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https://issues.apache.org/jira/browse/BEAM-4388?focusedWorklogId=108146&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-108146
]
ASF GitHub Bot logged work on BEAM-4388:
----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Jun/18 17:22
Start Date: 01/Jun/18 17:22
Worklog Time Spent: 10m
Work Description: vectorijk commented on a change in pull request #5481:
[BEAM-4388] Support optimized logical plan
URL: https://github.com/apache/beam/pull/5481#discussion_r192461895
##########
File path:
sdks/java/extensions/sql/src/main/java/org/apache/beam/sdk/extensions/sql/impl/rel/BeamRelNode.java
##########
@@ -17,15 +17,77 @@
*/
package org.apache.beam.sdk.extensions.sql.impl.rel;
+import java.util.List;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionTuple;
import org.apache.beam.sdk.values.Row;
import org.apache.calcite.rel.RelNode;
+import org.apache.calcite.rel.type.RelDataType;
+import org.apache.calcite.rel.type.RelDataTypeField;
/** A {@link RelNode} that can also give a {@link PTransform} that implements
the expression. */
public interface BeamRelNode extends RelNode {
+ default double estimateRowSize(RelDataType rowType) {
+ List<RelDataTypeField> fieldList = rowType.getFieldList();
+
+ return fieldList
+ .stream()
+ .mapToDouble(typeField -> estimateDataTypeSize(typeField.getType()))
+ .sum();
+ }
+
+ default double estimateDataTypeSize(RelDataType type) {
Review comment:
Didn't notice that. I think that's a good way and I will switch to
`RowCoder.estimatedSizeBytes()`
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Issue Time Tracking
-------------------
Worklog Id: (was: 108146)
Time Spent: 50m (was: 40m)
> Support optimized logical plan
> ------------------------------
>
> Key: BEAM-4388
> URL: https://issues.apache.org/jira/browse/BEAM-4388
> Project: Beam
> Issue Type: Sub-task
> Components: dsl-sql
> Reporter: Kai Jiang
> Assignee: Kai Jiang
> Priority: Major
> Time Spent: 50m
> Remaining Estimate: 0h
>
> Before converting into Beam Pipeline physical plan, logical plan should be
> optimized and it will be super helpful for efficiently executing Beam
> PTransforms pipeline.
> Calcite has two ways for optimizing logical plan (HepPlanner and
> VolcanoPlanner). We can support VolcanoPlanner first and apply calcite
> builtin optimize rules (like
> FilterJoinRule.FILTER_ON_JOIN) to sql query optimize plans.
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