# Google Trends Scraper (`scraperx/google-trends-scraper`) Actor 📈 Google Trends Scraper extracts trending topics, related topics/queries, rising & breakout keywords, plus interest over time and by region. 🔎 Ideal for SEO, keyword research, content planning & market analysis. ⚙️ Export clean data to CSV/JSON fast. - **URL**: https://apify.com/scraperx/google-trends-scraper.md - **Developed by:** [ScraperX](https://apify.com/scraperx) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## 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? 🤔 A Google Trends Scraper is a powerful tool that automatically extracts real-time and historical search trend data from Google Trends. Instead of manually checking topics one by one, a scraper gathers search volumes, interest over time, regional popularity, related topics, and related queries in seconds. This makes it a must-have for SEO professionals, content strategists, marketers, data analysts, and developers using Google Trends scraper Python, Google Trends scraper API, Google Trends scraper GitHub, or Google Trends scraper Apify solutions. Using a Google Trends web scraper improves SEO decision-making by revealing niche opportunities, keyword seasonality, and trending topics for your content calendar. Whether you're researching competitors, building dashboards, or harvesting data programmatically, a scraper accelerates your workflow and ensures you never miss a rising trend. * * * ## 📊 What Google Trends Data Can I Extract? Below is a clear breakdown of the data types you can scrape using a Google Trends Scraper (including related interests for industries like hotels, travel, and even Tripadvisor scraping research). Using a Google Trends Scraper, you can extract a wide range of high-value insights used by marketers, researchers, and developers. Whether you're learning how to get Google Trends data, building a custom dashboard, or automating daily SEO reports, the scraper compiles structured data ideal for business intelligence tools. 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 same structured extraction approach works for companion tools like the Google Hotels Scraper, Google Hotels scraper online, hotel scraper, and Google Maps scraper tool—making it ideal for large research workflows. * * * #### 🔥 Key Features of the Google Trends Scraper A high-quality Google Trends Scraper includes features that make data extraction fast, reliable, and scalable. This is essential whether you're exploring Google Trends scraper Reddit discussions, testing emastra Google Trends scraper alternatives, or integrating with internal SEO tools. #### ⭐ Key Features * 🚀 Automated Keyword Trend Extraction Fetch interest over time, regional stats, and related queries instantly. * 📈 Bulk Keyword Input Analyze hundreds of keywords at once—perfect for content planning or PPC campaigns. * 🔁 Scheduled Runs Automate daily or weekly scrapes to monitor trend shifts. * 🧩 API & Python Support Seamless integration with your stack whether you prefer Google Trends scraper API, Python scripts, or GitHub-based workflows. * 📥 Export to JSON, CSV, or Excel Ideal for data visualization in Looker Studio, Power BI, Tableau, or internal BI tools. * 🌎 Multi-Region Support Extract data globally, across categories, languages, and subregions. * 🔒 Privacy-Safe & No Private Data Scrapes only publicly available trend information. These features empower SEO teams, researchers, and analysts to stay ahead of trends and build accurate forecasting models. * * * ### 🛠️ How to Use a Google Trends Scraper Follow this beginner-friendly workflow to extract Google Trends data quickly using tools like Google Trends Scraper Apify: #### ✅ Step-by-Step Guide 1. 🔐 Log in to Apify Create a free account or sign in to get started. 2. 🔎 Select the Actor Search for “Google Trends Scraper” (similar to how you would find YouTube Transcript Scraper). 3. 📥 Enter Input Data Add your keyword list, comparison terms, category filters, or region parameters. 4. ⚙️ Choose Options * 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. 📤 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 A Google Trends Scraper unlocks powerful use cases across industries—especially when combined with related tools like a Google Hotels Scraper, hotel scraper, or Google Maps scraper tool free. #### 🌟 Popular Use Cases * 📚 SEO & Content Strategy Discover trending keywords for blogs, YouTube, ecommerce, and affiliate marketing. * 📊 Market Research Identify rising consumer interests, seasonal demand, and regional differences. * 🏨 Travel & Hospitality Analysis Combine Google Trends with hotel Google insights, Tripadvisor scraping, and scrape hotel data to forecast tourism demand. * 🛍️ Ecommerce Trend Tracking Predict which products will trend next—perfect for dropshipping and Amazon sellers. * 📈 Competitor Intelligence Compare search popularity of brands, products, or influencers. * 💡 Data Science Projects Build trend-prediction models using historical interest data. * 🎥 Social Media & Content Timing Identify when interest peaks for maximum visibility. These use cases highlight why both small businesses and large enterprises rely on Google Trends scraping for strategic insights. * * * ### 🏆 Why Choose Us? Our Google Trends Scraper stands out because it blends accuracy, speed, and reliability—powered by a clean API, flexible integrations, and enterprise-level scaling. #### 💎 What Makes Us Different? * ⚡ Fast & Scalable — Extract trends for thousands of keywords instantly * 🤝 Built for Teams — Perfect for SEO agencies, media companies, and research groups * 🧠 Smart NLP Optimizations — Cleaner outputs and semantic clustering * 💻 Developer Friendly — Works smoothly with Python, APIs, GitHub workflows * 📤 Flexible Exports — JSON, CSV, Excel, or API delivery * 🔍 Highly Accurate Structured Data — Ready for dashboards and analysis * 🛡️ Secure & Transparent — No hidden scraping logic, fully compliant with data policies Whether you’re analyzing search interest for hotels, planning content, or forecasting demand trends globally, our scraper gives you data-backed insights you can trust. ### 📈 How Many Results Can You Scrape with a Google Trends Scraper? Our Google Trends Scraper is built for massive scalability, whether you're scraping 1 keyword or 10,000. The scraper can process: * Multiple time ranges simultaneously * Global and regional interest * Related queries & topics * Real-time trending searches * Hundreds of comparisons at once #### ⚡ Performance Highlights * Thousands of Keywords per Run Ideal for enterprise SEO teams or large datasets. * Multi-Region Parallel Scraping Extract trends for dozens of countries without slowdown. * Automated Scheduling Perfect for long-term monitoring and forecasting. * Optimized for High-Volume API Calls Integrates seamlessly with Google Trends scraper API, internal BI tools, or Google Trends scraper Python pipelines. Even if you're running complex datasets—including hospitality-focused terms like scrape Google Hotels, Google hotel API, or travel market analysis—the scraper handles large-scale extraction effortlessly. This makes it ideal for: * Publishers * Market intelligence firms * Travel companies * Ecommerce analysts * Data scientists * Trend forecasting teams Our system ensures consistent performance, structured output, and precise results—even under intense workloads. * * * ### ⚖️ Is It Legal to Scrape Google Trends? Yes—scraping Google Trends is generally legal, as long as you follow ethical guidelines and respect platform terms. Google Trends only displays public, aggregated data, meaning no personal or sensitive information is involved. #### ✔ Best Practices * Do not overload Google’s servers * Use official APIs where applicable * Scrape responsibly and ethically * Use data for analysis, research, and compliant business use Scraping Google Trends is similar to using tools like Google Hotels web scraper, Google hotel API, or Tripadvisor scraping for public information. The key is ensuring your usage complies with local laws and Google’s terms. If you're unsure, always consult a legal expert—especially for large enterprise applications. * * * ### ⚙️ Input Parameters #### 📥 Example Input JSON ```json { "keywords": ["chatgpt", "AI", "python"], "timeRange": "today 3-m", "geo": "US", "category": 0, "sortOrder": "", "maxComments": 100, "proxyConfiguration": { "useApifyProxy": false } } ```` ## 📤 Output Format #### 📦 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 } ] } ``` ## ❓ FAQ #### 1️⃣ Is a Google Trends Scraper free? Some tools are free; advanced versions (like Apify) offer scalable plans. #### 2️⃣ Can I scrape Google Trends with Python? Yes—many use Google Trends scraper Python libraries or APIs. #### 3️⃣ Is scraping Google Trends safe? Yes, as it uses public aggregated search data. #### 4️⃣ Does this work with Google Hotels data? While separate, many users combine this with a Google Hotels Scraper for travel insights. #### 5️⃣ Can you scrape Google Maps or hotel listings? Yes—via a Google Maps scraper tool free or hotel scraper built for public data. #### 6️⃣ Can I download the scraper from GitHub? Yes—many Google Trends scraper GitHub repositories exist. #### 7️⃣ Is there an API? Yes—Apify and other providers offer a Google Trends scraper API. ### ✅ Conclusion A Google Trends Scraper empowers marketers, researchers, and businesses with real-time insights, trend forecasting, and data-driven decision-making. Whether using APIs, Python, or automated tools, it streamlines keyword analysis at scale. For SEO, travel, or market intelligence, it delivers fast, accurate, and actionable search-trend data. # Actor input Schema ## `searchTerms` (type: `array`): 📝 Add the keywords or topics you want to analyze. Required if you do not provide a Google Sheet ID. ## `isMultiple` (type: `boolean`): ✅ Enable this if commas in a line should split into multiple terms. ## `timeRange` (type: `string`): 📅 Choose how far back to analyze trend history (default: Past 12 months). ## `geo` (type: `string`): 🗺️ Select a country/region to localize trend data. Leave empty for Worldwide results. If you provide Google Trends URLs, location settings inside those URLs will be used first. ## `viewedFrom` (type: `string`): 🌐 Choose the country context used when viewing Trends pages to help match local results. ## `skipDebugScreen` (type: `boolean`): 💾 By default, page snapshots are saved for debugging. Turn this on to skip saving snapshots. ## `startUrls` (type: `array`): 🎯 Paste full Google Trends URLs when you need custom comparisons, advanced filters, or exact date windows. ## `spreadsheetId` (type: `string`): 📥 Optional: load terms automatically from a public Google Sheet. Use one column only; row 1 is treated as a header. ## `category` (type: `string`): 🎛️ Narrow results to a specific Trends category, or leave empty for all categories. ## `maxItems` (type: `integer`): 📦 Maximum number of output records. Use 0 for no limit. ## `customTimeRange` (type: `string`): 🧭 Set an exact date range in this format: YYYY-MM-DD YYYY-MM-DD. This overrides the preset time range. ## `maxConcurrency` (type: `integer`): 🚦 Number of pages to process in parallel. Higher values can be faster but use more resources. ## `maxRequestRetries` (type: `integer`): 🛟 Number of retry attempts for failed requests before giving up. ## `pageLoadTimeoutSecs` (type: `integer`): ⌛ Maximum wait time for a page to load before retrying. ## `proxyConfiguration` (type: `object`): 🌍 Configure proxy usage for stable access. Useful when you need geo-consistent or more resilient requests. ## 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("scraperx/google-trends-scraper").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 = { "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("scraperx/google-trends-scraper").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 '{ "searchTerms": [ "webscraping" ], "timeRange": "today 12-m", "geo": "", "viewedFrom": "", "startUrls": [], "category": "", "proxyConfiguration": { "useApifyProxy": false } }' | apify call scraperx/google-trends-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperx/google-trends-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Trends Scraper", "description": "📈 Google Trends Scraper extracts trending topics, related topics/queries, rising & breakout keywords, plus interest over time and by region. 🔎 Ideal for SEO, keyword research, content planning & market analysis. ⚙️ Export clean data to CSV/JSON fast.", "version": "0.1", "x-build-id": "BddMhYTOVxLgl8SVu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperx~google-trends-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperx-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperx~google-trends-scraper/runs": { "post": { "operationId": "runs-sync-scraperx-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperx~google-trends-scraper/run-sync": { "post": { "operationId": "run-sync-scraperx-google-trends-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "📝 Add the keywords or topics you want to analyze. Required if you do not provide a Google Sheet ID.", "items": { "type": "string" } }, "isMultiple": { "title": "🧩 Multiple terms mode", "type": "boolean", "description": "✅ Enable this if commas in a line should split into 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 how far back to analyze trend history (default: 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": "🗺️ Select a country/region to localize trend data. Leave empty for Worldwide results. If you provide Google Trends URLs, location settings inside those URLs will be used first.", "default": "" }, "viewedFrom": { "title": "🏠 Viewed from country", "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": "🌐 Choose the country context used when viewing Trends pages to help match local results.", "default": "" }, "skipDebugScreen": { "title": "🖼️ Do not save snapshots", "type": "boolean", "description": "💾 By default, page snapshots are saved for debugging. Turn this on to skip saving snapshots.", "default": false }, "startUrls": { "title": "🔗 Google Trends URLs", "type": "array", "description": "🎯 Paste full Google Trends URLs when you need custom comparisons, advanced filters, or exact date windows.", "items": { "type": "string" } }, "spreadsheetId": { "title": "📄 Google Sheet ID", "type": "string", "description": "📥 Optional: load terms automatically from a public Google Sheet. Use one column only; row 1 is treated as a header.", "default": "" }, "category": { "title": "🗂️ Trend category", "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": "🎛️ Narrow results to a specific Trends category, or leave empty for all categories.", "default": "" }, "maxItems": { "title": "🔢 Max items", "minimum": 0, "type": "integer", "description": "📦 Maximum number of output records. Use 0 for no limit.", "default": 0 }, "customTimeRange": { "title": "🕒 Custom time range", "type": "string", "description": "🧭 Set an exact date range in this format: YYYY-MM-DD YYYY-MM-DD. This overrides the preset time range.", "default": "" }, "maxConcurrency": { "title": "⚡ Max concurrency", "minimum": 1, "type": "integer", "description": "🚦 Number of pages to process in parallel. Higher values can be faster but use more resources.", "default": 10 }, "maxRequestRetries": { "title": "🔁 Max request retries", "minimum": 1, "type": "integer", "description": "🛟 Number of retry attempts for failed requests before giving up.", "default": 7 }, "pageLoadTimeoutSecs": { "title": "⏱️ Page load timeout", "minimum": 30, "type": "integer", "description": "⌛ Maximum wait time for a page to load before retrying.", "default": 180 }, "proxyConfiguration": { "title": "🛡️ Proxy configuration", "type": "object", "description": "🌍 Configure proxy usage for stable access. Useful when you need geo-consistent or more resilient requests." } } }, "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 } } } } } } } } } } ```