# X / Twitter Scraper (`alwaysprimedev/twitter-scraper-json`) Actor Scrape X (Twitter) search results, profiles, posts, and lists into clean structured JSON. Supports bulk URLs, pagination, normalized output, and easy exports through Apify for monitoring, research, lead generation, and automation. - **URL**: https://apify.com/alwaysprimedev/twitter-scraper-json.md - **Developed by:** [Always Prime](https://apify.com/alwaysprimedev) (community) - **Categories:** Lead generation, SEO tools, Social media - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 > Paste the X links, queries, handles, or list URLs you want to scrape. The deployment handles the rest. **X / Twitter Scraper** is a no-code Apify Actor for extracting public X (Twitter) data from search results, profiles, single posts, and lists. It is built for people who need a practical **Twitter scraper**, **X scraper**, or lightweight **Twitter API alternative** for monitoring, research, lead generation, content analysis, and workflow automation. Paste one target or many targets, choose how many posts you want per target, and export structured results to **JSON, CSV, Excel, HTML, RSS, or via API integrations**. The Actor stores normalized post data in the default dataset and keeps run metadata in the default key-value store. ### Why this X / Twitter scraper is useful - Scrape **search results**, **profiles**, **single posts**, and **lists** from one Actor. - Paste **multiple queries, URLs, handles, IDs, or list links** in one run. - Collect **up to your requested per-target limit** with automatic pagination for search, profile, and list runs. - Export clean, normalized results with author info, engagement counters, media, hashtags, URLs, and timestamps. - Use it in **Apify Console**, **API calls**, **schedules**, **webhooks**, **Make**, **Zapier**, or custom pipelines. - Start with a simple target-based workflow instead of dealing with technical scraping setup. ### Common use cases - Brand monitoring for keywords, product names, or competitor mentions - Tracking posts from creators, founders, journalists, or company accounts - Collecting post datasets for AI enrichment, classification, or sentiment analysis - Monitoring list feeds for niche communities, vertical experts, or local news - Building lead lists from keyword or profile-based discovery workflows - Saving tweet/post datasets to BI tools, spreadsheets, or internal dashboards ### What this Twitter scraper can extract The Actor supports these target types: | Mode | What you provide | What it returns | | --- | --- | --- | | `search` | Search queries or full search URLs | Matching posts from X search | | `profile` | `@handle` or profile URL | Posts, replies, or media from that profile | | `tweet` | Post URL or post ID | A normalized record for that single post | | `list` | List URL or list ID | Recent posts from the list timeline | ### Key features - **Bulk input support**: paste several targets one per line and process them in a single run - **Client-friendly input**: designed for non-developers, with minimal technical fields - **Simple operator experience**: users focus on targets and limits, not on scraping internals - **Normalized output**: results are standardized into a predictable JSON shape - **Dataset-ready output**: download data or feed it directly into Apify integrations - **Helpful run records**: `RUN_INFO`, `INPUT_USED`, and `OUTPUT` records make debugging and automation easier ### How to scrape X / Twitter data on Apify 1. Choose what you want to scrape: search results, a profile, posts, or a list. 2. Paste one target or many targets into the relevant field. 3. Set how many posts you want **per target** for paginated modes. 4. Run the Actor. 5. Open the dataset in the Output tab, or export the results through API and integrations. ### Simple user input The public input is intentionally minimal. End users only need to: - choose the target type - paste one or many links, handles, IDs, or search queries - choose the per-target result limit when relevant Any deployment-specific live-access setup stays outside the user input, so the experience remains clean and client-friendly. ### Bulk input examples #### Search multiple queries ```json { "whatToScrape": "search", "searchQuery": "from:NASA\nfrom:SpaceX\nhttps://x.com/search?q=OpenAI&src=typed_query&f=live", "searchSort": "Latest", "maxItems": 20 } ```` #### Scrape multiple profiles ```json { "whatToScrape": "profile", "profile": "NASA\nhttps://x.com/NASAHubble\nOpenAI", "profileContent": "tweets", "maxItems": 30 } ``` #### Scrape multiple posts ```json { "whatToScrape": "tweet", "postUrlOrId": "https://x.com/XDevelopers/status/1346889436626259968\nhttps://x.com/NASA/status/1872487026621776029" } ``` #### Scrape multiple lists ```json { "whatToScrape": "list", "listUrlOrId": "https://x.com/i/lists/1265545834667610117\nhttps://x.com/i/lists/1441717848968364036", "maxItems": 50 } ``` ### Output example Dataset items contain normalized X / Twitter post data such as: ```json { "type": "tweet", "id": "1346889436626259968", "url": "https://x.com/XDevelopers/status/1346889436626259968", "text": "Hello from X.", "createdAt": "Tue Apr 02 12:00:00 +0000 2026", "lang": "en", "likeCount": 120, "retweetCount": 10, "replyCount": 5, "quoteCount": 2, "viewCount": 4300, "mode": "search", "searchTerm": "from:NASA", "inputUrl": "https://x.com/search?q=from%3ANASA", "fetchedAt": "2026-04-03T10:00:00Z", "author": { "userName": "XDevelopers", "name": "Developers", "url": "https://x.com/XDevelopers" }, "hashtags": [], "media": [] } ``` The Output tab includes: - **Posts dataset**: the main extracted records - **Output summary**: a concise run summary in the `OUTPUT` record - **Run summary**: detailed metadata in `RUN_INFO` - **Parsed input**: the sanitized input in `INPUT_USED` ### What fields you get Important output fields include: - `id` - `url` - `twitterUrl` - `text` - `createdAt` - `lang` - `likeCount` - `retweetCount` - `replyCount` - `quoteCount` - `viewCount` - `author` - `hashtags` - `media` - `mode` - `searchTerm` - `sourceType` - `inputUrl` - `fetchedAt` ### Why teams use this Actor instead of building from scratch - No need to build and maintain your own X web request client - No need to normalize raw GraphQL responses yourself - No need to hand-roll pagination logic for common X timelines - No need to build a separate export layer for dataset downloads and integrations - Easier onboarding for operators or clients who prefer forms over code ### Automation and integrations This Actor works well when you want to: - trigger X data collection on a schedule - push results to webhooks after each run - connect the output to Make, Zapier, n8n, or custom APIs - enrich posts with AI or internal business logic in downstream Actors - use Apify dataset endpoints as a simple Twitter data API for your apps ### Cost and performance notes - The Actor is lightweight and does not depend on a browser for normal runs. - Search, profile, and list modes auto-paginate only until they hit your requested `maxItems`. - Bulk runs let you scrape many targets in one run, but each target still consumes requests on X. - For the best balance of cost and speed, start with smaller `maxItems` and scale up after you validate your workflow. ### FAQ #### Is this the official X API? No. This Actor mirrors the web requests used by `x.com` and normalizes the response into structured output. If you need an official, contract-based API with long-term compatibility guarantees, use the official X API instead. #### Why do I see demo or sample data? Some deployments may use sample preview mode for onboarding, testing, or schema validation. In production-style deployments, live access can be enabled behind the scenes without changing the public input UX. #### Can I scrape multiple URLs in one run? Yes. Search, profile, post, and list inputs all support bulk mode. Paste one target per line and the Actor will process them in a single run. #### Does `maxItems` apply to the whole run or each target? For `search`, `profile`, and `list`, `maxItems` applies **per target**. In `tweet` mode, it is ignored because each target is a single post. #### What if one target fails in a bulk run? The Actor keeps processing the remaining targets. Per-target errors are stored in `RUN_INFO`, so one bad target does not ruin the whole batch. #### Can I use this as a Twitter API alternative? For many public-page scraping use cases, yes. Teams often use this Actor as a practical API layer on top of Apify datasets and webhooks. It is still based on observed web behavior, so it can change when X changes its frontend. ### Troubleshooting - If a deployment is configured for live collection and a run fails, review the private live-access configuration for that deployment. - If a specific X endpoint changes, the Actor may need updated query IDs or request parameters. - If you only want to validate the workflow, sample preview mode can still be used for schema and integration testing. - If you paste many search queries, use **one query per line** for the cleanest parsing. ### Local development ```bash python -m venv .venv .venv\Scripts\activate pip install -r requirements.txt copy .env.example .env ``` Local CLI examples: ```bash python cli.py search --query "from:NASA" python cli.py profile --screen-name NASA --tab tweets python cli.py tweet --tweet-id 1346889436626259968 python cli.py list --list-id 1265545834667610117 ``` To run the Actor locally, save your input to `storage/key_value_stores/default/INPUT.json` and start: ```bash python -m src ``` ### Support If you run into an edge case, use the Actor's **Issues** tab on Apify or update the repository with a reproducible example input. Clear issue reports with target type, sample input, and error details make fixes much faster. ### Technical notes - Query IDs and feature flags on X can rotate over time - Live collection depends on deployment-level access configuration - Search, profile, and list modes auto-paginate; single-post mode performs one lookup per post - The Actor is designed for public-page data workflows and normalized export rather than official API parity # Actor input Schema ## `whatToScrape` (type: `string`): Choose the kind of X page you want to collect data from. ## `searchQuery` (type: `string`): Used when you scrape search results. You can paste one query or many queries/search URLs, one per line. ## `searchSort` (type: `string`): Choose how search results should be ordered. ## `profile` (type: `string`): Used when you scrape profiles. Paste one or many @handles or full profile URLs, one per line. ## `profileContent` (type: `string`): Choose which profile tab to collect. ## `postUrlOrId` (type: `string`): Used when you scrape specific posts. Paste one or many full post URLs or post IDs, one per line. ## `listUrlOrId` (type: `string`): Used when you scrape lists. Paste one or many full list URLs or list IDs, one per line. ## `maxItems` (type: `integer`): Maximum number of posts to collect for each pasted search/profile/list target. Ignored in single-post mode. ## `demoMode` (type: `boolean`): Legacy compatibility field retained for older tasks and inputs. ## `mode` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `query` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `product` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `screenName` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `profileTab` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `tweetId` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `listId` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `count` (type: `integer`): Legacy compatibility field retained for older tasks and inputs. ## `cursor` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `timeoutSeconds` (type: `integer`): Legacy compatibility field retained for older tasks and inputs. ## `bearerToken` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `authToken` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## `ct0` (type: `string`): Legacy compatibility field retained for older tasks and inputs. ## Actor input object example ```json { "whatToScrape": "search", "searchQuery": "from:NASA\nfrom:SpaceX", "searchSort": "Latest", "profile": "NASA\nhttps://x.com/NASAHubble", "profileContent": "tweets", "postUrlOrId": "https://x.com/XDevelopers/status/1346889436626259968\nhttps://x.com/NASA/status/1872487026621776029", "listUrlOrId": "https://x.com/i/lists/1265545834667610117\nhttps://x.com/i/lists/1441717848968364036", "maxItems": 5 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `outputSummary` (type: `string`): No description ## `runInfo` (type: `string`): No description ## `inputUsed` (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 = { "whatToScrape": "search", "searchQuery": `from:NASA from:SpaceX`, "profile": `NASA https://x.com/NASAHubble`, "postUrlOrId": `https://x.com/XDevelopers/status/1346889436626259968 https://x.com/NASA/status/1872487026621776029`, "listUrlOrId": `https://x.com/i/lists/1265545834667610117 https://x.com/i/lists/1441717848968364036`, "maxItems": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("alwaysprimedev/twitter-scraper-json").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 = { "whatToScrape": "search", "searchQuery": """from:NASA from:SpaceX""", "profile": """NASA https://x.com/NASAHubble""", "postUrlOrId": """https://x.com/XDevelopers/status/1346889436626259968 https://x.com/NASA/status/1872487026621776029""", "listUrlOrId": """https://x.com/i/lists/1265545834667610117 https://x.com/i/lists/1441717848968364036""", "maxItems": 5, } # Run the Actor and wait for it to finish run = client.actor("alwaysprimedev/twitter-scraper-json").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 '{ "whatToScrape": "search", "searchQuery": "from:NASA\\nfrom:SpaceX", "profile": "NASA\\nhttps://x.com/NASAHubble", "postUrlOrId": "https://x.com/XDevelopers/status/1346889436626259968\\nhttps://x.com/NASA/status/1872487026621776029", "listUrlOrId": "https://x.com/i/lists/1265545834667610117\\nhttps://x.com/i/lists/1441717848968364036", "maxItems": 5 }' | apify call alwaysprimedev/twitter-scraper-json --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=alwaysprimedev/twitter-scraper-json", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X / Twitter Scraper", "description": "Scrape X (Twitter) search results, profiles, posts, and lists into clean structured JSON. Supports bulk URLs, pagination, normalized output, and easy exports through Apify for monitoring, research, lead generation, and automation.", "version": "1.0", "x-build-id": "DXvWcb7J7sSVKLIfY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/alwaysprimedev~twitter-scraper-json/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-alwaysprimedev-twitter-scraper-json", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/alwaysprimedev~twitter-scraper-json/runs": { "post": { "operationId": "runs-sync-alwaysprimedev-twitter-scraper-json", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/alwaysprimedev~twitter-scraper-json/run-sync": { "post": { "operationId": "run-sync-alwaysprimedev-twitter-scraper-json", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "whatToScrape": { "title": "What do you want to scrape?", "enum": [ "search", "profile", "tweet", "list" ], "type": "string", "description": "Choose the kind of X page you want to collect data from.", "default": "search" }, "searchQuery": { "title": "Search queries or search URLs", "type": "string", "description": "Used when you scrape search results. You can paste one query or many queries/search URLs, one per line." }, "searchSort": { "title": "Search sort", "enum": [ "Latest", "Top" ], "type": "string", "description": "Choose how search results should be ordered.", "default": "Latest" }, "profile": { "title": "Profiles", "type": "string", "description": "Used when you scrape profiles. Paste one or many @handles or full profile URLs, one per line." }, "profileContent": { "title": "Profile content", "enum": [ "tweets", "replies", "media" ], "type": "string", "description": "Choose which profile tab to collect.", "default": "tweets" }, "postUrlOrId": { "title": "Posts", "type": "string", "description": "Used when you scrape specific posts. Paste one or many full post URLs or post IDs, one per line." }, "listUrlOrId": { "title": "Lists", "type": "string", "description": "Used when you scrape lists. Paste one or many full list URLs or list IDs, one per line." }, "maxItems": { "title": "How many posts per target do you want?", "minimum": 1, "type": "integer", "description": "Maximum number of posts to collect for each pasted search/profile/list target. Ignored in single-post mode.", "default": 20 }, "demoMode": { "title": "Demo mode", "type": "boolean", "description": "Legacy compatibility field retained for older tasks and inputs." }, "mode": { "title": "Legacy mode", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "query": { "title": "Legacy query", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "product": { "title": "Legacy product", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "screenName": { "title": "Legacy screen name", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "profileTab": { "title": "Legacy profile tab", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "tweetId": { "title": "Legacy tweet id", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "listId": { "title": "Legacy list id", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "count": { "title": "Legacy count", "type": "integer", "description": "Legacy compatibility field retained for older tasks and inputs." }, "cursor": { "title": "Cursor", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "timeoutSeconds": { "title": "Timeout", "type": "integer", "description": "Legacy compatibility field retained for older tasks and inputs." }, "bearerToken": { "title": "Legacy bearer token", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "authToken": { "title": "Legacy auth token", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." }, "ct0": { "title": "Legacy ct0", "type": "string", "description": "Legacy compatibility field retained for older tasks and inputs." } } }, "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 } } } } } } } } } } ```