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https://issues.apache.org/jira/browse/DRILL-5716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16151208#comment-16151208
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ASF GitHub Bot commented on DRILL-5716:
---------------------------------------
Github user paul-rogers commented on a diff in the pull request:
https://github.com/apache/drill/pull/928#discussion_r136669083
--- Diff:
exec/java-exec/src/main/java/org/apache/drill/exec/work/foreman/rm/DistributedQueryQueue.java
---
@@ -0,0 +1,272 @@
+/*
+ * 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.drill.exec.work.foreman.rm;
+
+import java.util.concurrent.TimeUnit;
+
+import javax.xml.bind.annotation.XmlRootElement;
+
+import org.apache.drill.exec.ExecConstants;
+import org.apache.drill.exec.coord.ClusterCoordinator;
+import org.apache.drill.exec.coord.DistributedSemaphore;
+import org.apache.drill.exec.coord.DistributedSemaphore.DistributedLease;
+import org.apache.drill.exec.proto.UserBitShared.QueryId;
+import org.apache.drill.exec.proto.helper.QueryIdHelper;
+import org.apache.drill.exec.server.DrillbitContext;
+import org.apache.drill.exec.server.options.SystemOptionManager;
+
+/**
+ * Distributed query queue which uses a Zookeeper distributed semaphore to
+ * control queuing across the cluster. The distributed queue is actually
two
+ * queues: one for "small" queries, another for "large" queries. Query
size is
+ * determined by the Planner's estimate of query cost.
+ * <p>
+ * This queue is configured using system options:
+ * <dl>
+ * <dt><tt>exec.queue.enable</tt>
+ * <dt>
+ * <dd>Set to true to enable the distributed queue.</dd>
+ * <dt><tt>exec.queue.large</tt>
+ * <dt>
+ * <dd>The maximum number of large queries to admit. Additional
+ * queries wait in the queue.</dd>
+ * <dt><tt>exec.queue.small</tt>
+ * <dt>
+ * <dd>The maximum number of small queries to admit. Additional
+ * queries wait in the queue.</dd>
+ * <dt><tt>exec.queue.threshold</tt>
+ * <dt>
+ * <dd>The cost threshold. Queries below this size are small, at
+ * or above this size are large..</dd>
+ * <dt><tt>exec.queue.timeout_millis</tt>
+ * <dt>
+ * <dd>The maximum time (in milliseconds) a query will wait in the
+ * queue before failing.</dd>
+ * </dl>
+ * <p>
+ * The above values are refreshed every five seconds. This aids performance
+ * a bit in systems with very high query arrival rates.
+ */
+
+public class DistributedQueryQueue implements QueryQueue {
+ private static final org.slf4j.Logger logger =
org.slf4j.LoggerFactory.getLogger(DistributedQueryQueue.class);
+
+ private class DistributedQueueLease implements QueueLease {
+ private final QueryId queryId;
+ private DistributedLease lease;
+ private final String queueName;
+ private long queryMemory;
+
+ public DistributedQueueLease(QueryId queryId, String queueName,
DistributedLease lease, long queryMemory) {
+ this.queryId = queryId;
+ this.queueName = queueName;
+ this.lease = lease;
+ this.queryMemory = queryMemory;
+ }
+
+ @Override
+ public String toString() {
+ return String.format("Lease for %s queue to query %s",
+ queueName, QueryIdHelper.getQueryId(queryId));
+ }
+
+ @Override
+ public long queryMemoryPerNode() { return queryMemory; }
+
+ @Override
+ public void release() {
+ DistributedQueryQueue.this.release(this);
+ }
+
+ @Override
+ public String queueName() { return queueName; }
+ }
+
+ /**
+ * Exposes a snapshot of internal state information for use in status
+ * reporting, such as in the UI.
+ */
+
+ @XmlRootElement
+ public static class ZKQueueInfo {
+ public final int smallQueueSize;
+ public final int largeQueueSize;
+ public final double queueThreshold;
+ public final long memoryPerNode;
+ public final long memoryPerSmallQuery;
+ public final long memoryPerLargeQuery;
+
+ public ZKQueueInfo(DistributedQueryQueue queue) {
+ smallQueueSize = queue.smallQueueSize;
+ largeQueueSize = queue.largeQueueSize;
+ queueThreshold = queue.queueThreshold;
+ memoryPerNode = queue.memoryPerNode;
+ memoryPerSmallQuery = queue.memoryPerSmallQuery;
+ memoryPerLargeQuery = queue.memoryPerLargeQuery;
+ }
+ }
+
+ private long memoryPerNode;
+ private int largeQueueSize;
+ private int smallQueueSize;
+ private SystemOptionManager optionManager;
+ private ClusterCoordinator clusterCoordinator;
+ private long queueThreshold;
+ private long queueTimeout;
+ private long refreshTime;
+ private long memoryPerSmallQuery;
+ private long memoryPerLargeQuery;
+ private double largeToSmallRatio;
+
+ public DistributedQueryQueue(DrillbitContext context) {
+ optionManager = context.getOptionManager();
+ clusterCoordinator = context.getClusterCoordinator();
+ }
+
+ @Override
+ public void setMemoryPerNode(long memoryPerNode) {
+ this.memoryPerNode = memoryPerNode;
+ refreshConfig();
+ }
+
+ private void assignMemory() {
+
+ // Divide up memory between queues using admission rate
+ // to give more memory to larger queries and less to
+ // smaller queries. We assume that large queries are
+ // larger than small queries by a factor of
+ // largeToSmallRatio.
+
+ double totalUnits = largeToSmallRatio * largeQueueSize +
smallQueueSize;
+ double memoryUnit = memoryPerNode / totalUnits;
+ memoryPerLargeQuery = Math.round(memoryUnit * largeToSmallRatio);
+ memoryPerSmallQuery = Math.round(memoryUnit);
+
+ logger.debug("Distributed queue memory config: total memory = {},
large/small memory ratio = {}",
+ memoryPerNode, largeToSmallRatio);
+ logger.debug("Small queue: {} slots, {} bytes per slot",
smallQueueSize, memoryPerSmallQuery);
+ logger.debug("Large queue: {} slots, {} bytes per slot",
largeQueueSize, memoryPerLargeQuery);
+ }
+
+ @Override
+ public long getDefaultMemoryPerNode(double cost) {
+ return (cost < queueThreshold) ? memoryPerSmallQuery :
memoryPerLargeQuery;
+ }
+
+ /**
+ * This limits the number of "small" and "large" queries that a Drill
cluster will run
+ * simultaneously, if queuing is enabled. If the query is unable to run,
this will block
+ * until it can. Beware that this is called under run(), and so will
consume a Thread
+ * while it waits for the required distributed semaphore.
+ *
+ * @param queryId query identifier
+ * @param totalCost the query plan
+ * @throws QueryQueueException
+ * @throws QueueTimeoutException
+ */
+
+ @SuppressWarnings("resource")
+ @Override
+ public QueueLease enqueue(QueryId queryId, double cost) throws
QueryQueueException, QueueTimeoutException {
+ final String queueName;
+ DistributedLease lease = null;
+ long queryMemory;
+ final DistributedSemaphore distributedSemaphore;
+ try {
+
+ // Only the refresh and queue computation is synchronized.
+
+ synchronized(this) {
+ refreshConfig();
+
+ // get the appropriate semaphore
+ if (cost >= queueThreshold) {
+ distributedSemaphore =
clusterCoordinator.getSemaphore("query.large", largeQueueSize);
+ queueName = "large";
+ queryMemory = memoryPerLargeQuery;
+ } else {
+ distributedSemaphore =
clusterCoordinator.getSemaphore("query.small", smallQueueSize);
+ queueName = "small";
+ queryMemory = memoryPerSmallQuery;
+ }
+ }
+ logger.debug("Query {} with cost {} placed into the {} queue.",
+ QueryIdHelper.getQueryId(queryId), cost, queueName);
+
+ lease = distributedSemaphore.acquire(queueTimeout,
TimeUnit.MILLISECONDS);
--- End diff --
Don't turn on queueing for high concurrency. This option should never be
used for versions of Drill designed for very high query rates.
In such a high-concurrency scenario, the app using Drill might consider the
in-process queue based on Java concurrency objects. This would be particularly
useful if the high-concurrency case ran the query only in the Foreman Drillbit.
Of course, if the goal is very high concurrency, then queueing is not very
useful as its whole point is to limit concurrency (to avoid overloading memory
and CPU.)
In testing, the ZK queueing overhead turned out to be (surprisingly) quite
negligible for a normal, fully-distributed query. Probably the cost of one
additional network request is trivial compared to the number of network hops
needed to get a distributed query running across Drillbits.
> Queue-based memory assignment for buffering operators
> -----------------------------------------------------
>
> Key: DRILL-5716
> URL: https://issues.apache.org/jira/browse/DRILL-5716
> Project: Apache Drill
> Issue Type: Improvement
> Affects Versions: 1.12.0
> Reporter: Paul Rogers
> Assignee: Paul Rogers
>
> Apache Drill already has a queueing feature based on ZK semaphores. We did a
> bit of testing to show that the feature does, in fact work. We propose to
> enhance the feature with some light revisions to make work with the "managed"
> external sort and the newly-added spilling feature for the hash agg operator.
> The key requirement is to build on what we have for now; we may want to
> tackle a larger project to create a more complete solution later.
> Existing functionality:
> * Two ZK-based queues called the “small” and “large” query queues.
> * A threshold, call it T, given as a query cost, to determine the queue into
> which a query will go.
> * Admit levels for the two queues: call them Qs and Ql.
> Basically, when a query comes in:
> * Plan the query as usual.
> * Obtain the final query cost from the planner, call this C.
> * If C<T, the query goes into the small queue, else it goes into the large
> queue.
> * Suppose the small queue. Ask ZK if the query can run.
> * ZK checks if Qs queries are already running. If so, the query waits, else
> the query runs.
> The proposed changes include:
> * Refactor the code to provide a queueing API that supports a variety of
> queuing mechanisms.
> * Provide three: the null queue (default), an in-process queue (for testing)
> and the ZK queues.
> * Modify the query profile web UI to show two new bits of information about
> queues:
> - The queue to which the query was sent.
> - The total planning cost.
> * Modify the query profile web UI to show two memory assignment numbers:
> - Total memory allocated to the query
> - Memory per sort or hash-add operator
> Then, add to the queue mechanism the ability to do memory assignment:
> * Provide a weight, W: every small query gets 1 unit, every large query gets
> W units.
> * Use the queue admit levels to determine total units: U = Qs + W * Ql.
> * Obtain total direct memory from the system. M.
> * Subtract a reserve percent R for overhead.
> * Do the math to get the memory per query for each query:
> * For the small queue: (M - R) / U
> * For the large queue: (M - R) / U * W
> * Use this memory amount as the “memory per query” number in the existing
> sort/hash-agg memory assignment (instead of the fixed 2 GB.)
> The result will be a nice incremental addition to what we already have, and
> should make it a bit easier people to actually use the feature (because they
> can see the planning numbers and see the queues used, allowing them to
> effectively tune the system.)
> The API used for the above features also allow third parties to add on a more
> robust admission control feature as needed, perhaps tying into an existing
> queueing mechanism of their choice.
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