[GitHub] [arrow-datafusion] metesynnada commented on a diff in pull request #6049: MemoryExec INSERT INTO refactor to use ExecutionPlan

["metesynnada (via GitHub)" <gi...@apache.org>]

metesynnada commented on code in PR #6049:
URL: https://github.com/apache/arrow-datafusion/pull/6049#discussion_r1176075832


##########
datafusion/core/src/datasource/memory.rs:
##########
@@ -19,33 +19,31 @@
 //! 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, 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 futures::StreamExt;
 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;
+use crate::physical_plan::memory_insert::MemoryWriteExec;
 use crate::physical_plan::ExecutionPlan;
 use crate::physical_plan::{repartition::RepartitionExec, Partitioning};
 
 /// In-memory table
 #[derive(Debug)]
 pub struct MemTable {
     schema: SchemaRef,
-    batches: Arc<RwLock<Vec<Vec<RecordBatch>>>>,
+    pub(crate) batches: Arc<Vec<Arc<RwLock<Vec<RecordBatch>>>>>,

Review Comment:
   You are right, we can move on with `Vec<Arc<RwLock<Vec<RecordBatch>>>>`.



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