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Usage by Tasks Actor

This actor scans your Apify account and provides a detailed summary of your platform usage and costs, broken down by task. It helps you understand which tasks are consuming the most resources over a specific period.

🚀 Key Features

• Comprehensive Analysis: Fetches all tasks and their historical runs with efficient pagination
• Cost Tracking: Focuses on runs with actual USD costs (usageTotalUsd > 0)
• Dual Reports: Always generates both aggregated and daily breakdown reports
• High Performance: Optimized with 1000-run batches and direct usage data access
• Progress Tracking: Real-time progress updates with detailed batch logging
• Duplicate Prevention: Tracks individual runs to prevent reprocessing

📊 Functionality

Data Processing Flow

1. Task Discovery: Fetches all tasks using paginated API calls
2. Run Analysis: For each task, retrieves all historical runs in 1000-run batches
3. Date Filtering: Filters runs by specified date range (UTC)
4. Cost Filtering: Only includes runs with usageTotalUsd > 0
5. Data Aggregation: Groups results by task and date
6. Report Generation: Creates both aggregated and daily breakdowns

Report Types

Aggregated Report (summary.csv):

• Groups all runs by task across the entire date range
• Shows total run count and USD cost per task
• Sorted by total cost (highest first)

Daily Report (summary_daily.csv):

• Breaks down usage by task and individual day
• Shows daily run count and USD cost
• Sorted chronologically by date

📝 Input Configuration

The actor takes its input from the Apify platform UI or API:

Note: Date fields are interpreted in UTC timezone. This may differ from times displayed in the Apify Console.

📤 Output

Console Logs

• Progress Indicators: ✅ for success/info, ❌ for errors
• Run Details: Each qualifying run logged with date, status, and cost
• Summary Tables: Final aggregated and daily breakdowns

Key-Value Store (Storage > Key-value store)

• summary.csv: Aggregated summary by task (always generated)
• summary_daily.csv: Day-by-day breakdown by task (always generated)

Dataset (Output tab or Storage > Dataset)

Format depends on "Dataset Report Type" selection:

• "Summarized": Aggregated data by task
• "Day by Day": Daily breakdown by task

💡 Example Output

Console Log Example

✅ Starting newprocessing run 🚀
✅ Fetching all tasks(starting from offset 0)...
✅ Found 1500 tasks.
✅ Processing task: My Web Scraper(abc123)[Actor: Web Scraper(xyz789)]
✅ Filtering runs by date range:2024-07-01 to 2024-07-31 📅
✅ Run batch saved for task My Web Scraper:1000 runs fetched 📊
✅ Fetched 2500 runs for task My Web Scraper within date range
✅ Runs processed for task My Web Scraper:15 qualifying runs 📊
✅ ➤ Run 2xMPaRmWfByTrc5yZ |17 Jun 2024,18:21:37CEST|SUCCEEDED|USD: $0.0009
✅ Task completed: My Web Scraper(abc123)-15 qualifying runs, $0.0234 total 📊
✅ Usage Summary(Aggregated):
┌─────────────┬─────────────┬─────────────┬─────────────┬───────────┬───────────┐
│ Actor Name │ Actor ID │ Task Name │ Task ID │ Run Count │ Total $ │
├─────────────┼─────────────┼─────────────┼─────────────┼───────────┼───────────┤
│ Web Scraper │ xyz789 │ My Scraper │ abc123 │ 15 │ $0.0234 │
└─────────────┴─────────────┴─────────────┴─────────────┴───────────┴───────────┘
✅ Aggregated summary has been saved to KVSas summary.csv 📥
✅ Daily summary has been saved to KVSas summary_daily.csv 📥
✅ Processing completed successfully! 🎉

CSV Output Examples

summary.csv (Aggregated):

Date Range (From):,2024-07-01
Date Range (To):,2024-07-31
Actor Name,Actor ID,Task Name,Task ID,Run Count,Total $
Web Scraper,xyz789,My Scraper,abc123,15,$0.0234

summary_daily.csv (Daily):

Date Range (From):,2024-07-01
Date Range (To):,2024-07-31
Day,Actor Name,Actor ID,Task Name,Task ID,Run Count,Total $
2024-07-15,Web Scraper,xyz789,My Scraper,abc123,8,$0.0123
2024-07-16,Web Scraper,xyz789,My Scraper,abc123,7,$0.0111

⚠️ Important Notes

Performance Considerations

• Large Accounts: Processing time scales with number of tasks and runs
• API Limits: Uses efficient pagination to handle large datasets
• Memory Usage: Optimized for minimal memory footprint with batch processing
• Timeout Settings: Critical for accounts with extensive history or large amount of runs
• Batch Processing: 1000-run batches for optimal performance

Data Limitations

• Retention Policy: Historical data availability depends on subscription plan
• API Access: Requires valid Apify API token (auto-detected)
• Cost Filtering: Only includes runs with actual USD costs
• Date Precision: Uses UTC date comparison (date-only, not time)

Technical Details

• Pagination: Handles unlimited tasks and runs efficiently
• Error Recovery: Graceful handling of API failures and timeouts
• Memory Management: Efficient data structures for large datasets
• Duplicate Prevention: Individual run tracking prevents data loss

For more information, see the Apify documentation.