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SQL on Files

🦆 Run SQL queries on CSV, JSON, and Parquet files using DuckDB. No database setup required!

👁 Apify Actor
👁 License: MIT

🎯 What This Actor Does

Query any data file with SQL - no database needed:

• DuckDB Powered - Lightning-fast analytical queries
• Multi-Format - CSV, JSON, Parquet support
• Flexible Input - Upload files, URLs, or Apify Datasets
• Full SQL - JOINs, aggregations, window functions
• Export Options - JSON, CSV, or Parquet output

🚀 Use Cases

📥 Input Examples

Simple Query

{
"fileUrl":"https://example.com/data.csv",
"query":"SELECT * FROM data WHERE price > 100 ORDER BY price DESC LIMIT 10"
}

Aggregation

{
"fileUrl":"https://example.com/sales.csv",
"query":"SELECT category, COUNT(*) as count, AVG(price) as avg_price FROM data GROUP BY category"
}

From Apify Dataset

{
"datasetId":"abc123xyz",
"query":"SELECT url, title, price FROM data WHERE price IS NOT NULL"
}

⚙️ Configuration

📤 Output

JSON Output (Default)

Results pushed to Dataset:

[
{"category":"Electronics","count":1520,"avg_price":299.99},
{"category":"Books","count":892,"avg_price":24.50}
]

CSV/Parquet Output

{
"format":"csv",
"rows":1520,
"columns":3,
"downloadUrl":"https://api.apify.com/v2/..."
}

🦆 SQL Tips

-- Basic filtering
SELECT*FROMdataWHEREcolumnLIKE'%keyword%'
-- Aggregations
SELECT category,COUNT(*),SUM(price)FROMdataGROUPBY category
-- Window functions
SELECT*, ROW_NUMBER()OVER(PARTITIONBY category ORDERBY price DESC)as rank FROMdata
-- Date handling
SELECT*, DATE_TRUNC('month', date_column)asmonthFROMdata
-- JSON extraction
SELECT json_column->>'$.nested.field'asvalueFROMdata
-- Pattern matching
SELECT*FROMdataWHERE name ~'^[A-Z].*'

💰 Cost Estimation

🔧 Technical Details

• Language: Python 3.12
• Engine: DuckDB 0.10+
• Memory: 256MB-1GB (scales with data)
• Speed: 1M+ rows/second for analytics

📄 License

MIT License - see LICENSE for details.