//! Example demonstrating the streaming API concept for TegDB
//!
//! This example shows the architectural improvements that streaming API provides
//! even though the exact API integration may need refinement.

fn main() {
    println!("=== TegDB Streaming API Architecture Demo ===\n");

    println!("The streaming API provides several key benefits:\n");

    println!("1. MEMORY EFFICIENCY:");
    println!("   - Traditional: SELECT * FROM large_table loads ALL rows into Vec<Vec<SqlValue>>");
    println!("   - Streaming: Processes one row at a time, using O(1) memory instead of O(n)");
    println!("   - Critical for tables with millions of rows\n");

    println!("2. REDUCED LATENCY:");
    println!("   - Traditional: Wait for ALL rows to be processed before getting any results");
    println!("   - Streaming: Get first results immediately as they're found");
    println!("   - Better user experience for interactive queries\n");

    println!("3. EARLY TERMINATION:");
    println!("   - LIMIT clauses can stop processing as soon as enough rows are found");
    println!("   - Filtering happens during iteration, not after loading everything");
    println!("   - Massive performance gains for selective queries\n");

    println!("4. COMPOSABLE OPERATIONS:");
    println!("   - stream.take(10) - get first 10 rows");
    println!("   - stream.filter(|row| condition) - apply additional filtering");
    println!("   - stream.collect() - convert back to Vec for compatibility");
    println!("   - Lazy evaluation means only necessary work is done\n");

    println!("5. REAL-WORLD SCENARIOS:");
    println!("   - ETL processes: Stream data from source to destination");
    println!("   - Analytics: Calculate aggregates without loading full dataset");
    println!("   - Reporting: Generate reports with pagination");
    println!("   - Real-time processing: Handle data as it comes in\n");

    // Demonstrate the conceptual API structure
    demonstrate_streaming_concept();
}

fn demonstrate_streaming_concept() {
    println!("=== Conceptual API Usage ===\n");

    // This represents the structure of how the streaming API would work
    println!("// Example 1: Basic streaming usage");
    println!("let streaming_result = executor.execute_streaming_query(\"users\", None, None, Some(100))?;");
    println!("for row_result in streaming_result.rows {{");
    println!("    match row_result {{");
    println!("        Ok(row) => process_row(row),");
    println!("        Err(e) => handle_error(e),");
    println!("    }}");
    println!("}}\n");

    println!("// Example 2: Memory-efficient aggregation");
    println!("let stream = executor.execute_streaming_query(\"sales\", Some(&[\"amount\"]), None, None)?;");
    println!("let total: f64 = stream.rows");
    println!("    .filter_map(|row| row.ok()?.get(0)?.as_real())");
    println!("    .sum();");
    println!("// Processes millions of rows using constant memory!\n");

    println!("// Example 3: Pagination");
    println!("let stream = executor.execute_streaming_query(\"products\", None, None, None)?;");
    println!(
        "let page: Vec<_> = stream.rows.skip(page_size * page_num).take(page_size).collect();"
    );
    println!("// Only processes the rows actually needed\n");

    println!("// Example 4: Early termination");
    println!(
        "let stream = executor.execute_streaming_query(\"logs\", None, Some(error_filter), None)?;"
    );
    println!("if let Some(first_error) = stream.rows.next() {{");
    println!("    // Found the error immediately, no need to scan entire table");
    println!("    handle_first_error(first_error);");
    println!("}}\n");

    println!("=== Architecture Benefits ===\n");
    println!("1. The RowIterator struct implements lazy evaluation");
    println!("2. StorageFormat.deserialize_row() is called on-demand");
    println!("3. Transaction.scan() returns an iterator, not a Vec");
    println!("4. Filters are applied during iteration, not after");
    println!("5. LIMIT is enforced by the iterator, stopping early");
    println!("6. Memory usage is bounded by single row size, not result set size\n");

    println!("This architecture is especially important for:");
    println!("- IoT data processing (millions of sensor readings)");
    println!("- Log analysis (large log files)");
    println!("- ETL workflows (transforming large datasets)");
    println!("- Real-time analytics (processing data streams)");
    println!("- Large reporting queries (financial reports, etc.)");
}
