cloud-fan commented on a change in pull request #29724:
URL: https://github.com/apache/spark/pull/29724#discussion_r486837991
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -246,13 +244,14 @@ case class StreamingSymmetricHashJoinExec(
// Join one side input using the other side's buffered/state rows. Here
is how it is done.
//
- // - `leftJoiner.joinWith(rightJoiner)` generates all rows from matching
new left input with
Review comment:
I'm not familiar with this part, cc @zsxwing @HeartSaVioR @xuanyuanking
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -246,13 +244,14 @@ case class StreamingSymmetricHashJoinExec(
// Join one side input using the other side's buffered/state rows. Here
is how it is done.
//
- // - `leftJoiner.joinWith(rightJoiner)` generates all rows from matching
new left input with
Review comment:
I'm not familiar with this part, cc @zsxwing @HeartSaVioR @xuanyuanking
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -246,13 +244,14 @@ case class StreamingSymmetricHashJoinExec(
// Join one side input using the other side's buffered/state rows. Here
is how it is done.
//
- // - `leftJoiner.joinWith(rightJoiner)` generates all rows from matching
new left input with
Review comment:
I'm not familiar with this part, cc @zsxwing @HeartSaVioR @xuanyuanking
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -246,13 +244,14 @@ case class StreamingSymmetricHashJoinExec(
// Join one side input using the other side's buffered/state rows. Here
is how it is done.
//
- // - `leftJoiner.joinWith(rightJoiner)` generates all rows from matching
new left input with
Review comment:
I'm not familiar with this part, cc @zsxwing @HeartSaVioR @xuanyuanking
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -56,8 +56,8 @@ import org.apache.spark.util.{CompletionIterator,
SerializableConfiguration}
* - Apply the optional condition to filter the joined rows as the final
output.
*
* If a timestamp column with event time watermark is present in the join keys
or in the input
- * data, then the it uses the watermark figure out which rows in the buffer
will not join with
- * and the new data, and therefore can be discarded. Depending on the provided
query conditions, we
+ * data, then it uses the watermark figure out which rows in the buffer will
not join with
Review comment:
`uses the watermark to figure out`
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinExec.scala
##########
@@ -246,13 +244,14 @@ case class StreamingSymmetricHashJoinExec(
// Join one side input using the other side's buffered/state rows. Here
is how it is done.
//
- // - `leftJoiner.joinWith(rightJoiner)` generates all rows from matching
new left input with
Review comment:
I'm not familiar with this part, cc @zsxwing @HeartSaVioR @xuanyuanking
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