# Interior Designers Email Scraper (`scraperoka/interior-designers-email-scraper`) Actor 🎨 Interior Designers Email Scraper extracts emails from interior design professionals with targeted accuracy. Perfect for B2B outreach, lead generation, and sales campaigns. 🚀 Faster sourcing, cleaner lists, higher response rates. - **URL**: https://apify.com/scraperoka/interior-designers-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Automation, Other - **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 ### Interior Designers Email Scraper 📬 **Interior Designers Email Scraper** automatically searches for interior design businesses in your target locations and extracts their contact information—including emails—so you can build lead lists faster than manual research. Whether you’re looking for an **interior design leads email scraper**, an **interior designer email finder**, or an **interior design contact list scraper**, this actor helps you collect usable outreach data at scale. It’s built for marketers, data analysts, and researchers who need reliable results while saving hours of manual work. --- ### 🚀 Why choose Interior Designers Email Scraper? | Feature | Benefit | |---|---| | ✅ All-in-one business contact extraction | Finds interior design businesses and extracts contact info including emails, phone numbers, and social links | | ✅ Structured dataset output | Produces consistent fields like `name`, `website`, `scraped_emails`, `scraped_phones`, and `scraped_social_media` for easy importing | | ✅ Reliability-focused scraping pipeline | Includes resilience via retries/fallback logic during website scraping (where applicable) | | ✅ Built for scale across multiple locations | Supports searching multiple `googleMapsLocation` entries and stopping when limits are reached | | ✅ Proxy configuration support | Lets you configure `proxyConfiguration` for web scraping reliability at larger volumes | | ✅ Real-time dataset saving | Pushes scraped results to the Apify dataset as soon as they’re ready | --- ### 🔑 Key features - 🎯 **Location and niche targeting**: Use `googleMapsSearchTerm` (prefilled as “Interior Designers”) and one or more `googleMapsLocation` values to focus results - 🏙️ **City-bounds filtering**: Filters businesses to keep results within the target city boundaries - 🌐 **Website contact enrichment**: Scrapes the business `website` to collect `scraped_emails`, plus `scraped_phones` and `scraped_social_media` - 📞 **Phone extraction support**: Extracts phone numbers when available (`extract_phones` is enabled by default) - 🔗 **Social profile extraction support**: Extracts social media links when available (`extract_social` is enabled by default) - 💾 **Immediate dataset writes**: Each email result is pushed to the dataset as it’s found for faster downstream processing - 🛡️ **Proxy configuration for scraping**: Includes `proxyConfiguration` input (with `proxy support` prefilled) for more robust scraping runs - 🔍 **Email-only saving option**: Supports an email-only mode that only saves businesses that actually have emails found (`email_only_results` behavior in the code path) --- ### 📝 Input Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "Interior Designers", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | |---|---:|---| | `googleMapsSearchTerm` | ✅ | The business type or niche you want to search for (prefilled with `Interior Designers`). | | `googleMapsLocation` | ✅ | A list of target geographic locations (for example, `New York`). You can provide multiple locations in one run. | | `maxBusinesses` | ❌ | Maximum number of businesses to find (bounded in the UI as 1–1000). The scraper stops when this target is reached. | | `scrapeMaxBusinessesPerLocation` | ❌ | If enabled, collects up to `maxBusinesses` results per location. If disabled, combines all locations up to a single total limit. | | `proxyConfiguration` | ❌ | Proxy settings for scraping. Recommended for large-scale scraping. Currently supports `proxy support` (prefilled to `true`). | *** ### 📦 Output The actor saves results to the **Apify dataset** in JSON format. The storage view (dataset transformation) includes the following fields for the default dataset view: `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`. In addition, during website scraping the actor pushes **flattened rows** where each row corresponds to a specific extracted email via the `email_found` field. Example dataset row shape (flattened per email): ```json { "street_address": "123 Example St", "city": "New York", "zip": "10001", "state": "NY", "country_code": "US", "full_address": "123 Example St New York NY 10001 US", "website": "https://exampledesignco.com", "avg_rating": 4.6, "total_reviews": 128, "name": "Example Interior Design Co", "place_id": "ChIExamplePlaceId", "phone": "212-555-0123", "lat": 40.7128, "long": -74.006, "scraped_phones": ["212-555-0123"], "scraped_social_media": ["https://www.linkedin.com/company/example/"], "emails_found": 3, "pages_scraped": 8, "scrape_status": "success", "email_found": "hello@exampledesignco.com" } ``` #### Output Fields | Field | Type | Description | |---|---|---| | `name` | string | Business name | | `website` | string | Business website (also pushed as a dataset field) | | `phone` | string | Phone value from the business listing data | | `full_address` | string | Combined address string (`street_address city state zip country_code`) | | `city` | string | City extracted from the listing data | | `state` | string | State extracted from the listing data | | `zip` | string | ZIP code extracted from the listing data | | `country_code` | string | Country code extracted from the listing data | | `scraped_emails` | array | Emails found from the business website (array form exists in the dataset view transformation) | | `scraped_phones` | array | Phone numbers extracted from the website | | `scraped_social_media` | array | Social links extracted from the website | | `emails_found` | number | Count of emails found for the business | | `pages_scraped` | number | Number of processed URLs/pages during website scraping | | `avg_rating` | number | Average rating from the listing data | | `total_reviews` | number | Total reviews from the listing data | | `lat` | number | Latitude | | `long` | number | Longitude | | `place_id` | string | Place identifier from the listing data | | `scrape_status` | string | Scraping status (`success`, `failed`, `no_website`, `error`, etc. as set in code) | | `email_found` | string | Email address for the flattened row pushed to the dataset (added during “one row per email” pushes) | | `scrape_error` | string | Error message when scraping fails (`scrape_error` is set in error paths) | ✅ Export note: dataset results are available from the Apify console output tab and can typically be exported from the dataset to JSON/CSV for analysis and outreach. *** ### 🚀 How to use Interior Designers Email Scraper (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and go to the **Actors** tab. 2. **Find this actor**\ Search for **Interior Designers Email Scraper** and open the actor page. 3. **Enter your INPUT**\ In the **INPUT** panel, set: `googleMapsSearchTerm` (default is `Interior Designers`)\ and provide one or more `googleMapsLocation` values. 4. **Set your limits (optional but recommended)**\ Use `maxBusinesses` to control how many businesses you want.\ If you’re running multiple locations, toggle `scrapeMaxBusinessesPerLocation` to control whether the limit applies per location or globally. 5. **Configure proxies (optional)**\ If you’re scraping at higher volume, use `proxyConfiguration`. By default, `proxy support` is set to `true`. 6. **Run the actor**\ Click **Run**. During the run, you’ll see progress logs for location scraping and website scraping. 7. **Review results in the dataset**\ When the run completes, open the **OUTPUT** tab and view the dataset.\ You can inspect fields like business address, `website`, `scraped_emails`, and per-email rows via `email_found`. 8. **Export for your workflow**\ Export to JSON/CSV from the dataset to load into a CRM, outreach tool, or analysis pipeline. No coding required—get qualified interior designer contact data in minutes with Interior Designers Email Scraper. 💡 *** ### 🌟 Advanced features & SEO optimization - 🧩 **Engineered for interior design marketing email list building**: Designed specifically for interior design contact discovery and email harvesting use cases (like `interior design CRM email scraper` workflows). - 🔄 **Email-only mode support**: When enabled in the code path, only businesses with emails are saved/pushed—ideal for keeping your outreach lists clean (useful for “best interior designers email scraper” comparisons). - 🛡️ **Proxy-ready for reliable scraping**: Includes `proxyConfiguration` support to help keep large campaigns stable (handy for “bulk email scraper for interior designers” needs). - 📊 **Data completeness focus**: Outputs not just emails, but also `scraped_phones` and `scraped_social_media`, supporting richer lead enrichment beyond just “find interior designer emails”. *** ### 🎯 Best use cases - 📈 **Marketing teams building lead lists**: Rapidly generate an interior design marketing email list by collecting business websites and extracting contact emails - 🧠 **Researchers and data analysts**: Build an interior design database emails table with consistent fields like ratings, reviews, and geolocation (`lat`, `long`) - 💼 **Agencies sourcing outreach prospects**: Compile an interior design agency email scraper dataset for targeted campaigns by location - 🏢 **Business development roles**: Find interior designer prospecting email list leads that combine `website`, address, and verified counts (`emails_found`) - 🔁 **CRM enrichment workflows**: Export and import results into your CRM using structured fields to support an interior design CRM email scraper pipeline - 🗂️ **Prospecting at scale**: Use max limits and multi-location inputs to power a bulk email scraper for interior designers approach - 📬 **Cold outreach preparation**: Create qualified leads interior design email outputs that also include phone and social links for better personalization *** ### 🔧 Technical specifications - **Supported Input Formats** - ✅ `googleMapsLocation` as an array of strings - ✅ `googleMapsSearchTerm` as a string - ✅ Optional controls: `maxBusinesses`, `scrapeMaxBusinessesPerLocation`, `proxyConfiguration` - **Proxy Support** - ✅ `proxyConfiguration` object is supported - ✅ `proxy support` is included in the schema (prefilled as `true`) - **Retry Mechanism** - ✅ Includes resilience during website scraping via retries/fallback logic (as indicated by the pipeline behavior) - **Dataset Structure** - ✅ Default dataset view includes business and scraped contact fields such as: `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` - ✅ Flattened email rows include `email_found` (pushed one row per extracted email) - **Limitations** - ❌ Businesses without a `website` are pushed with `scrape_status` set to `no_website` (and `scraped_emails` is an empty array in those paths) - ❌ Results quality depends on publicly available contact info found on business websites *** ### ❓ FAQ #### Does Interior Designers Email Scraper extract phone numbers and social media links too? ✅ Yes. The actor is set up with phone and social extraction enabled, and it outputs `scraped_phones` and `scraped_social_media` alongside `scraped_emails`. #### How do I control how many businesses are collected? You can set `maxBusinesses`. The scraper stops when the target is reached. If you provide multiple locations, you can also set `scrapeMaxBusinessesPerLocation` to control whether the limit is per location or global across locations. #### What if a business doesn’t have a website? ❌ If a business has no `website`, it will be pushed with empty `scraped_emails`, empty `scraped_phones`, empty `scraped_social_media`, `emails_found` set to `0`, `pages_scraped` set to `0`, and `scrape_status` set to `no_website` (unless email-only behavior prevents it in the relevant code path). #### Does the output include one row per email address? ✅ Yes. During website scraping, the actor pushes flattened results where each email produces a dataset row, using the `email_found` field. #### Can I use proxies for better reliability? ✅ Yes. Configure `proxyConfiguration` (including `proxy support`) to improve reliability for larger scraping runs. #### Is email validation enabled? The actor reads `validateEmails` from input as `validate_emails = actor_input.get('validateEmails', False)`. If you include `validateEmails` in your run input, it will affect email validation behavior. #### How can I export the results after the run? You can open the actor **OUTPUT** dataset in Apify Console and export the dataset (commonly to JSON or CSV) for use in your CRM, outreach system, or analysis. *** ### 🤝 Support & feature requests Want to improve Interior Designers Email Scraper for your interior design leads email scraper workflow? 💡 - 💡 Feature Requests: ideas like additional export formats (CSV), extra enrichment fields, or custom email validation behavior are welcome. - 📧 Contact: email . Your feedback helps shape the roadmap for future improvements to the Interior Designers Email Scraper. *** ### ✨ Final thoughts *If you need an SEO-optimized, purpose-built way to build interior design marketing email lists from public web sources, Interior Designers Email Scraper is designed to deliver.*\ *Run it at small or large scale, then export the dataset for your outreach and analysis workflows.* *** ### Disclaimer **This actor accesses publicly accessible sources** to collect business contact information. It does not access private profiles, authenticated content, or password-protected data. It is **your responsibility** to ensure your use complies with applicable laws and regulations (including GDPR/CCPA where relevant), spam regulations, and each website’s terms of service. For data removal requests, contact . Please use Interior Designers Email Scraper responsibly, ethically, and for legitimate purposes only. # 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": "Interior Designers", "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": "Interior Designers", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/interior-designers-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": "Interior Designers", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scraperoka/interior-designers-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": "Interior Designers", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call scraperoka/interior-designers-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/interior-designers-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Interior Designers Email Scraper", "description": "🎨 Interior Designers Email Scraper extracts emails from interior design professionals with targeted accuracy. Perfect for B2B outreach, lead generation, and sales campaigns. 🚀 Faster sourcing, cleaner lists, higher response rates.", "version": "1.0", "x-build-id": "qs4vOQpSoPZxJHMgU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~interior-designers-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-interior-designers-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~interior-designers-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-interior-designers-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~interior-designers-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-interior-designers-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Interior Designers" }, "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 } } } } } } } } } } ```