# StubHub Events Discovery Scraper (`automation-lab/stubhub-events-discovery-scraper`) Actor 🎟️ Discover public StubHub events around any coordinates. Export schedules, venues, IDs, canonical URLs, images, displayed prices, and availability flags. - **URL**: https://apify.com/automation-lab/stubhub-events-discovery-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Travel - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## StubHub Events Discovery Scraper Discover public events on StubHub by geographic coordinates and export clean, structured schedules for analysis, aggregation, and monitoring. This Apify Actor reads StubHub's public Explore data surface. It returns one dataset row per event with event and category IDs, dates, venues, canonical URLs, images, displayed prices, and availability flags. Use it when you need repeatable event discovery without manually browsing city pages. - 🎟️ Find events around any latitude and longitude - πŸ“… Normalize event dates while preserving StubHub's displayed labels - 🏟️ Export venue IDs, names, and locations - πŸ”Ž Filter by category ID, date, active listings, and parking status - πŸ”— Send results to datasets, APIs, webhooks, Google Sheets, or your database ### What does the StubHub Events Discovery Scraper do? The Actor queries public StubHub Explore batches for the coordinates you provide. It paginates conservatively, deduplicates events by `eventId`, applies your filters, and writes typed records to the default Apify dataset. The Actor is deliberately scoped to event discovery. It does not claim to scrape protected seat maps or seat-level ticket inventory. That narrow scope keeps runs fast, predictable, and useful for schedule intelligence. ### Who is it for? #### Event aggregators Build or refresh city event calendars with stable event IDs, canonical links, dates, and venue information. #### Venue and promoter intelligence teams Watch which events appear around a market, compare venue schedules, and detect additions over time. #### Travel and hospitality planners Monitor future concerts, sports, and live entertainment that may affect visitor demand. #### Ticket-market analysts Collect public availability flags, price classes, and displayed starting-price labels for recurring analysis. #### Developers and data engineers Feed normalized StubHub event records into an application, warehouse, alerting workflow, or enrichment pipeline. ### Why use this StubHub event scraper? Manual Explore browsing is hard to repeat and awkward to export. This Actor provides a stable input and output contract suitable for automation. - βœ… No StubHub login is required for the supported Explore scope - βœ… Coordinates make geographic provenance explicit - βœ… Pagination and deduplication are handled automatically - βœ… Date and category filters fail closed instead of leaking unknown records - βœ… Every output row links back to its public StubHub event URL - βœ… Pay-per-event billing aligns spend with delivered data ### What data can you extract? | Field | Description | |---|---| | `eventId` | Stable StubHub event identifier | | `categoryId` | StubHub category identifier | | `name` | Public event name | | `url` | Canonical StubHub event URL | | `eventDate` | Date normalized to `YYYY-MM-DD` when present in the URL | | `formattedDate` | Date label displayed by StubHub | | `formattedTime` | Time label displayed by StubHub | | `isDateConfirmed` | Whether StubHub marks the date as confirmed | | `isTimeConfirmed` | Whether StubHub marks the time as confirmed | | `isTbd` | Whether date or time is still to be determined | | `eventState` | Numeric source event-state value | | `venueId` | StubHub venue identifier | | `venueName` | Venue name | | `venueLocation` | Displayed city, region, and country | | `formattedFromPrice` | Source-formatted starting-price label when provided | | `hasActiveListings` | Active-listings flag from Explore | | `allowPublicPurchase` | Public-purchase flag from Explore | | `isParkingEvent` | Whether the row is a separate parking event | | `imageUrl` | Public event image URL | | `sourceLatitude` | Latitude used for discovery | | `sourceLongitude` | Longitude used for discovery | | `sourcePage` | Explore page that produced the row | | `scrapedAt` | UTC extraction timestamp | ### How to scrape StubHub events 1. Open the Actor input form. 2. Enter the latitude and longitude for your target market. 3. Set a small `maxItems` value for your first run. 4. Optionally add category IDs or date boundaries. 5. Click **Start**. 6. Preview the `StubHub events` dataset view. 7. Export results as JSON, CSV, Excel, XML, or RSS. 8. Schedule the Actor if you want recurring monitoring. ### Input parameters #### Location `latitude` and `longitude` define the geographic center used by StubHub Explore. New York City (`40.7128`, `-74.006`) is prefilled for a quick smoke test. Use decimal coordinates and keep them within normal geographic ranges. #### Event collection `maxItems` controls how many unique matching events are saved. The Actor may inspect more source rows than it saves when filters are active. #### Filters `categoryIds` accepts exact numeric IDs found in earlier discovery results. `fromDate` and `toDate` use `YYYY-MM-DD`. `activeListingsOnly` keeps only events whose Explore record reports active listings. `includeParkingEvents` controls separate parking-event records. #### Advanced safety limit `maxPages` bounds the number of Explore batches checked. Increase it only when narrow filters require deeper traversal. ### Example input ```json { "latitude": 40.7128, "longitude": -74.006, "maxItems": 20, "maxPages": 5, "includeParkingEvents": false } ```` A category-monitoring input can reuse an ID learned from an earlier dataset: ```json { "latitude": 40.7128, "longitude": -74.006, "categoryIds": [20519], "maxItems": 50, "maxPages": 25 } ``` ### Example output ```json { "eventId": 160262128, "categoryId": 33253, "name": "BTS", "url": "https://www.stubhub.com/bts-east-rutherford-tickets-8-1-2026/event/160262128/", "eventDate": "2026-08-01", "formattedDate": "Aug 01", "formattedTime": "8:00 PM", "isTbd": false, "isDateConfirmed": true, "isTimeConfirmed": true, "venueId": 12236, "venueName": "MetLife Stadium", "venueLocation": "East Rutherford, NJ, USA", "allowPublicPurchase": true, "hasActiveListings": false, "isParkingEvent": false, "sourceLatitude": 40.7128, "sourceLongitude": -74.006, "sourcePage": 0, "source": "stubhub-explore" } ``` Fields that StubHub omits are omitted rather than replaced with misleading values. ### How much does it cost to scrape StubHub events? The Actor uses pay-per-event pricing. A small one-time start charge covers run initialization, followed by a charge for each unique event saved. The start charge is currently **$0.005**. Per-event prices use Apify plan tiers and are shown in the Actor pricing panel before each run. For a quick estimate, multiply your expected event count by your displayed tier price and add the start charge. The Apify Free plan can be used for small evaluations subject to your account credits and the live pricing panel. ### Tips for reliable results - 🎯 Start with 10–20 events before scheduling a larger job. - πŸ“ Use coordinates near the center of the market you care about. - 🧭 Run neighboring coordinates separately when market boundaries matter. - 🏷️ Discover category IDs broadly before using a category-only monitor. - πŸ“† Keep date windows realistic because Explore prioritizes upcoming events. - πŸ…ΏοΈ Leave parking records disabled unless they are part of your workflow. - πŸ” Use `eventId` as the primary key when comparing scheduled runs. ### Scheduling and event monitoring Apify schedules can run the Actor hourly, daily, or weekly. Store each run in its default dataset or send results to a webhook. For change detection, compare the newest dataset with your prior snapshot using `eventId`. Useful signals include: - Newly discovered event IDs - Changes to confirmation flags - Changes to active-listing status - New displayed starting-price labels - Venue schedule additions ### Integrations #### Google Sheets Export a dataset as CSV or connect through Make to refresh an event-planning sheet. #### Webhooks and Slack Trigger a webhook after a run, diff the results, and send new events to a Slack channel. #### Data warehouses Load JSON records into BigQuery, Snowflake, PostgreSQL, or another warehouse keyed by `eventId` and `scrapedAt`. #### Travel demand models Join event dates and venue locations to hotel, flight, or foot-traffic datasets. #### Venue intelligence Group rows by `venueId` to build recurring venue calendars and market comparisons. ### JavaScript API example ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/stubhub-events-discovery-scraper').call({ latitude: 40.7128, longitude: -74.006, maxItems: 20, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); ``` ### Python API example ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("automation-lab/stubhub-events-discovery-scraper").call(run_input={ "latitude": 34.0522, "longitude": -118.2437, "maxItems": 20, }) items = client.dataset(run["defaultDatasetId"]).list_items().items print(items) ``` ### cURL API example ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~stubhub-events-discovery-scraper/runs?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"latitude":40.7128,"longitude":-74.006,"maxItems":20}' ``` Fetch results with the dataset ID returned by the run: ```bash curl "https://api.apify.com/v2/datasets/DATASET_ID/items?clean=true&format=json" ``` ### Use with Apify MCP Connect the Actor to Claude Code: ```bash claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/stubhub-events-discovery-scraper" ``` For Claude Desktop, Cursor, or VS Code, add an HTTP MCP server: ```json { "mcpServers": { "apify": { "url": "https://mcp.apify.com?tools=automation-lab/stubhub-events-discovery-scraper" } } } ``` Example prompts: - β€œFind upcoming StubHub events near New York and summarize them by venue.” - β€œRun the StubHub event extractor for Los Angeles and return active listings only.” - β€œCompare the newest event IDs with yesterday's dataset.” ### Data quality and limitations The Actor reports the public Explore fields available at run time. A displayed starting price is a formatted source label, not a guaranteed checkout price. Explore ordering and availability may change between runs. Coordinates influence the discovery market but do not define a strict radius filter. Date normalization relies on the canonical event URL; when the source URL lacks a parseable date, `eventDate` is omitted. Seat maps, individual ticket listings, and checkout details are outside this Actor's scope. ### Is it legal to scrape StubHub events? Public event information can have legitimate uses such as aggregation, research, and market monitoring. You are responsible for your use case, applicable laws, contractual restrictions, and personal-data obligations. Avoid excessive request volumes and do not use the Actor to bypass access controls. This Actor accesses only the supported public Explore surface and uses conservative sequential requests. ### Troubleshooting #### Why did my filtered run return no events? Your category or date window may not occur in the Explore pages checked. Remove filters, run a broad discovery, inspect category IDs, and then narrow the next run. The Actor exits with an error instead of silently reporting success with an empty dataset. #### Why does `hasActiveListings` look different later? It is a live source flag and can change as inventory changes. Use `scrapedAt` to distinguish snapshots. #### Why is a nearby event outside my city? Coordinates select StubHub's discovery market; they are not a hard-radius boundary. Filter `venueLocation` downstream or run multiple precise coordinates when needed. #### Why is `eventDate` missing? The Actor only emits a normalized date when it can parse one safely from the canonical URL. It preserves `formattedDate` and confirmation flags for the remaining cases. ### FAQ #### Does it require a StubHub account? No account is required for the supported public Explore discovery workflow. #### Can it scrape ticket seats and rows? No. Seat-level listings are intentionally outside v1 because they use a different, more protected detail workflow. #### Can I search by city name? Use the city's latitude and longitude. This avoids ambiguous city names and makes repeat runs reproducible. #### Can I filter by category? Yes. Supply numeric `categoryIds` discovered in a previous broad run. #### Can I export CSV or Excel? Yes. Apify datasets support JSON, CSV, Excel, XML, RSS, and API access. #### Are duplicate events removed? Yes. Each run deduplicates by StubHub `eventId`. ### Related event scrapers Combine this Actor with other automation-lab event data tools: - [TickPick Events & Tickets Scraper](https://apify.com/automation-lab/tickpick-events-tickets-scraper) for another ticket-market event source - [Luma Events Scraper](https://apify.com/automation-lab/luma-events-scraper) for community and professional event listings Cross-source workflows can join records by date, venue, performer name, and canonical source ID while preserving each platform's provenance. ### Support If a run fails, share its Apify run URL through the Actor issue form. Include the coordinates, filters, expected event, and approximate run time. That context helps distinguish an input mismatch from a source change quickly. # Actor input Schema ## `latitude` (type: `number`): Set the center latitude for StubHub Explore discovery. New York City is prefilled. ## `longitude` (type: `number`): Set the center longitude for StubHub Explore discovery. New York City is prefilled. ## `maxItems` (type: `integer`): Stop after saving this many unique matching events. ## `categoryIds` (type: `array`): Optionally keep only exact StubHub category IDs, such as IDs discovered in previous runs. ## `fromDate` (type: `string`): Optionally keep events on or after this date (YYYY-MM-DD). ## `toDate` (type: `string`): Optionally keep events on or before this date (YYYY-MM-DD). ## `activeListingsOnly` (type: `boolean`): Keep only records currently marked by Explore as having active listings. ## `includeParkingEvents` (type: `boolean`): Include separate parking records when StubHub returns them. ## `maxPages` (type: `integer`): Safety limit for source pages checked when filters are narrow. ## Actor input object example ```json { "latitude": 40.7128, "longitude": -74.006, "maxItems": 10, "categoryIds": [], "activeListingsOnly": false, "includeParkingEvents": false, "maxPages": 5 } ``` # Actor output Schema ## `overview` (type: `string`): One row per unique event with schedule, venue, category, URL, availability, price label, and source metadata. # 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 = { "latitude": 40.7128, "longitude": -74.006, "maxItems": 10, "maxPages": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/stubhub-events-discovery-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "latitude": 40.7128, "longitude": -74.006, "maxItems": 10, "maxPages": 5, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/stubhub-events-discovery-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "latitude": 40.7128, "longitude": -74.006, "maxItems": 10, "maxPages": 5 }' | apify call automation-lab/stubhub-events-discovery-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/stubhub-events-discovery-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "StubHub Events Discovery Scraper", "description": "🎟️ Discover public StubHub events around any coordinates. Export schedules, venues, IDs, canonical URLs, images, displayed prices, and availability flags.", "version": "0.1", "x-build-id": "dJ1Q03iUTxhpCFhKd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~stubhub-events-discovery-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-stubhub-events-discovery-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/automation-lab~stubhub-events-discovery-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-stubhub-events-discovery-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/automation-lab~stubhub-events-discovery-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-stubhub-events-discovery-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "latitude": { "title": "πŸ“ Latitude", "minimum": -90, "maximum": 90, "type": "number", "description": "Set the center latitude for StubHub Explore discovery. New York City is prefilled.", "default": 40.7128 }, "longitude": { "title": "πŸ“ Longitude", "minimum": -180, "maximum": 180, "type": "number", "description": "Set the center longitude for StubHub Explore discovery. New York City is prefilled.", "default": -74.006 }, "maxItems": { "title": "🎟️ Maximum events", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Stop after saving this many unique matching events.", "default": 20 }, "categoryIds": { "title": "Category IDs", "type": "array", "description": "Optionally keep only exact StubHub category IDs, such as IDs discovered in previous runs.", "items": { "type": "integer" }, "default": [] }, "fromDate": { "title": "From date", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "Optionally keep events on or after this date (YYYY-MM-DD)." }, "toDate": { "title": "To date", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "type": "string", "description": "Optionally keep events on or before this date (YYYY-MM-DD)." }, "activeListingsOnly": { "title": "Only events with active listings", "type": "boolean", "description": "Keep only records currently marked by Explore as having active listings.", "default": false }, "includeParkingEvents": { "title": "Include parking events", "type": "boolean", "description": "Include separate parking records when StubHub returns them.", "default": false }, "maxPages": { "title": "Maximum Explore pages", "minimum": 1, "maximum": 50, "type": "integer", "description": "Safety limit for source pages checked when filters are narrow.", "default": 25 } } }, "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 } } } } } } } } } } ```