docs/source/library-user-guide/extending-operators.md

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# Extending Operators

DataFusion supports extending operators by transforming [`LogicalPlan`] and [`ExecutionPlan`] through customized [optimizer rules](https://docs.rs/datafusion/latest/datafusion/optimizer/trait.OptimizerRule.html). This section will use the µWheel project to illustrate such capabilities.

[`logicalplan`]: https://docs.rs/datafusion/latest/datafusion/logical_expr/enum.LogicalPlan.html
[`executionplan`]: https://docs.rs/datafusion/latest/datafusion/physical_plan/trait.ExecutionPlan.html

## About DataFusion µWheel

[DataFusion µWheel](https://github.com/uwheel/datafusion-uwheel/tree/main) is a native DataFusion optimizer which improves query performance for time-based analytics through fast temporal aggregation and pruning using custom indices. The integration of µWheel into DataFusion is a joint effort with the DataFusion community.

### Optimizing Logical Plan

The `rewrite` function transforms logical plans by identifying temporal patterns and aggregation functions that match the stored wheel indices. When match is found, it queries the corresponding index to retrieve pre-computed aggregate values, stores these results in a [MemTable](https://docs.rs/datafusion/latest/datafusion/datasource/memory/struct.MemTable.html), and returns as a new `LogicalPlan::TableScan`. If no match is found, the original plan proceeds unchanged through DataFusion's standard execution path.

```rust,ignore
fn rewrite(
  &self,
  plan: LogicalPlan,
  _config: &dyn OptimizerConfig,
) -> Result<Transformed<LogicalPlan>> {
    // Attempts to rewrite a logical plan to a uwheel-based plan that either provides
    // plan-time aggregates or skips execution based on min/max pruning.
    if let Some(rewritten) = self.try_rewrite(&plan) {
        Ok(Transformed::yes(rewritten))
    } else {
        Ok(Transformed::no(plan))
    }
}
```

```rust,ignore
// Converts a uwheel aggregate result to a TableScan with a MemTable as source
fn agg_to_table_scan(result: f64, schema: SchemaRef) -> Result<LogicalPlan> {
  let data = Float64Array::from(vec![result]);
  let record_batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(data)])?;
  let df_schema = Arc::new(DFSchema::try_from(schema.clone())?);
  let mem_table = MemTable::try_new(schema, vec![vec![record_batch]])?;
  mem_table_as_table_scan(mem_table, df_schema)
}
```

To get a deeper dive into the usage of the µWheel project, visit the [blog post](https://uwheel.rs/post/datafusion_uwheel/) by Max Meldrum.