INSERT INTO support for MemTable

datafusion/core/src/datasource/datasource.rs

@@ -21,7 +21,7 @@ use std::any::Any;
 use std::sync::Arc;
 
 use async_trait::async_trait;
-use datafusion_common::Statistics;
+use datafusion_common::{DataFusionError, Statistics};
 use datafusion_expr::{CreateExternalTable, LogicalPlan};
 pub use datafusion_expr::{TableProviderFilterPushDown, TableType};
 
@@ -97,6 +97,16 @@ pub trait TableProvider: Sync + Send {
     fn statistics(&self) -> Option<Statistics> {
         None
     }
+
+    /// Insert into this table
+    async fn insert_into(
+        &self,
+        _state: &SessionState,
+        _input: &LogicalPlan,
+    ) -> Result<()> {
+        let msg = "Insertion not implemented for this table".to_owned();
+        Err(DataFusionError::NotImplemented(msg))
+    }
 }
 
 /// A factory which creates [`TableProvider`]s at runtime given a URL.

datafusion/core/src/datasource/memory.rs

@@ -19,18 +19,22 @@
 //! queried by DataFusion. This allows data to be pre-loaded into memory and then
 //! repeatedly queried without incurring additional file I/O overhead.
 
-use futures::StreamExt;
+use futures::{StreamExt, TryStreamExt};
 use std::any::Any;
 use std::sync::Arc;
 
 use arrow::datatypes::SchemaRef;
 use arrow::record_batch::RecordBatch;
 use async_trait::async_trait;
+use datafusion_expr::LogicalPlan;
+use tokio::sync::RwLock;
+use tokio::task;
 
 use crate::datasource::{TableProvider, TableType};
 use crate::error::{DataFusionError, Result};
 use crate::execution::context::SessionState;
 use crate::logical_expr::Expr;
+use crate::physical_plan::coalesce_partitions::CoalescePartitionsExec;
 use crate::physical_plan::common;
 use crate::physical_plan::common::AbortOnDropSingle;
 use crate::physical_plan::memory::MemoryExec;
@@ -41,7 +45,7 @@ use crate::physical_plan::{repartition::RepartitionExec, Partitioning};
 #[derive(Debug)]
 pub struct MemTable {
     schema: SchemaRef,
-    batches: Vec<Vec<RecordBatch>>,
+    batches: Arc<RwLock<Vec<Vec<RecordBatch>>>>,
 }
 
 impl MemTable {
@@ -54,7 +58,7 @@ impl MemTable {
         {
             Ok(Self {
                 schema,
-                batches: partitions,
+                batches: Arc::new(RwLock::new(partitions)),
             })
         } else {
             Err(DataFusionError::Plan(
@@ -143,22 +147,102 @@ impl TableProvider for MemTable {
         _filters: &[Expr],
         _limit: Option<usize>,
     ) -> Result<Arc<dyn ExecutionPlan>> {
+        let batches = &self.batches.read().await;
         Ok(Arc::new(MemoryExec::try_new(
-            &self.batches.clone(),
+            batches,
             self.schema(),
             projection.cloned(),
         )?))
     }
+
+    /// Inserts the execution results of a given [LogicalPlan] into this [MemTable].
+    /// The `LogicalPlan` must have the same schema as this `MemTable`.
+    ///
+    /// # Arguments
+    ///
+    /// * `state` - The [SessionState] containing the context for executing the plan.
+    /// * `input` - The [LogicalPlan] to execute and insert.
+    ///
+    /// # Returns
+    ///
+    /// * A `Result` indicating success or failure.
+    async fn insert_into(&self, state: &SessionState, input: &LogicalPlan) -> Result<()> {
+        // Create a physical plan from the logical plan.
+        let plan = state.create_physical_plan(input).await?;
+
+        // Check that the schema of the plan matches the schema of this table.
+        if !plan.schema().eq(&self.schema) {
+            return Err(DataFusionError::Plan(
+                "Inserting query must have the same schema with the table.".to_string(),
+            ));
+        }
+
+        // Get the number of partitions in the plan and the table.
+        let plan_partition_count = plan.output_partitioning().partition_count();
+        let table_partition_count = self.batches.read().await.len();
+
+        // Adjust the plan as necessary to match the number of partitions in the table.
+        let plan: Arc<dyn ExecutionPlan> = if plan_partition_count
+            == table_partition_count
+            || table_partition_count == 0
+        {
+            plan
+        } else if table_partition_count == 1 {
+            // If the table has only one partition, coalesce the partitions in the plan.
+            Arc::new(CoalescePartitionsExec::new(plan))
+        } else {
+            // Otherwise, repartition the plan using a round-robin partitioning scheme.
+            Arc::new(RepartitionExec::try_new(
+                plan,
+                Partitioning::RoundRobinBatch(table_partition_count),
+            )?)
+        };
+
+        // Get the task context from the session state.
+        let task_ctx = state.task_ctx();
+
+        // Execute the plan and collect the results into batches.
+        let mut tasks = vec![];
+        for idx in 0..plan.output_partitioning().partition_count() {
+            let stream = plan.execute(idx, task_ctx.clone())?;
+            let handle = task::spawn(async move {
+                stream.try_collect().await.map_err(DataFusionError::from)
+            });
+            tasks.push(AbortOnDropSingle::new(handle));
+        }
+        let results = futures::future::join_all(tasks)
+            .await
+            .into_iter()
+            .map(|result| {
+                result.map_err(|e| DataFusionError::Execution(format!("{e}")))?
+            })
+            .collect::<Result<Vec<Vec<RecordBatch>>>>()?;
+
+        // Write the results into the table.
+        let mut all_batches = self.batches.write().await;
+
+        if all_batches.is_empty() {
+            *all_batches = results
+        } else {
+            for (batches, result) in all_batches.iter_mut().zip(results.into_iter()) {
+                batches.extend(result);
+            }
+        }
+
+        Ok(())
+    }
 }
 
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::datasource::provider_as_source;
     use crate::from_slice::FromSlice;
     use crate::prelude::SessionContext;
     use arrow::array::Int32Array;
     use arrow::datatypes::{DataType, Field, Schema};
     use arrow::error::ArrowError;
+    use datafusion_expr::LogicalPlanBuilder;
     use futures::StreamExt;
     use std::collections::HashMap;
 
@@ -388,4 +472,135 @@ mod tests {
 
         Ok(())
     }
+
+    fn create_mem_table_scan(
+        schema: SchemaRef,
+        data: Vec<Vec<RecordBatch>>,
+    ) -> Result<Arc<LogicalPlan>> {
+        // Convert the table into a provider so that it can be used in a query
+        let provider = provider_as_source(Arc::new(MemTable::try_new(schema, data)?));
+        // Create a table scan logical plan to read from the table
+        Ok(Arc::new(
+            LogicalPlanBuilder::scan("source", provider, None)?.build()?,
+        ))
+    }
+
+    fn create_initial_ctx() -> Result<(SessionContext, SchemaRef, RecordBatch)> {
+        // Create a new session context
+        let session_ctx = SessionContext::new();
+        // Create a new schema with one field called "a" of type Int32
+        let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)]));
+
+        // Create a new batch of data to insert into the table
+        let batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![Arc::new(Int32Array::from_slice([1, 2, 3]))],
+        )?;
+        Ok((session_ctx, schema, batch))
+    }
+
+    #[tokio::test]
+    async fn test_insert_into_single_partition() -> Result<()> {
+        let (session_ctx, schema, batch) = create_initial_ctx()?;
+        let initial_table = Arc::new(MemTable::try_new(
+            schema.clone(),
+            vec![vec![batch.clone()]],
+        )?);
+        // Create a table scan logical plan to read from the table
+        let single_partition_table_scan =
+            create_mem_table_scan(schema.clone(), vec![vec![batch.clone()]])?;
+        // Insert the data from the provider into the table
+        initial_table
+            .insert_into(&session_ctx.state(), &single_partition_table_scan)
+            .await?;
+        // Ensure that the table now contains two batches of data in the same partition
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 2);
+
+        // Create a new provider with 2 partitions
+        let multi_partition_table_scan = create_mem_table_scan(
+            schema.clone(),
+            vec![vec![batch.clone()], vec![batch]],
+        )?;
+
+        // Insert the data from the provider into the table. We expect coalescing partitions.
+        initial_table
+            .insert_into(&session_ctx.state(), &multi_partition_table_scan)
+            .await?;
+        // Ensure that the table now contains 4 batches of data with only 1 partition
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 4);
+        assert_eq!(initial_table.batches.read().await.len(), 1);
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_insert_into_multiple_partition() -> Result<()> {
+        let (session_ctx, schema, batch) = create_initial_ctx()?;
+        // create a memory table with two partitions, each having one batch with the same data
+        let initial_table = Arc::new(MemTable::try_new(
+            schema.clone(),
+            vec![vec![batch.clone()], vec![batch.clone()]],
+        )?);
+
+        // scan a data source provider from a memory table with a single partition
+        let single_partition_table_scan = create_mem_table_scan(
+            schema.clone(),
+            vec![vec![batch.clone(), batch.clone()]],
+        )?;
+
+        // insert the data from the 1 partition data source provider into the initial table
+        initial_table
+            .insert_into(&session_ctx.state(), &single_partition_table_scan)
+            .await?;
+
+        // We expect round robin repartition here, each partition gets 1 batch.
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 2);
+        assert_eq!(initial_table.batches.read().await.get(1).unwrap().len(), 2);
+
+        // scan a data source provider from a memory table with 2 partition
+        let multi_partition_table_scan = create_mem_table_scan(
+            schema.clone(),
+            vec![vec![batch.clone()], vec![batch]],
+        )?;
+        // We expect one-to-one partition mapping.
+        initial_table
+            .insert_into(&session_ctx.state(), &multi_partition_table_scan)
+            .await?;
+        // Ensure that the table now contains 3 batches of data with 2 partitions.
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 3);
+        assert_eq!(initial_table.batches.read().await.get(1).unwrap().len(), 3);
+        Ok(())
+    }
+
+    #[tokio::test]
+    async fn test_insert_into_empty_table() -> Result<()> {
+        let (session_ctx, schema, batch) = create_initial_ctx()?;
+        // create empty memory table
+        let initial_table = Arc::new(MemTable::try_new(schema.clone(), vec![])?);
+
+        // scan a data source provider from a memory table with a single partition
+        let single_partition_table_scan = create_mem_table_scan(
+            schema.clone(),
+            vec![vec![batch.clone(), batch.clone()]],
+        )?;
+
+        // insert the data from the 1 partition data source provider into the initial table
+        initial_table
+            .insert_into(&session_ctx.state(), &single_partition_table_scan)
+            .await?;
+
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 2);
+
+        // scan a data source provider from a memory table with 2 partition
+        let single_partition_table_scan = create_mem_table_scan(
+            schema.clone(),
+            vec![vec![batch.clone()], vec![batch]],
+        )?;
+        // We expect coalesce partitions here.
+        initial_table
+            .insert_into(&session_ctx.state(), &single_partition_table_scan)
+            .await?;
+        // Ensure that the table now contains 3 batches of data with 2 partitions.
+        assert_eq!(initial_table.batches.read().await.get(0).unwrap().len(), 4);
+        Ok(())
+    }
 }

datafusion/core/src/execution/context.rs

@@ -31,7 +31,7 @@ use crate::{
         optimizer::PhysicalOptimizerRule,
     },
 };
-use datafusion_expr::{DescribeTable, StringifiedPlan};
+use datafusion_expr::{DescribeTable, DmlStatement, StringifiedPlan, WriteOp};
 pub use datafusion_physical_expr::execution_props::ExecutionProps;
 use datafusion_physical_expr::var_provider::is_system_variables;
 use parking_lot::RwLock;
@@ -308,7 +308,8 @@ impl SessionContext {
 
     /// Creates a [`DataFrame`] that will execute a SQL query.
     ///
-    /// Note: This API implements DDL such as `CREATE TABLE` and `CREATE VIEW` with in-memory
+    /// Note: This API implements DDL statements such as `CREATE TABLE` and
+    /// `CREATE VIEW` and DML statements such as `INSERT INTO` with in-memory
     /// default implementations.
     ///
     /// If this is not desirable, consider using [`SessionState::create_logical_plan()`] which
@@ -318,6 +319,24 @@ impl SessionContext {
         let plan = self.state().create_logical_plan(sql).await?;
 
         match plan {
+            LogicalPlan::Dml(DmlStatement {
+                table_name,
+                op: WriteOp::Insert,
+                input,
+                ..
+            }) => {
+                if self.table_exist(&table_name)? {
+                    let name = table_name.table();
+                    let provider = self.table_provider(name).await?;
+                    provider.insert_into(&self.state(), &input).await?;
+                } else {
+                    return Err(DataFusionError::Execution(format!(
+                        "Table '{}' does not exist",
+                        table_name
+                    )));
+                }
+                self.return_empty_dataframe()
+            }
             LogicalPlan::CreateExternalTable(cmd) => {
                 self.create_external_table(&cmd).await
             }