# Apacheinc Product Discovery Ec Spider (`getdataforme/apacheinc-product-discovery-ec-spider`) Actor The Apacheinc Product Discovery EC Spider efficiently extracts product information from the Apache Inc. website using keyword-based searches, ensuring high-quality data extraction. It supports flexible item limits for comprehensive analysis, optimized performance, and easy configuration.... - **URL**: https://apify.com/getdataforme/apacheinc-product-discovery-ec-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 --- ## Apacheinc Product Discovery Ec Spider ### Introduction The Apacheinc Product Discovery EC Spider is a powerful web scraping tool designed to efficiently extract product information from the Apache Inc. website. It allows users to discover and analyze products based on specified keywords, making it an invaluable resource for market research, competitive analysis, and business intelligence. ### Features - **Keyword-Based Scraping**: Customize searches with specific keywords to target relevant products. - **High Data Quality**: Ensures accurate and reliable product data extraction. - **Flexible Item Limit**: Set a maximum number of items to scrape per run or leave unlimited for comprehensive data collection. - **Efficient Performance**: Optimized for speed and accuracy, reducing the time needed for large-scale data gathering. - **User-Friendly Configuration**: Simple setup with intuitive input parameters. ### Input Parameters Table | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|--------------------------| | Keywords | array | No | The keywords for the spider. | `["chemical"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Keywords": ["chemical"], "item_limit": 10 } ```` #### Output JSON ```json [ { "type": "site", "product_name": "Hose Safety, Care, & Maintenance", "product_url": "https://www.apache-inc.com/CMS/apache.nsf/weblinks/AHBD-999SRY?open", "description": "Correct Hose Use, Care & Maintenance Information", "part_no": null, "post_image": null, "pagination": 1, "actor_id": "8CZ381bjcXyK0Z8op", "run_id": "2B8a9VRRjtHlWnY0e" }, { "type": "product", "product_name": "2-Ply 75# Polyester Monofilament White Urethane Cover x Bare Antistatic", "product_url": "https://www.apache-inc.com/cms/apache.nsf/Product_Public.xsp?action=openDocument&documentId=E97BF169CFF6231586257EFF0024C86E", "part_no": "20103859", "post_image": "https://www.apache-inc.com/cms/wscentral.nsf/weblinks/AHBD-9QDQUA/$File/AHBD-9QDQUA.png", "pagination": 1, "actor_id": "8CZ381bjcXyK0Z8op", "run_id": "2B8a9VRRjtHlWnY0e" }, { "type": "product", "product_name": "#2 to #4 Clipperยฎ Microlacer Face Strips", "product_url": "https://www.apache-inc.com/cms/apache.nsf/Product_Public.xsp?action=openDocument&documentId=7886233806BB77E686257F30000FB0A3", "part_no": "25091943", "post_image": "https://www.apache-inc.com/CMS/WSCentral.nsf/weblinks/AHBD-8TWKVM/$File/AHBD-8TWKVM.png", "pagination": 1, "actor_id": "8CZ381bjcXyK0Z8op", "run_id": "2B8a9VRRjtHlWnY0e" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather comprehensive product data to analyze market trends. - **Competitive Intelligence**: Monitor competitor products and pricing strategies. - **Price Monitoring**: Track changes in product prices over time for strategic decision-making. - **Content Aggregation**: Collect product information for content creation or aggregation platforms. - **Academic Research**: Use scraped data for academic studies related to commerce and marketing. - **Business Automation**: Integrate with business systems for automated inventory management. ### Installation and Usage 1. Search for "Apacheinc Product Discovery Ec 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 containing objects with the following fields: - **type**: Indicates whether the entry is a site or product. - **product\_name**: The name of the product. - **product\_url**: URL to the product page. - **description**: A brief description of the product (if available). - **part\_no**: Part number associated with the product (if available). - **post\_image**: URL to an image related to the product (if available). - **pagination**: The pagination number for multi-page results. - **actor\_id**: Unique identifier for the actor instance. - **run\_id**: Unique identifier for the run. ### Error Handling The spider is designed to handle common web scraping errors such as network issues, timeouts, and missing elements. If an error occurs, it will be logged in the Apify Console, allowing you to review and address any issues promptly. ### Rate Limiting and Best Practices To ensure optimal performance and avoid being blocked by the target website: - Respect the site's `robots.txt` file. - Implement polite scraping practices, such as adding delays between requests. - Monitor your usage to stay within acceptable limits. ### Limitations and Considerations - The spider is tailored for Apache Inc.'s website structure; changes in their layout may affect performance. - Ensure compliance with legal requirements regarding web scraping and data use. # Actor input Schema ## `Keywords` (type: `array`): The keywords 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 { "Keywords": [ "chemical" ], "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/apacheinc-product-discovery-ec-spider").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/apacheinc-product-discovery-ec-spider").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 '{}' | apify call getdataforme/apacheinc-product-discovery-ec-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/apacheinc-product-discovery-ec-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Apacheinc Product Discovery Ec Spider", "description": "The Apacheinc Product Discovery EC Spider efficiently extracts product information from the Apache Inc. website using keyword-based searches, ensuring high-quality data extraction. It supports flexible item limits for comprehensive analysis, optimized performance, and easy configuration....", "version": "0.0", "x-build-id": "YrBQE3XIUtE0pAbmS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~apacheinc-product-discovery-ec-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-apacheinc-product-discovery-ec-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~apacheinc-product-discovery-ec-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-apacheinc-product-discovery-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~apacheinc-product-discovery-ec-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-apacheinc-product-discovery-ec-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Keywords": { "title": "Keywords", "type": "array", "description": "The keywords for the spider.", "default": [ "chemical" ], "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 } } } } } } } } } } ```