andygrove commented on a change in pull request #7993:
URL: https://github.com/apache/arrow/pull/7993#discussion_r472583526
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File path: rust/datafusion/src/dataframe.rs
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@@ -174,4 +174,18 @@ pub trait DataFrame {
/// Return the logical plan represented by this DataFrame.
fn to_logical_plan(&self) -> LogicalPlan;
+
+ /// Return a DataFrame with the explanation of its plan so far.
+ ///
+ /// ```
+ /// # use datafusion::prelude::*;
+ /// # use datafusion::error::Result;
+ /// # fn main() -> Result<()> {
+ /// let mut ctx = ExecutionContext::new();
+ /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
+ /// let batches = df.limit(100)?.explain(false)?.collect()?;
+ /// # Ok(())
+ /// # }
+ /// ```
+ fn explain(&self, verbose: bool) -> Result<Arc<dyn DataFrame>>;
Review comment:
So Spark has a couple different things to help people debug. `explain`
literally prints the query plan(s) to stdout. It is also possible to access the
underlying logical and physical query plans using `df.queryExecution.plan` or
`df.queryExecution.executedPlan` for example. We already have `to_logical_plan`
and I do think that we eventually should expose the physical plan as well, but
not until we have made a decision on how we're implementing the physical plan
enum and optimizer (there are JIRAs open for these issues already).
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