# SEPE Spain Job Scraper – Ofertas de Empleo ES (`x_dabit_x/sepe-empleo-es-pro`) Actor Scrapes job offers from SEPE Spain (sepe.es) with stealth Camoufox, proxy rotation, skills filtering (regex + TF-IDF ML), deduplication, change-detection alerts, and Prometheus metrics export. Supports province/CCAA filtering with Vizcaya/Bizkaia focus. - **URL**: https://apify.com/x\_dabit\_x/sepe-empleo-es-pro.md - **Developed by:** [David Cortes](https://apify.com/x_dabit_x) (community) - **Categories:** Jobs, AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## SEPE Spain Job Scraper – Ofertas de Empleo ES Pro **The #1 Apify Actor for scraping SEPE Spain job offers** with full stealth, smart skills filtering, and K8s-ready Prometheus metrics. - **Anti-bot max**: Camoufox (Firefox stealth) + Apify residential proxies + random delays + cookie handling - **Smart skills filter**: regex + scikit-learn TF-IDF cosine similarity (catches "contenedores" → Docker, "orquestación" → Kubernetes) - **Change-detection alerts**: compares every run against the previous one → new / changed / removed offers - **Deduplication**: SHA-256 hash per offer, persisted across runs - **K8s-ready**: Prometheus metrics exported to KV store (scrapeable by any Prometheus server) - **Province focus**: Vizcaya / Bizkaia by default, all 52 Spanish provinces supported --- ### Output Example ```json { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/...", "titulo_oferta": "DevOps Engineer – Kubernetes / AWS", "empresa": "Tecnología Vasca S.L.", "provincia": "Vizcaya", "salario": "35.000 € - 50.000 €/año", "skills_requeridas": ["Kubernetes", "Docker", "AWS", "Terraform", "Linux", "CI/CD"], "fecha_publicacion": "2026-04-15", "enlace_aplicar": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/.../solicitar" } ```` *** ### Input Schema | Field | Type | Default | Description | |---|---|---|---| | `start_urls` | array | SEPE national pages | Extra entry-point URLs | | `provincias` | array | `["Vizcaya","Bizkaia"]` | Province/CCAA filter (52 provinces supported) | | `skills` | array | `["Kubernetes","Docker","Python"]` | Skills to filter by (regex + ML) | | `max_pages` | int | `50` | Max listing pages per entry-point | | `use_ml_skills` | bool | `true` | Enable TF-IDF ML skill matching | | `use_proxy` | bool | `true` | Use Apify residential proxy | | `proxy_groups` | array | `["RESIDENTIAL"]` | Proxy groups | | `proxy_country` | string | `"ES"` | Proxy country (ES = Spanish IP) | | `headless` | bool | `true` | Headless browser mode | | `min_delay` | float | `2.0` | Min delay between requests (s) | | `max_delay` | float | `5.0` | Max delay between requests (s) | *** ### Quick Start #### Run locally ```bash ## 1. Clone / enter the project cd sepe-empleo-es-pro ## 2. Create virtual env and install dependencies python -m venv .venv source .venv/bin/activate ## Windows: .venv\Scripts\activate pip install -r requirements.txt ## 3. Install Camoufox browser binaries python -m camoufox fetch ## 4. Put test input in place cp test_input.json storage/key_value_stores/default/INPUT.json ## 5. Run python -m my_actor ``` #### Run on Apify ```bash ## Login (one-time) apify login ## Push and deploy apify push ## Run with test input apify run --input-file test_input.json ``` #### Run via API ```bash curl -X POST \ "https://api.apify.com/v2/acts/YOUR_USERNAME~sepe-empleo-es-pro/runs" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_TOKEN" \ -d '{ "provincias": ["Vizcaya", "Bizkaia"], "skills": ["Kubernetes", "Python", "DevOps"], "max_pages": 50 }' ``` *** ### Architecture ``` my_actor/ ├── main.py ## Actor entry point, crawler setup, post-processing ├── routes.py ## Crawlee router: NAV / LIST / DETAIL handlers + XHR interception ├── extractors.py ## Multi-selector SEPE data extraction with JSON-LD + regex fallbacks ├── skills_matcher.py ## Regex + TF-IDF scikit-learn skills detection (30+ tech skills) ├── dedup.py ## SHA-256 offer deduplication, cross-run persistence ├── alerts.py ## Change-detection: new / changed / removed offers diff ├── metrics.py ## Prometheus metrics (counters, gauges, histograms) └── config.py ## Province codes (52), SEPE URLs, CSS selectors, rate-limit settings ``` #### Anti-bot Stack | Layer | Technology | Config | |---|---|---| | Browser fingerprint | Camoufox (Firefox stealth) | `os=windows/macos`, `locale=es-ES`, `geoip=true` | | IP rotation | Apify Residential proxies | `countryCode=ES` (Spanish IPs) | | Timing | Random delays | 2–5 s per request (configurable) | | Detection evasion | Cookie auto-accept | Handles SEPE's cookie banner | | CAPTCHA detection | Text/title heuristics | Auto-retry on fresh session | | Header generation | Camoufox built-in | Realistic browser headers | | Scrolling | JS scroll simulation | Triggers lazy-loaded content | | XHR interception | Playwright response hook | Catches SEPE's JSON API calls | #### Skills Matching Pipeline ``` Input text (job description) │ ▼ ┌───────────────────┐ ┌─────────────────────────┐ │ Regex matcher │ │ TF-IDF cosine similarity │ │ (30+ skills, │ + │ (scikit-learn, threshold │ │ 50+ aliases) │ │ 0.25, ngram 1-2) │ └───────────────────┘ └─────────────────────────┘ │ │ └───────────┬───────────────┘ ▼ Canonical skill names ["Kubernetes","Docker","Python"] ``` *** ### Prometheus Metrics (K8s-ready) Metrics are exported in standard Prometheus text format to the Actor's Key-Value Store under the key `prometheus_metrics`. Retrieve them via: ```bash curl "https://api.apify.com/v2/key-value-stores/STORE_ID/records/prometheus_metrics" \ -H "Authorization: Bearer YOUR_TOKEN" ``` #### Available metrics | Metric | Type | Description | |---|---|---| | `sepe_offers_scraped_total` | Counter | Total offers stored | | `sepe_offers_new_total` | Counter | New vs previous run | | `sepe_offers_changed_total` | Counter | Changed offers | | `sepe_offers_removed_total` | Counter | Removed offers | | `sepe_requests_total{status}` | Counter | Requests by status (success/failed/retried) | | `sepe_pages_skipped_duplicates_total` | Counter | Dedup skips | | `sepe_skills_matched_total{skill}` | Counter | Matches per skill | | `sepe_offers_in_dataset` | Gauge | Current dataset size | | `sepe_dedup_ratio` | Gauge | Duplicate ratio (0–1) | | `sepe_pages_crawled_total` | Gauge | Pages visited | | `sepe_proxy_errors_total` | Gauge | Proxy/network errors | | `sepe_scrape_duration_seconds` | Histogram | Total run duration | | `sepe_page_load_duration_seconds` | Histogram | Per-page load time | #### Kubernetes scraping example ```yaml ## prometheus-scrape-config.yaml - job_name: sepe_scraper metrics_path: /v2/key-value-stores/STORE_ID/records/prometheus_metrics scheme: https bearer_token: YOUR_APIFY_TOKEN static_configs: - targets: [api.apify.com] ``` *** ### Change Detection Alerts After each run an `alerts_report` is saved to the Key-Value Store and also pushed to the dataset as a record with `_record_type: "alerts_summary"`: ```json { "_record_type": "alerts_summary", "generated_at": "2026-04-15T10:30:00Z", "stats": { "new_count": 47, "changed_count": 12, "removed_count": 3, "total_current": 1024, "total_previous": 980 }, "sample_new_offers": [ ... ], "changed_offers": [ {"offer": {...}, "changed_fields": ["salario"]} ], "removed_offers": [ ... ] } ``` Integrate with Zapier / Make / Slack via the Apify webhook → trigger on run completion. *** ### Province Codes Reference All 52 Spanish provinces are supported. Examples: | Input | Province | Code | |---|---|---| | `"Vizcaya"` or `"Bizkaia"` | Vizcaya / Bizkaia | 48 | | `"Madrid"` | Madrid | 28 | | `"Barcelona"` | Barcelona | 08 | | `"Guipúzcoa"` or `"Gipuzkoa"` | Guipúzcoa | 20 | | `"Valencia"` or `"València"` | Valencia | 46 | | `"Sevilla"` | Sevilla | 41 | *** ### Legal & Compliance - Only public, freely accessible data is scraped - Rate-limited to ≤ 1 request/second (configurable) - Respects SEPE's robots.txt structure - No login, no personal data, no GDPR-protected content - Data is from sepe.es which is a Spanish public institution *** ### Troubleshooting | Problem | Solution | |---|---| | 0 offers returned | SEPE may have changed page structure; check Actor logs for CSS selector misses | | CAPTCHA detected | Enable Apify Residential proxy (`use_proxy: true`, `proxy_groups: ["RESIDENTIAL"]`) | | Slow runs | Increase `max_concurrency` in `main.py` or reduce `max_delay` | | Missing skills | Add aliases to `SKILLS_TAXONOMY` in `config.py` | | Stale dedup | Delete `previous_offers_hashes` and `previous_offers_snapshot` from KV store | *** ### Deploy to Apify ```bash apify login apify push ``` [![Deploy to Apify](https://apify.com/ext/open_in_apify_button.svg)](https://console.apify.com/actors/new) # Actor input Schema ## `start_urls` (type: `array`): Optional extra SEPE URLs to start from. Defaults to the main SEPE employment pages. ## `provincias` (type: `array`): Filter offers by Spanish province or autonomous community name. Case-insensitive. E.g. \["Vizcaya", "Bizkaia", "Madrid", "Barcelona"]. Leave empty to crawl all Spain. ## `skills` (type: `array`): Only keep offers that mention at least one of these skills. Uses both regex and ML (TF-IDF) matching. Leave empty to return all offers. E.g. \["Kubernetes", "Python", "DevOps"]. ## `max_pages` (type: `integer`): Maximum number of listing pages to crawl per province/entry-point. Each page typically contains 10–25 offers. ## `use_ml_skills` (type: `boolean`): Enable scikit-learn TF-IDF cosine similarity in addition to regex for fuzzy skill matching. Catches terms like 'contenedores' → Docker, 'orquestación' → Kubernetes. ## `use_proxy` (type: `boolean`): Rotate requests through Apify proxy pool to avoid IP bans. Requires an Apify plan with proxy access. ## `proxy_groups` (type: `array`): Apify proxy groups to use. RESIDENTIAL gives best results against SEPE anti-bot. Falls back to SHADER if not available. ## `proxy_country` (type: `string`): ISO country code for proxy geo. ES for Spanish IP (best for SEPE). ## `headless` (type: `boolean`): Run Camoufox in headless mode. Set to false only for local debugging. ## `min_delay` (type: `number`): Minimum random delay between page loads (rate-limiting). Must be ≥ 1 to respect SEPE's servers. ## `max_delay` (type: `number`): Maximum random delay between page loads. ## Actor input object example ```json { "start_urls": [ { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo.html" }, { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/empleo-en-la-red.html" } ], "provincias": [ "Vizcaya", "Bizkaia", "Madrid", "Barcelona", "Guipúzcoa" ], "skills": [ "Kubernetes", "Docker", "Python", "AWS", "Terraform", "DevOps", "Linux" ], "max_pages": 50, "use_ml_skills": true, "use_proxy": true, "proxy_groups": [ "RESIDENTIAL", "SHADER" ], "proxy_country": "ES", "headless": true, "min_delay": 2, "max_delay": 5 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "start_urls": [ { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo.html" }, { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/empleo-en-la-red.html" } ], "provincias": [ "Vizcaya", "Bizkaia" ], "skills": [ "Kubernetes", "Docker", "Python" ] }; // Run the Actor and wait for it to finish const run = await client.actor("x_dabit_x/sepe-empleo-es-pro").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 = { "start_urls": [ { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo.html" }, { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/empleo-en-la-red.html" }, ], "provincias": [ "Vizcaya", "Bizkaia", ], "skills": [ "Kubernetes", "Docker", "Python", ], } # Run the Actor and wait for it to finish run = client.actor("x_dabit_x/sepe-empleo-es-pro").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 '{ "start_urls": [ { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo.html" }, { "url": "https://www.sepe.es/HomeSepe/Personas/encontrar-empleo/empleo-en-la-red.html" } ], "provincias": [ "Vizcaya", "Bizkaia" ], "skills": [ "Kubernetes", "Docker", "Python" ] }' | apify call x_dabit_x/sepe-empleo-es-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=x_dabit_x/sepe-empleo-es-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEPE Spain Job Scraper – Ofertas de Empleo ES", "description": "Scrapes job offers from SEPE Spain (sepe.es) with stealth Camoufox, proxy rotation, skills filtering (regex + TF-IDF ML), deduplication, change-detection alerts, and Prometheus metrics export. Supports province/CCAA filtering with Vizcaya/Bizkaia focus.", "version": "1.0", "x-build-id": "RNbDPWxGRsRAgkPKa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/x_dabit_x~sepe-empleo-es-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-x_dabit_x-sepe-empleo-es-pro", "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/x_dabit_x~sepe-empleo-es-pro/runs": { "post": { "operationId": "runs-sync-x_dabit_x-sepe-empleo-es-pro", "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/x_dabit_x~sepe-empleo-es-pro/run-sync": { "post": { "operationId": "run-sync-x_dabit_x-sepe-empleo-es-pro", "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": { "start_urls": { "title": "Start URLs", "type": "array", "description": "Optional extra SEPE URLs to start from. Defaults to the main SEPE employment pages.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "provincias": { "title": "Provincias / CCAA", "type": "array", "description": "Filter offers by Spanish province or autonomous community name. Case-insensitive. E.g. [\"Vizcaya\", \"Bizkaia\", \"Madrid\", \"Barcelona\"]. Leave empty to crawl all Spain.", "items": { "type": "string" } }, "skills": { "title": "Skills Filter", "type": "array", "description": "Only keep offers that mention at least one of these skills. Uses both regex and ML (TF-IDF) matching. Leave empty to return all offers. E.g. [\"Kubernetes\", \"Python\", \"DevOps\"].", "items": { "type": "string" } }, "max_pages": { "title": "Max Listing Pages", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of listing pages to crawl per province/entry-point. Each page typically contains 10–25 offers.", "default": 50 }, "use_ml_skills": { "title": "Use ML Skill Matching (TF-IDF)", "type": "boolean", "description": "Enable scikit-learn TF-IDF cosine similarity in addition to regex for fuzzy skill matching. Catches terms like 'contenedores' → Docker, 'orquestación' → Kubernetes.", "default": true }, "use_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Rotate requests through Apify proxy pool to avoid IP bans. Requires an Apify plan with proxy access.", "default": true }, "proxy_groups": { "title": "Proxy Groups", "type": "array", "description": "Apify proxy groups to use. RESIDENTIAL gives best results against SEPE anti-bot. Falls back to SHADER if not available.", "default": [ "RESIDENTIAL" ], "items": { "type": "string" } }, "proxy_country": { "title": "Proxy Country Code", "maxLength": 2, "type": "string", "description": "ISO country code for proxy geo. ES for Spanish IP (best for SEPE).", "default": "ES" }, "headless": { "title": "Headless Browser", "type": "boolean", "description": "Run Camoufox in headless mode. Set to false only for local debugging.", "default": true }, "min_delay": { "title": "Minimum Delay Between Requests (seconds)", "minimum": 1, "maximum": 30, "type": "number", "description": "Minimum random delay between page loads (rate-limiting). Must be ≥ 1 to respect SEPE's servers.", "default": 2 }, "max_delay": { "title": "Maximum Delay Between Requests (seconds)", "minimum": 1, "maximum": 60, "type": "number", "description": "Maximum random delay between page loads.", "default": 5 } } }, "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 } } } } } } } } } } ```