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/*
* 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.kafka.common.internals;
import org.apache.kafka.common.TopicPartition;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.function.BiConsumer;
/**
* This class is a useful building block for doing fetch requests where topic partitions have to be rotated via
* round-robin to ensure fairness and some level of determinism given the existence of a limit on the fetch response
* size. Because the serialization of fetch requests is more efficient if all partitions for the same topic are grouped
* together, we do such grouping in the method `set`.
*
* As partitions are moved to the end, the same topic may be repeated more than once. In the optimal case, a single
* topic would "wrap around" and appear twice. However, as partitions are fetched in different orders and partition
* leadership changes, we will deviate from the optimal. If this turns out to be an issue in practice, we can improve
* it by tracking the partitions per node or calling `set` every so often.
*
* Note that this class is not thread-safe with the exception of {@link #size()} which returns the number of
* partitions currently tracked.
*/
public class PartitionStates {
private final LinkedHashMap map = new LinkedHashMap<>();
private final Set partitionSetView = Collections.unmodifiableSet(map.keySet());
/* the number of partitions that are currently assigned available in a thread safe manner */
private volatile int size = 0;
public PartitionStates() {}
public void moveToEnd(TopicPartition topicPartition) {
S state = map.remove(topicPartition);
if (state != null)
map.put(topicPartition, state);
}
public void updateAndMoveToEnd(TopicPartition topicPartition, S state) {
map.remove(topicPartition);
map.put(topicPartition, state);
updateSize();
}
public void update(TopicPartition topicPartition, S state) {
map.put(topicPartition, state);
updateSize();
}
public void remove(TopicPartition topicPartition) {
map.remove(topicPartition);
updateSize();
}
/**
* Returns an unmodifiable view of the partitions in random order.
* changes to this PartitionStates instance will be reflected in this view.
*/
public Set partitionSet() {
return partitionSetView;
}
public void clear() {
map.clear();
updateSize();
}
public boolean contains(TopicPartition topicPartition) {
return map.containsKey(topicPartition);
}
public Iterator stateIterator() {
return map.values().iterator();
}
public void forEach(BiConsumer biConsumer) {
map.forEach(biConsumer);
}
public Map partitionStateMap() {
return Collections.unmodifiableMap(map);
}
/**
* Returns the partition state values in order.
*/
public List partitionStateValues() {
return new ArrayList<>(map.values());
}
public S stateValue(TopicPartition topicPartition) {
return map.get(topicPartition);
}
/**
* Get the number of partitions that are currently being tracked. This is thread-safe.
*/
public int size() {
return size;
}
/**
* Update the builder to have the received map as its state (i.e. the previous state is cleared). The builder will
* "batch by topic", so if we have a, b and c, each with two partitions, we may end up with something like the
* following (the order of topics and partitions within topics is dependent on the iteration order of the received
* map): a0, a1, b1, b0, c0, c1.
*/
public void set(Map partitionToState) {
map.clear();
update(partitionToState);
updateSize();
}
private void updateSize() {
size = map.size();
}
private void update(Map partitionToState) {
LinkedHashMap> topicToPartitions = new LinkedHashMap<>();
for (TopicPartition tp : partitionToState.keySet()) {
List partitions = topicToPartitions.computeIfAbsent(tp.topic(), k -> new ArrayList<>());
partitions.add(tp);
}
for (Map.Entry> entry : topicToPartitions.entrySet()) {
for (TopicPartition tp : entry.getValue()) {
S state = partitionToState.get(tp);
map.put(tp, state);
}
}
}
public static class PartitionState {
private final TopicPartition topicPartition;
private final S value;
public PartitionState(TopicPartition topicPartition, S state) {
this.topicPartition = Objects.requireNonNull(topicPartition);
this.value = Objects.requireNonNull(state);
}
public S value() {
return value;
}
@Override
public boolean equals(Object o) {
if (this == o)
return true;
if (o == null || getClass() != o.getClass())
return false;
PartitionState that = (PartitionState) o;
return topicPartition.equals(that.topicPartition) && value.equals(that.value);
}
@Override
public int hashCode() {
int result = topicPartition.hashCode();
result = 31 * result + value.hashCode();
return result;
}
public TopicPartition topicPartition() {
return topicPartition;
}
@Override
public String toString() {
return "PartitionState(" + topicPartition + "=" + value + ')';
}
}
}