# K12 Schools Email Scraper (`scraperoka/k12-schools-email-scraper`) Actor 📧 K12 Schools Email Scraper extracts verified email addresses from K-12 school websites—great for targeted outreach, sales, and lead research. ⚡ Fast, accurate data collection. 🚀 Perfect for B2B prospecting and marketing campaigns. - **URL**: https://apify.com/scraperoka/k12-schools-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### K12 Schools Email Scraper 📬 **K12 Schools Email Scraper** is an Apify actor that helps you find K12 schools in targeted locations and extract contact information—especially email addresses—from publicly available sources. If you’re looking for a **K12 schools email scraper**, a **school district email scraping tool**, or a **K-12 school contact email extractor**, this actor is built to save you time by turning school and district listings into usable email and phone contact data. Whether you’re a marketer, recruiter, researcher, or data analyst, you can use K12 leads email scraper workflows to build outreach-ready contact lists at scale—without manually digging through directories. --- ### 🚀 Why choose K12 Schools Email Scraper? | Feature | Benefit | |---|---| | ✅ **Email-first website contact scraping** | Extracts emails (and also phones + social links) from the school/district websites found in your results | | ✅ **Proxy support** | Helps improve reliability for large-scale scraping by reducing the chance of IP blocks and rate limits | | ✅ **Resilience for real-world pages** | Includes fallbacks so the run can keep going even when some sites are incomplete or unavailable | | ✅ **Structured, dataset-ready output** | Saves consistent fields like `name`, `website`, `scraped_emails`, and `scrape_status` for easy import into CRMs and analysis | | ✅ **Scales across multiple locations** | Lets you search multiple `googleMapsLocation` values and collect up to your defined limits | | ✅ **Progress-aware limits** | Stops when the target number of results is reached, so you control how much data you collect | --- ### 🔑 Key features - 🎯 **Location-driven K12 contact discovery**: Uses your `googleMapsLocation` list and `googleMapsSearchTerm` to focus on K12 school listings. - 📄 **Website email harvesting**: Scrapes school or district websites to extract `scraped_emails`, and can also extract `scraped_phones` and `scraped_social_media`. - 🧾 **Accurate dataset structure**: Outputs consistent dataset columns for downstream processing (including per-email rows via `email_found`). - 🛡️ **Built-in proxy support**: Works with your `proxyConfiguration` (recommended for larger batches). - 🔄 **Control your volume**: Use `maxBusinesses` and `scrapeMaxBusinessesPerLocation` to manage totals per location vs globally. - 💾 **Real-time data saving**: Pushes results to the Apify dataset immediately during execution (so you don’t lose work on interruptions). - 📞 **Phone and social enrichment included**: Alongside emails, it also stores phone numbers and social links when available. - ✅ **Optional email validation**: Through `validateEmails`, it can validate emails for higher-confidence outreach lists. --- ### 📝 Input Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "K12 Schools", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `googleMapsSearchTerm` | ❌ | Search term describing the business type or niche to target (prefilled with `K12 Schools`). | | `googleMapsLocation` | ✅ | One or more geographic locations to search (example format: `New York`). | | `maxBusinesses` | ❌ | Target number of businesses to find with emails. Allowed range is `1-1000` (default: `5`). | | `scrapeMaxBusinessesPerLocation` | ❌ | Controls how `maxBusinesses` is applied: when enabled, it collects up to `maxBusinesses` per location; when disabled, it combines all locations up to a single total limit. | | `proxyConfiguration` | ❌ | Proxy settings for scraping. Includes `proxy support` (prefilled to `true`). | *** ### 📦 Output After execution, the actor saves the results to the **Business Contact Data** dataset view. The dataset transformation includes the following fields: `name`, `website`, `phone`, `full_address`, `city`, `state`, `zip`, `country_code`, `scraped_emails`, `scraped_phones`, `scraped_social_media`, `emails_found`, `pages_scraped`, `avg_rating`, `total_reviews`, `lat`, `long`, `place_id`, `scrape_status`. ```json [ { "name": "Sample School District", "website": "https://example.edu", "phone": "+1 555 123 4567", "full_address": "123 Main St City ST 12345 US", "city": "City", "state": "ST", "zip": "12345", "country_code": "US", "scraped_emails": ["info@example.edu"], "scraped_phones": ["+1 555 987 6543"], "scraped_social_media": ["https://www.facebook.com/example"], "emails_found": 1, "pages_scraped": 12, "avg_rating": 4.2, "total_reviews": 38, "lat": 40.7128, "long": -74.006, "place_id": "ChIJ...", "scrape_status": "success" } ] ``` #### Output Fields | Field | Type | Description | |---|---|---| | `name` | string | Business/school name. | | `website` | string | Website URL for the business/school. | | `phone` | string | Phone value from the initial business listing (when present). | | `full_address` | string | Combined address string built from address parts. | | `city` | string | City from the business listing. | | `state` | string | State from the business listing. | | `zip` | string | ZIP/postal code from the business listing. | | `country_code` | string | Country code from the business listing. | | `scraped_emails` | array | Extracted emails found on the website. | | `scraped_phones` | array | Extracted phone numbers found on the website. | | `scraped_social_media` | array | Extracted social media links found on the website. | | `emails_found` | number | Count of emails found for that business. | | `pages_scraped` | number | Number of processed URLs/pages during website scraping (`job.progress.processed_urls`). | | `avg_rating` | number | Average rating value from the business listing. | | `total_reviews` | number | Total reviews count from the business listing. | | `lat` | number | Latitude coordinate. | | `long` | number | Longitude coordinate. | | `place_id` | string | Place identifier from the initial listing. | | `scrape_status` | string | Status of the scrape (e.g. `success`, `failed`, `no_website`, `error`). | Note: during execution, results are also pushed in a flattened way per extracted email using `email_found`. However, the dataset view transformation focuses on the fields listed above for table display and analysis. *** ### How to use K12 Schools Email Scraper (via Apify Console) 1. **Open Apify Console** Go to [console.apify.com](https://console.apify.com) and sign in. 2. **Find the actor** Search for **K12 Schools Email Scraper** in the Actors marketplace. 3. **Add your input** In the **INPUT** panel, set: - `googleMapsSearchTerm` (default `K12 Schools`) - `googleMapsLocation` (add one or more locations) 4. **Set your limits** Choose: - `maxBusinesses` (how many business contacts you want to collect) - `scrapeMaxBusinessesPerLocation` (per-location cap vs one global cap) 5. **Configure proxies (recommended)** In **Proxy configuration**, use `proxyConfiguration` and keep `proxy support` enabled for more reliable runs at scale. 6. **Run the actor** Click **Run**. While it runs, you’ll see progress in the logs as it processes each location and scrapes websites. 7. **Review your dataset** When finished, open the dataset named **Business Contact Data** (default dataset view: *Businesses with Contact Information*), then export the results (JSON/CSV) for your outreach pipeline. No coding required—get K12 school contact email lists in minutes with this K12 schools email scraper. *** ### 🧠 Advanced features & SEO optimization - 🔍 **Engineered for K12 outreach lists**: Built specifically for the **K12 Schools Email Scraper** use case—extracting emails plus additional contact details like phones and social links. - 🌍 **Location-aware scaling**: Use `scrapeMaxBusinessesPerLocation` to decide whether you want a per-city quota or one combined global quota across all `googleMapsLocation` entries. - 🛡️ **Proxy-ready for reliability**: Includes proxy configuration support to help keep scraping stable when you collect large numbers of contacts. - 📬 **Optimized for email lead building**: Works well as a **school district email scraping tool** when you need consistent structured fields for bulk analysis and CRM import. - 📊 **Dataset-first workflow**: Results are pushed to the dataset during the run, so you can start exporting even before you finish experimenting with parameters. *** ### 🎯 Best use cases - 📈 **Marketers building K12 school contact email lists**: Gather district and school emails for campaigns while enriching with phones and social links. - 🏫 **School staffing researchers**: Compile school staff email discovery lists from public web information for outreach and analysis. - 🧾 **Data analysts running contact coverage studies**: Quantify how many schools include email contact details and track scrape outcomes with `scrape_status`. - 🛠️ **CRM operations and sales development teams**: Feed a bulk school emails scraper output into pipelines for follow-ups and segmentation by location. - 💼 **Education program coordinators**: Find campus email scraping leads quickly for event invitations, partnerships, and applications. - 💻 **API/data automation workflows**: Use this **K12 domain email scraper** output as structured inputs for ETL jobs and automated enrichment. *** ### 🔧 Technical specifications #### Supported Input Formats ✅ `googleMapsSearchTerm` (string)\ ✅ `googleMapsLocation` (array of strings)\ ✅ `maxBusinesses` (integer)\ ✅ `scrapeMaxBusinessesPerLocation` (boolean)\ ✅ `proxyConfiguration` (object with `proxy support`) #### Proxy Support ✅ `proxyConfiguration` support via `proxy support` (Apify Proxy) #### Retry Mechanism ✅ Includes retries and fallbacks for resilience (high level behavior) #### Dataset Structure ✅ Dataset view fields include: `name`, `website`, `phone`, `full_address`, `city`, `state`, `zip`, `country_code`, `scraped_emails`, `scraped_phones`, `scraped_social_media`, `emails_found`, `pages_scraped`, `avg_rating`, `total_reviews`, `lat`, `long`, `place_id`, `scrape_status` #### Rate Limits & Performance - Designed for practical bulk runs using controlled concurrency during website scraping. - The total amount of work is bounded by `maxBusinesses` and `scrapeMaxBusinessesPerLocation`. #### Limitations - ❌ Some listings may not have a `website`, resulting in `scrape_status` like `no_website` and empty `scraped_emails`. - ❌ Some websites may not expose emails in publicly accessible pages, producing `scrape_status` like `failed` or `success` with `emails_found` of 0. *** ### ❓ FAQ #### How many emails can I scrape with K12 Schools Email Scraper? It’s controlled primarily by `maxBusinesses`, which sets the target number of businesses to find with emails. If `scrapeMaxBusinessesPerLocation` is enabled, the actor aims for up to `maxBusinesses` per location; otherwise it uses a combined global limit across locations. #### What locations can I use for the search? Use `googleMapsLocation` as an array of location strings (for example, `["New York"]`). Add multiple locations to expand your reach and generate a larger K12 school contact email list. #### Does it extract phones and social media links too? ✅ Yes. The dataset includes `scraped_phones` and `scraped_social_media` alongside `scraped_emails` when a website is available and public contact details can be extracted. #### Will it validate emails? It can validate emails if you enable `validateEmails` in the actor input (the code reads `validateEmails` from actor input). When enabled, emails are validated during the website scraping step. #### What happens if a school listing has no website? If no `website` is available, the actor sets `scraped_emails` to an empty array, sets `emails_found` to `0`, and uses `scrape_status` such as `no_website`. In email-only mode behavior, it may skip saving those rows depending on the run settings. #### Can I run this at scale for bulk school emails scraping? ✅ Yes—use `proxyConfiguration` (recommended) and set `maxBusinesses` appropriately. The actor also pushes results during processing, so you can manage large jobs without losing completed work. #### How do I integrate the output into my workflow? You can export from the Apify dataset (JSON/CSV options depend on the console). The **Business Contact Data** dataset includes fields like `scraped_emails`, `emails_found`, `pages_scraped`, and `scrape_status`, which are convenient for importing into CRMs and analytics tools. #### Is this legal to use for outreach? ✅ The actor collects information only from **publicly accessible sources**. It’s your responsibility to comply with GDPR, CCPA, anti-spam laws, and platform terms when using the output for outreach. *** ### 🤝 Support & feature requests Want to improve the **K12 Schools Email Scraper** output for your K12 leads email scraper workflows? 💡 - 💡 **Feature Requests**: Ask for enhancements like richer exports (CSV), additional dataset fields, or custom filtering logic for email domains and roles. - 📧 **Contact**: Email us at with your request or feedback. Your feedback helps shape what we build next for this K12 schools email scraper. *** ### *Final thoughts on K12 Schools Email Scraper* *If you need a reliable way to extract K12 school contact email lists with structured output, **K12 Schools Email Scraper** is a strong choice for scaling your outreach data.*\ *Get started today and let this SEO-optimized K12 schools email scraper do the heavy lifting.* *** ### Disclaimer **This tool only accesses publicly accessible sources**. It does not access private profiles, authenticated data, or password-protected pages. You are responsible for ensuring your use complies with applicable laws (including GDPR/CCPA), anti-spam regulations, and relevant platform terms of service. If you need data removal, contact . Please use K12 Schools Email Scraper responsibly, ethically, and only for legitimate purposes. # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "K12 Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "googleMapsSearchTerm": "K12 Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/k12-schools-email-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 = { "googleMapsSearchTerm": "K12 Schools", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scraperoka/k12-schools-email-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 '{ "googleMapsSearchTerm": "K12 Schools", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call scraperoka/k12-schools-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/k12-schools-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "K12 Schools Email Scraper", "description": "📧 K12 Schools Email Scraper extracts verified email addresses from K-12 school websites—great for targeted outreach, sales, and lead research. ⚡ Fast, accurate data collection. 🚀 Perfect for B2B prospecting and marketing campaigns.", "version": "1.0", "x-build-id": "cFYuQcfNaf4cBI0V3" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~k12-schools-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-k12-schools-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~k12-schools-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-k12-schools-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~k12-schools-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-k12-schools-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "K12 Schools" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "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 } } } } } } } } } } ```