Explain the doc

docs/structured-streaming-programming-guide.md

@@ -1674,6 +1674,16 @@ Any of the stateful operation(s) after any of below stateful operations can have
 As Spark cannot check the state function of `mapGroupsWithState`/`flatMapGroupsWithState`, Spark assumes that the state function
 emits late rows if the operator uses Append mode.
 
+Spark provides two ways to check the number of late rows on stateful operators which would help you identify the issue:
+
+1. On Spark UI: check the metrics in stateful operator nodes in query execution details page in SQL tab
+2. On Streaming Query Listener: check "numLateInputs" in "stateOperators" in QueryProcessEvent.
+
+Please note that the definition of "input" is relative: it doesn't always mean "input rows" for the operator.
+Streaming aggregation does pre-aggregate input rows and checks the late inputs against pre-aggregated inputs,
+hence the number is not same as the number of original input rows. You'd like to check the fact whether the value is zero
+or non-zero.
+
 There's a known workaround: split your streaming query into multiple queries per stateful operator, and ensure
 end-to-end exactly once per query. Ensuring end-to-end exactly once for the last query is optional.