alamb commented on code in PR #10558:
URL: https://github.com/apache/datafusion/pull/10558#discussion_r1605494227
##########
datafusion-examples/examples/plan_to_sql.rs:
##########
@@ -77,3 +81,132 @@ fn simple_expr_to_sql_demo_escape_mysql_style() ->
Result<()> {
assert_eq!(sql, r#"((`a` < 5) OR (`a` = 8))"#);
Ok(())
}
+
+/// DataFusion can convert a logic plan created using the DataFrames API to
read from a parquet file
+/// to SQL, using column name escaping PostgreSQL style.
+async fn simple_plan_to_sql_parquest_dataframe_demo() -> Result<()> {
+ // create local execution context
+ let ctx = SessionContext::new(); // define the query using the DataFrame
trait
+
+ let testdata = datafusion::test_util::parquet_test_data();
+ let df = ctx
+ .read_parquet(
+ &format!("{testdata}/alltypes_plain.parquet"),
+ ParquetReadOptions::default(),
+ )
+ .await?
+ .select_columns(&["id", "int_col", "double_col", "date_string_col"])?;
+
+ let ast = plan_to_sql(df.logical_plan())?;
+
+ let sql = format!("{}", ast);
+
+ assert_eq!(
+ sql,
+ r#"SELECT "?table?"."id", "?table?"."int_col", "?table?"."double_col",
"?table?"."date_string_col" FROM "?table?""#
+ );
+
+ Ok(())
+}
+
+/// DataFusion can convert a logic plan created using the DataFrames API to
read from a csv file
Review Comment:
I am not sure we need to have both parquet and csv demos -- one or the other
serves as a good demonstration I think
##########
datafusion-examples/examples/plan_to_sql.rs:
##########
@@ -77,3 +81,132 @@ fn simple_expr_to_sql_demo_escape_mysql_style() ->
Result<()> {
assert_eq!(sql, r#"((`a` < 5) OR (`a` = 8))"#);
Ok(())
}
+
+/// DataFusion can convert a logic plan created using the DataFrames API to
read from a parquet file
+/// to SQL, using column name escaping PostgreSQL style.
+async fn simple_plan_to_sql_parquest_dataframe_demo() -> Result<()> {
+ // create local execution context
+ let ctx = SessionContext::new(); // define the query using the DataFrame
trait
+
+ let testdata = datafusion::test_util::parquet_test_data();
+ let df = ctx
+ .read_parquet(
+ &format!("{testdata}/alltypes_plain.parquet"),
+ ParquetReadOptions::default(),
+ )
+ .await?
+ .select_columns(&["id", "int_col", "double_col", "date_string_col"])?;
+
+ let ast = plan_to_sql(df.logical_plan())?;
+
+ let sql = format!("{}", ast);
+
+ assert_eq!(
+ sql,
+ r#"SELECT "?table?"."id", "?table?"."int_col", "?table?"."double_col",
"?table?"."date_string_col" FROM "?table?""#
+ );
+
+ Ok(())
+}
+
+/// DataFusion can convert a logic plan created using the DataFrames API to
read from a csv file
+/// to SQL, using column name escaping PostgreSQL style.
+async fn simple_plan_to_sql_csv_dataframe_demo() -> Result<()> {
+ // create local execution context
+ let ctx = SessionContext::new(); // define the query using the DataFrame
trait
+
+ let testdata = datafusion::test_util::arrow_test_data();
+ let df = ctx
+ .read_csv(
+ &format!("{testdata}/csv/aggregate_test_100.csv"),
+ CsvReadOptions::default(),
+ )
+ .await?
+ .select(vec![col("c1"), min(col("c12")), max(col("c12"))])?;
+
+ let ast = plan_to_sql(df.logical_plan())?;
+
+ let sql = format!("{}", ast);
+
+ assert_eq!(
+ sql,
+ r#"SELECT "?table?"."c1", MIN("?table?"."c12"), MAX("?table?"."c12")
FROM "?table?""#
+ );
+
+ Ok(())
+}
+
+async fn round_trip_plan_to_sql_parquest_dataframe_demo() -> Result<()> {
+ // create local execution context
+ let ctx = SessionContext::new(); // define the query using the DataFrame
trait
+
+ let testdata = datafusion::test_util::parquet_test_data();
+
+ // register parquet file with the execution context
+ ctx.register_parquet(
+ "alltypes_plain",
+ &format!("{testdata}/alltypes_plain.parquet"),
+ ParquetReadOptions::default(),
+ )
+ .await?;
+
+ // execute the query
Review Comment:
I don't think this executes a query -- it does something more like
```suggestion
// create a logical plan from a SQL string and then programmatically add
new filters
```
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