Senior Software Engineer", "company": "Stripe", "location": "Remote", "workplaceType": "Remote", "seniorityLevel": "Mid-Senior level", "employmentType": "Full-time", "jobFunction": "Engineering", "descriptionText": "We're looking for a senior engineer to...", "salary": "USD 150000-200000 YEAR", "applicantCount": 87, "easyApply": true, "listedAtISO": "2026-07-06T10:00:00.000Z", "jobUrl": "https://www.linkedin.com/jobs/view/1234567890", "searchQuery": "software engineer", "scrapedAt": "2026-07-08T12:00:00.000Z" } ```` #### Apify MCP (Model Context Protocol) Connect the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) so AI assistants can: - **Run** this Actor from natural-language instructions - **Read** job results directly in the chat - **Chain** with other Actors — enrich contacts, send alerts, update spreadsheets **Example MCP conversation:** ``` User: "Find 50 remote data engineer jobs posted this week and summarize top employers" → MCP runs LinkedIn Jobs Scraper with jobKeywords, jobLocation=Remote, jobDatePosted=week → MCP reads dataset items → LLM summarizes hiring trends and top companies ``` #### API quick start ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "jobKeywords": ["data engineer"], "jobLocation": "Remote", "jobDatePosted": "week", "jobsPerQuery": 50, "fetchJobDetails": true }' ``` **Fetch results:** `GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json` **Dataset views in Apify Console:** Overview (core fields) · Full details (includes descriptions) Export as **JSON**, **CSV**, **Excel**, **RSS**, or via **API**. *** ### Input reference | Input | Type | Default | Description | |-------|------|---------|-------------| | `jobKeywords` | string\[] | `["software engineer"]` | Search keywords — one run per keyword | | `jobTitle` | string | — | Title filter | | `jobCompany` | string | — | Company filter | | `jobLocation` | string | `"United States"` | Location | | `jobExperienceLevels` | enum\[] | all levels | internship → executive | | `jobTypes` | enum\[] | all types | full\_time, part\_time, contract, … | | `jobRemote` | enum\[] | all types | onsite, remote, hybrid | | `jobDatePosted` | enum | `any` | any, month, week, day | | `jobSort` | enum | `relevance` | relevance or date | | `jobsPerQuery` | integer | `25` | Max jobs per keyword (1–1000) | | `fetchJobDetails` | boolean | `true` | Full description + criteria | | `includeRaw` | boolean | `false` | Attach extended source payload | | `proxyConfiguration` | object | residential US | Connection settings (US residential recommended) | Full schema: **Input** tab on Apify Console or `.actor/input_schema.json` in this repository. *** ### Quick start examples **Remote software roles — full details** ```json { "jobKeywords": ["software engineer", "backend developer"], "jobLocation": "Remote", "jobRemote": ["remote"], "jobsPerQuery": 100, "fetchJobDetails": true } ``` **Jobs at a specific company** ```json { "jobKeywords": ["engineer"], "jobCompany": "Google", "jobLocation": "United States", "jobsPerQuery": 50 } ``` **Fresh listings only — fast summary mode** ```json { "jobKeywords": ["product manager"], "jobLocation": "London", "jobDatePosted": "week", "jobSort": "date", "jobsPerQuery": 75, "fetchJobDetails": false } ``` **High-volume market scan** ```json { "jobKeywords": ["machine learning", "AI engineer", "data scientist"], "jobLocation": "United States", "jobExperienceLevels": ["mid_senior", "director"], "jobsPerQuery": 500, "fetchJobDetails": true } ``` *** ### Performance & reliability - **Residential proxy (US recommended)** — best results and consistent throughput. - **Default memory: 512 MB** — enough for high-volume runs with multiple keywords. - **Streaming output** — jobs appear in your dataset as they are collected; long runs stay efficient. - **Smart search expansion** — automatically broadens coverage when you request large result sets. *** ### Limitations & compliance - This is an unofficial tool — not affiliated with LinkedIn. - Use responsibly and comply with applicable laws and LinkedIn's Terms of Service. - Some fields (e.g. salary) appear only when employers publish them. - Listing availability can change; expired postings may return summary data only. *** ### Contact & custom work Need something beyond this Actor? I design and build **custom data solutions** and **full-stack products** for teams that want results — not experiments. **Email:** \ **GitHub:** [github.com/DrunkCodes](https://github.com/DrunkCodes) Reach out for: - **Custom Apify Actors** — any website, job board, or API - **LinkedIn & hiring data projects** — pipelines, alerts, and dashboards at scale - **LLM & MCP integrations** — connect your data to ChatGPT, Claude, and agent workflows - **Web apps & automation** — scrapers, internal tools, SaaS products, and client-facing platforms Whether you need a one-off data feed or a production system, I can help you ship it. *** *LinkedIn Jobs Scraper · by [DrunkCodes](https://github.com/DrunkCodes)* # Actor input Schema ## `jobKeywords` (type: `array`): Main job search keywords — same as the LinkedIn Jobs search box. One search per keyword. ## `jobTitle` (type: `string`): Title filter (e.g. "Software Engineer"). Combined with keywords for precise LinkedIn Jobs results. ## `jobCompany` (type: `string`): Company filter — search jobs at a specific company (e.g. "Google", "Stripe"). Leave empty for any company. ## `jobLocation` (type: `string`): Location to search jobs in (e.g. "United States", "London", "Remote"). ## `jobExperienceLevels` (type: `array`): Filter jobs by experience level. ## `jobTypes` (type: `array`): Filter jobs by employment type. ## `jobRemote` (type: `array`): Filter jobs by on-site / remote / hybrid. ## `jobDatePosted` (type: `string`): Only return jobs posted within this window. ## `jobSort` (type: `string`): Relevance or most recent. ## `jobsPerQuery` (type: `integer`): Maximum jobs to return per keyword (1–1000). ## `fetchJobDetails` (type: `boolean`): Fetch each job's full description and criteria (seniority, employment type, function, industry). Slightly slower but much richer. ## `includeRaw` (type: `boolean`): Attach the raw source payload under the raw field. Larger output. ## `proxyConfiguration` (type: `object`): Use Residential US proxy for best results and to avoid throttling. ## Actor input object example ```json { "jobKeywords": [ "software engineer" ], "jobTitle": "Software Engineer", "jobCompany": "", "jobLocation": "United States", "jobExperienceLevels": [ "internship", "entry", "associate", "mid_senior", "director", "executive" ], "jobTypes": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer" ], "jobRemote": [ "onsite", "remote", "hybrid" ], "jobDatePosted": "any", "jobSort": "relevance", "jobsPerQuery": 25, "fetchJobDetails": true, "includeRaw": false, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # Actor output Schema ## `allResults` (type: `string`): Complete dataset with every job field from this run. ## `overview` (type: `string`): Core job fields table view. ## `full` (type: `string`): All job fields including descriptions. # 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 = { "jobKeywords": [ "software engineer" ], "jobTitle": "Software Engineer", "jobCompany": "", "jobLocation": "United States", "jobExperienceLevels": [ "internship", "entry", "associate", "mid_senior", "director", "executive" ], "jobTypes": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer" ], "jobRemote": [ "onsite", "remote", "hybrid" ], "jobDatePosted": "any", "jobSort": "relevance", "jobsPerQuery": 25, "fetchJobDetails": true, "includeRaw": false }; // Run the Actor and wait for it to finish const run = await client.actor("b2b_leads/linkedin-jobs-scraper-hiring-job-listings-data").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 = { "jobKeywords": ["software engineer"], "jobTitle": "Software Engineer", "jobCompany": "", "jobLocation": "United States", "jobExperienceLevels": [ "internship", "entry", "associate", "mid_senior", "director", "executive", ], "jobTypes": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer", ], "jobRemote": [ "onsite", "remote", "hybrid", ], "jobDatePosted": "any", "jobSort": "relevance", "jobsPerQuery": 25, "fetchJobDetails": True, "includeRaw": False, } # Run the Actor and wait for it to finish run = client.actor("b2b_leads/linkedin-jobs-scraper-hiring-job-listings-data").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 '{ "jobKeywords": [ "software engineer" ], "jobTitle": "Software Engineer", "jobCompany": "", "jobLocation": "United States", "jobExperienceLevels": [ "internship", "entry", "associate", "mid_senior", "director", "executive" ], "jobTypes": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer" ], "jobRemote": [ "onsite", "remote", "hybrid" ], "jobDatePosted": "any", "jobSort": "relevance", "jobsPerQuery": 25, "fetchJobDetails": true, "includeRaw": false }' | apify call b2b_leads/linkedin-jobs-scraper-hiring-job-listings-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=b2b_leads/linkedin-jobs-scraper-hiring-job-listings-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper | Hiring & Job Listings Data", "description": "Scrape LinkedIn job postings at scale. Full search filters: keywords, title, company, location, remote/hybrid, experience & date posted. Structured JSON for recruiting, talent acquisition, job boards, hiring intelligence, salary research & LLM pipelines. 1,000 jobs/keyword. Real-time Apify export.", "version": "0.0", "x-build-id": "IjkjWQAJ1A1iG3W3f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/b2b_leads~linkedin-jobs-scraper-hiring-job-listings-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-b2b_leads-linkedin-jobs-scraper-hiring-job-listings-data", "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/b2b_leads~linkedin-jobs-scraper-hiring-job-listings-data/runs": { "post": { "operationId": "runs-sync-b2b_leads-linkedin-jobs-scraper-hiring-job-listings-data", "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/b2b_leads~linkedin-jobs-scraper-hiring-job-listings-data/run-sync": { "post": { "operationId": "run-sync-b2b_leads-linkedin-jobs-scraper-hiring-job-listings-data", "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", "properties": { "jobKeywords": { "title": "Job keywords", "type": "array", "description": "Main job search keywords — same as the LinkedIn Jobs search box. One search per keyword.", "default": [ "software engineer" ], "items": { "type": "string" } }, "jobTitle": { "title": "Job title", "type": "string", "description": "Title filter (e.g. \"Software Engineer\"). Combined with keywords for precise LinkedIn Jobs results.", "default": "Software Engineer" }, "jobCompany": { "title": "Company", "type": "string", "description": "Company filter — search jobs at a specific company (e.g. \"Google\", \"Stripe\"). Leave empty for any company.", "default": "" }, "jobLocation": { "title": "Job location", "type": "string", "description": "Location to search jobs in (e.g. \"United States\", \"London\", \"Remote\").", "default": "United States" }, "jobExperienceLevels": { "title": "Experience levels", "type": "array", "description": "Filter jobs by experience level.", "items": { "type": "string", "enum": [ "internship", "entry", "associate", "mid_senior", "director", "executive" ], "enumTitles": [ "Internship", "Entry level", "Associate", "Mid-Senior", "Director", "Executive" ] }, "default": [ "internship", "entry", "associate", "mid_senior", "director", "executive" ] }, "jobTypes": { "title": "Job types", "type": "array", "description": "Filter jobs by employment type.", "items": { "type": "string", "enum": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer" ], "enumTitles": [ "Full-time", "Part-time", "Contract", "Temporary", "Internship", "Volunteer" ] }, "default": [ "full_time", "part_time", "contract", "temporary", "internship", "volunteer" ] }, "jobRemote": { "title": "Workplace type", "type": "array", "description": "Filter jobs by on-site / remote / hybrid.", "items": { "type": "string", "enum": [ "onsite", "remote", "hybrid" ], "enumTitles": [ "On-site", "Remote", "Hybrid" ] }, "default": [ "onsite", "remote", "hybrid" ] }, "jobDatePosted": { "title": "Date posted", "enum": [ "any", "month", "week", "day" ], "type": "string", "description": "Only return jobs posted within this window.", "default": "any" }, "jobSort": { "title": "Sort jobs by", "enum": [ "relevance", "date" ], "type": "string", "description": "Relevance or most recent.", "default": "relevance" }, "jobsPerQuery": { "title": "Jobs per keyword", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum jobs to return per keyword (1–1000).", "default": 25 }, "fetchJobDetails": { "title": "Fetch full job details", "type": "boolean", "description": "Fetch each job's full description and criteria (seniority, employment type, function, industry). Slightly slower but much richer.", "default": true }, "includeRaw": { "title": "Include raw data", "type": "boolean", "description": "Attach the raw source payload under the raw field. Larger output.", "default": false }, "proxyConfiguration": { "title": "Proxy settings", "type": "object", "description": "Use Residential US proxy for best results and to avoid throttling.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } } }, "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 } } } } } } } } } } ```