📈 Google Trends Scraper

An Apify Actor that retrieves data from Google Trends: realtime trending searches, interest over time, interest by region, and related queries/topics.

🚀 Quickstart (no input required)

Want to get started in a flash? ⚡️ The actor has smart defaults, so you can run it immediately and get the latest realtime trending searches for the US.

• In the Apify UI: Just click Run.

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🔍 What you can fetch

• Trending now (realtime): ⏰ Find out what's rapidly rising in searches in the last few hours.
• Interest over time: 📊 Get a timeline of search interest for one or more keywords.
• Interest by region: 🗺️ Discover where your keywords are searched the most.
• Related queries/topics: 💡 Explore new queries and topics related to your main keyword.

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💰 Pricing (rental)

• $12/month
• Recommended for ongoing tracking and monitoring workflows.
• You can configure inputs freely; heavy configurations (very long timeframes, CITY/DMA resolution, inc_low_vol) may take longer.

⚙️ Input (overview)

The actor's UI is dynamically generated from the .actor/INPUT_SCHEMA.json file. Here are the key fields you'll interact with:

• scrape_type (select, default: trending_now)
• keywords (array, optional for trending_now; required for other types)
• gprop (select: web/images/news/shopping/youtube)
• timeframe_type (select: predefined or custom, default: predefined)
• predefined_timeframe (select, default: today 12-m)
• custom_timeframe (string, e.g. "2023-01-01 2023-06-30")
• geo_selection_type (select, default: Common Countries)
• common_geo (string country code, default: US)
• custom_geo_code (string for regions like US-CA)
• geo_resolution (COUNTRY/REGION/DMA/CITY; for interest_by_region)
• inc_low_vol (boolean; include low-volume regions; Pro recommended)
• trending_language (string, default: en)
• trending_hours (integer 1-191, default: 24)

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📋 Example Inputs

Here are some common use cases to get you started!

1. Realtime trending (US, 24 hours)

   {
     "scrape_type": "trending_now",
     "common_geo": "US",
     "trending_hours": 24,
     "trending_language": "en"
   }
   

2. Interest over time for multiple keywords (US)

   {
     "scrape_type": "interest_over_time",
     "keywords": ["data science", "machine learning"],
     "timeframe_type": "predefined",
     "predefined_timeframe": "today 12-m",
     "geo_selection_type": "Common Countries",
     "common_geo": "US"
   }
   

3. Related queries for a single keyword (global)

   {
     "scrape_type": "related_queries",
     "keywords": ["python"],
     "timeframe_type": "predefined",
     "predefined_timeframe": "today 12-m"
   }
   

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💾 Output Format

The actor pushes a single JSON object to the default dataset. This object will contain:

• scrape_type: The type of scrape performed.
• data: An array containing the results of your query.

Example data structures:

• interest_over_time / interest_by_region: Data is an array of objects (rows), like a table.
• related_queries / related_topics: Data is an array with top and rising lists of related searches.
• trending_now: Data is a list of trending keyword objects with details like keyword, geo, volume, and topic_names.

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⚠️ Reliability & Troubleshooting

Encountering issues? Don't worry, here are some common solutions:

• Quotas and rate limits: 🚫 If you see 429/302 errors or quota issues, try reducing the frequency of your runs or using shorter timeframes/geos. You can also use a proxy to rotate your IP address.
• Big results: 🐢 Scraping with geo_resolution: CITY or inc_low_vol can return large datasets and be slower. Start with COUNTRY or REGION for faster results.
• Hours limit: ⌛ The trending_now scrape type supports a range of 1 to 191 hours.
• Empty results: 🤷 Check that your keywords are popular enough in the specified timeframe and geo.

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🧰 Runtime defaults & limits

• Memory: 1024 MB (default)
• Timeout: 30 minutes (default)
• CPU: 1 (default)
• Dataset writes: The actor always writes at least one item per run. If no data is found, it pushes an informative item with input summary.

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📄 Notes

• Alias support: You can use "trending_searches" as a supported alias for "trending_now".