milenkovicm commented on code in PR #1993:
URL:
https://github.com/apache/datafusion-ballista/pull/1993#discussion_r3734935689
##########
ballista/client/src/extension.rs:
##########
@@ -234,3 +237,46 @@ impl Extension {
Ok(scheduler_url)
}
}
+
+/// Providing [DataFrameExt] for an extended functionality on DataFusion
DataFrame.
+///
+#[async_trait::async_trait]
+pub trait DataFrameExt {
+ /// Checkpointing DataFrame - storing intermediate result to disk and
breaking lineage in the plan
+ async fn checkpoint(self) -> datafusion::error::Result<DataFrame>;
+}
+
+#[async_trait::async_trait]
+impl DataFrameExt for DataFrame {
+ async fn checkpoint(self) -> datafusion::error::Result<DataFrame> {
+ let (state, plan) = self.into_parts();
+ let ctx = SessionContext::new_with_state(state);
+
+ let base_dir =
+ ctx.state()
+ .config()
+ .ballista_checkpoint_dir()
+ .ok_or_else(|| {
+ DataFusionError::Configuration(
+ "ballista.checkpoint.dir must be set to use
DataFrame::checkpoint()"
+ .to_string(),
+ )
+ })?;
+
+ let path = format!(
+ "{}/{}/{}",
+ base_dir.trim_end_matches('/'),
+ ctx.state().session_id(),
+ Uuid::new_v4()
+ );
+
+ // Executes the original plan as a normal distributed job.
+ ctx.execute_logical_plan(plan)
Review Comment:
this is one way to implement this, but difference from spark that ballista
checkpoint will materialize the dag (up to that point) instead of waiting
standard actions, such as write.
this will work for first version, but maybe we should consider adding new
logical plan extension which would represent checkpoint, wait for write op and
then split plan on scheduler side using planner.
wdyt @sandugood
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