perf(v2): tune parquet row iterator batching (#3726)

pkg/experiment/query_backend/query_profile_entry.go

@@ -13,6 +13,20 @@ import (
 	"github.com/grafana/pyroscope/pkg/phlaredb/tsdb/index"
 )
 
+// As we expect rows to be very small, we want to fetch a bigger
+// batch of rows at once to amortize the latency of reading.
+const bigBatchSize = 2 << 10
+
+type ProfileEntry struct {
+	RowNum      int64
+	Timestamp   model.Time
+	Fingerprint model.Fingerprint
+	Labels      phlaremodel.Labels
+	Partition   uint64
+}
+
+func (e ProfileEntry) RowNumber() int64 { return e.RowNum }
+
 func profileEntryIterator(q *queryContext, groupBy ...string) (iter.Iterator[ProfileEntry], error) {
 	series, err := getSeriesLabels(q.ds.Index(), q.req.matchers, groupBy...)
 	if err != nil {
@@ -28,7 +42,7 @@ func profileEntryIterator(q *queryContext, groupBy ...string) (iter.Iterator[Pro
 
 	buf := make([][]parquet.Value, 3)
 	entries := iter.NewAsyncBatchIterator[*parquetquery.IteratorResult, ProfileEntry](
-		results, 128,
+		results, bigBatchSize,
 		func(r *parquetquery.IteratorResult) ProfileEntry {
 			buf = r.Columns(buf,
 				schemav1.SeriesIndexColumnName,
@@ -48,16 +62,6 @@ func profileEntryIterator(q *queryContext, groupBy ...string) (iter.Iterator[Pro
 	return entries, nil
 }
 
-type ProfileEntry struct {
-	RowNum      int64
-	Timestamp   model.Time
-	Fingerprint model.Fingerprint
-	Labels      phlaremodel.Labels
-	Partition   uint64
-}
-
-func (e ProfileEntry) RowNumber() int64 { return e.RowNum }
-
 type seriesLabels struct {
 	fingerprint model.Fingerprint
 	labels      phlaremodel.Labels

pkg/experiment/query_backend/query_time_series.go

@@ -40,7 +40,7 @@ func queryTimeSeries(q *queryContext, query *queryv1.Query) (r *queryv1.Report,
 		return nil, err
 	}
 
-	rows := parquetquery.NewRepeatedRowIterator(q.ctx, entries, q.ds.Profiles().RowGroups(), column.ColumnIndex)
+	rows := parquetquery.NewRepeatedRowIteratorBatchSize(q.ctx, entries, q.ds.Profiles().RowGroups(), bigBatchSize, column.ColumnIndex)
 	defer runutil.CloseWithErrCapture(&err, rows, "failed to close column iterator")
 
 	builder := phlaremodel.NewTimeSeriesBuilder(query.TimeSeries.GroupBy...)

pkg/phlaredb/query/repeated.go

@@ -38,24 +38,43 @@ const (
 	//
 	// How many values we expect per a row, the upper boundary?
 	repeatedRowColumnIteratorReadSize = 2 << 10
+
+	// Batch size specifies how many rows to read from a column at once.
+	// Note that the batched rows are buffered in-memory but do not reference
+	// the pages from which they were read.
+	//
+	// The default value is extremely conservative, as in most cases, rows are
+	// quite large (e.g., profile samples). Given that we run many queries
+	// concurrently, the memory waste outweighs the benefits of the "read-ahead"
+	// optimization that batching is intended to provide.
+	//
+	// However, in cases where the rows are small (such as in time series),
+	// the value should be increased significantly.
+	defaultBatchSize = 4
 )
 
 func NewRepeatedRowIterator[T any](
 	ctx context.Context,
 	rows iter.Iterator[T],
 	rowGroups []parquet.RowGroup,
 	columns ...int,
+) iter.Iterator[RepeatedRow[T]] {
+	return NewRepeatedRowIteratorBatchSize(ctx, rows, rowGroups, defaultBatchSize, columns...)
+}
+
+func NewRepeatedRowIteratorBatchSize[T any](
+	ctx context.Context,
+	rows iter.Iterator[T],
+	rowGroups []parquet.RowGroup,
+	batchSize int64,
+	columns ...int,
 ) iter.Iterator[RepeatedRow[T]] {
 	rows, rowNumbers := iter.Tee(rows)
 	return &repeatedRowIterator[T]{
 		rows: rows,
 		columns: NewMultiColumnIterator(ctx,
 			WrapWithRowNumber(rowNumbers),
-			// Batch size specifies how many rows to be read
-			// from a column at once. Note that the batched rows
-			// are buffered in-memory, but not reference pages
-			// they were read from.
-			4,
+			int(batchSize),
 			rowGroups,
 			columns...,
 		),