Extending DataFusion’s operators: custom LogicalPlan and Execution Plans¶
DataFusion supports extension of operators by transforming logical plan and execution plan through customized optimizer rules. This section will use the µWheel project to illustrate such capabilities.
About DataFusion µWheel¶
DataFusion µWheel 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, and returns as a new LogicalPlan::TableScan. If no match is found, the original plan proceeds unchanged through DataFusion’s standard execution path.
,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))
}
}
,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 by Max Meldrum.