Dandandan commented on pull request #9865:
URL: https://github.com/apache/arrow/pull/9865#issuecomment-813411456
This is the optimized plan, which looks ok, there is a `RepartitionExec`
wrapping the `CsvExec` (which is ok) and a `MergeExec` just before `TopKExec`:
```
TopKExec { input: MergeExec { input: ProjectionExec { expr: [(Column { name:
"customer_id" }, "customer_id"), (Column { name: "revenue" }, "revenue")],
schema: Schema { fields: [Field { name: "customer_id", data_type: Utf8,
nullable: false, dict_id: 0, dict_is_ordered: false, metadata: None }, Field {
name: "revenue", data_type: Int64, nullable: false, dict_id: 0,
dict_is_ordered: false, metadata: None }], metadata: {} }, input:
CoalesceBatchesExec { input: RepartitionExec { input: CsvExec { path:
"tests/customer.csv", filenames: ["tests/customer.csv"], schema: Schema {
fields: [Field { name: "customer_id", data_type: Utf8, nullable: false,
dict_id: 0, dict_is_ordered: false, metadata: None }, Field { name: "revenue",
data_type: Int64, nullable: false, dict_id: 0, dict_is_ordered: false,
metadata: None }], metadata: {} }, has_header: false, delimiter: Some(44),
file_extension: ".csv", projection: Some([0, 1]), projected_schema: Schema {
fields: [Field { name: "customer_id", data_t
ype: Utf8, nullable: false, dict_id: 0, dict_is_ordered: false, metadata: None
}, Field { name: "revenue", data_type: Int64, nullable: false, dict_id: 0,
dict_is_ordered: false, metadata: None }], metadata: {} }, batch_size: 8192,
limit: None }, partitioning: RoundRobinBatch(16), channels: Mutex { data: {} }
}, target_batch_size: 4096 } } }, k: 3 }
```
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