Updated extending operators documentation

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

@@ -19,4 +19,41 @@
 
 # Extending DataFusion's operators: custom LogicalPlan and Execution Plans
 
-Coming soon
+DataFusion supports extension of operators by transforming logical plan and execution plan 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.
+
+## 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>> {
+    // Attemps 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.