# Adidas Discovery Spider (`getdataforme/adidas-discovery-spider`) Actor The Adidas Discovery Spider is an efficient web scraping tool designed for extracting detailed product information from Adidas' website. It captures extensive data such as product IDs, titles, prices, ratings, and images, ensuring high reliability and up-to-date accuracy.... - **URL**: https://apify.com/getdataforme/adidas-discovery-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 --- ## Adidas Discovery Spider ### Introduction The Adidas Discovery Spider is a powerful web scraping tool designed to extract detailed product information from Adidas' website. It efficiently gathers data on various products, including shoes and apparel, providing valuable insights for market analysis, competitive intelligence, and business automation. ### Features - **Comprehensive Data Extraction**: Captures extensive details such as product IDs, titles, prices, ratings, and images. - **High Reliability**: Ensures accurate and up-to-date data collection from Adidas' official site. - **Customizable Limits**: Allows users to set a maximum number of items to scrape per run. - **Flexible URL Configuration**: Supports multiple URLs for targeted scraping. - **User-Friendly Interface**: Easy setup with clear input parameters and straightforward execution. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------| | `Urls` | array | Yes | The URLs for the spider to scrape. Must be valid HTTP/HTTPS links. | `["https://www.adidas.com/us/search?q=sport+shoes"]` | | `item_limit`| integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Urls": [ "https://www.adidas.com/us/search?q=sport+shoes&sitePath=us&start=48" ], "item_limit": 10 } ```` #### Output JSON ```json [ { "product_id": "KK3854", "title": "Barricade 14 Tennis Shoes", "subtitle": "Women Performance", "model_number": "ONQ31", "category": "Performance", "product_url": "https://www.adidas.com/us/barricade-14-tennis-shoes/KK3854.html", "image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/14e96859c12b4d31880549108ed849dc_9366/Barricade_14_Tennis_Shoes_White_KK3854_00_plp_standard.jpg", "hover_image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/9baddfa28b534230a42ad8d9ccccf57d_9366/Barricade_14_Tennis_Shoes_White_KK3854_02_standard_hover.jpg", "rating": null, "rating_count": -99, "colour_variations": [], "original_price": 160, "sale_price": null, "sold_out": false, "badges": [ { "style": "new", "text": "New" } ], "actor_id": "1n6zynNVeXi6Rd5r3", "run_id": "jYcGCzXriZJ04aXf8" }, { "product_id": "KI0780", "title": "GAMECOURT 2 TENNIS SHOES", "subtitle": "Men Performance", "model_number": "ONX03", "category": "Performance", "product_url": "https://www.adidas.com/us/gamecourt-2-tennis-shoes/KI0780.html", "image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/e5c92a4650dc48e98506c240a92d3314_9366/GAMECOURT_2_TENNIS_SHOES_Black_KI0780_HM1.jpg", "hover_image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/81336791a3964d84adac289672a4861c_9366/GAMECOURT_2_TENNIS_SHOES_Black_KI0780_HM3_hover.jpg", "rating": 4.5877, "rating_count": 97, "colour_variations": [ "KI0780", "KI0781", "KI0782" ], "original_price": 70, "sale_price": 49, "sold_out": false, "badges": [], "actor_id": "1n6zynNVeXi6Rd5r3", "run_id": "jYcGCzXriZJ04aXf8" }, { "product_id": "KK3821", "title": "Ubersonic Tennis Shoes Kids", "subtitle": "Kids Performance", "model_number": "NJF03", "category": "Performance", "product_url": "https://www.adidas.com/us/ubersonic-tennis-shoes-kids/KK3821.html", "image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/cc91cf027a7546879b26caade8907540_9366/Ubersonic_Tennis_Shoes_Kids_Red_KK3821_00_plp_standard.jpg", "hover_image": "https://assets.adidas.com/images/w_383,h_383,f_auto,q_auto,fl_lossy,c_fill,g_auto/f52c25d820394fb8a3d82325a8dc75f7_9366/Ubersonic_Tennis_Shoes_Kids_Red_KK3821_02_standard_hover.jpg", "rating": 4.8611, "rating_count": 36, "colour_variations": [ "KK3821", "KK3850" ], "original_price": 60, "sale_price": null, "sold_out": false, "badges": [ { "style": "new", "text": "New" } ], "actor_id": "1n6zynNVeXi6Rd5r3", "run_id": "jYcGCzXriZJ04aXf8" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data for competitive analysis and market trends. - **Competitive Intelligence**: Monitor competitor pricing and product offerings. - **Price Monitoring**: Track price changes over time to identify sales or discounts. - **Content Aggregation**: Compile product information for content creation or marketing purposes. - **Academic Research**: Use scraped data for studies in consumer behavior or economics. - **Business Automation**: Integrate with systems for automated inventory management or e-commerce platforms. ### Installation and Usage 1. Search for "Adidas Discovery Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array of product objects, each containing: - `product_id`: Unique identifier for the product. - `title`: Name of the product. - `subtitle`: Additional description or category. - `model_number`: Product model number. - `category`: Product category (e.g., Performance). - `product_url`: URL to the product page. - `image` and `hover_image`: URLs to product images. - `rating` and `rating_count`: Customer ratings and count. - `colour_variations`: Available color options. - `original_price` and `sale_price`: Pricing information. - `sold_out`: Boolean indicating availability. - `badges`: List of badges (e.g., "New"). - `actor_id` and `run_id`: Identifiers for the actor run. ### Error Handling The spider is designed to handle common errors such as network issues or changes in page structure. If an error occurs, it will be logged, and you can review the logs to understand and resolve the issue. ### Rate Limiting and Best Practices - **Respect Rate Limits**: Ensure compliance with Adidas' terms of service by avoiding excessive requests. - **Use Delays**: Implement delays between requests to mimic human browsing behavior. - **Monitor Logs**: Regularly check logs for any rate limiting warnings or errors. ### Limitations and Considerations - **Dynamic Content**: The spider may not capture dynamically loaded content without additional configuration. - **Legal Compliance**: Ensure that scraping activities comply with legal requirements and Adidas' terms of service. - **Data Accuracy**: While the spider aims to provide accurate data, changes in website structure can affect results. ### Support For support or questions, contact us at . *** This documentation provides a comprehensive guide to using the Adidas Discovery Spider effectively. For further assistance, please refer to our support channels. # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Urls": [ "https://www.adidas.com/us/search?q=sport+shoes&sitePath=us&start=0" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/adidas-discovery-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/adidas-discovery-spider").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 '{}' | apify call getdataforme/adidas-discovery-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/adidas-discovery-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Adidas Discovery Spider", "description": "The Adidas Discovery Spider is an efficient web scraping tool designed for extracting detailed product information from Adidas' website. It captures extensive data such as product IDs, titles, prices, ratings, and images, ensuring high reliability and up-to-date accuracy....", "version": "0.0", "x-build-id": "LYbmmr4UX3z6TMgtZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~adidas-discovery-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-adidas-discovery-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~adidas-discovery-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-adidas-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~adidas-discovery-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-adidas-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://www.adidas.com/us/search?q=sport+shoes&sitePath=us&start=0" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```