📊 Google Trends Fast Scraper — Flat Tabular & CSV Output (5 Endpoints, 99.9% Success)

The easiest and most reliable Google Trends extractor on Apify. Extract realtime trending searches, 2004–present historical timelines, geographic heatmaps, and breakout queries formatted as clean, flat rows ready for 1-click export to Excel, CSV, Google Sheets, or databases. No nested arrays, no custom parsing code required!

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🏆 Why Choose This Scraper?

Most Google Trends scrapers return deeply nested JSON structures that are difficult to work with unless you write custom scripts to flatten arrays. Furthermore, they frequently suffer from Google's strict rate limits (429 Too Many Requests).

This scraper is built from the ground up to solve both problems:

• 📋 1-Click Clean CSV/Excel Ready: Every single data point is saved as an unnested, flat tabular row. Simply click "Export to CSV" or "Export to Excel" in the Apify Console and your dataset is instantly ready for analysis.
• ⚡ All 5 Endpoints in 1 Actor: Retrieve Trending Now, Interest Over Time, Interest by Region, Related Queries, and Related Topics without paying for multiple actors.
• 🛡️ Automated Anti-Blocking & Smart IP Rotation: Automated Apify residential proxy rotation cycles IPs and handles Google's rate limits behind the scenes.
• 💰 Transparent Pay-Per-Result (PPE): You only pay for successfully extracted rows. Check the Pricing tab for current rates. No recurring monthly rentals, zero proxy fees, and zero compute runtime fees.
• 🔄 Multi-Keyword Support: Compare up to 5 keywords simultaneously or loop through keyword lists automatically up to your specified limit.
• 🤖 AI Agent & No-Code Ready: Natively compatible with the Apify MCP Server (Cursor, Claude Desktop), Make, N8N, and Google Sheets.

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📡 5 Core Intelligence Endpoints

Select your desired mode using the scrape_type parameter:

1. 🚀 trending_now — Real-Time Search Pulses

Discover what the world is searching for right now. Monitor viral breakout spikes hourly with estimated search volumes, percentage growth, category tags, and context-rich news articles.

Flat CSV / JSON Row Preview:

{
  "scrape_type": "trending_now",
  "keyword": "Champions League Final",
  "volume": 250000,
  "volume_growth_pct": 850,
  "topic_names": ["Sports", "Soccer"],
  "article_title": "Match Preview and Lineups Announced",
  "article_source": "BBC Sport",
  "article_url": "https://www.bbc.com/sport/football/...",
  "started_timestamp": "2026-06-01T18:00:00+00:00"
}

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2. 📈 interest_over_time — Historical Timeline (2004 – Present)

Track relative search popularity on a normalized 0–100 scale across any timeframe—from the past hour to over 20 years of historical data. Compare up to 5 keywords on the exact same scale.

Flat CSV / JSON Row Preview:

{
  "scrape_type": "interest_over_time",
  "keyword": "Artificial Intelligence",
  "date": "2025-01-01T00:00:00+00:00",
  "value": 84,
  "isPartial": false
}

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3. 🗺️ interest_by_region — Geographic Demand Breakdown

Pinpoint exactly where consumer demand is centralized. Filter by Country, Region (State/Province), Metro DMA, or City.

Flat CSV / JSON Row Preview:

{
  "scrape_type": "interest_by_region",
  "keyword": "Electric Vehicles",
  "geo_code": "US-CA",
  "geo_name": "California",
  "value": 100,
  "max_value_index": 0
}

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4. 🔍 related_queries — Long-Tail & Breakout Queries

Unearth the exact search queries people enter alongside your target keywords. Automatically separates established "Top" queries from explosive "Rising" breakout queries (up to +5000% growth).

Flat CSV / JSON Row Preview:

{
  "scrape_type": "related_queries",
  "keyword": "SaaS Marketing",
  "query_type": "rising",
  "query": "ai marketing automation tools",
  "value": 450,
  "formatted_value": "+450%"
}

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5. 🏷️ related_topics — Knowledge Graph Entity Extraction

Extract Google's contextual entity groupings. Understand how Google's search algorithms categorize your market or brand.

Flat CSV / JSON Row Preview:

{
  "scrape_type": "related_topics",
  "keyword": "Notion",
  "topic_type": "top",
  "topic_title": "Productivity software",
  "topic_type_label": "Software",
  "value": 100
}

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⏱️ Timeframe vs. Data Granularity Guide

Google Trends automatically adjusts data point frequency based on your chosen timeframe. Use this cheat sheet to choose the right setting:

Timeframe Option	Granularity Returned	Ideal Use Case
now 1-H, now 4-H	Minute-by-minute	Breaking news, viral spikes, live sports events
now 1-d, now 7-d	Hourly intervals	Weekly campaign tracking, product launch momentum
today 1-m, today 3-m	Daily data points	Monthly performance audits, seasonal trend tracking
today 12-m, today 5-y	Weekly data points	Multi-year demand forecasting, competitor benchmarking
all (2004 – Present)	Monthly data points	Macro industry evolution, historical retrospective analysis

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💎 Transparent Pay-Per-Event Pricing

Stop paying high recurring monthly subscriptions or paying for idle compute time. This actor operates on Apify's Pay-Per-Event (PPE) model:

Benefit	Details
Pay-Per-Result	You only pay for successfully extracted results. Check the Pricing tab above for the current live rate.
Residential Proxies Included	Built-in smart residential proxy rotation is included at no additional charge.
No Compute Time Fees	You never pay for startup time, queueing, or scrape runtime delays.
Zero-Risk Guarantee	Failed requests or empty queries are never charged.

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🎯 Use Cases

📦 E-Commerce & Dropshipping

Identify breakout products before they saturate the market. Run trending_now daily across multiple countries and cross-reference with interest_over_time to separate viral fads from sustained commercial demand. Catch consumer product waves on TikTok and Instagram weeks before they peak on Amazon.

✍️ SEO & Content Marketing

Use related_queries to discover explosive long-tail queries with rising search volume (+5000% breakout) that competitors haven't targeted yet. Use interest_over_time to time content publishing to peak seasonal demand.

💸 Quantitative Finance & Trading

Correlate interest_over_time spikes for ticker symbols, crypto, commodities, or brand names with market volatility. Search volume trends often predict retail trading volume and momentum shifts.

📊 Market Research & Brand Intelligence

Track brand awareness over time against rivals using interest_over_time with up to 5 keywords simultaneously. Use interest_by_region to find underserved geographic markets and plan localized expansion campaigns.

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🤖 AI & Workflow Automations

Because results are saved as clean flat rows, you can plug this actor directly into your automation pipelines:

• 📊 Google Sheets & Airtable: Automatically append new trending rows to your spreadsheets without needing formatting scripts.
• 🔄 Make (Integromat) & N8N: Trigger alerts or content generation workflows when keyword search volume crosses thresholds.
• 🤖 Model Context Protocol (MCP): Connect this scraper directly to Cursor, Claude Desktop, or custom AI Agents via the Apify MCP server to give your LLM live Google Trends research capabilities.
• 🐍 LangChain & LlamaIndex: Load tabular search trends into your RAG pipelines and research agents.

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💻 Quick Start Code Examples

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Run Google Trends Fast Scraper
run = client.actor("scraperpro/google-trends-ppe").call(run_input={
    "scrape_type": "interest_over_time",
    "keywords": ["OpenAI", "Anthropic", "DeepSeek"],
    "timeframe_type": "predefined",
    "predefined_timeframe": "today 12-m",
    "common_geo": "US",
    "max_events": 500
})

# Iterate directly through flat rows
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['date']}] {item['keyword']}: {item['value']}")

Node.js (JavaScript / TypeScript)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('scraperpro/google-trends-ppe').call({
    scrape_type: 'related_queries',
    keywords: ['AI Tools'],
    timeframe_type: 'predefined',
    predefined_timeframe: 'today 3-m',
    common_geo: 'US',
    max_events: 50
});

// Fetch flat rows
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(row => {
    console.log(`${row.query_type.toUpperCase()}: ${row.query} (${row.formatted_value})`);
});

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⚙️ Input Parameters

Parameter	Type	Required	Default	Description
scrape_type	string	Yes	trending_now	One of: trending_now, interest_over_time, interest_by_region, related_queries, related_topics
keywords	array	Cond.	[]	List of 1 to 5 keywords (required for all types except trending_now)
timeframe_type	string	No	predefined	predefined or custom
predefined_timeframe	string	No	today 12-m	Predefined window (e.g., now 7-d, today 3-m, today 12-m, all)
custom_timeframe	string	No	""	Exact date range in YYYY-MM-DD YYYY-MM-DD format
common_geo	string	No	""	2-letter country code (e.g., US, GB, DE). Leave blank for Worldwide
geo_resolution	string	No	REGION	For interest_by_region: COUNTRY, REGION, CITY, or DMA
gprop	string	No	""	Search property: "" (Web), images, news, froogle (Shopping), youtube
max_events	integer	No	100	Maximum number of data rows to extract

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💡 Pro Tips for Best Results

1. Instant CSV Download: In the Apify Console, click Export $\rightarrow$ CSV to download your dataset. Because this actor uses flat rows, it opens seamlessly in Microsoft Excel, Apple Numbers, and Google Sheets without any column splitting.
2. Leave Proxies on Default: The scraper automatically assigns Apify Residential Proxies and cycles session IPs. You do not need to configure custom proxies.
3. Worldwide vs. Country-Specific: For worldwide data, leave common_geo blank. Regional breakdown (interest_by_region) is most detailed when a specific country (e.g. US, GB, DE) is chosen.
4. Discovering Hidden Breakouts: Check the rising section of related_queries—queries marked with high percentages or "Breakout" represent emerging search trends with low SEO competition.
5. Need Single-Envelope Nested JSON?: If your backend API integration specifically requires all data points wrapped inside a single nested JSON object ({"data": [...]}), check out our companion actor: Google Trends Scraper (Nested Batch API).

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❓ Frequently Asked Questions

Q: Why use this actor instead of scrapers that output nested JSON?
A: Most scrapers return all data nested inside single records or complex arrays. This actor formats every observation as a flat tabular row, making it ideal for immediate CSV export, spreadsheet analysis, and no-code automations without writing parsing code.

Q: Do I need to buy or configure my own proxies?
A: No. High-speed residential proxies are integrated directly into the scraping engine at zero extra charge.

Q: What happens if Google returns a 429 rate-limit error?
A: The scraper handles rate limits automatically. It detects blocks, automatically switches proxy sessions, and seamlessly resumes extraction so your scraping jobs finish reliably without manual intervention.

Q: Is scraping Google Trends legal?
A: Yes. This actor extracts publicly available search statistics published by Google Trends. It does not access private account information.

Q: Can I export data to Excel or CSV?
A: Yes. In the Apify Console, click "Export" on your dataset to download cleanly formatted Excel, CSV, JSON, or XML files.

Q: How does billing work?
A: You are billed strictly per successful result extracted via Apify's Pay-Per-Event model (see the Pricing tab for current rates). You are never charged for actor runtime, proxy bandwidth, or failed requests.

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