# Wikidata Entity Search Scraper (`parseforge/wikidata-entity-search-scraper`) Actor Search Wikidata's open knowledge graph of 100M+ entities (people, places, brands, books, films) by name. Returns Q-ID, label, description, aliases, all claims (P-properties), sitelinks to every Wikipedia language, structured facts and image. Filter by entity type, language and full-claims fetching. - **URL**: https://apify.com/parseforge/wikidata-entity-search-scraper.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** Education, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $14.00 / 1,000 result items 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 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 ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## ๐ŸŒ Wikidata Entity Search Scraper > ๐Ÿš€ **Search Wikidata's open knowledge graph of 100M+ entities by name.** > ๐Ÿ•’ **Last updated:** 2026-05-06 ยท **๐Ÿ“Š 22 fields** per record ยท **100M+ entities** ยท people, places, brands, books, films, concepts ยท all claims, sitelinks, multilingual labels The **Wikidata Entity Search Scraper** searches Wikidata's open knowledge graph of **100M+ entities** by name. Output includes the canonical Q-ID, label, description, aliases, all claims (P-properties), sitelinks to every Wikipedia language edition, and structured facts. Wikidata is the structured-data backbone of Wikipedia and one of the largest open knowledge graphs in the world. Filters run server-side, so a single run can resolve every entity matching a name, fetch full claim trees, or pull entities in non-English languages. | ๐ŸŽฏ Target Audience | ๐Ÿ’ก Primary Use Cases | |---|---| | ML pipelines, knowledge-graph engineers, journalists, fact-checkers, content recommendation engines, search developers | Entity resolution, knowledge-graph augmentation, fact-checking, content enrichment, multilingual search, ML training datasets | --- ### ๐Ÿ“‹ What the Wikidata Entity Search Scraper does Five filtering workflows in a single run: - ๐Ÿ” **Free-text search.** Match entity labels and aliases. - ๐ŸŒ **Multilingual.** Search in 20+ languages (en, es, fr, de, it, ja, zh, ko, ar, hi, pt, nl, ru). - ๐Ÿ†” **Item or property.** Search Q-entities (items) or P-entities (properties). - ๐Ÿ“Š **Full claims fetch.** Optional: pull every statement, sitelink, and structured fact per entity. - ๐Ÿท๏ธ **Image extraction.** Auto-extracts the entity's primary image from claim P18. > ๐Ÿ’ก **Why it matters:** clean, server-side filtering removes the parser-and-pagination work from your team and keeps your dataset fresh on every run. --- ### ๐ŸŽฌ Full Demo _๐Ÿšง Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded dataset._ --- ### โš™๏ธ Input
InputTypeDefaultBehavior
maxItemsinteger10Records to return. Free plan caps at 10, paid plan up to 1,000,000.
querystring"tesla"Term to search Wikidata entities.
languagestring"en"Search language code (ISO 639).
entityTypestring"item"`item` (Q) or `property` (P).
fetchClaimsbooleantrueFetch full claims, sitelinks, aliases per entity.
**Example: all entities matching Tesla.** ```json { "maxItems": 50, "query": "tesla", "language": "en", "fetchClaims": true } ```` **Example: Spanish-language Madrid entities.** ```json { "maxItems": 100, "query": "madrid", "language": "es" } ``` *** ### ๐Ÿ“Š Output Each record contains **22 fields**. Download the dataset as CSV, Excel, JSON, or XML. #### ๐Ÿงพ Schema | Field | Type | Example | |---|---|---| | ๐Ÿ–ผ๏ธ `thumbnailUrl` | string | null | `"https://commons.wikimedia.org/wiki/Special:FilePath/Tesla_Inc_Logo.svg?width=300"` | | ๐Ÿ†” `entityId` | string | `"Q478214"` | | ๐Ÿ“› `label` | string | `"Tesla"` | | ๐Ÿ“ `description` | string | `"American automotive, energy storage and solar power company"` | | ๐Ÿท๏ธ `aliases` | array | `["Tesla Inc","Tesla Motors"]` | | ๐Ÿ†” `instanceOfId` | string | null | `"Q43229"` | | ๐Ÿ“Š `sitelinkCount` | number | `106` | | ๐Ÿ“Š `claimCount` | number | `147` | | ๐Ÿ“Š `claims` | object | `{P31:[Q43229],P159:[Q485176]}` | | ๐ŸŒ `wikidataUrl` | string | `"https://www.wikidata.org/wiki/Q478214"` | | ๐Ÿ“š `wikipediaEnUrl` | string | `"https://en.wikipedia.org/wiki/Tesla,_Inc."` | #### ๐Ÿ“ฆ Sample records
๐Ÿš— Company entity ```json {"entityId":"Q478214","label":"Tesla","description":"American automotive, energy storage and solar power company","sitelinkCount":106,"claimCount":147,"wikidataUrl":"https://www.wikidata.org/wiki/Q478214","wikipediaEnUrl":"https://en.wikipedia.org/wiki/Tesla,_Inc."} ```
โšก SI unit ```json {"entityId":"Q163343","label":"tesla","description":"SI unit of magnetic flux density","sitelinkCount":71,"claimCount":35} ```
*** ### โœจ Why choose this Actor | | Capability | |---|---| | ๐Ÿ“š | **100M+ entities.** People, places, brands, books, films, concepts in a single query. | | ๐ŸŒ | **Multilingual.** 20+ languages with native-language labels and aliases. | | ๐Ÿ“Š | **Full structured facts.** All P-property claims for entity-resolution pipelines. | | ๐Ÿ”— | **Sitelinks to Wikipedia.** Direct links to every Wikipedia language edition. | | โšก | **Fast.** 100 entities in under 30 seconds. | *** ### ๐Ÿ“ˆ How it compares to alternatives | Approach | Cost | Coverage | Refresh | Filters | Setup | |---|---|---|---|---|---| | **โญ This Actor** | $5 free credit | **100M+** entities | Live per run | query, language, type, claims | โšก 2 min | | Wikidata SPARQL endpoint | Free | All | Live | SPARQL | ๐Ÿข SPARQL knowledge | | Manual Wikidata browse | Free | Manual | Live | Web filters | ๐Ÿ•’ Manual | | DBpedia | Free | Subset | Stale | SPARQL | ๐Ÿข Setup | Pick this Actor when you want broad coverage, server-side filtering, and no pipeline maintenance. *** ### ๐Ÿš€ How to use 1. ๐Ÿ“ **Sign up.** [Create a free account with $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) (takes 2 minutes). 2. ๐ŸŒ **Open the Actor.** Go to the Wikidata Entity Search Scraper page on the Apify Store. 3. ๐ŸŽฏ **Set input.** Pick your filters and `maxItems`. 4. ๐Ÿš€ **Run it.** Click **Start** and let the Actor collect your data. 5. ๐Ÿ“ฅ **Download.** Grab your results in the **Dataset** tab as CSV, Excel, JSON, or XML. > โฑ๏ธ Total time from signup to downloaded dataset: **3-5 minutes.** No coding required. *** ### ๐Ÿ’ผ Business use cases
#### ๐Ÿค– Knowledge Graphs - Entity resolution and disambiguation - Augment internal KGs with Wikidata facts - Build cross-language entity links - Train named-entity-recognition models #### ๐Ÿ” Search & Discovery - Power semantic search with structured facts - Build autocomplete with multilingual labels - Resolve ambiguous entity names - Cross-language search experiments
#### ๐Ÿ“ฐ Journalism & Fact-Checking - Verify entities mentioned in stories - Pull biographical and corporate facts - Cross-reference claims via P-properties - Map relationship networks #### ๐Ÿค– ML & NLP - Train entity-linking models - Build retrieval-augmented agents - Generate training datasets for NER - Multilingual KB embedding
*** ### ๐Ÿ”Œ Automating Wikidata Entity Search Scraper Control the scraper programmatically for scheduled runs and pipeline integrations: - ๐ŸŸข **Node.js.** Install the `apify-client` NPM package. - ๐Ÿ **Python.** Use the `apify-client` PyPI package. - ๐Ÿ“š See the [Apify API documentation](https://docs.apify.com/api/v2) for full details. The [Apify Schedules feature](https://docs.apify.com/platform/schedules) lets you trigger this Actor on any cron interval. Hourly, daily, or weekly refreshes keep downstream databases in sync automatically. *** ### ๐ŸŒŸ Beyond business use cases Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.
#### ๐ŸŽ“ Research and academia - Knowledge-graph research - Reproducible KB snapshots - Cross-cultural KB studies - Course material on Wikidata #### ๐ŸŽจ Personal and creative - Personal knowledge dashboards - Side projects with structured facts - Newsletter content - Hobbyist KB exploration
#### ๐Ÿค Non-profit and civic - Open-knowledge contributions - Civic literacy datasets - Cultural heritage cataloging - Multilingual literacy projects #### ๐Ÿงช Experimentation - Train entity-linking ML models - Prototype KB-aware chat agents - Build entity-resolution pipelines - Test cross-language search
*** ### ๐Ÿค– Ask an AI assistant about this scraper Open a ready-to-send prompt about this ParseForge actor in the AI of your choice: - ๐Ÿ’ฌ [**ChatGPT**](https://chat.openai.com/?q=How%20do%20I%20use%20the%20Wikidata%20Entity%20Search%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿง  [**Claude**](https://claude.ai/new?q=How%20do%20I%20use%20the%20Wikidata%20Entity%20Search%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ” [**Perplexity**](https://perplexity.ai/search?q=How%20do%20I%20use%20the%20Wikidata%20Entity%20Search%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) - ๐Ÿ…’ [**Copilot**](https://copilot.microsoft.com/?q=How%20do%20I%20use%20the%20Wikidata%20Entity%20Search%20Scraper%20by%20ParseForge%20on%20Apify%3F%20Show%20me%20input%20examples%2C%20output%20fields%2C%20common%20use%20cases%2C%20and%20how%20to%20integrate%20it%20into%20a%20workflow.) *** ### โ“ Frequently Asked Questions #### ๐Ÿงฉ How does it work? Provide a query and language. The Actor queries Wikidata's wbsearchentities endpoint and optionally fetches full claims via wbgetentities. #### ๐Ÿ“Š How many fields per record? 22 base fields plus a `claims` object with every P-property and a `sitelinks` map across Wikipedia languages. #### ๐ŸŒ Which languages are supported? 20+, including English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Japanese, Chinese, Korean, Arabic, Hindi, Turkish, Polish, Swedish, Finnish, Danish, Norwegian, Czech. #### ๐Ÿ†” What's the difference between Q and P entities? Q-entities are items (people, places, things). P-entities are properties (relations like 'instance of', 'located in', 'date of birth'). #### ๐Ÿ” Can I schedule runs? Yes. Use Apify Schedules to refresh entity caches or track entity creations on a topic. #### โš–๏ธ Is this data public? Yes. Wikidata publishes under CC0; you can use the data freely without attribution. #### ๐Ÿ’ณ Do I need a paid Apify plan? No. The free plan covers preview runs. A paid plan unlocks higher item counts and scheduling. #### ๐Ÿ†˜ What if a run fails? Apify retries transient errors. Partial datasets are preserved. #### ๐Ÿ–ผ๏ธ Does it return entity images? Yes when the entity has claim P18 set. The Actor extracts the Commons image URL automatically. #### ๐Ÿ“š How do I use the claims field? Each P-property maps to an array of values. Decode P-IDs via [Wikidata's property page](https://www.wikidata.org/wiki/Wikidata:List_of_properties). *** ### ๐Ÿ”Œ Integrate with any app Wikidata Entity Search Scraper connects to any cloud service via [Apify integrations](https://apify.com/integrations): - [**Make**](https://docs.apify.com/platform/integrations/make) - Automate multi-step workflows - [**Zapier**](https://docs.apify.com/platform/integrations/zapier) - Connect with 5,000+ apps - [**Slack**](https://docs.apify.com/platform/integrations/slack) - Get run notifications in your channels - [**Airbyte**](https://docs.apify.com/platform/integrations/airbyte) - Pipe data into your warehouse - [**GitHub**](https://docs.apify.com/platform/integrations/github) - Trigger runs from commits and releases - [**Google Drive**](https://docs.apify.com/platform/integrations/drive) - Export datasets straight to Sheets You can also use webhooks to trigger downstream actions when a run finishes. *** ### ๐Ÿ”— Recommended Actors - [**๐Ÿ“š Open Library Books**](https://apify.com/parseforge/openlibrary-books-scraper) - 30M+ books and editions - [**๐Ÿ“– Project Gutenberg Books**](https://apify.com/parseforge/project-gutenberg-books-scraper) - 75,000+ free public-domain books - [**๐ŸŽจ Openverse Media**](https://apify.com/parseforge/openverse-media-scraper) - 800M+ openly licensed images and audio - [**๐Ÿ“ฐ Hacker News Search**](https://apify.com/parseforge/hackernews-algolia-scraper) - Every HN story since 2007 - [**๐ŸŒ World Bank Indicators**](https://apify.com/parseforge/worldbank-indicators-scraper) - Country economic indicators > ๐Ÿ’ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more reference-data scrapers. *** **๐Ÿ†˜ Need Help?** [**Open our contact form**](https://tally.so/r/BzdKgA) to request a new scraper, propose a custom data project, or report an issue. *** > **โš ๏ธ Disclaimer:** this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the Wikimedia Foundation, Wikidata, Wikipedia, or any contributing editor. All trademarks mentioned are the property of their respective owners. Only publicly available open data is collected. # Actor input Schema ## `maxItems` (type: `integer`): Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000 ## `query` (type: `string`): Term to search Wikidata entities (people, places, things, concepts). ## `language` (type: `string`): Wikidata search language code. ## `entityType` (type: `string`): Search items (Q-entities) or properties (P-entities). ## `fetchClaims` (type: `boolean`): If checked, fetches full statements/claims, sitelinks, and aliases per entity (1 extra API call per entity). ## Actor input object example ```json { "maxItems": 10, "query": "tesla", "language": "en", "entityType": "item", "fetchClaims": true } ``` # Actor output Schema ## `overview` (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 = { "maxItems": 10, "query": "tesla", "language": "en", "entityType": "item" }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/wikidata-entity-search-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "maxItems": 10, "query": "tesla", "language": "en", "entityType": "item", } # Run the Actor and wait for it to finish run = client.actor("parseforge/wikidata-entity-search-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "maxItems": 10, "query": "tesla", "language": "en", "entityType": "item" }' | apify call parseforge/wikidata-entity-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/wikidata-entity-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikidata Entity Search Scraper", "description": "Search Wikidata's open knowledge graph of 100M+ entities (people, places, brands, books, films) by name. Returns Q-ID, label, description, aliases, all claims (P-properties), sitelinks to every Wikipedia language, structured facts and image. Filter by entity type, language and full-claims fetching.", "version": "1.0", "x-build-id": "JJoh1AJ4UmPNZtfZz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/parseforge~wikidata-entity-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-parseforge-wikidata-entity-search-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/parseforge~wikidata-entity-search-scraper/runs": { "post": { "operationId": "runs-sync-parseforge-wikidata-entity-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/parseforge~wikidata-entity-search-scraper/run-sync": { "post": { "operationId": "run-sync-parseforge-wikidata-entity-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Free users: Limited to 10 items (preview). Paid users: Optional, max 1,000,000" }, "query": { "title": "Search query", "type": "string", "description": "Term to search Wikidata entities (people, places, things, concepts)." }, "language": { "title": "Search language", "enum": [ "en", "es", "fr", "de", "it", "pt", "nl", "ru", "ja", "zh", "ko", "ar", "hi", "tr", "pl", "sv", "fi", "da", "no", "cs" ], "type": "string", "description": "Wikidata search language code.", "default": "en" }, "entityType": { "title": "Entity type", "enum": [ "item", "property" ], "type": "string", "description": "Search items (Q-entities) or properties (P-entities).", "default": "item" }, "fetchClaims": { "title": "Fetch full claims", "type": "boolean", "description": "If checked, fetches full statements/claims, sitelinks, and aliases per entity (1 extra API call per entity).", "default": true } } }, "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 } } } } } } } } } } ```