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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/03/23 08:07:06 UTC

[GitHub] [spark] cloud-fan commented on a change in pull request #31476: [SPARK-34366][SQL] Add interface for DS v2 metrics

cloud-fan commented on a change in pull request #31476:
URL: https://github.com/apache/spark/pull/31476#discussion_r599346059



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File path: sql/catalyst/src/main/java/org/apache/spark/sql/connector/CustomTaskMetric.java
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@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.connector;
+
+import org.apache.spark.annotation.Evolving;
+import org.apache.spark.sql.connector.read.PartitionReader;
+
+/**
+ * A custom task metric. This is a logical representation of a metric reported by data sources
+ * at the executor side. During query execution, Spark will collect the task metrics per partition
+ * by {@link PartitionReader} and update internal metrics based on collected metric values.
+ * For streaming query, Spark will collect and combine metrics for a final result per micro batch.
+ *
+ * The metrics will be gathered during query execution back to the driver and then combined. How
+ * the task metrics are combined is defined by corresponding {@link CustomMetric} with same metric
+ * name. The final result will be shown up in the physical operator in Spark UI.

Review comment:
       `physical operator` -> `data source scan operator`




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