alamb commented on a change in pull request #716:
URL: https://github.com/apache/arrow-datafusion/pull/716#discussion_r669916709
##########
File path: datafusion/src/execution/context.rs
##########
@@ -2364,6 +2365,75 @@ mod tests {
assert_batches_sorted_eq!(expected, &results);
}
+ #[tokio::test]
+ async fn case_builtin_math_expression() {
+ let mut ctx = ExecutionContext::new();
+
+ let type_values = vec![
+ (
+ DataType::Int8,
+ Arc::new(Int8Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::Int16,
+ Arc::new(Int16Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::Int32,
+ Arc::new(Int32Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::Int64,
+ Arc::new(Int64Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::UInt8,
+ Arc::new(UInt8Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::UInt16,
+ Arc::new(UInt16Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::UInt32,
+ Arc::new(UInt32Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::UInt64,
+ Arc::new(UInt64Array::from(vec![1])) as ArrayRef,
+ ),
+ (
+ DataType::Float32,
+ Arc::new(Float32Array::from(vec![1.0_f32])) as ArrayRef,
+ ),
+ (
+ DataType::Float64,
+ Arc::new(Float64Array::from(vec![1.0_f64])) as ArrayRef,
+ ),
+ ];
+
+ for (data_type, array) in type_values.iter() {
+ let schema =
+ Arc::new(Schema::new(vec![Field::new("v", data_type.clone(),
false)]));
+ let batch =
+ RecordBatch::try_new(schema.clone(),
vec![array.clone()]).unwrap();
+ let provider = MemTable::try_new(schema,
vec![vec![batch]]).unwrap();
+ ctx.register_table("t", Arc::new(provider)).unwrap();
+ let expected = vec![
+ "+---------+",
+ "| sqrt(v) |",
+ "+---------+",
+ "| 1 |",
+ "+---------+",
+ ];
+ let results = plan_and_collect(&mut ctx, "SELECT sqrt(v) FROM t")
Review comment:
👍 it is a good test. We can finagle the types later if needed (e.g. if
we ever want to support `sqrt(numeric)` --> `numeric`.
I think this PR makes DataFusion better than it is on master.
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