diff --git a/datafusion-examples/examples/simple_udtf.rs b/datafusion-examples/examples/simple_udtf.rs
index 260c6cbee710..05ad231949a8 100644
--- a/datafusion-examples/examples/simple_udtf.rs
+++ b/datafusion-examples/examples/simple_udtf.rs
@@ -15,7 +15,6 @@
 // specific language governing permissions and limitations
 // under the License.
 
-use arrow::array::Int64Array;
 use arrow::csv::reader::Format;
 use arrow::csv::ReaderBuilder;
 use async_trait::async_trait;
@@ -24,22 +23,22 @@ use datafusion::arrow::record_batch::RecordBatch;
 use datafusion::datasource::function::TableFunctionImpl;
 use datafusion::datasource::TableProvider;
 use datafusion::error::Result;
-use datafusion::execution::context::SessionState;
-use datafusion::execution::TaskContext;
+use datafusion::execution::context::{ExecutionProps, SessionState};
 use datafusion::physical_plan::memory::MemoryExec;
-use datafusion::physical_plan::{collect, ExecutionPlan};
+use datafusion::physical_plan::ExecutionPlan;
 use datafusion::prelude::SessionContext;
-use datafusion_common::{DFSchema, ScalarValue};
-use datafusion_expr::{EmptyRelation, Expr, LogicalPlan, Projection, TableType};
+use datafusion_common::{plan_err, DataFusionError, ScalarValue};
+use datafusion_expr::{Expr, TableType};
+use datafusion_optimizer::simplify_expressions::{ExprSimplifier, SimplifyContext};
 use std::fs::File;
 use std::io::Seek;
 use std::path::Path;
 use std::sync::Arc;
 
 // To define your own table function, you only need to do the following 3 things:
-// 1. Implement your own TableProvider
-// 2. Implement your own TableFunctionImpl and return your TableProvider
-// 3. Register the function using ctx.register_udtf
+// 1. Implement your own [`TableProvider`]
+// 2. Implement your own [`TableFunctionImpl`] and return your [`TableProvider`]
+// 3. Register the function using [`SessionContext::register_udtf`]
 
 /// This example demonstrates how to register a TableFunction
 #[tokio::main]
@@ -47,14 +46,15 @@ async fn main() -> Result<()> {
     // create local execution context
     let ctx = SessionContext::new();
 
+    // register the table function that will be called in SQL statements by `read_csv`
     ctx.register_udtf("read_csv", Arc::new(LocalCsvTableFunc {}));
 
     let testdata = datafusion::test_util::arrow_test_data();
     let csv_file = format!("{testdata}/csv/aggregate_test_100.csv");
 
-    // read csv with at most 2 rows
+    // Pass 2 arguments, read csv with at most 2 rows (simplify logic makes 1+1 --> 2)
     let df = ctx
-        .sql(format!("SELECT * FROM read_csv('{csv_file}', 2);").as_str())
+        .sql(format!("SELECT * FROM read_csv('{csv_file}', 1 + 1);").as_str())
         .await?;
     df.show().await?;
 
@@ -67,9 +67,14 @@ async fn main() -> Result<()> {
     Ok(())
 }
 
+/// Table Function that mimics the [`read_csv`] function in DuckDB.
+///
+/// Usage: `read_csv(filename, [limit])`
+///
+/// [`read_csv`]: https://duckdb.org/docs/data/csv/overview.html
 struct LocalCsvTable {
     schema: SchemaRef,
-    exprs: Vec<Expr>,
+    limit: Option<usize>,
     batches: Vec<RecordBatch>,
 }
 
@@ -89,13 +94,12 @@ impl TableProvider for LocalCsvTable {
 
     async fn scan(
         &self,
-        state: &SessionState,
+        _state: &SessionState,
         projection: Option<&Vec<usize>>,
         _filters: &[Expr],
         _limit: Option<usize>,
     ) -> Result<Arc<dyn ExecutionPlan>> {
-        let batches = if !self.exprs.is_empty() {
-            let max_return_lines = self.interpreter_expr(state).await?;
+        let batches = if let Some(max_return_lines) = self.limit {
             // get max return rows from self.batches
             let mut batches = vec![];
             let mut lines = 0;
@@ -121,56 +125,35 @@ impl TableProvider for LocalCsvTable {
         )?))
     }
 }
-
-impl LocalCsvTable {
-    async fn interpreter_expr(&self, state: &SessionState) -> Result<i64> {
-        use datafusion::logical_expr::expr_rewriter::normalize_col;
-        use datafusion::logical_expr::utils::columnize_expr;
-        let plan = LogicalPlan::EmptyRelation(EmptyRelation {
-            produce_one_row: true,
-            schema: Arc::new(DFSchema::empty()),
-        });
-        let logical_plan = Projection::try_new(
-            vec![columnize_expr(
-                normalize_col(self.exprs[0].clone(), &plan)?,
-                plan.schema(),
-            )],
-            Arc::new(plan),
-        )
-        .map(LogicalPlan::Projection)?;
-        let rbs = collect(
-            state.create_physical_plan(&logical_plan).await?,
-            Arc::new(TaskContext::from(state)),
-        )
-        .await?;
-        let limit = rbs[0]
-            .column(0)
-            .as_any()
-            .downcast_ref::<Int64Array>()
-            .unwrap()
-            .value(0);
-        Ok(limit)
-    }
-}
-
 struct LocalCsvTableFunc {}
 
 impl TableFunctionImpl for LocalCsvTableFunc {
     fn call(&self, exprs: &[Expr]) -> Result<Arc<dyn TableProvider>> {
-        let mut new_exprs = vec![];
-        let mut filepath = String::new();
-        for expr in exprs {
-            match expr {
-                Expr::Literal(ScalarValue::Utf8(Some(ref path))) => {
-                    filepath = path.clone()
+        let Some(Expr::Literal(ScalarValue::Utf8(Some(ref path)))) = exprs.get(0) else {
+            return plan_err!("read_csv requires at least one string argument");
+        };
+
+        let limit = exprs
+            .get(1)
+            .map(|expr| {
+                // try to simpify the expression, so 1+2 becomes 3, for example
+                let execution_props = ExecutionProps::new();
+                let info = SimplifyContext::new(&execution_props);
+                let expr = ExprSimplifier::new(info).simplify(expr.clone())?;
+
+                if let Expr::Literal(ScalarValue::Int64(Some(limit))) = expr {
+                    Ok(limit as usize)
+                } else {
+                    plan_err!("Limit must be an integer")
                 }
-                expr => new_exprs.push(expr.clone()),
-            }
-        }
-        let (schema, batches) = read_csv_batches(filepath)?;
+            })
+            .transpose()?;
+
+        let (schema, batches) = read_csv_batches(path)?;
+
         let table = LocalCsvTable {
             schema,
-            exprs: new_exprs.clone(),
+            limit,
             batches,
         };
         Ok(Arc::new(table))