hengfeiyang opened a new issue, #4288:
URL: https://github.com/apache/arrow-datafusion/issues/4288
**Describe the bug**
It can't work like this:
```sql
SELECT foo AS bar FROM t WHERE bar > 10
```
I have the field `foo` just give it an alias `bar` then as a where clause,
it will report the error:
```
Error: SchemaError(FieldNotFound { qualifier: None, name: "bar",
valid_fields: Some(["t.foo"]) })
```
**To Reproduce**
Steps to reproduce the behavior:
**Expected behavior**
It can work.
**Additional context**
My demo code:
```rust
use std::sync::Arc;
use datafusion::arrow::array::Int32Array;
use datafusion::arrow::datatypes::{DataType, Field, Schema};
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::datasource::MemTable;
use datafusion::error::Result;
use datafusion::from_slice::FromSlice;
use datafusion::prelude::SessionContext;
/// This example demonstrates how to use the DataFrame API against in-memory
data.
#[tokio::main]
async fn main() -> Result<()> {
// define a schema.
let schema = Arc::new(Schema::new(vec![Field::new("foo",
DataType::Int32, false)]));
// define data.
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(Int32Array::from_slice([1, 10, 10, 100]))],
)?;
// declare a new context. In spark API, this corresponds to a new spark
SQLsession
let ctx = SessionContext::new();
// declare a table in memory. In spark API, this corresponds to
createDataFrame(...).
let provider = MemTable::try_new(schema.clone(), vec![vec![batch]])?;
ctx.register_table("t", Arc::new(provider))?;
let df = ctx.sql("SELECT foo AS bar FROM t WHERE bar > 10").await?;
// print the results
df.show().await?;
Ok(())
}
```
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