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fix: Various metrics bug fixes and improvements #1111

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Merged
andygrove merged 25 commits into from
Dec 2, 2024
Merged

fix: Various metrics bug fixes and improvements #1111

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725ccb4
Refactor native planner to build tree of SparkPlan that maps to origi…
andygrove Nov 22, 2024
581984e
add SparkPlan children
andygrove Nov 22, 2024
d25f2d3
clippy
andygrove Nov 22, 2024
2027dc9
add some more documentation
andygrove Nov 22, 2024
8c8c9a5
aggregate metrics
andygrove Nov 22, 2024
baae1ba
simplify approach
andygrove Nov 22, 2024
68d0fdf
add a unit test
andygrove Nov 22, 2024
2347203
save progress
andygrove Nov 22, 2024
d0aeda1
remove debug, add specific jvm timer
andygrove Nov 22, 2024
3359738
fix
andygrove Nov 22, 2024
0a8a06b
clippy
andygrove Nov 22, 2024
40e90d5
clippy
andygrove Nov 22, 2024
5bfe334
format
andygrove Nov 22, 2024
0a3044e
Revert some changes, update documentation
andygrove Nov 24, 2024
d430175
fix typo
andygrove Nov 24, 2024
ff5076d
fix typo
andygrove Nov 24, 2024
ca66095
measure more FFI time
andygrove Nov 24, 2024
6fd5723
record FFI time for CollectLimitExec and TakeOrderedAndProject
andygrove Nov 26, 2024
c512187
upmerge:
andygrove Nov 26, 2024
56e3ead
save
andygrove Nov 26, 2024
36e6233
Revert "save"
andygrove Nov 26, 2024
ff26736
revert FFI timing changes, which are now in a separate PR
andygrove Nov 30, 2024
82c9c7c
remove println
andygrove Dec 1, 2024
c77b144
address feedback
andygrove Dec 1, 2024
11b5fb6
address feedback: explain how Spark plan ID is used
andygrove Dec 2, 2024
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1 change: 1 addition & 0 deletions native/core/src/execution/datafusion/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -21,4 +21,5 @@ pub mod expressions;
mod operators;
pub mod planner;
pub mod shuffle_writer;
pub(crate) mod spark_plan;
mod util;
165 changes: 122 additions & 43 deletions native/core/src/execution/datafusion/planner.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,7 @@ use datafusion::{
use datafusion_functions_nested::concat::ArrayAppend;
use datafusion_physical_expr::aggregate::{AggregateExprBuilder, AggregateFunctionExpr};

use crate::execution::datafusion::spark_plan::SparkPlan;
use datafusion_comet_proto::{
spark_expression::{
self, agg_expr::ExprStruct as AggExprStruct, expr::ExprStruct, literal::Value, AggExpr,
Expand Down Expand Up @@ -861,11 +862,11 @@ impl PhysicalPlanner {
///
/// Note that we return created `Scan`s which will be kept at JNI API. JNI calls will use it to
/// feed in new input batch from Spark JVM side.
pub fn create_plan<'a>(
pub(crate) fn create_plan<'a>(
&'a self,
spark_plan: &'a Operator,
inputs: &mut Vec<Arc<GlobalRef>>,
) -> Result<(Vec<ScanExec>, Arc<dyn ExecutionPlan>), ExecutionError> {
) -> Result<(Vec<ScanExec>, Arc<SparkPlan>), ExecutionError> {
let children = &spark_plan.children;
match spark_plan.op_struct.as_ref().unwrap() {
OpStruct::Projection(project) => {
Expand All @@ -880,15 +881,23 @@ impl PhysicalPlanner {
.map(|r| (r, format!("col_{}", idx)))
})
.collect();
Ok((scans, Arc::new(ProjectionExec::try_new(exprs?, child)?)))
let projection = Arc::new(ProjectionExec::try_new(exprs?, child.wrapped.clone())?);
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, projection, vec![child])),
))
}
OpStruct::Filter(filter) => {
assert!(children.len() == 1);
let (scans, child) = self.create_plan(&children[0], inputs)?;
let predicate =
self.create_expr(filter.predicate.as_ref().unwrap(), child.schema())?;

Ok((scans, Arc::new(FilterExec::try_new(predicate, child)?)))
let filter = Arc::new(FilterExec::try_new(predicate, child.wrapped.clone())?);
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, filter, vec![child])),
))
}
OpStruct::HashAgg(agg) => {
assert!(children.len() == 1);
Expand Down Expand Up @@ -926,7 +935,7 @@ impl PhysicalPlanner {
group_by,
aggr_expr,
vec![None; num_agg], // no filter expressions
Arc::clone(&child),
Arc::clone(&child.wrapped),
Arc::clone(&schema),
)?,
);
Expand All @@ -940,8 +949,11 @@ impl PhysicalPlanner {
})
.collect();

let exec: Arc<dyn ExecutionPlan> = if agg.result_exprs.is_empty() {
aggregate
if agg.result_exprs.is_empty() {
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, aggregate, vec![child])),
))
} else {
// For final aggregation, DF's hash aggregate exec doesn't support Spark's
// aggregate result expressions like `COUNT(col) + 1`, but instead relying
Expand All @@ -950,17 +962,29 @@ impl PhysicalPlanner {
//
// Note that `result_exprs` should only be set for final aggregation on the
// Spark side.
Arc::new(ProjectionExec::try_new(result_exprs?, aggregate)?)
};

Ok((scans, exec))
let projection =
Arc::new(ProjectionExec::try_new(result_exprs?, aggregate.clone())?);
Ok((
scans,
Arc::new(SparkPlan::new_with_additional(
spark_plan.plan_id,
projection,
vec![child],
vec![aggregate],
)),
Comment on lines +983 to +988

@andygrove andygrove Nov 22, 2024 •
edited
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This is an example where we are currently dropping the aggregate metrics and just capturing the projection metrics

))
}
}
OpStruct::Limit(limit) => {
assert!(children.len() == 1);
let num = limit.limit;
let (scans, child) = self.create_plan(&children[0], inputs)?;

Ok((scans, Arc::new(LocalLimitExec::new(child, num as usize))))
let limit = Arc::new(LocalLimitExec::new(child.wrapped.clone(), num as usize));
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, limit, vec![child])),
))
}
OpStruct::Sort(sort) => {
assert!(children.len() == 1);
Expand All @@ -978,11 +1002,20 @@ impl PhysicalPlanner {
// SortExec fails in some cases if we do not unpack dictionary-encoded arrays, and
// it would be more efficient if we could avoid that.
// https://github.com/apache/datafusion-comet/issues/963
let child = Self::wrap_in_copy_exec(child);
let child_copied = Self::wrap_in_copy_exec(child.wrapped.clone());

let sort = Arc::new(
SortExec::new(LexOrdering::new(exprs?), child_copied.clone()).with_fetch(fetch),
);

Ok((
scans,
Arc::new(SortExec::new(LexOrdering::new(exprs?), child).with_fetch(fetch)),
Arc::new(SparkPlan::new_with_additional(
spark_plan.plan_id,
sort,
vec![child.clone()],
vec![child.wrapped.clone()],
)),
))
}
OpStruct::Scan(scan) => {
Expand All @@ -1008,7 +1041,10 @@ impl PhysicalPlanner {
// The `ScanExec` operator will take actual arrays from Spark during execution
let scan =
ScanExec::new(self.exec_context_id, input_source, &scan.source, data_types)?;
Ok((vec![scan.clone()], Arc::new(scan)))
Ok((
vec![scan.clone()],
Arc::new(SparkPlan::new(spark_plan.plan_id, Arc::new(scan), vec![])),
))
}
OpStruct::ShuffleWriter(writer) => {
assert!(children.len() == 1);
Expand All @@ -1017,14 +1053,22 @@ impl PhysicalPlanner {
let partitioning = self
.create_partitioning(writer.partitioning.as_ref().unwrap(), child.schema())?;

let shuffle_writer = Arc::new(ShuffleWriterExec::try_new(
child.wrapped.clone(),
partitioning,
writer.output_data_file.clone(),
writer.output_index_file.clone(),
)?);

// TODO this assumes that the child of a shuffle is always a ScanExec
Ok((
scans,
Arc::new(ShuffleWriterExec::try_new(
child,
partitioning,
writer.output_data_file.clone(),
writer.output_index_file.clone(),
)?),
Arc::new(SparkPlan::new_with_additional(
spark_plan.plan_id,
shuffle_writer,
vec![child.clone()],
vec![child.wrapped.clone()],
)),
))
}
OpStruct::Expand(expand) => {
Expand Down Expand Up @@ -1068,16 +1112,32 @@ impl PhysicalPlanner {
// the data corruption. Note that we only need to copy the input batch
// if the child operator is `ScanExec`, because other operators after `ScanExec`
// will create new arrays for the output batch.
let child = if can_reuse_input_batch(&child) {
Arc::new(CopyExec::new(child, CopyMode::UnpackOrDeepCopy))
if can_reuse_input_batch(&child.wrapped) {
let child_copied = Arc::new(CopyExec::new(
child.wrapped.clone(),
CopyMode::UnpackOrDeepCopy,
));
let expand = Arc::new(CometExpandExec::new(projections, child_copied, schema));
Ok((
scans,
Arc::new(SparkPlan::new_with_additional(
spark_plan.plan_id,
expand,
vec![child.clone()],
vec![child.wrapped.clone()],
)),
))
} else {
child
};

Ok((
scans,
Arc::new(CometExpandExec::new(projections, child, schema)),
))
let expand = Arc::new(CometExpandExec::new(
projections,
child.wrapped.clone(),
schema,
));
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, expand, vec![child])),
))
}
}
OpStruct::SortMergeJoin(join) => {
let (join_params, scans) = self.parse_join_parameters(
Expand Down Expand Up @@ -1115,7 +1175,12 @@ impl PhysicalPlanner {
false,
)?);

Ok((scans, join))
// TODO pass in additional plans (CopyExec, ScanExec)
// TODO pass in child spark plans
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, join, vec![])),
))
}
OpStruct::HashJoin(join) => {
let (join_params, scans) = self.parse_join_parameters(
Expand Down Expand Up @@ -1154,7 +1219,12 @@ impl PhysicalPlanner {
swap_hash_join(hash_join.as_ref(), PartitionMode::Partitioned)?
};

Ok((scans, hash_join))
// TODO pass in additional plans (CopyExec, ScanExec)
// TODO pass in child spark plans
Ok((
scans,
Arc::new(SparkPlan::new(spark_plan.plan_id, hash_join, vec![])),
))
}
OpStruct::Window(wnd) => {
let (scans, child) = self.create_plan(&children[0], inputs)?;
Expand Down Expand Up @@ -1187,14 +1257,16 @@ impl PhysicalPlanner {
})
.collect();

let window_agg = Arc::new(BoundedWindowAggExec::try_new(
window_expr?,
child.wrapped.clone(),
partition_exprs.to_vec(),
InputOrderMode::Sorted,
)?);
Ok((
scans,
Arc::new(BoundedWindowAggExec::try_new(
window_expr?,
child,
partition_exprs.to_vec(),
InputOrderMode::Sorted,
)?),
// TODO additional metrics?
Arc::new(SparkPlan::new(spark_plan.plan_id, window_agg, vec![child])),
))
}
}
Expand Down Expand Up @@ -1331,8 +1403,8 @@ impl PhysicalPlanner {

Ok((
JoinParameters {
left,
right,
left: left.wrapped.clone(),
right: right.wrapped.clone(),
join_on,
join_type,
join_filter,
Expand Down Expand Up @@ -2207,6 +2279,7 @@ mod tests {
#[test]
fn test_unpack_dictionary_primitive() {
let op_scan = Operator {
plan_id: 0,
children: vec![],
op_struct: Some(OpStruct::Scan(spark_operator::Scan {
fields: vec![spark_expression::DataType {
Expand All @@ -2232,7 +2305,7 @@ mod tests {

let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let mut stream = datafusion_plan.execute(0, task_ctx).unwrap();
let mut stream = datafusion_plan.wrapped.execute(0, task_ctx).unwrap();

let runtime = tokio::runtime::Runtime::new().unwrap();
let (tx, mut rx) = mpsc::channel(1);
Expand Down Expand Up @@ -2279,6 +2352,7 @@ mod tests {
#[test]
fn test_unpack_dictionary_string() {
let op_scan = Operator {
plan_id: 0,
children: vec![],
op_struct: Some(OpStruct::Scan(spark_operator::Scan {
fields: vec![spark_expression::DataType {
Expand Down Expand Up @@ -2315,7 +2389,7 @@ mod tests {

let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let mut stream = datafusion_plan.execute(0, task_ctx).unwrap();
let mut stream = datafusion_plan.wrapped.execute(0, task_ctx).unwrap();

let runtime = tokio::runtime::Runtime::new().unwrap();
let (tx, mut rx) = mpsc::channel(1);
Expand Down Expand Up @@ -2365,6 +2439,7 @@ mod tests {
#[allow(clippy::field_reassign_with_default)]
async fn to_datafusion_filter() {
let op_scan = spark_operator::Operator {
plan_id: 0,
children: vec![],
op_struct: Some(spark_operator::operator::OpStruct::Scan(
spark_operator::Scan {
Expand All @@ -2388,7 +2463,10 @@ mod tests {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();

let stream = datafusion_plan.execute(0, Arc::clone(&task_ctx)).unwrap();
let stream = datafusion_plan
.wrapped
.execute(0, Arc::clone(&task_ctx))
.unwrap();
let output = collect(stream).await.unwrap();
assert!(output.is_empty());
}
Expand Down Expand Up @@ -2442,6 +2520,7 @@ mod tests {
};

Operator {
plan_id: 0,
children: vec![child_op],
op_struct: Some(OpStruct::Filter(spark_operator::Filter {
predicate: Some(expr),
Expand Down
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