elusion 8.2.0

Elusion is a modern DataFrame / Data Engineering / Data Analysis library that combines the familiarity of DataFrame operations (like those in PySpark, Pandas, and Polars) with the power of SQL query building. It provides flexible query construction without enforcing strict operation ordering, enabling developers to write intuitive and maintainable data transformations.
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use crate::prelude::*;

pub async fn register_df_as_table(
        ctx: &SessionContext,
        table_name: &str,
        df: &DataFrame,
    ) -> ElusionResult<()> {
        let batches = df.clone().collect().await
        .map_err(|e| ElusionError::InvalidOperation {
            operation: "Data Collection".to_string(),
            reason: format!("Failed to collect DataFrame: {}", e),
            suggestion: "💡 Check if DataFrame contains valid data".to_string()
        })?;

        let schema = df.schema();

        let mem_table = MemTable::try_new(schema.clone().into(), vec![batches])
        .map_err(|e| ElusionError::SchemaError {
            message: format!("Failed to create in-memory table: {}", e),
            schema: Some(schema.to_string()),
            suggestion: "💡 Verify schema compatibility and data types".to_string()
        })?;

        ctx.register_table(table_name, Arc::new(mem_table))
        .map_err(|e| ElusionError::InvalidOperation {
            operation: "Table Registration".to_string(),
            reason: format!("Failed to register table '{}': {}", table_name, e),
            suggestion: "💡 Check if table name is unique and valid".to_string()
        })?;

        Ok(())
    }