# Google Trends Scraper (`scraper-engine/google-trends-scraper`) Actor Scrape real-time and historical Google Trends data, including interest over time, related queries, topics, and regional popularity. Perfect for SEO, market research, forecasting, and trend analysis. Fast, accurate, and ideal for building data-driven insights. - **URL**: https://apify.com/scraper-engine/google-trends-scraper.md - **Developed by:** [Scraper Engine](https://apify.com/scraper-engine) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 288 total users, 2 monthly users, 100.0% runs succeeded, 3 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing $19.99/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### ⭐ What Does a Google Trends Scraper Do? πŸ€” A Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper that helps you scrape google trends data automatically. Instead of manual analysis, this google trends data scraper extracts search volumes, interest over time, regional trends, and related queries in seconds. This google trends extractor supports bulk google trends scraper workflows and google trends scraping automation, making large-scale data collection efficient. As a reliable google data extraction tool, it enables SEO professionals, marketers, analysts, and developers to gather structured insights at scale. It can also integrate with google trends scraper api solutions or be extended using google trends scraper github and Python-based workflows. ### πŸ› οΈ Support and Feedback - 🐞 **Bug reports:** Open a ticket in the repository **Issues** section - ✨ **Custom solutions or feature requests:** Contact our team πŸ“§ **Email:** dev.scraperengine@gmail.com ### πŸ“Š What Google Trends Data Can I Extract? The Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper that helps you scrape google trends and extract structured insights for SEO, analytics, and research. This google trends data scraper supports bulk google trends scraper workflows and google trends scraping automation, making large-scale data collection efficient. Using this google trends extractor, you can gather high-value data for dashboards, reporting, and market intelligence. It also works with google trends scraper api integrations or can be extended via google trends scraper github. Here’s a detailed overview: | Data Type | Description | | --- | --- | | Interest Over Time | Daily, weekly, or monthly trend values for any keyword. | | Interest by Region | Popularity across countries, states, or cities. | | Related Queries | Rising and top associated keywords. | | Related Topics | Contextual categories Google associates with the search term. | | Real-Time Trends | Hot topics trending right now. | | Comparison Data | Compare multiple keywords side-by-side. | | Category-specific Trends | Topics in travel, hotels, shopping, tech, etc. | This google data extraction tool is widely used for SEO analysis, competitor research, and trend forecasting. The same structured approach applies to other google scraper tools used in large-scale research workflows. * * * #### πŸ”₯ Key Features of the Google Trends Scraper The Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper built to scrape google trends efficiently. This google trends data scraper supports bulk google trends scraper workflows and google trends scraping automation for scalable data extraction. * πŸš€ **Automated Keyword Trend Extraction** β€” Instantly extract insights using a reliable **google trends extractor** and **google data extraction tool** * πŸ“ˆ **Bulk Keyword Input** β€” Analyze large datasets with **bulk google trends scraper** and **google trends scraping automation** * πŸ” **Scheduled Runs** β€” Automate workflows for continuous tracking using **google scraper** systems * 🧩 **API & Python Support** β€” Integrate via **google trends scraper api** or extend with **google trends scraper github** * πŸ“₯ **Export Options** β€” Download structured data for BI tools using this **google data extraction tool** * 🌎 **Multi-Region Support** β€” Extract global insights with a scalable **google scraper** * πŸ”’ **Privacy-Safe Data** β€” Collect only public data using a compliant **google trends scraper tool** ### πŸ› οΈ How to Use a Google Trends Scraper Using a Google Trends Scraper is simple and works as a reliable google trends scraper tool and advanced google scraper to scrape google trends data efficiently. This google trends data scraper supports google trends scraping automation and bulk google trends scraper workflows. #### βœ… Step-by-Step Guide 1. πŸ” Log in to Apify Create a free account or sign in to get started. 2. πŸ”Ž Select the Actor Search for β€œGoogle Trends Scraper” (similar to how you would find YouTube Transcript Scraper). 3. πŸ“₯ Enter Input Data Add keywords, filters, or regions for bulk google trends scraper processing. 4. βš™οΈ Choose Options Configure settings using this google data extraction tool: * Select time range (past day, week, 30 days, 5 years) * Choose geography * Enable/disable related topics or real-time trend scraping 6. ▢️ Run the Actor Hit Start and the scraper will automatically process your keywords and extract all trend metrics. 7. πŸ“€ Download Results Export as JSON or CSV for deeper analysis in Python, Excel, Looker Studio, or Power BI. This workflow is ideal for marketing, travel research (e.g., scrape Google Hotels data), and competitive trend monitoring. * * * ### 🎯 Use Cases The Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper designed to scrape google trends for multiple industries. This google trends data scraper supports bulk google trends scraper workflows and google trends scraping automation for scalable insights. #### 🌟 Popular Use Cases πŸ“š SEO & Content Strategy Use this google data extraction tool to discover trending keywords and optimize content while you scrape google trends πŸ“Š Market Research Analyze demand patterns and regional trends using a reliable google scraper 🏨 Travel & Hospitality Analysis Combine insights from google trends extractor with other tools for demand forecasting πŸ›οΈ Ecommerce Trend Tracking Identify product trends using bulk google trends scraper and google trends scraping automation πŸ“ˆ Competitor Intelligence Compare brand performance using structured data from the google trends scraper tool πŸ’‘ Data Science Projects Build predictive models using outputs from a scalable google data extraction tool πŸŽ₯ Social Media & Content Timing Track peak interest periods while you scrape google trends for better visibility * * * ### πŸ† Why Choose Us? Our Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper built for accuracy, speed, and scalability. This google trends data scraper supports bulk google trends scraper workflows and google trends scraping automation for enterprise-level performance. #### πŸ’Ž What Makes Us Different? * ⚑ **Fast & Scalable** β€” Extract large datasets with **bulk google trends scraper** and **google trends scraping automation** * 🀝 **Built for Teams** β€” Ideal for agencies using a reliable **google scraper** * 🧠 **Smart NLP Optimization** β€” Clean insights via **google data extraction tool** * πŸ’» **Developer Friendly** β€” Integrate with **google trends scraper api** or extend using **google trends scraper github** * πŸ“€ **Flexible Exports** β€” Structured output from a scalable **google scraper** * πŸ” **Accurate Data** β€” Use the **google trends extractor** for analysis-ready datasets * πŸ›‘οΈ **Secure & Transparent** β€” Compliant **google trends scraper tool** for responsible data use Whether you want to scrape google trends for SEO, research, or forecasting, this google scraper delivers reliable, data-driven insights at scale. ### πŸ“ˆ How Many Results Can You Scrape with a Google Trends Scraper? The Google Trends Scraper is a scalable google trends scraper tool and advanced google scraper built to scrape google trends across small and large datasets. This google trends data scraper supports bulk google trends scraper workflows and google trends scraping automation, allowing thousands of keywords per run. It can process multiple time ranges, regional trends, related queries, and real-time data simultaneously. With google trends scraper api integration and google data extraction tool capabilities, it enables seamless automation and high-volume data collection. #### ⚑ **Performance Highlights** * Handle large datasets with **bulk google trends scraper** * Run parallel regional extraction using **google scraper** * Automate recurring tasks with **google trends scraping automation** This makes it ideal for SEO teams, analysts, and researchers who need consistent, structured insights at scale. ### βš–οΈ Is It Legal to Scrape Google Trends? Using a Google Trends Scraper or any google scraper to scrape google trends is generally acceptable since the platform provides public, aggregated data. This google trends scraper tool supports google trends scraping automation while focusing on responsible data usage. #### βœ” Best Practices * Use a **google data extraction tool** without overloading servers * Prefer **google trends scraper api** integrations where possible * Follow ethical guidelines when running **bulk google trends scraper** workflows * Ensure compliance with local laws and platform policies Since this **google trends data scraper** works with non-personal, trend-based data, it is widely used for research, SEO, and analytics. However, responsible usage is key when using any **google scraper** at scale. If you are unsure, consult legal guidance before implementing large-scale **google trends scraping automation**. ### βš™οΈ Input Parameters #### πŸ“₯ Example Input JSON ```json { "keywords": ["chatgpt", "AI", "python"], "timeRange": "today 3-m", "geo": "US", "category": 0, "sortOrder": "", "maxComments": 100, "proxyConfiguration": { "useApifyProxy": false } } ```` ## πŸ“€ Output Format #### πŸ“¦ Example Output JSON ```json { "inputUrlOrTerm": "chatgpt, AI, python", "searchTerm": "chatgpt, AI, python", "interestOverTime_timelineData": [ { "date": "2025-08-24", "chatgpt": 87, "AI": 65, "python": 42 }, { "date": "2025-08-25", "chatgpt": 96, "AI": 72, "python": 48 } ] } ``` ### πŸ”— What are other Google scraping tools? If you want to scrape specific Google data, you can use any of the dedicated scrapers below for faster and more targeted results.\ Just enter keywords, search queries, place names, app links, or URLs and start scraping instantly. | [πŸ–ΌοΈ Google Images Scraper](https://apify.com/scraper-engine/google-images-scraper) | [✈️ Google Flights Scraper](https://apify.com/scraper-engine/google-flights-scraper) | |---|---| | [⌨️ Google Search Autocomplete API](https://apify.com/scraper-engine/google-search-autocomplete-api) | [πŸ“’ Google Ads Scraper](https://apify.com/scraper-engine/google-ads-scraper) | | [πŸ“° Google News Scraper](https://apify.com/scraper-engine/google-news-scraper) | [🏨 Google Hotels Scraper](https://apify.com/scraper-engine/google-hotels-scraper) | | [πŸ“± Google Play Data Extractor](https://apify.com/scraper-engine/google-play-scraper) | [⭐ Google Play Store Reviews Scraper](https://apify.com/scraper-engine/google-play-store-reviews-scraper) | | [πŸ—ΊοΈ Google Maps Reviews Scraper](https://apify.com/scraper-engine/google-maps-reviews-scraper) | [πŸ“§ Google Play Email Scraper](https://apify.com/scraper-engine/google-play-email-scraper) | | [πŸ“ Google Maps Extractor](https://apify.com/scraper-engine/google-maps-extractor) | [🧭 Google Maps Scraper](https://apify.com/scraper-engine/google-maps-scraper) | | [πŸ“§ Google Maps Email Scraper](https://apify.com/scraper-engine/google-maps-email-scraper) | [πŸ“± Google Play Store App Search Scraper](https://apify.com/scraper-engine/google-play-store-app-search-scraper) | | [πŸ“¨ Google Maps Email Extractor](https://apify.com/scraper-engine/google-maps-email-extractor) | [πŸ“ž Google Maps Phone Number Extractor](https://apify.com/scraper-engine/google-maps-phone-number-extractor) | | [☎️ Google Play Phone Number Scraper](https://apify.com/scraper-engine/google-play-phone-number-scraper) | [πŸ“ž Google Maps Phone Number Scraper](https://apify.com/scraper-engine/google-maps-phone-number-scraper) | ### ❓ FAQ #### **1️⃣ Is a Google Trends Scraper free?** Some **Google Trends Scraper** tools are free, but scalable **google trends scraper tool** solutions with **bulk google trends scraper** features usually require paid plans. #### **2️⃣ Can I scrape Google Trends with Python?** Yes, developers use Python-based solutions or **google trends scraper api**, though managed **google scraper** tools offer better scalability. #### **3️⃣ Is scraping Google Trends safe?** Yes, using a **google trends data scraper** is safe since it relies on public aggregated data. #### **4️⃣ Does this work with Google Hotels data?** While separate, many combine a **google scraper** with other tools for broader research workflows. #### **5️⃣ Can you scrape Google Maps or hotel listings?** Yes, with specialized tools, though this **google data extraction tool** focuses on trends data. #### **6️⃣ Can I download the scraper from GitHub?** Yes, many **google trends scraper github** projects exist, but require setup and maintenance. #### **7️⃣ Is there an API?** Yes, you can integrate via **google trends scraper api** for **google trends scraping automation**. ### βœ… Conclusion The Google Trends Scraper is a powerful google trends scraper tool and advanced google scraper built for speed and scale. With support for bulk google trends scraper, google trends scraping automation, and google data extraction tool workflows, it simplifies trend analysis and keyword research. Whether you want to scrape google trends for SEO, market research, or forecasting, it delivers structured, reliable insights for data-driven decisions. # Actor input Schema ## `searchTerms` (type: `array`): (Required if 'spreadsheetId' is not provided) List of search terms to be scraped. ## `isMultiple` (type: `boolean`): If checked comma will be handled as search for multiple terms ## `timeRange` (type: `string`): Choose a predefined search's time range (defaults to 'Past 12 months') ## `geo` (type: `string`): Get results from a specific geo area (defaults to 'Worldwide'). This maps to different fields in the dataset results: smaller countries like Greece return just 'interestByCity' data, while larger ones like the United States return 'interestBySubregion' data as well. When omitted (Worldwide), 'interestBy' is returned instead, which corresponds to the 'Interest by area' section on Google Trends. Note: When using Google Trends URLs from the 'πŸ”— Use a Google Trends URL instead of search terms' section, the geo parameter from the URL's query string takes precedence over this setting. You can also use start URLs to retrieve 'interestByMetro' data for metropolitan areas by providing URLs with more specific geo regions like California (US) directly copied from Google Trends. ## `viewedFrom` (type: `string`): Country for residential proxies to ensure correct results ## `skipDebugScreen` (type: `boolean`): By default snapshot of each visited Google Trends page saved to KV Store ## `startUrls` (type: `array`): Useful when default search is not enough, i.e. for custom date range or comparing trends. Copy a Google Trends URL and paste it here. ## `spreadsheetId` (type: `string`): (Optional) Id of the google sheet from where search terms will be loaded. Spreadsheet must have only one column. Row 1 is considered the title of the column so it will not be loaded as a search term. The provided spreadsheet must be publicly available. Check the actor documentation for more info. ## `category` (type: `string`): Choose a category to filter the search for (defaults to 'All categories') ## `maxItems` (type: `integer`): Limit of product items to be scraped. Zero value means no limit. ## `customTimeRange` (type: `string`): Provide custom time range. If present, it takes precedence over regular timeRange. Format is: YYYY-MM-DD YYYY-MM-DD. See examples in the documentation. ## `maxConcurrency` (type: `integer`): How many pages will be opened in parallel if enough CPU is available. ## `maxRequestRetries` (type: `integer`): How many times will the actor retry a request if it fails. ## `pageLoadTimeoutSecs` (type: `integer`): How long it will wait for page loading before it abandons the loading and tries again. ## `proxyConfiguration` (type: `object`): Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked. ## Actor input object example ```json { "searchTerms": [ "webscraping" ], "isMultiple": false, "timeRange": "today 12-m", "geo": "", "viewedFrom": "", "skipDebugScreen": false, "startUrls": [], "spreadsheetId": "", "category": "", "maxItems": 0, "customTimeRange": "", "maxConcurrency": 10, "maxRequestRetries": 7, "pageLoadTimeoutSecs": 180, "proxyConfiguration": { "useApifyProxy": false } } ``` # 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 = { "searchTerms": [ "webscraping" ], "timeRange": "today 12-m", "geo": "", "viewedFrom": "", "startUrls": [], "category": "", "proxyConfiguration": { "useApifyProxy": false } }; // Run the Actor and wait for it to finish const run = await client.actor("scraper-engine/google-trends-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "searchTerms": ["webscraping"], "timeRange": "today 12-m", "geo": "", "viewedFrom": "", "startUrls": [], "category": "", "proxyConfiguration": { "useApifyProxy": False }, } # Run the Actor and wait for it to finish run = client.actor("scraper-engine/google-trends-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "webscraping" ], "timeRange": "today 12-m", "geo": "", "viewedFrom": "", "startUrls": [], "category": "", "proxyConfiguration": { "useApifyProxy": false } }' | apify call scraper-engine/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraper-engine/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper", "description": "Scrape real-time and historical Google Trends data, including interest over time, related queries, topics, and regional popularity. Perfect for SEO, market research, forecasting, and trend analysis. Fast, accurate, and ideal for building data-driven insights.", "version": "0.1", "x-build-id": "w09lHrsBPFResDAnG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraper-engine~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraper-engine-google-trends-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraper-engine~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-scraper-engine-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraper-engine~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-scraper-engine-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchTerms": { "title": "Search terms", "type": "array", "description": "(Required if 'spreadsheetId' is not provided) List of search terms to be scraped.", "items": { "type": "string" } }, "isMultiple": { "title": "Multiple terms", "type": "boolean", "description": "If checked comma will be handled as search for multiple terms", "default": false }, "timeRange": { "title": "Time range", "enum": [ "now 1-H", "now 4-H", "now 1-d", "now 7-d", "today 1-m", "today 3-m", "today 12-m", "today 5-y", "all" ], "type": "string", "description": "Choose a predefined search's time range (defaults to 'Past 12 months')", "default": "today 12-m" }, "geo": { "title": "Geo area", "enum": [ "", "AF", "AL", "DZ", "AS", "AD", "AO", "AI", "AQ", "AG", "AR", "AM", "AW", "AU", "AT", "AZ", "BS", "BH", "BD", "BB", "BY", "BE", "BZ", "BJ", "BM", "BT", "BO", "BQ", "BA", "BW", "BV", "BR", "IO", "BN", "BG", "BF", "BI", "CV", "KH", "CM", "CA", "KY", "CF", "TD", "CL", "CN", "CX", "CO", "KM", "CG", "CD", "CK", "CR", "CI", "HR", "CU", "CW", "CY", "CZ", "DK", "DJ", "DM", "DO", "EC", "EG", "SV", "GQ", "ER", "EE", "ET", "FK", "FO", "FJ", "FI", "FR", "GF", "PF", "TF", "GA", "GM", "GE", "DE", "GH", "GI", "GR", "GL", "GD", "GP", "GU", "GT", "GG", "GN", "GW", "GY", "HT", "HM", "VA", "HN", "HK", "HU", "IS", "IN", "ID", "IR", "IQ", "IE", "IM", "IL", "IT", "JM", "JP", "JE", "JO", "KZ", "KE", "KI", "KP", "KR", "KW", "KG", "LA", "LV", "LB", "LS", "LR", "LY", "LI", "LT", "LU", "MO", "MK", "MG", "MW", "MY", "MV", "ML", "MT", "MH", "MQ", "MR", "MU", "YT", "MX", "FM", "MD", "MC", "MN", "ME", "MS", "MA", "MZ", "MM", "NA", "NR", "NP", "NL", "NC", "NZ", "NI", "NE", "NG", "NU", "NF", "MP", "NO", "OM", "PK", "PW", "PS", "PA", "PG", "PY", "PE", "PH", "PN", "PL", "PT", "PR", "QA", "RE", "RO", "RU", "RW", "BL", "SH", "KN", "LC", "MF", "PM", "VC", "WS", "SM", "ST", "SA", "SN", "RS", "SC", "SL", "SG", "SX", "SK", "SI", "SB", "SO", "ZA", "GS", "SS", "ES", "LK", "SD", "SR", "SJ", "SZ", "SE", "CH", "SY", "TW", "TJ", "TZ", "TH", "TL", "TG", "TK", "TO", "TT", "TN", "TR", "TM", "TC", "TV", "UG", "UA", "AE", "GB", "US", "UM", "UY", "UZ", "VU", "VE", "VN", "VG", "VI", "WF", "EH", "YE", "ZM", "ZW" ], "type": "string", "description": "Get results from a specific geo area (defaults to 'Worldwide'). This maps to different fields in the dataset results: smaller countries like Greece return just 'interestByCity' data, while larger ones like the United States return 'interestBySubregion' data as well. When omitted (Worldwide), 'interestBy' is returned instead, which corresponds to the 'Interest by area' section on Google Trends. Note: When using Google Trends URLs from the 'πŸ”— Use a Google Trends URL instead of search terms' section, the geo parameter from the URL's query string takes precedence over this setting. You can also use start URLs to retrieve 'interestByMetro' data for metropolitan areas by providing URLs with more specific geo regions like California (US) directly copied from Google Trends.", "default": "" }, "viewedFrom": { "title": "Country to view Trends from", "enum": [ "", "us", "af", "al", "dz", "as", "ad", "ao", "ai", "aq", "ag", "ar", "am", "aw", "au", "at", "az", "bs", "bh", "bd", "bb", "by", "be", "bz", "bj", "bm", "bt", "bo", "ba", "bw", "bv", "br", "io", "bn", "bg", "bf", "bi", "kh", "cm", "ca", "cv", "ky", "cf", "td", "cl", "cn", "cx", "cc", "co", "km", "cg", "cd", "ck", "cr", "ci", "hr", "cu", "cy", "cz", "dk", "dj", "dm", "do", "ec", "eg", "sv", "gq", "er", "ee", "et", "fk", "fo", "fj", "fi", "fr", "gf", "pf", "tf", "ga", "gm", "ge", "de", "gh", "gi", "gr", "gl", "gd", "gp", "gu", "gt", "gn", "gw", "gy", "ht", "hm", "va", "hn", "hk", "hu", "is", "in", "id", "ir", "iq", "ie", "il", "it", "jm", "jp", "jo", "kz", "ke", "ki", "kp", "kr", "kw", "kg", "la", "lv", "lb", "ls", "lr", "ly", "li", "lt", "lu", "mo", "mk", "mg", "mw", "my", "mv", "ml", "mt", "mh", "mq", "mr", "mu", "yt", "mx", "fm", "md", "mc", "mn", "me", "ms", "ma", "mz", "mm", "na", "nr", "np", "nl", "an", "nc", "nz", "ni", "ne", "ng", "nu", "nf", "mp", "no", "om", "pk", "pw", "ps", "pa", "pg", "py", "pe", "ph", "pn", "pl", "pt", "pr", "qa", "re", "ro", "ru", "rw", "sh", "kn", "lc", "pm", "vc", "ws", "sm", "st", "sa", "sn", "rs", "sc", "sl", "sg", "sk", "si", "sb", "so", "za", "gs", "ss", "es", "lk", "sd", "sr", "sj", "sz", "se", "ch", "sy", "tw", "tj", "tz", "th", "tl", "tg", "tk", "to", "tt", "tn", "tr", "tm", "tc", "tv", "ug", "ua", "ae", "gb", "um", "uy", "uz", "vu", "ve", "vn", "vg", "vi", "wf", "eh", "ye", "zm", "zw" ], "type": "string", "description": "Country for residential proxies to ensure correct results", "default": "" }, "skipDebugScreen": { "title": "Do not save snapshot", "type": "boolean", "description": "By default snapshot of each visited Google Trends page saved to KV Store", "default": false }, "startUrls": { "title": "Google Trends URLs", "type": "array", "description": "Useful when default search is not enough, i.e. for custom date range or comparing trends. Copy a Google Trends URL and paste it here.", "items": { "type": "string" } }, "spreadsheetId": { "title": "Google sheet ID (To get input data from Google sheet)", "type": "string", "description": "(Optional) Id of the google sheet from where search terms will be loaded. Spreadsheet must have only one column. Row 1 is considered the title of the column so it will not be loaded as a search term. The provided spreadsheet must be publicly available. Check the actor documentation for more info.", "default": "" }, "category": { "title": "Category of trends", "enum": [ "", "3", "47", "44", "22", "12", "5", "7", "71", "8", "45", "65", "11", "13", "958", "19", "16", "299", "14", "66", "29", "533", "174", "18", "20", "67" ], "type": "string", "description": "Choose a category to filter the search for (defaults to 'All categories')", "default": "" }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Limit of product items to be scraped. Zero value means no limit.", "default": 0 }, "customTimeRange": { "title": "Custom time range", "type": "string", "description": "Provide custom time range. If present, it takes precedence over regular timeRange. Format is: YYYY-MM-DD YYYY-MM-DD. See examples in the documentation.", "default": "" }, "maxConcurrency": { "title": "Max concurrency", "minimum": 1, "type": "integer", "description": "How many pages will be opened in parallel if enough CPU is available.", "default": 10 }, "maxRequestRetries": { "title": "Max request retries", "minimum": 1, "type": "integer", "description": "How many times will the actor retry a request if it fails.", "default": 7 }, "pageLoadTimeoutSecs": { "title": "Page load timeout", "minimum": 30, "type": "integer", "description": "How long it will wait for page loading before it abandons the loading and tries again.", "default": 180 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxy settings. Actor will start with no proxy and fallback to datacenter/residential if blocked." } } }, "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 } } } } } } } } } } ```