kgyrtkirk commented on code in PR #17038: URL: https://github.com/apache/druid/pull/17038#discussion_r1794782761
########## processing/src/main/java/org/apache/druid/query/operator/GlueingPartitioningOperator.java: ########## @@ -0,0 +1,282 @@ +/* + * 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 org.apache.druid.error.InvalidInput; +import org.apache.druid.java.util.common.ISE; +import org.apache.druid.query.rowsandcols.ConcatRowsAndColumns; +import org.apache.druid.query.rowsandcols.LimitedRowsAndColumns; +import org.apache.druid.query.rowsandcols.RowsAndColumns; +import org.apache.druid.query.rowsandcols.column.Column; +import org.apache.druid.query.rowsandcols.column.ColumnAccessor; +import org.apache.druid.query.rowsandcols.semantic.ClusteredGroupPartitioner; +import org.apache.druid.query.rowsandcols.semantic.DefaultClusteredGroupPartitioner; + +import java.io.Closeable; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.Iterator; +import java.util.List; +import java.util.NoSuchElementException; +import java.util.concurrent.atomic.AtomicReference; + +/** + * This glueing partitioning operator is supposed to continuously receive data, and output batches of partitioned RACs. + * It maintains a last-partitioning-boundary of the last-pushed-RAC, and attempts to glue it with the next RAC it receives, + * ensuring that partitions are handled correctly, even across multiple RACs. + * <p> + * Additionally, this assumes that data has been pre-sorted according to the partitioning columns. + */ +public class GlueingPartitioningOperator extends AbstractPartitioningOperator +{ + private final int maxRowsMaterialized; + private RowsAndColumns previousRac; + + private static final Integer MAX_ROWS_MATERIALIZED_NO_LIMIT = Integer.MAX_VALUE; + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns + ) + { + this(child, partitionColumns, MAX_ROWS_MATERIALIZED_NO_LIMIT); + } + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns, + Integer maxRowsMaterialized + ) + { + super(partitionColumns, child); + this.maxRowsMaterialized = maxRowsMaterialized; + } + + @Override + public Closeable goOrContinue(Closeable continuation, Receiver receiver) + { + if (continuation != null) { + Continuation cont = (Continuation) continuation; + + if (cont.iter != null) { + while (cont.iter.hasNext()) { + RowsAndColumns next = cont.iter.next(); + + if (!cont.iter.hasNext()) { + // We are at the last RAC. Process it only if subContinuation is null, otherwise save it in previousRac. + if (cont.subContinuation == null) { + receiver.push(next); + receiver.completed(); + return null; + } else { + previousRac = next; + break; + } + } + + final Signal signal = receiver.push(next); + if (signal != Signal.GO) { + return handleNonGoCases(signal, cont.iter, receiver, cont); + } + } + + if (cont.subContinuation == null) { + receiver.completed(); + return null; + } + } + + continuation = cont.subContinuation; + } + + AtomicReference<Iterator<RowsAndColumns>> iterHolder = new AtomicReference<>(); + + final Closeable retVal = child.goOrContinue( + continuation, + new Receiver() + { + @Override + public Signal push(RowsAndColumns rac) + { + ensureMaxRowsMaterializedConstraint(rac.numRows()); + return handlePush(rac, receiver, iterHolder); + } + + @Override + public void completed() + { + if (previousRac != null) { + receiver.push(previousRac); + previousRac = null; + } + if (iterHolder.get() == null) { + receiver.completed(); + } + } + } + ); + + if (iterHolder.get() != null || retVal != null) { + return new Continuation( + iterHolder.get(), + retVal + ); + } else { + return null; + } + } + + /** + * Iterator implementation for gluing partitioned RowsAndColumns. + * It handles the boundaries of partitions within a single RAC and potentially glues + * the first partition of the current RAC with the previous RAC if needed. + */ + private class GluedRACsIterator implements Iterator<RowsAndColumns> + { + private final RowsAndColumns rac; + private final int[] boundaries; + private int currentIndex = 0; + private boolean firstPartitionHandled = false; + + public GluedRACsIterator(RowsAndColumns rac) + { + this.rac = rac; + ClusteredGroupPartitioner groupPartitioner = rac.as(ClusteredGroupPartitioner.class); + if (groupPartitioner == null) { + groupPartitioner = new DefaultClusteredGroupPartitioner(rac); + } + this.boundaries = groupPartitioner.computeBoundaries(partitionColumns); + } + + @Override + public boolean hasNext() + { + return currentIndex < boundaries.length - 1; + } + + /** + * Retrieves the next partition in the RowsAndColumns. If the first partition has not been handled yet, + * it may be glued with the previous RowsAndColumns if the partition columns match. + * + * @return The next RowsAndColumns partition, potentially glued with the previous one. + * @throws NoSuchElementException if there are no more partitions. + */ + @Override + public RowsAndColumns next() + { + if (!hasNext()) { + throw new NoSuchElementException(); + } + + if (!firstPartitionHandled) { Review Comment: note: you don't necessarily need this boolean; couldn't `(previousRac != null)` act like it? or `currentIndex==0` ? having `(previousRac != null)` at a higher level could also cleanup some conditionals ########## processing/src/main/java/org/apache/druid/query/operator/GlueingPartitioningOperator.java: ########## @@ -0,0 +1,282 @@ +/* + * 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 org.apache.druid.error.InvalidInput; +import org.apache.druid.java.util.common.ISE; +import org.apache.druid.query.rowsandcols.ConcatRowsAndColumns; +import org.apache.druid.query.rowsandcols.LimitedRowsAndColumns; +import org.apache.druid.query.rowsandcols.RowsAndColumns; +import org.apache.druid.query.rowsandcols.column.Column; +import org.apache.druid.query.rowsandcols.column.ColumnAccessor; +import org.apache.druid.query.rowsandcols.semantic.ClusteredGroupPartitioner; +import org.apache.druid.query.rowsandcols.semantic.DefaultClusteredGroupPartitioner; + +import java.io.Closeable; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.Iterator; +import java.util.List; +import java.util.NoSuchElementException; +import java.util.concurrent.atomic.AtomicReference; + +/** + * This glueing partitioning operator is supposed to continuously receive data, and output batches of partitioned RACs. + * It maintains a last-partitioning-boundary of the last-pushed-RAC, and attempts to glue it with the next RAC it receives, + * ensuring that partitions are handled correctly, even across multiple RACs. + * <p> + * Additionally, this assumes that data has been pre-sorted according to the partitioning columns. + */ +public class GlueingPartitioningOperator extends AbstractPartitioningOperator +{ + private final int maxRowsMaterialized; + private RowsAndColumns previousRac; + + private static final Integer MAX_ROWS_MATERIALIZED_NO_LIMIT = Integer.MAX_VALUE; + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns + ) + { + this(child, partitionColumns, MAX_ROWS_MATERIALIZED_NO_LIMIT); + } + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns, + Integer maxRowsMaterialized + ) + { + super(partitionColumns, child); + this.maxRowsMaterialized = maxRowsMaterialized; Review Comment: could this be `null`? ########## processing/src/main/java/org/apache/druid/query/operator/GlueingPartitioningOperator.java: ########## @@ -0,0 +1,279 @@ +/* + * 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 org.apache.druid.error.InvalidInput; +import org.apache.druid.query.rowsandcols.ConcatRowsAndColumns; +import org.apache.druid.query.rowsandcols.LimitedRowsAndColumns; +import org.apache.druid.query.rowsandcols.RowsAndColumns; +import org.apache.druid.query.rowsandcols.column.Column; +import org.apache.druid.query.rowsandcols.column.ColumnAccessor; +import org.apache.druid.query.rowsandcols.semantic.ClusteredGroupPartitioner; +import org.apache.druid.query.rowsandcols.semantic.DefaultClusteredGroupPartitioner; + +import java.io.Closeable; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Iterator; +import java.util.List; +import java.util.NoSuchElementException; +import java.util.concurrent.atomic.AtomicReference; + +/** + * This glueing partitioning operator is supposed to continuously receive data, and output batches of partitioned RACs. + * It maintains a last-partitioning-boundary of the last-pushed-RAC, and attempts to glue it with the next RAC it receives, + * ensuring that partitions are handled correctly, even across multiple RACs. + * <p> + * Additionally, this assumes that data has been pre-sorted according to the partitioning columns. + */ +public class GlueingPartitioningOperator extends AbstractPartitioningOperator +{ + private final int maxRowsMaterialized; + private RowsAndColumns previousRac; + + private static final int MAX_ROWS_MATERIALIZED_NO_LIMIT = -1; + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns + ) + { + this(child, partitionColumns, MAX_ROWS_MATERIALIZED_NO_LIMIT); + } + + public GlueingPartitioningOperator( + Operator child, + List<String> partitionColumns, + int maxRowsMaterialized + ) + { + super(partitionColumns, child); + this.maxRowsMaterialized = maxRowsMaterialized; + } + + @Override + public Closeable goOrContinue(Closeable continuation, Receiver receiver) + { + if (continuation != null) { + Continuation cont = (Continuation) continuation; + + if (cont.iter != null) { + while (cont.iter.hasNext()) { + RowsAndColumns next = cont.iter.next(); + + if (!cont.iter.hasNext()) { + // We are at the last RAC. Process it only if subContinuation is null, otherwise save it in previousRac. + if (cont.subContinuation == null) { + receiver.push(next); + receiver.completed(); + return null; + } else { + previousRac = next; + break; + } + } + + final Signal signal = receiver.push(next); + if (signal != Signal.GO) { + return handleNonGoCases(signal, cont.iter, receiver, cont); + } + } + + if (cont.subContinuation == null) { + receiver.completed(); + return null; + } + } + + continuation = cont.subContinuation; + } + + AtomicReference<Iterator<RowsAndColumns>> iterHolder = new AtomicReference<>(); + + final Closeable retVal = child.goOrContinue( + continuation, + new Receiver() + { + @Override + public Signal push(RowsAndColumns rac) + { + ensureMaxRowsMaterializedConstraint(rac.numRows()); + return handlePush(rac, receiver, iterHolder); + } + + @Override + public void completed() + { + if (previousRac != null) { + receiver.push(previousRac); + previousRac = null; + } + if (iterHolder.get() == null) { + receiver.completed(); + } + } + } + ); + + if (iterHolder.get() != null || retVal != null) { + return new Continuation( + iterHolder.get(), + retVal + ); + } else { + return null; + } + } + + /** + * Iterator implementation for gluing partitioned RowsAndColumns. + * It handles the boundaries of partitions within a single RAC and potentially glues + * the first partition of the current RAC with the previous RAC if needed. + */ + private class GluedRACsIterator implements Iterator<RowsAndColumns> + { + private final RowsAndColumns rac; + private final int[] boundaries; + private int currentIndex = 0; + private boolean firstPartitionHandled = false; + + public GluedRACsIterator(RowsAndColumns rac) + { + this.rac = rac; + ClusteredGroupPartitioner groupPartitioner = rac.as(ClusteredGroupPartitioner.class); + if (groupPartitioner == null) { + groupPartitioner = new DefaultClusteredGroupPartitioner(rac); + } + this.boundaries = groupPartitioner.computeBoundaries(partitionColumns); + } + + @Override + public boolean hasNext() + { + return currentIndex < boundaries.length - 1; + } + + /** + * Retrieves the next partition in the RowsAndColumns. If the first partition has not been handled yet, + * it may be glued with the previous RowsAndColumns if the partition columns match. + * + * @return The next RowsAndColumns partition, potentially glued with the previous one. + * @throws NoSuchElementException if there are no more partitions. + */ + @Override + public RowsAndColumns next() + { + if (!hasNext()) { + throw new NoSuchElementException(); + } + + if (!firstPartitionHandled) { + firstPartitionHandled = true; + int start = boundaries[currentIndex]; + int end = boundaries[currentIndex + 1]; + LimitedRowsAndColumns limitedRAC = new LimitedRowsAndColumns(rac, start, end); + + if (isGlueingNeeded(previousRac, limitedRAC)) { Review Comment: note: not sure if there a need to pass 2 integers - could compare the 1st and last row ########## processing/src/test/java/org/apache/druid/query/operator/NaivePartitioningOperatorTest.java: ########## @@ -68,6 +68,49 @@ public void testDefaultImplementation() .runToCompletion(op); } + @Test + public void testPartitioningWithMultipleRACs() + { + RowsAndColumns rac1 = MapOfColumnsRowsAndColumns.fromMap( + ImmutableMap.of( + "sorted", new IntArrayColumn(new int[]{0, 0, 0, 1, 1}), + "unsorted", new IntArrayColumn(new int[]{3, 54, 21, 1, 5}) + ) Review Comment: update api to the more readable one ########## extensions-core/multi-stage-query/src/main/java/org/apache/druid/msq/querykit/WindowOperatorQueryFrameProcessor.java: ########## @@ -445,100 +318,60 @@ private void convertRowFrameToRowsAndColumns(Frame frame) null, OffsetLimit.limit(Integer.MAX_VALUE), null, - null + null, + (int) frameWriterFactory.allocatorCapacity() Review Comment: it would be more futureproof to not limit something to 4GB ; at least in the new api don't use `int` ; you may cast at the end... ########## extensions-core/multi-stage-query/src/main/java/org/apache/druid/msq/querykit/WindowOperatorQueryFrameProcessor.java: ########## @@ -158,174 +131,74 @@ 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(); + LazilyDecoratedRowsAndColumns ldrc = convertRowFrameToRowsAndColumns(frame); + frameRowsAndColsBuilder.add(ldrc); + ensureMaxRowsInAWindowConstraint(frameRowsAndColsBuilder.getNumRows()); Review Comment: inline `ensureMaxRowsInAWindowConstraint` into the builder's `add` ########## processing/src/main/java/org/apache/druid/query/operator/GlueingPartitioningOperatorFactory.java: ########## @@ -0,0 +1,82 @@ +/* + * 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.fasterxml.jackson.annotation.JsonCreator; +import com.fasterxml.jackson.annotation.JsonProperty; + +import java.util.List; +import java.util.Objects; + +public class GlueingPartitioningOperatorFactory extends AbstractPartitioningOperatorFactory +{ + private final int maxRowsMaterialized; + + @JsonCreator + public GlueingPartitioningOperatorFactory( + @JsonProperty("partitionColumns") List<String> partitionColumns, + @JsonProperty("maxRowsMaterialized") int maxRowsMaterialized + ) + { + super(partitionColumns); + this.maxRowsMaterialized = maxRowsMaterialized; + } + + @JsonProperty("maxRowsMaterialized") + public int getMaxRowsMaterialized() + { + return maxRowsMaterialized; + } + + @Override + public Operator wrap(Operator op) + { + return new GlueingPartitioningOperator(op, partitionColumns, maxRowsMaterialized); + } + + @Override + public boolean validateEquivalent(OperatorFactory other) + { + return super.validateEquivalent(other) && + maxRowsMaterialized == ((GlueingPartitioningOperatorFactory) other).getMaxRowsMaterialized(); Review Comment: I don't think it matters ########## processing/src/main/java/org/apache/druid/query/operator/AbstractPartitioningOperator.java: ########## @@ -0,0 +1,121 @@ +/* + * 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 org.apache.druid.error.DruidException; +import org.apache.druid.java.util.common.RE; +import org.apache.druid.query.rowsandcols.RowsAndColumns; + +import java.io.Closeable; +import java.io.IOException; +import java.util.Iterator; +import java.util.List; +import java.util.concurrent.atomic.AtomicReference; + +public abstract class AbstractPartitioningOperator implements Operator +{ + protected final List<String> partitionColumns; + protected final Operator child; + + public AbstractPartitioningOperator( + List<String> partitionColumns, + Operator child + ) + { + this.partitionColumns = partitionColumns; + this.child = child; + } + + protected static class Continuation implements Closeable + { + Iterator<RowsAndColumns> iter; + Closeable subContinuation; + + public Continuation(Iterator<RowsAndColumns> iter, Closeable subContinuation) + { + this.iter = iter; + this.subContinuation = subContinuation; + } + + @Override + public void close() throws IOException + { + if (subContinuation != null) { + subContinuation.close(); + } + } + } + + protected Signal handlePush(RowsAndColumns rac, Receiver receiver, AtomicReference<Iterator<RowsAndColumns>> iterHolder) + { + if (rac == null) { + throw DruidException.defensive("Should never get a null rac here."); + } + + Iterator<RowsAndColumns> partitionsIter = getIteratorForRAC(rac); + + AtomicReference<Signal> keepItGoing = new AtomicReference<>(Signal.GO); Review Comment: then it maybe return an `Optional<Signal>` ? ########## 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: thank you ; much more readable! ########## processing/src/main/java/org/apache/druid/query/rowsandcols/LazilyDecoratedRowsAndColumns.java: ########## @@ -95,6 +110,7 @@ public LazilyDecoratedRowsAndColumns( this.limit = limit; this.ordering = ordering; this.viewableColumns = viewableColumns; + this.allocatorCapacity = allocatorCapacity != null ? allocatorCapacity : 200 << 20; Review Comment: note: the codebase is full of passing 2 integers and 3 booleans thru methods which don't care about them...I don't think its okay to burn in a constant like `200 << 20` but it was there before...and it got better... -- This is an automated message from the Apache Git Service. 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