himanshug commented on a change in pull request #8578: parallel broker merges on fork join pool URL: https://github.com/apache/incubator-druid/pull/8578#discussion_r334759043
########## File path: core/src/main/java/org/apache/druid/java/util/common/guava/ParallelMergeCombiningSequence.java ########## @@ -0,0 +1,1071 @@ +/* + * 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.java.util.common.guava; + +import com.google.common.collect.Lists; +import com.google.common.collect.Ordering; +import org.apache.druid.java.util.common.RE; +import org.apache.druid.java.util.common.logger.Logger; +import org.apache.druid.utils.JvmUtils; + +import javax.annotation.Nullable; +import java.io.IOException; +import java.util.ArrayDeque; +import java.util.ArrayList; +import java.util.Iterator; +import java.util.List; +import java.util.NoSuchElementException; +import java.util.Objects; +import java.util.PriorityQueue; +import java.util.Queue; +import java.util.concurrent.ArrayBlockingQueue; +import java.util.concurrent.BlockingQueue; +import java.util.concurrent.ForkJoinPool; +import java.util.concurrent.RecursiveAction; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.TimeoutException; +import java.util.concurrent.atomic.AtomicReference; +import java.util.function.BinaryOperator; + +/** + * Artisanal, locally-sourced, hand-crafted, gluten and GMO free, bespoke, small-batch parallel merge combinining sequence + */ +public class ParallelMergeCombiningSequence<T> extends YieldingSequenceBase<T> +{ + private static final Logger LOG = new Logger(ParallelMergeCombiningSequence.class); + + private final ForkJoinPool workerPool; + private final List<Sequence<T>> baseSequences; + private final Ordering<T> orderingFn; + private final BinaryOperator<T> combineFn; + private final int queueSize; + private final boolean hasTimeout; + private final long timeoutAtNanos; + private final int queryPriority; // not currently used :( + private final int yieldAfter; + private final int batchSize; + private final int parallelism; + private final CancellationGizmo cancellationGizmo; + + public ParallelMergeCombiningSequence( + ForkJoinPool workerPool, + List<Sequence<T>> baseSequences, + Ordering<T> orderingFn, + BinaryOperator<T> combineFn, + boolean hasTimeout, + long timeoutMillis, + int queryPriority, + int parallelism, + int yieldAfter, + int batchSize + ) + { + this.workerPool = workerPool; + this.baseSequences = baseSequences; + this.orderingFn = orderingFn; + this.combineFn = combineFn; + this.hasTimeout = hasTimeout; + this.timeoutAtNanos = System.nanoTime() + TimeUnit.NANOSECONDS.convert(timeoutMillis, TimeUnit.MILLISECONDS); + this.queryPriority = queryPriority; + this.parallelism = parallelism; + this.yieldAfter = yieldAfter; + this.batchSize = batchSize; + this.queueSize = 4 * (yieldAfter / batchSize); + this.cancellationGizmo = new CancellationGizmo(); + } + + @Override + public <OutType> Yielder<OutType> toYielder(OutType initValue, YieldingAccumulator<OutType, T> accumulator) + { + if (baseSequences.isEmpty()) { + return Sequences.<T>empty().toYielder(initValue, accumulator); + } + + final BlockingQueue<ResultBatch<T>> outputQueue = new ArrayBlockingQueue<>(queueSize); + MergeCombinePartitioningAction<T> finalMergeAction = new MergeCombinePartitioningAction<>( + baseSequences, + orderingFn, + combineFn, + outputQueue, + queueSize, + parallelism, + yieldAfter, + batchSize, + hasTimeout, + timeoutAtNanos, + cancellationGizmo + ); + workerPool.execute(finalMergeAction); + Sequence<T> finalOutSequence = makeOutputSequenceForQueue(outputQueue, hasTimeout, timeoutAtNanos, cancellationGizmo); + return finalOutSequence.toYielder(initValue, accumulator); + } + + /** + * Create an output {@link Sequence} that wraps the output {@link BlockingQueue} of a + * {@link MergeCombinePartitioningAction} + */ + static <T> Sequence<T> makeOutputSequenceForQueue( + BlockingQueue<ResultBatch<T>> queue, + boolean hasTimeout, + long timeoutAtNanos, + CancellationGizmo cancellationGizmo + ) + { + return new BaseSequence<>( + new BaseSequence.IteratorMaker<T, Iterator<T>>() + { + @Override + public Iterator<T> make() + { + return new Iterator<T>() + { + private ResultBatch<T> currentBatch; + + @Override + public boolean hasNext() + { + final long thisTimeoutNanos = timeoutAtNanos - System.nanoTime(); + if (thisTimeoutNanos < 0) { + throw new RE(new TimeoutException("Sequence iterator timed out")); + } + + if (currentBatch != null && !currentBatch.isTerminalResult() && !currentBatch.isDrained()) { + return true; + } + try { + if (currentBatch == null || currentBatch.isDrained()) { + if (hasTimeout) { + currentBatch = queue.poll(thisTimeoutNanos, TimeUnit.NANOSECONDS); + } else { + currentBatch = queue.take(); + } + } + if (currentBatch == null) { + throw new RE(new TimeoutException("Sequence iterator timed out waiting for data")); + } + + if (cancellationGizmo.isCancelled()) { + throw cancellationGizmo.getRuntimeException(); + } + + if (currentBatch.isTerminalResult()) { + return false; + } + return true; + } + catch (InterruptedException e) { + throw new RE(e); + } + } + + @Override + public T next() + { + if (cancellationGizmo.isCancelled()) { + throw cancellationGizmo.getRuntimeException(); + } + + if (currentBatch == null || currentBatch.isDrained() || currentBatch.isTerminalResult()) { + throw new NoSuchElementException(); + } + return currentBatch.next(); + } + }; + } + + @Override + public void cleanup(Iterator<T> iterFromMake) + { + // nothing to cleanup + } + } + ); + } + + /** + * This {@link RecursiveAction} is the initial task of the parallel merge-combine process. Capacity and input sequence + * count permitting, it will partition the input set of {@link Sequence} to do 2 layer parallel merge. + * + * For the first layer, the partitions of input sequences are each wrapped in {@link YielderBatchedResultsCursor}, and + * for each partition a {@link PrepareMergeCombineInputsAction} will be executed to wait for each of the yielders to + * yield {@link ResultBatch}. After the cursors all have an initial set of results, the + * {@link PrepareMergeCombineInputsAction} will execute a {@link MergeCombineAction} + * to perform the actual work of merging sequences and combining results. The merged and combined output of each + * partition will itself be put into {@link ResultBatch} and pushed to a {@link BlockingQueue} with a + * {@link ForkJoinPool} {@link QueuePusher}. + * + * The second layer will execute a single {@link PrepareMergeCombineInputsAction} to wait for the {@link ResultBatch} + * from each partition to be available in their 'output' {@link BlockingQueue} which each is wrapped in + * {@link BlockingQueueuBatchedResultsCursor}. Like the first layer, after the {@link PrepareMergeCombineInputsAction} + * is complete and some {@link ResultBatch} are ready to merge from each partition, it will execute a + * {@link MergeCombineAction} do a final merge combine of all the parallel computed results, again pushing + * {@link ResultBatch} into a {@link BlockingQueue} with a {@link QueuePusher}. + */ + private static class MergeCombinePartitioningAction<T> extends RecursiveAction + { + private final List<Sequence<T>> sequences; + private final Ordering<T> orderingFn; + private final BinaryOperator<T> combineFn; + private final BlockingQueue<ResultBatch<T>> out; + private final int queueSize; + private final int parallelism; + private final int yieldAfter; + private final int batchSize; + private final boolean hasTimeout; + private final long timeoutAt; + private final CancellationGizmo cancellationGizmo; + + private MergeCombinePartitioningAction( + List<Sequence<T>> sequences, + Ordering<T> orderingFn, + BinaryOperator<T> combineFn, + BlockingQueue<ResultBatch<T>> out, + int queueSize, + int parallelism, + int yieldAfter, + int batchSize, + boolean hasTimeout, + long timeoutAt, + CancellationGizmo cancellationGizmo + ) + { + this.sequences = sequences; + this.combineFn = combineFn; + this.orderingFn = orderingFn; + this.out = out; + this.queueSize = queueSize; + this.parallelism = parallelism; + this.yieldAfter = yieldAfter; + this.batchSize = batchSize; + this.hasTimeout = hasTimeout; + this.timeoutAt = timeoutAt; + this.cancellationGizmo = cancellationGizmo; + } + + @Override + protected void compute() + { + try { + final int parallelTaskCount = computeNumTasks(); + + // if we have a small number of sequences to merge, or computed paralellism is too low, do not run in parallel, + // just serially perform the merge-combine with a single task + if (sequences.size() < 4 || parallelTaskCount < 2) { + LOG.debug( + "Input sequence count (%s) or available parallel merge task count (%s) too small to perform parallel" + + " merge-combine, performing serially with a single merge-combine task", + sequences.size(), + parallelTaskCount + ); + + QueuePusher<ResultBatch<T>> resultsPusher = new QueuePusher<>(out, hasTimeout, timeoutAt); + + List<BatchedResultsCursor<T>> sequenceCursors = new ArrayList<>(sequences.size()); + for (Sequence<T> s : sequences) { + sequenceCursors.add(new YielderBatchedResultsCursor<>(new SequenceBatcher<>(s, batchSize), orderingFn)); + } + PrepareMergeCombineInputsAction<T> blockForInputsAction = new PrepareMergeCombineInputsAction<>( + sequenceCursors, + resultsPusher, + orderingFn, + combineFn, + yieldAfter, + batchSize, + cancellationGizmo + ); + getPool().execute(blockForInputsAction); + } else { + // 2 layer parallel merge done in fjp + LOG.debug("Spawning %s parallel merge-combine tasks for %s sequences", parallelTaskCount, sequences.size()); + spawnParallelTasks(parallelTaskCount); + } + } + catch (Exception ex) { + cancellationGizmo.cancel(ex); + out.offer(ResultBatch.TERMINAL); + } + } + + private void spawnParallelTasks(int parallelMergeTasks) + { + List<RecursiveAction> tasks = new ArrayList<>(); + List<BlockingQueue<ResultBatch<T>>> intermediaryOutputs = new ArrayList<>(parallelMergeTasks); + + List<? extends List<Sequence<T>>> partitions = + Lists.partition(sequences, sequences.size() / parallelMergeTasks); + + for (List<Sequence<T>> partition : partitions) { + BlockingQueue<ResultBatch<T>> outputQueue = new ArrayBlockingQueue<>(queueSize); + intermediaryOutputs.add(outputQueue); + QueuePusher<ResultBatch<T>> pusher = new QueuePusher<>(outputQueue, hasTimeout, timeoutAt); + + List<BatchedResultsCursor<T>> partitionCursors = new ArrayList<>(sequences.size()); + for (Sequence<T> s : partition) { + partitionCursors.add(new YielderBatchedResultsCursor<>(new SequenceBatcher<>(s, batchSize), orderingFn)); + } + PrepareMergeCombineInputsAction<T> blockForInputsAction = new PrepareMergeCombineInputsAction<>( + partitionCursors, + pusher, + orderingFn, + combineFn, + yieldAfter, + batchSize, + cancellationGizmo + ); + tasks.add(blockForInputsAction); + } + + for (RecursiveAction task : tasks) { + getPool().execute(task); + } + + QueuePusher<ResultBatch<T>> outputPusher = new QueuePusher<>(out, hasTimeout, timeoutAt); + List<BatchedResultsCursor<T>> intermediaryOutputsCursors = new ArrayList<>(intermediaryOutputs.size()); + for (BlockingQueue<ResultBatch<T>> queue : intermediaryOutputs) { + intermediaryOutputsCursors.add( + new BlockingQueueuBatchedResultsCursor<>(queue, orderingFn, hasTimeout, timeoutAt) + ); + } + PrepareMergeCombineInputsAction<T> finalMergeAction = new PrepareMergeCombineInputsAction<>( + intermediaryOutputsCursors, + outputPusher, + orderingFn, + combineFn, + yieldAfter, + batchSize, + cancellationGizmo + ); + + getPool().execute(finalMergeAction); + } + + /** + * Computes maximum number of layer 1 parallel merging tasks given available processors and an estimate of current + * {@link ForkJoinPool} utilization. A return value of 1 or less indicates that a serial merge will be done on + * the pool instead. + */ + private int computeNumTasks() + { + final int availableProcessors = JvmUtils.getRuntimeInfo().getAvailableProcessors(); + final int runningThreadCount = getPool().getRunningThreadCount(); + final int submissionCount = getPool().getQueuedSubmissionCount(); + // max is minimum of either number of processors or user suggested parallelism + final int maxParallelism = Math.min(availableProcessors, parallelism); + // adjust max to be no more than total pool parallelism less the number of running threads + submitted tasks + final int utilizationEstimate = runningThreadCount + submissionCount; + // minimum of 'max computed parallelism' and pool parallelism less current 'utilization estimate' + final int computedParallelism = Math.min(maxParallelism, getPool().getParallelism() - utilizationEstimate); + // compute total number of layer 1 'parallel' tasks, the final merge task will take the remaining slot + // we divide the sequences by 2 because we need at least 2 sequences per partition for it to make sense to need + // an additional parallel task to compute the merge + final int computedOptimalParallelism = Math.min( + (int) Math.floor((double) sequences.size() / 2.0), + computedParallelism - 1 + ); + + final int computedNumParallelTasks = Math.max(computedOptimalParallelism, 1); + + LOG.debug("Computed parallel tasks: [%s]; ForkJoinPool details - processors: [%s] parallelism: [%s] " + + "active threads: [%s] running threads: [%s] queued submissions: [%s] queued tasks: [%s] " + + "pool size: [%s] steal count: [%s]", + computedNumParallelTasks, + availableProcessors, + parallelism, + getPool().getActiveThreadCount(), + runningThreadCount, + submissionCount, + getPool().getQueuedTaskCount(), + getPool().getPoolSize(), + getPool().getStealCount() + ); + + return computedNumParallelTasks; + } + } + + + /** + * This {@link RecursiveAction} is the work-horse of the {@link ParallelMergeCombiningSequence}, it merge-combines + * a set of {@link BatchedResultsCursor} and produces output to a {@link BlockingQueue} with the help of a + * {@link QueuePusher}. This is essentially a composite of logic taken from {@link MergeSequence} and + * {@link org.apache.druid.common.guava.CombiningSequence}, where the {@link Ordering} is used to both set the sort + * order for a {@link PriorityQueue}, and as a comparison to determine if 'same' ordered results need to be combined + * with a supplied {@link BinaryOperator} combining function. + * + * This task takes a {@link #yieldAfter} parameter which controls how many input result rows will be processed before + * this task completes and executes a new task to continue where it left off. This value is initially set by the + * {@link MergeCombinePartitioningAction} to a default value, but after that this process is timed to try and compute + * an 'optimal' number of rows to yield to achieve a task runtime of ~10ms, on the assumption that the time to process + * n results will be approximately the same. {@link #recursionDepth} is used to track how many times a task has + * continued executing, and utilized to compute a cumulative moving average of task run time per amount yielded in + * order to 'smooth' out the continual adjustment. + */ + private static class MergeCombineAction<T> extends RecursiveAction + { + private final PriorityQueue<BatchedResultsCursor<T>> pQueue; + private final Ordering<T> orderingFn; + private final BinaryOperator<T> combineFn; + private final QueuePusher<ResultBatch<T>> outputQueue; + private final T initialValue; + private final int yieldAfter; + private final int batchSize; + private final int recursionDepth; + private final CancellationGizmo cancellationGizmo; + + private MergeCombineAction( + PriorityQueue<BatchedResultsCursor<T>> pQueue, + QueuePusher<ResultBatch<T>> outputQueue, + Ordering<T> orderingFn, + BinaryOperator<T> combineFn, + T initialValue, + int yieldAfter, + int batchSize, + int recursionDepth, + CancellationGizmo cancellationGizmo + ) + { + this.pQueue = pQueue; + this.orderingFn = orderingFn; + this.combineFn = combineFn; + this.outputQueue = outputQueue; + this.initialValue = initialValue; + this.yieldAfter = yieldAfter; + this.batchSize = batchSize; + this.recursionDepth = recursionDepth; + this.cancellationGizmo = cancellationGizmo; + } + + @Override + protected void compute() + { + try { + long start = System.nanoTime(); + + int counter = 0; + int batchCounter = 0; + ResultBatch<T> outputBatch = new ResultBatch<>(batchSize); + + T currentCombinedValue = initialValue; + while (counter++ < yieldAfter && !pQueue.isEmpty()) { + BatchedResultsCursor<T> cursor = pQueue.poll(); + + // push the queue along + if (!cursor.isDone()) { + T nextValueToAccumulate = cursor.get(); + + cursor.advance(); + if (!cursor.isDone()) { + pQueue.offer(cursor); + } else { + cursor.close(); + } + + // if current value is null, combine null with next value + if (currentCombinedValue == null) { + currentCombinedValue = combineFn.apply(null, nextValueToAccumulate); + continue; + } + + // if current value is "same" as next value, combine them + if (orderingFn.compare(currentCombinedValue, nextValueToAccumulate) == 0) { + currentCombinedValue = combineFn.apply(currentCombinedValue, nextValueToAccumulate); + continue; + } + + // else, push accumulated value to the queue, accumulate again with next value as initial + outputBatch.add(currentCombinedValue); + batchCounter++; + if (batchCounter >= batchSize) { + outputQueue.offer(outputBatch); + outputBatch = new ResultBatch<>(batchSize); + batchCounter = 0; + } + + // next value is now current value + currentCombinedValue = combineFn.apply(null, nextValueToAccumulate); + } else { + cursor.close(); + } + } + + if (!pQueue.isEmpty() && !cancellationGizmo.isCancelled()) { + // if there is still work to be done, execute a new task with the current accumulated value to continue + // combining where we left off + if (!outputBatch.isDrained()) { + outputQueue.offer(outputBatch); + } + + // measure the time it took to process 'yieldAfter' elements in order to project a next 'yieldAfter' value + // which we want to target a 10ms task run time. smooth this value with a cumulative moving average in order + // to prevent normal jitter in processing time from skewing the next yield value too far in any direction + final long elapsedMillis = Math.max( + TimeUnit.MILLISECONDS.convert(System.nanoTime() - start, TimeUnit.NANOSECONDS), + 1L + ); + final double nextYieldAfter = Math.max(10.0 * ((double) yieldAfter / elapsedMillis), 1.0); + final double cumulativeMovingAverage = (nextYieldAfter + (recursionDepth * yieldAfter)) / (recursionDepth + 1); + final int adjustedNextYieldAfter = (int) Math.ceil(cumulativeMovingAverage); Review comment: I know giving ideas is easy and totally agree about the "easier said than done" part , and you have the first right to any choice or to degree of experimentations done as the author :) Looked at `fjp.getQueuedSubmissionCount()` and indeed it is fairly expensive at busy times, OTOH noticed that there is a `fjp.hasQueuedSubmissions()` which is much faster and just tells if `fjp.getQueuedSubmissionCount() > 0` > At least the way stuff is currently structured, I don't believe I can currently fully take the potentially blocking parts out of the time measurement unless I do a lot more, smaller measurements and combine them, measuring only periods where I know blocking will not happen. yeah, Even if you did that , cpu time is more effective because so called "blocking" code in `ManagedBlocker` isn't necessarily blocking from OS perspective every time and could consume cpu time. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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