jeffreyssmith2nd commented on code in PR #10716:
URL: https://github.com/apache/datafusion/pull/10716#discussion_r1620812645
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datafusion/core/src/datasource/schema_adapter.rs:
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@@ -75,9 +75,16 @@ pub trait SchemaAdapter: Send + Sync {
/// Creates a `SchemaMapping` that can be used to cast or map the columns
/// from the file schema to the table schema.
-pub trait SchemaMapper: Send + Sync {
+pub trait SchemaMapper: Debug + Send + Sync {
/// Adapts a `RecordBatch` to match the `table_schema` using the stored
mapping and conversions.
fn map_batch(&self, batch: RecordBatch) ->
datafusion_common::Result<RecordBatch>;
+
+ /// Adapts a `RecordBatch` that does not have all the columns (as defined
in the schema).
Review Comment:
As I understand it, when `DatafusionArrowPredicate::evaluate` is called, the
`RecordBatch` only contains one column. If we use the `map_batch` function, it
indexes into the `RecordBatch` as if all the columns are there.
For example, if we have a schema with fields: `[{name: "value", type:
"Float64"},{name: "time", type: "Timestamp"}]`, then map_batch will try to
index at `1` for time, but the `RecordBatch` won't actually have that index
since only one column is passed in.
This definitely may be the wrong way to achieve what I wanted, but the
intent was to look the field up by name in the `table_schema` so that we don't
have that indexing problem.
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