Support Group By Aggregation Queries

Author: LYZJU2019

datafusion-uwheel/src/lib.rs

@@ -47,7 +47,7 @@ use uwheel::{
         sum::{F64SumAggregator, U32SumAggregator},
     },
     wheels::read::ReaderWheel,
-    Aggregator, Conf, Entry, HawConf, RwWheel, WheelRange,
+    Aggregator, Conf, Duration, Entry, HawConf, RwWheel, WheelRange,
 };
 
 /// Custom aggregator implementations that are used by this crate.
@@ -314,6 +314,64 @@ impl UWheelOptimizer {
                     _ => None,
                 }
             }
+
+            LogicalPlan::Aggregate(agg) => {
+                let group_expr = agg.group_expr.first()?;
+
+                // Only continue if the aggregation has a filter
+                let LogicalPlan::Filter(filter) = agg.input.as_ref() else {
+                    return None;
+                };
+
+                let (wheel_range, _) = extract_filter_expr(&filter.predicate, &self.time_column)?;
+
+                match group_expr {
+                    Expr::ScalarFunction(func) if func.name() == "date_trunc" => {
+                        let interval = func.args.first()?;
+                        if let Expr::Literal(ScalarValue::Utf8(duration)) = interval {
+                            match duration.as_ref()?.as_str() {
+                                "second" => {
+                                    unimplemented!("date_trunc('second') group by is not supported")
+                                }
+                                "minute" => {
+                                    unimplemented!("date_trunc('minute') group by is not supported")
+                                }
+                                "hour" => {
+                                    unimplemented!("date_trunc('hour') group by is not supported")
+                                }
+                                "day" => {
+                                    let res = self
+                                        .wheels
+                                        .count
+                                        .group_by(wheel_range, Duration::DAY)
+                                        .unwrap_or_default()
+                                        .iter()
+                                        .map(|(k, v)| ((*k * 1_000) as i64, *v as i64)) // transform milliseconds to microseconds by multiplying by 1_000
+                                        .collect();
+
+                                    let schema = Arc::new(plan.schema().clone().as_arrow().clone());
+
+                                    return uwheel_group_by_to_table_scan(res, schema).ok();
+                                }
+                                "week" => {
+                                    unimplemented!("date_trunc('week') group by is not supported")
+                                }
+                                "month" => {
+                                    unimplemented!("date_trunc('month') group by is not supported")
+                                }
+                                "year" => {
+                                    unimplemented!("date_trunc('year') group by is not supported")
+                                }
+                                _ => {}
+                            }
+                        }
+                    }
+                    _ => {
+                        unimplemented!("We only support scalar function date_trunc for group by expression now")
+                    }
+                }
+                None
+            }
             // Check whether it follows the pattern: SELECT * FROM X WHERE TIME >= X AND TIME <= Y
             LogicalPlan::Filter(filter) => self.try_rewrite_filter(filter, plan),
             _ => None,
@@ -440,6 +498,25 @@ fn uwheel_agg_to_table_scan(result: f64, schema: SchemaRef) -> Result<LogicalPla
     mem_table_as_table_scan(mem_table, df_schema)
 }
 
+// Converts a uwheel group by result to a TableScan with a MemTable as source
+// currently only supports timestamp group by
+fn uwheel_group_by_to_table_scan(
+    result: Vec<(i64, i64)>,
+    schema: SchemaRef,
+) -> Result<LogicalPlan> {
+    let group_by =
+        TimestampMicrosecondArray::from(result.iter().map(|(k, _)| *k).collect::<Vec<_>>());
+
+    let agg = Int64Array::from(result.iter().map(|(_, v)| *v).collect::<Vec<_>>());
+
+    let record_batch =
+        RecordBatch::try_new(schema.clone(), vec![Arc::new(group_by), Arc::new(agg)])?;
+
+    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)
+}
+
 // helper for possibly removing the table name from the expression key
 fn maybe_replace_table_name(expr: &Expr, table_name: &str) -> String {
     let expr_str = expr.to_string();
@@ -568,7 +645,7 @@ fn is_count_star_aggregate(aggregate_function: &AggregateFunction) -> bool {
             func,
             args,
             ..
-        } if func.name() == "COUNT" && (args.len() == 1 && is_wildcard(&args[0]) || args.is_empty()))
+        } if (func.name() == "COUNT" || func.name() == "count") && (args.len() == 1 && is_wildcard(&args[0]) || args.is_empty()))
 }
 
 // Helper methods to build the UWheelOptimizer
@@ -942,9 +1019,11 @@ mod tests {
     use chrono::TimeZone;
     use datafusion::arrow::datatypes::{Field, Schema, TimeUnit};
     use datafusion::execution::SessionStateBuilder;
+    use datafusion::functions_aggregate::count::count;
     use datafusion::functions_aggregate::expr_fn::avg;
     use datafusion::functions_aggregate::min_max::{max, min};
-    use datafusion::logical_expr::test::function_stub::{count, sum};
+    use datafusion::functions_aggregate::sum::sum;
+    use datafusion::prelude::date_trunc;
 
     use super::*;
     use builder::Builder;
@@ -1405,4 +1484,95 @@ mod tests {
 
         Ok(())
     }
+
+    #[tokio::test]
+    async fn group_by_count_aggregation_rewrite() -> Result<()> {
+        let optimizer = test_optimizer().await?;
+
+        let temporal_filter = col("timestamp")
+            .gt_eq(lit("2024-05-10T00:00:00Z"))
+            .and(col("timestamp").lt(lit("2024-05-10T00:00:10Z")));
+
+        let plan =
+            LogicalPlanBuilder::scan("test", provider_as_source(optimizer.provider()), None)?
+                .filter(temporal_filter)?
+                .aggregate(
+                    vec![date_trunc(lit("day"), col("timestamp"))], // GROUP BY date_trunc('day', timestamp)
+                    vec![count(wildcard())],
+                )?
+                .project(vec![
+                    date_trunc(lit("day"), col("timestamp")),
+                    count(wildcard()),
+                ])?
+                .build()?;
+
+        // Assert that the original plan is a Projection
+        assert!(matches!(plan, LogicalPlan::Projection(_)));
+
+        let rewritten = optimizer.try_rewrite(&plan).unwrap();
+        // assert it was rewritten to a TableScan
+        assert!(matches!(rewritten, LogicalPlan::TableScan(_)));
+
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn group_by_count_aggregation_exec() -> Result<()> {
+        let optimizer = test_optimizer().await?;
+
+        let temporal_filter = col("timestamp")
+            .gt_eq(lit("2024-05-10T00:00:00Z"))
+            .and(col("timestamp").lt(lit("2024-05-10T00:00:10Z")));
+
+        let plan =
+            LogicalPlanBuilder::scan("test", provider_as_source(optimizer.provider()), None)?
+                .filter(temporal_filter)?
+                .aggregate(
+                    vec![date_trunc(lit("day"), col("timestamp"))], // GROUP BY date_trunc('day', timestamp)
+                    vec![count(wildcard())],
+                )?
+                .project(vec![
+                    date_trunc(lit("day"), col("timestamp")),
+                    count(wildcard()),
+                ])?
+                .build()?;
+
+        let ctx = SessionContext::new();
+        ctx.register_table("test", optimizer.provider().clone())?;
+
+        // Set UWheelOptimizer as optimizer rule
+        let session_state = SessionStateBuilder::new()
+            .with_optimizer_rules(vec![optimizer.clone()])
+            .build();
+        let uwheel_ctx = SessionContext::new_with_state(session_state);
+
+        // Run the query through the ctx that has our OptimizerRule
+        let df = uwheel_ctx.execute_logical_plan(plan).await?;
+        let results = df.collect().await?;
+
+        assert_eq!(results.len(), 1);
+
+        assert_eq!(
+            results[0]
+                .column(0)
+                .as_any()
+                .downcast_ref::<TimestampMicrosecondArray>()
+                .unwrap()
+                .value(0)
+                / 1000,
+            1_715_299_200_000
+        );
+
+        assert_eq!(
+            results[0]
+                .column(1)
+                .as_any()
+                .downcast_ref::<Int64Array>()
+                .unwrap()
+                .value(0),
+            10
+        );
+
+        Ok(())
+    }
 }