# X/Twitter Preset Trends Scrapper (`simoit/x-twitter-preset-trends-scrapper`) Actor Preset X trend feeds from Explore tabs like news, sports, and entertainment - **URL**: https://apify.com/simoit/x-twitter-preset-trends-scrapper.md - **Developed by:** [simoit](https://apify.com/simoit) (community) - **Categories:** Social media - **Stats:** 3 total users, 2 monthly users, 42.9% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $0.30 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Trends Scraper ![SIMO IT logo](https://simoit.tech/favicon.ico) Work with preset X trend feeds from selected Explore tabs. This Actor can list preset trend timelines, fetch tweets for one concrete trend topic, and fetch relevant users for that trend. ### About SIMO IT SIMO IT builds practical social data products that support fast insight loops for marketing, PR, and growth teams. ### Features - collect posts from selected preset trend timelines - follow preset categories like `trending`, `news`, `sport`, and `entertainment` - preset categories map to X Explore tab feeds such as: - `news`: [https://x.com/explore/tabs/news](https://x.com/explore/tabs/news) - `sport`: [https://x.com/explore/tabs/sports](https://x.com/explore/tabs/sports) - `entertainment`: [https://x.com/explore/tabs/entertainment](https://x.com/explore/tabs/entertainment) - fetch tweets for one concrete trend topic using numeric `trendId` - collect `TrendRelevantUsers` for one concrete trend topic by numeric trend rest_id - monitor topic momentum and context - paginate for deeper trend analysis ### Use cases - trendwatching and social listening - real-time content planning - spotting opportunities for rapid PR and marketing reactions ### Output Preset trend-feed content that can be used immediately in analysis, planning, and decision workflows. Note: backend endpoint returns an envelope: `{ data: [...], page, limit, has_next }`. This Actor pushes only records from `data[]` to the dataset. ### How to use 1. Start with `What do you want to fetch?`. 2. For `Discover trends`, choose one preset `category`: `trending`, `news`, `sport`, or `entertainment`. 3. For tweets or relevant users, leave `category` empty and put the numeric value into `trendId`. 4. You can get that numeric id from a returned deep link such as `twitter://trending/2025383938727051697`. 5. Do not use the top-level trend item `id` like `trend-Southampton Stun Arsenal 2-1 to Reach FA Cup Semis`. Use the numeric value from `trend_url.url` or the helper field `trend_id`. 6. For tweet runs, choose `Tweet feed`: `Latest` or `Top`. 7. In most cases you do not need to touch `Data mode` in the Advanced section. 8. Configure pagination and caps as needed. Important: - this Actor works on preset trend categories, not the newer global-trending country/category feed - if you want the fuller country/category-based trends product, use [https://apify.com/simoit/x-twitter-trends-scrapper](https://apify.com/simoit/x-twitter-trends-scrapper) - `category` is only for trends discovery - `trendId` is required for tweets and relevant users - if both are filled, the Actor prioritizes `trendId` - invalid user input is reported as a successful run with a clear `USER_ERROR_REPORT` in the default key-value store instead of a generic crash - dataset shape stays unchanged for existing clients; error details are written to key-value store and status message, not pushed as dataset records - each run stores a `RUN_REPORT` key in the default key-value store with counts, warnings, and structured debug events No technical setup is required from your side. Backend infrastructure is managed by SIMO IT. Need a custom plan, custom limits, or a tailored data workflow? Contact `contact@simoit.tech`. ### Related Actors - Need country and category feeds from the newer global-trending surface? Use [Global Trending Scraper](https://apify.com/simoit/x-twitter-trends-scrapper). - Need tweet search after discovering a trend topic? Use [Tweet Scraper](https://apify.com/simoit/x-twitter-search-tweets-scrapper). - Need to inspect what one account posted around a trend? Use [Profile Scraper](https://apify.com/simoit/x-twitter-profile-scrapper). - Need account metadata or follower graphs for relevant users? Use [User Scraper](https://apify.com/simoit/x-twitter-user-scrapper). ### Input - `inputTargetType` (string, recommended): Primary UI selector for the run intent. - `discover_trends`: list trends from one preset category - `trend_tweets`: fetch tweets for one concrete numeric trend id - `relevant_users`: fetch relevant users for one concrete numeric trend id - `trend_tweets_and_users`: fetch both datasets for one concrete numeric trend id - `trendId` (string, conditionally required): - for `trends`: preset category (`trending`, `news`, `sport`, `entertainment`) or another backend-supported custom identifier - for `tweets` and `relevant_users`: numeric trend rest_id only, extracted from `trend_url.url` like `twitter://trending/2040024758503477259` - `trendName` (string, optional): Optional label stored together with returned tweets/users. - `sort` (string, optional, default: `latest`): Tweet timeline selection for one concrete trend. - `latest`: most recent tweets for the trend - `top`: top-ranked tweets for the trend - `category` (string, optional): Preset trend category: `trending`, `news`, `sport`, or `entertainment`. These are preset Explore-style feeds, not the newer global-trending categories. Use only for `discover_trends`. - `trendUrl` (string, optional): Single concrete X/Twitter trend URL. - `trendUrls` (string, optional): Batch concrete trend URLs (comma-separated or new lines). - `startUrls` (string, optional): Alias batch URL input (comma-separated or new lines). - `dataMode` (string, optional, default: `auto`): Advanced override. Usually leave it untouched. - `auto`: choose the right flow automatically from the provided target - `trends`: list current trends/posts from preset categories or another backend-supported custom identifier - `tweets`: fetch tweets for one concrete numeric trend id - `relevant_users`: list relevant users for one concrete numeric trend id - `both`: run both flows explicitly - `page` (integer, optional, default: `1`): Starting page number. - `limit` (integer, optional, default: `20`, max: `100`): Records per page. - `maxPages` (integer, optional, default: `1`): Maximum number of pages to fetch. - `maxItems` (integer, optional): Hard cap for trend records. - `usersPage` (integer, optional, default: `1`): Starting page for trend relevant users. - `usersLimit` (integer, optional, default: `20`, max: `100`): Relevant users per page. - `usersMaxPages` (integer, optional, default: `1`): Maximum pages for trend relevant users. - `usersMaxItems` (integer, optional): Hard cap for trend relevant user records. #### Filters and pagination - trend target: `trendId`, `category`, `trendUrl`, `trendUrls`, `startUrls` - mode switch: `dataMode` - trend page controls: `page`, `limit`, `maxPages`, `maxItems` - relevant users page controls: `usersPage`, `usersLimit`, `usersMaxPages`, `usersMaxItems` #### Example input Discover current sport trends: ```json { "inputTargetType": "discover_trends", "category": "sport", "limit": 10, "maxItems": 20 } ```` Fetch relevant users for one specific trend topic: ```json { "inputTargetType": "relevant_users", "trendId": "2025383938727051697", "usersLimit": 10, "usersMaxItems": 30 } ``` Fetch tweets for one specific trend topic: ```json { "inputTargetType": "trend_tweets", "trendId": "2025383938727051697", "sort": "latest", "limit": 10, "maxItems": 30 } ``` Fetch both tweets and relevant users for one specific trend topic: ```json { "inputTargetType": "trend_tweets_and_users", "trendId": "2025383938727051697", "sort": "top", "limit": 10, "maxItems": 30, "usersLimit": 10, "usersMaxItems": 30 } ``` Do not use this for tweets: ```json { "inputTargetType": "trend_tweets", "category": "trending" } ``` Trend timeline items now include helper fields you can reuse directly: ```json { "trend_id": "2025383938727051697", "trend_slug": "trend-Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide", "trend_name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide" } ``` Use `trend_id` as the numeric trend identifier for tweet and relevant-user runs. `trend_slug` is only a display-style slug derived from the trend item, for example `trend-Southampton Stun Arsenal 2-1 to Reach FA Cup Semis`. #### Example output ```json { "id": "trend-AI agents", "rank": null, "name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide", "trend_url": { "url": "twitter://trending/2025383938727051697", "urlType": "DeepLink", "urlEndpointOptions": [] }, "trend_metadata": { "domain_context": null, "url": { "url": "twitter://trending/2025383938727051697", "urlType": "DeepLink", "urlEndpointOptions": [] }, "meta_description": null, "name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide" }, "grouped_trends": [], "trend_id": "2025383938727051697", "trend_name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide", "_type": "timelinetrend" } ``` #### Returned fields For trend timeline items: - `trend_id`: numeric trend identifier reused for tweet and relevant-user runs - `trend_name`: human-readable trend title - `trend_slug`: display-style slug derived from the trend item - `trend_url.url`: deep link like `twitter://trending/2040024758503477259` - `trend_metadata`: extra trend metadata from X For tweet items: - `id`, `id_str`: tweet identifier - `trend_id`, `trend_name`: trend context attached by the Actor - `url`, `date`, `user`, `rawContent`: core tweet fields - `replyCount`, `retweetCount`, `likeCount`, `quoteCount`: engagement counters - `media`, `viewCount`: media and reach signals when available For relevant-user items: - `id`, `id_str`: user identifier - `trend_id`, `trend_name`: trend context attached by the Actor - `username`, `name`, `followersCount`, `friendsCount`, `verified`: key profile fields ```json { "id": 1899999999999999999, "trend_id": "2025383938727051697", "trend_name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide", "user": { "username": "espn" }, "rawContent": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide ...", "_type": "snscrape.modules.twitter.Tweet" } ``` ```json { "id": 44196397, "trend_id": "2025383938727051697", "trend_name": "Vikings Receiver Rondale Moore Dies at 25 in Apparent Suicide", "username": "elonmusk", "name": "Elon Musk", "followersCount": 235240499, "friendsCount": 1288, "verified": true, "_type": "snscrape.modules.twitter.User" } ``` ### FAQ **Do I need my own backend or API setup?**\ No. This Actor is ready to run on Apify, and backend infrastructure is managed by SIMO IT. **Which values can I use for `trendId`?**\ For `trends`, use one of `trending`, `news`, `sport`, `entertainment`, or a raw timeline id. For tweets and `relevant_users`, use a numeric trend rest\_id from a concrete trend topic. **Can I collect trend data continuously?**\ Yes. Run the Actor on a schedule and paginate with `maxPages`. **How do I get the list of available categories first?**\ Use one of the built-in preset values shown in the Actor: `trending`, `news`, `sport`, `entertainment`. **How do I get the list of available trends first?**\ Run the Actor with one preset category. The returned trend items include a deep link such as `twitter://trending/2025383938727051697`. The numeric suffix is the trend id you can reuse. In the Actor output use `trend_id`. **How do I get users for a specific trend topic?**\ First fetch trends from a preset category, then take one concrete numeric `trend_id` from the returned items and run the Actor with that `trendId`. In normal usage `auto` is enough. **How do I get tweets for a specific trend topic?** First fetch trends from a preset category, then reuse `trend_id`. That numeric id is enough to fetch the tweet feed for that trend. Use `sort=latest` for the freshest posts or `sort=top` for the ranked feed. # Actor input Schema ## `inputTargetType` (type: `string`): Choose the run type first. Then fill only the fields relevant to that path. ## `dataMode` (type: `string`): Advanced override. Auto picks the right mode from your input. Numeric trendId can fetch tweets and relevant users for one concrete trend. ## `trendId` (type: `string`): Numeric trend rest\_id for tweets and relevant users about one concrete trend, for example 2040024758503477259. You can extract it from trend\_url like twitter://trending/2040024758503477259. ## `trendName` (type: `string`): Optional label stored together with returned tweets/users. Not required for fetching tweets anymore. ## `sort` (type: `string`): Choose which tweet timeline to fetch for one concrete trend. Applies only to tweets. ## `category` (type: `string`): Optional preset trend timeline. Use this only for trends discovery, not for tweets or relevant users. ## `trendUrl` (type: `string`): Single concrete trend URL like https://x.com/i/trends/2039438188637196629. ## `trendUrls` (type: `string`): Optional concrete x.com/twitter.com trend URLs (comma-separated or new lines). ## `startUrls` (type: `string`): Alias for concrete trend URLs (comma-separated or new lines). ## `page` (type: `integer`): Starting page number. ## `limit` (type: `integer`): Items per page. ## `maxPages` (type: `integer`): Maximum number of pages to fetch. ## `maxItems` (type: `integer`): Maximum number of trend items to push. ## `usersPage` (type: `integer`): Starting page for relevant users of one concrete trend. ## `usersLimit` (type: `integer`): Users per page for one concrete trend rest\_id. ## `usersMaxPages` (type: `integer`): Maximum pages to fetch for one concrete trend rest\_id. ## `usersMaxItems` (type: `integer`): Maximum number of relevant users to push for one concrete trend rest\_id. ## Actor input object example ```json { "inputTargetType": "discover_trends", "dataMode": "auto", "sort": "latest", "page": 1, "limit": 10, "maxPages": 1, "maxItems": 10, "usersPage": 1, "usersLimit": 10, "usersMaxPages": 1, "usersMaxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `runReport` (type: `string`): No description ## `userErrorReport` (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 = { "inputTargetType": "discover_trends", "dataMode": "auto", "sort": "latest", "limit": 10, "maxItems": 10, "usersLimit": 10, "usersMaxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("simoit/x-twitter-preset-trends-scrapper").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 = { "inputTargetType": "discover_trends", "dataMode": "auto", "sort": "latest", "limit": 10, "maxItems": 10, "usersLimit": 10, "usersMaxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("simoit/x-twitter-preset-trends-scrapper").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 '{ "inputTargetType": "discover_trends", "dataMode": "auto", "sort": "latest", "limit": 10, "maxItems": 10, "usersLimit": 10, "usersMaxItems": 10 }' | apify call simoit/x-twitter-preset-trends-scrapper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=simoit/x-twitter-preset-trends-scrapper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X/Twitter Preset Trends Scrapper", "description": "Preset X trend feeds from Explore tabs like news, sports, and entertainment", "version": "0.0", "x-build-id": "9D9ldNFP6P4oV79te" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/simoit~x-twitter-preset-trends-scrapper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-simoit-x-twitter-preset-trends-scrapper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/simoit~x-twitter-preset-trends-scrapper/runs": { "post": { "operationId": "runs-sync-simoit-x-twitter-preset-trends-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/simoit~x-twitter-preset-trends-scrapper/run-sync": { "post": { "operationId": "run-sync-simoit-x-twitter-preset-trends-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "inputTargetType" ], "properties": { "inputTargetType": { "title": "What do you want to fetch?", "enum": [ "discover_trends", "trend_tweets", "relevant_users", "trend_tweets_and_users" ], "type": "string", "description": "Choose the run type first. Then fill only the fields relevant to that path.", "default": "discover_trends" }, "dataMode": { "title": "Data mode", "enum": [ "auto", "trends", "tweets", "relevant_users", "both" ], "type": "string", "description": "Advanced override. Auto picks the right mode from your input. Numeric trendId can fetch tweets and relevant users for one concrete trend.", "default": "auto" }, "trendId": { "title": "Trend ID (numeric)", "type": "string", "description": "Numeric trend rest_id for tweets and relevant users about one concrete trend, for example 2040024758503477259. You can extract it from trend_url like twitter://trending/2040024758503477259." }, "trendName": { "title": "Trend name", "type": "string", "description": "Optional label stored together with returned tweets/users. Not required for fetching tweets anymore." }, "sort": { "title": "Tweet feed", "enum": [ "latest", "top" ], "type": "string", "description": "Choose which tweet timeline to fetch for one concrete trend. Applies only to tweets.", "default": "latest" }, "category": { "title": "Category", "enum": [ "trending", "news", "sport", "entertainment" ], "type": "string", "description": "Optional preset trend timeline. Use this only for trends discovery, not for tweets or relevant users." }, "trendUrl": { "title": "Trend URL", "pattern": "^https?:\\/\\/(www\\.)?(x|twitter)\\.com\\/(i\\/)?trends\\/\\d+.*$", "type": "string", "description": "Single concrete trend URL like https://x.com/i/trends/2039438188637196629." }, "trendUrls": { "title": "Trend URLs", "type": "string", "description": "Optional concrete x.com/twitter.com trend URLs (comma-separated or new lines)." }, "startUrls": { "title": "Start URLs", "type": "string", "description": "Alias for concrete trend URLs (comma-separated or new lines)." }, "page": { "title": "Page", "minimum": 1, "type": "integer", "description": "Starting page number.", "default": 1 }, "limit": { "title": "Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Items per page.", "default": 20 }, "maxPages": { "title": "Max pages", "minimum": 1, "type": "integer", "description": "Maximum number of pages to fetch.", "default": 1 }, "maxItems": { "title": "Max trend items", "minimum": 1, "type": "integer", "description": "Maximum number of trend items to push." }, "usersPage": { "title": "Users page", "minimum": 1, "type": "integer", "description": "Starting page for relevant users of one concrete trend.", "default": 1 }, "usersLimit": { "title": "Users limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Users per page for one concrete trend rest_id.", "default": 20 }, "usersMaxPages": { "title": "Users max pages", "minimum": 1, "type": "integer", "description": "Maximum pages to fetch for one concrete trend rest_id.", "default": 1 }, "usersMaxItems": { "title": "Max relevant users", "minimum": 1, "type": "integer", "description": "Maximum number of relevant users to push for one concrete trend rest_id." } } }, "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 } } } } } } } } } } ```