Make DataFrame API consuming (#4621)

datafusion-examples/examples/custom_datasource.rs

@@ -69,7 +69,7 @@ async fn search_accounts(
     )?
     .build()?;
 
-    let mut dataframe = DataFrame::new(ctx.state, &logical_plan)
+    let mut dataframe = DataFrame::new(ctx.state, logical_plan)
         .select_columns(&["id", "bank_account"])?;
 
     if let Some(f) = filter {

datafusion-examples/examples/dataframe.rs

@@ -19,7 +19,6 @@ use datafusion::arrow::datatypes::{DataType, Field, Schema};
 use datafusion::error::Result;
 use datafusion::prelude::*;
 use std::fs;
-use std::sync::Arc;
 
 /// This example demonstrates executing a simple query against an Arrow data source (Parquet) and
 /// fetching results, using the DataFrame trait
@@ -64,7 +63,7 @@ a2,"08 9, 2013",2,1376006400,4.5"#;
 }
 
 // Example to read data from a csv file with inferred schema
-async fn example_read_csv_file_with_inferred_schema() -> Arc<DataFrame> {
+async fn example_read_csv_file_with_inferred_schema() -> DataFrame {
     let path = "example.csv";
     // Create a csv file using the predefined function
     create_csv_file(path.to_string());
@@ -75,7 +74,7 @@ async fn example_read_csv_file_with_inferred_schema() -> Arc<DataFrame> {
 }
 
 // Example to read csv file with a defined schema for the csv file
-async fn example_read_csv_file_with_schema() -> Arc<DataFrame> {
+async fn example_read_csv_file_with_schema() -> DataFrame {
     let path = "example.csv";
     // Create a csv file using the predefined function
     create_csv_file(path.to_string());

datafusion-examples/examples/deserialize_to_struct.rs

@@ -57,7 +57,7 @@ impl Data {
                 .sql("SELECT int_col, double_col FROM alltypes_plain")
                 .await?;
 
-            df.show().await?;
+            df.clone().show().await?;
 
             df.collect().await?
         };

datafusion-examples/examples/flight_server.rs

@@ -114,15 +114,15 @@ impl FlightService for FlightServiceImpl {
                 let df = ctx.sql(sql).await.map_err(to_tonic_err)?;
 
                 // execute the query
+                let schema = df.schema().clone().into();
                 let results = df.collect().await.map_err(to_tonic_err)?;
                 if results.is_empty() {
                     return Err(Status::internal("There were no results from ticket"));
                 }
 
                 // add an initial FlightData message that sends schema
                 let options = datafusion::arrow::ipc::writer::IpcWriteOptions::default();
-                let schema_flight_data =
-                    SchemaAsIpc::new(&df.schema().clone().into(), &options).into();
+                let schema_flight_data = SchemaAsIpc::new(&schema, &options).into();
 
                 let mut flights: Vec<Result<FlightData, Status>> =
                     vec![Ok(schema_flight_data)];