Akshat-Jain commented on code in PR #17038: URL: https://github.com/apache/druid/pull/17038#discussion_r1793844642
########## processing/src/test/java/org/apache/druid/query/operator/GlueingPartitioningOperatorTest.java: ########## @@ -0,0 +1,417 @@ +/* + * 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.druid.query.operator; + +import com.google.common.collect.ImmutableList; +import com.google.common.collect.ImmutableMap; +import org.apache.druid.error.DruidException; +import org.apache.druid.query.operator.window.RowsAndColumnsHelper; +import org.apache.druid.query.rowsandcols.MapOfColumnsRowsAndColumns; +import org.apache.druid.query.rowsandcols.RowsAndColumns; +import org.apache.druid.query.rowsandcols.column.IntArrayColumn; +import org.junit.Assert; +import org.junit.Test; + +import java.util.Collections; +import java.util.function.BiFunction; + +public class GlueingPartitioningOperatorTest +{ + @Test + public void testDefaultImplementation() Review Comment: Done ########## extensions-core/multi-stage-query/src/main/java/org/apache/druid/msq/querykit/WindowOperatorQueryFrameProcessor.java: ########## @@ -158,174 +131,73 @@ public List<WritableFrameChannel> outputChannels() @Override public ReturnOrAwait<Object> runIncrementally(IntSet readableInputs) throws IOException { - /* - There are 2 scenarios: - - *** Scenario 1: Query has atleast one window function with an OVER() clause without a PARTITION BY *** - - In this scenario, we add all the RACs to a single RowsAndColumns to be processed. We do it via ConcatRowsAndColumns, and run all the operators on the ConcatRowsAndColumns. - This is done because we anyway need to run the operators on the entire set of rows when we have an OVER() clause without a PARTITION BY. - This scenario corresponds to partitionColumnNames.isEmpty()=true code flow. - - *** Scenario 2: All window functions in the query have OVER() clause with a PARTITION BY *** - - In this scenario, we need to process rows for each PARTITION BY group together, but we can batch multiple PARTITION BY keys into the same RAC before passing it to the operators for processing. - Batching is fine since the operators list would have the required NaivePartitioningOperatorFactory to segregate each PARTITION BY group during the processing. - - The flow for this scenario can be summarised as following: - 1. Frame Reading and Cursor Initialization: We start by reading a frame from the inputChannel and initializing frameCursor to iterate over the rows in that frame. - 2. Row Comparison: For each row in the frame, we decide whether it belongs to the same PARTITION BY group as the previous row. - This is determined by comparePartitionKeys() method. - Please refer to the Javadoc of that method for further details and an example illustration. - 2.1. If the PARTITION BY columns of current row matches the PARTITION BY columns of the previous row, - they belong to the same PARTITION BY group, and gets added to rowsToProcess. - If the number of total rows materialized exceed maxRowsMaterialized, we process the pending batch via processRowsUpToLastPartition() method. - 2.2. If they don't match, then we have reached a partition boundary. - In this case, we update the value for lastPartitionIndex. - 3. End of Input: If the input channel is finished, any remaining rows in rowsToProcess are processed. - - *Illustration of Row Comparison step* - - Let's say we have window_function() OVER (PARTITION BY A ORDER BY B) in our query, and we get 3 frames in the input channel to process. - - Frame 1 - A, B - 1, 2 - 1, 3 - 2, 1 --> PARTITION BY key (column A) changed from 1 to 2. - 2, 2 - - Frame 2 - A, B - 3, 1 --> PARTITION BY key (column A) changed from 2 to 3. - 3, 2 - 3, 3 - 3, 4 - - Frame 3 - A, B - 3, 5 - 3, 6 - 4, 1 --> PARTITION BY key (column A) changed from 3 to 4. - 4, 2 - - *Why batching?* - We batch multiple PARTITION BY keys for processing together to avoid the overhead of creating different RACs for each PARTITION BY keys, as that would be unnecessary in scenarios where we have a large number of PARTITION BY keys, but each key having a single row. - - *Future thoughts: https://github.com/apache/druid/issues/16126* - Current approach with R&C and operators materialize a single R&C for processing. In case of data with low cardinality a single R&C might be too big to consume. Same for the case of empty OVER() clause. - Most of the window operations like SUM(), RANK(), RANGE() etc. can be made with 2 passes of the data. We might think to reimplement them in the MSQ way so that we do not have to materialize so much data. - */ - // If there are rows pending flush, flush them and run again before processing any more rows. if (frameHasRowsPendingFlush()) { flushAllRowsAndCols(); return ReturnOrAwait.runAgain(); } - if (partitionColumnNames.isEmpty()) { - // Scenario 1: Query has atleast one window function with an OVER() clause without a PARTITION BY. - if (inputChannel.canRead()) { - final Frame frame = inputChannel.read(); - convertRowFrameToRowsAndColumns(frame); - return ReturnOrAwait.runAgain(); - } - - if (inputChannel.isFinished()) { - // If no rows are flushed yet, process all rows. - if (rowId.get() == 0) { - runAllOpsOnMultipleRac(frameRowsAndCols); - } + if (inputChannel.canRead()) { + final Frame frame = inputChannel.read(); + convertRowFrameToRowsAndColumns(frame); Review Comment: Done -- This is an automated message from the Apache Git Service. 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