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new 439726f443 Add examples of how to convert logical plan to/from sql
strings (#10558)
439726f443 is described below
commit 439726f4434259a4d15f4dfe1ff86da77bed667a
Author: Xin Li <[email protected]>
AuthorDate: Tue May 21 01:35:30 2024 +0800
Add examples of how to convert logical plan to/from sql strings (#10558)
* Add examples of how to convert logical plan to/from sql strings
* Fix clippy
* fix comments
* fix comments
---
datafusion-examples/examples/plan_to_sql.rs | 71 ++++++++++++++++++++++++++++-
1 file changed, 70 insertions(+), 1 deletion(-)
diff --git a/datafusion-examples/examples/plan_to_sql.rs
b/datafusion-examples/examples/plan_to_sql.rs
index 3915d3991f..0e9485ba7f 100644
--- a/datafusion-examples/examples/plan_to_sql.rs
+++ b/datafusion-examples/examples/plan_to_sql.rs
@@ -20,7 +20,7 @@ use datafusion::error::Result;
use datafusion::prelude::*;
use datafusion::sql::unparser::expr_to_sql;
use datafusion_sql::unparser::dialect::CustomDialect;
-use datafusion_sql::unparser::Unparser;
+use datafusion_sql::unparser::{plan_to_sql, Unparser};
/// This example demonstrates the programmatic construction of
/// SQL using the DataFusion Expr [`Expr`] and LogicalPlan [`LogicalPlan`] API.
@@ -41,6 +41,8 @@ async fn main() -> Result<()> {
simple_expr_to_sql_demo()?;
simple_expr_to_sql_demo_no_escape()?;
simple_expr_to_sql_demo_escape_mysql_style()?;
+ simple_plan_to_sql_parquest_dataframe_demo().await?;
+ round_trip_plan_to_sql_parquest_dataframe_demo().await?;
Ok(())
}
@@ -77,3 +79,70 @@ 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();
+
+ 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 could parse a SQL into a DataFrame, adding a Filter, and
converting that back to sql.
+async fn round_trip_plan_to_sql_parquest_dataframe_demo() -> Result<()> {
+ // create local execution context
+ let ctx = SessionContext::new();
+
+ 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?;
+
+ // create a logical plan from a SQL string and then programmatically add
new filters
+ let df = ctx
+ .sql(
+ "SELECT int_col, double_col, CAST(date_string_col as VARCHAR) \
+ FROM alltypes_plain",
+ )
+ .await?
+ .filter(
+ col("id")
+ .gt(lit(1))
+ .and(col("tinyint_col").lt(col("double_col"))),
+ )?;
+
+ let ast = plan_to_sql(df.logical_plan())?;
+
+ let sql = format!("{}", ast);
+
+ assert_eq!(
+ sql,
+ r#"SELECT "alltypes_plain"."int_col", "alltypes_plain"."double_col",
CAST("alltypes_plain"."date_string_col" AS VARCHAR) FROM "alltypes_plain" WHERE
(("alltypes_plain"."id" > 1) AND ("alltypes_plain"."tinyint_col" <
"alltypes_plain"."double_col"))"#
+ );
+
+ Ok(())
+}
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