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https://issues.apache.org/jira/browse/FLINK-8479?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16344448#comment-16344448
]
ASF GitHub Bot commented on FLINK-8479:
---------------------------------------
Github user hequn8128 commented on a diff in the pull request:
https://github.com/apache/flink/pull/5342#discussion_r164631884
--- Diff:
flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/TimeBoundedStreamJoinOperator.java
---
@@ -0,0 +1,398 @@
+/*
+ * 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.flink.streaming.api.functions;
+
+import org.apache.flink.annotation.VisibleForTesting;
+import org.apache.flink.api.common.state.MapState;
+import org.apache.flink.api.common.state.MapStateDescriptor;
+import org.apache.flink.api.common.state.ValueState;
+import org.apache.flink.api.common.state.ValueStateDescriptor;
+import org.apache.flink.api.common.typeutils.TypeSerializer;
+import org.apache.flink.api.common.typeutils.base.BooleanSerializer;
+import org.apache.flink.api.common.typeutils.base.ListSerializer;
+import org.apache.flink.api.common.typeutils.base.LongSerializer;
+import org.apache.flink.api.java.tuple.Tuple2;
+import org.apache.flink.api.java.tuple.Tuple3;
+import org.apache.flink.api.java.typeutils.runtime.TupleSerializer;
+import org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator;
+import org.apache.flink.streaming.api.operators.TimestampedCollector;
+import org.apache.flink.streaming.api.operators.TwoInputStreamOperator;
+import org.apache.flink.streaming.api.watermark.Watermark;
+import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import static
org.apache.flink.api.common.typeinfo.BasicTypeInfo.LONG_TYPE_INFO;
+
+// TODO: Make bucket granularity adaptable
+/**
+ * A TwoInputStreamOperator to execute time-bounded stream inner joins.
+ *
+ * <p>By using a configurable lower and upper bound this operator will
emit exactly those pairs
+ * (T1, T2) where t2.ts ∈ [T1.ts + lowerBound, T1.ts + upperBound]. Both
the lower and the
+ * upper bound can be configured to be either inclusive or exclusive.
+ *
+ * <p>As soon as elements are joined they are passed to a user-defined
{@link JoinedProcessFunction},
+ * as a {@link Tuple2}, with f0 being the left element and f1 being the
right element
+ *
+ * @param <T1> The type of the elements in the left stream
+ * @param <T2> The type of the elements in the right stream
+ * @param <OUT> The output type created by the user-defined function
+ */
+public class TimeBoundedStreamJoinOperator<T1, T2, OUT>
+ extends AbstractUdfStreamOperator<OUT, JoinedProcessFunction<T1, T2,
OUT>>
+ implements TwoInputStreamOperator<T1, T2, OUT> {
+
+ private final long lowerBound;
+ private final long upperBound;
+
+ private final long inverseLowerBound;
+ private final long inverseUpperBound;
+
+ private final boolean lowerBoundInclusive;
+ private final boolean upperBoundInclusive;
+
+ private final long bucketGranularity = 1;
+
+ private static final String LEFT_BUFFER = "LEFT_BUFFER";
+ private static final String RIGHT_BUFFER = "RIGHT_BUFFER";
+ private static final String LAST_CLEANUP_LEFT = "LAST_CLEANUP_LEFT";
+ private static final String LAST_CLEANUP_RIGHT = "LAST_CLEANUP_RIGHT";
+
+ private transient ValueState<Long> lastCleanupRightBuffer;
+ private transient ValueState<Long> lastCleanupLeftBuffer;
+
+ private transient MapState<Long, List<Tuple3<T1, Long, Boolean>>>
leftBuffer;
+ private transient MapState<Long, List<Tuple3<T2, Long, Boolean>>>
rightBuffer;
+
+ private final TypeSerializer<T1> leftTypeSerializer;
+ private final TypeSerializer<T2> rightTypeSerializer;
+
+ private transient TimestampedCollector<OUT> collector;
+
+ private ContextImpl context;
+
+ /**
+ * Creates a new TimeBoundedStreamJoinOperator.
+ *
+ * @param lowerBound The lower bound for evaluating if
elements should be joined
+ * @param upperBound The upper bound for evaluating if
elements should be joined
+ * @param lowerBoundInclusive Whether or not to include elements where
the timestamp matches
+ * the lower bound
+ * @param upperBoundInclusive Whether or not to include elements where
the timestamp matches
+ * the upper bound
+ * @param udf A user-defined {@link
JoinedProcessFunction} that gets called
+ * whenever two elements of T1 and T2 are
joined
+ */
+ public TimeBoundedStreamJoinOperator(
+ long lowerBound,
+ long upperBound,
+ boolean lowerBoundInclusive,
+ boolean upperBoundInclusive,
+ TypeSerializer<T1> leftTypeSerializer,
+ TypeSerializer<T2> rightTypeSerializer,
+ JoinedProcessFunction<T1, T2, OUT> udf
+ ) {
+
+ super(udf);
+
+ this.lowerBound = lowerBound;
+ this.upperBound = upperBound;
+
+ this.inverseLowerBound = -1 * upperBound;
+ this.inverseUpperBound = -1 * lowerBound;
+
+ this.lowerBoundInclusive = lowerBoundInclusive;
+ this.upperBoundInclusive = upperBoundInclusive;
+ this.leftTypeSerializer = leftTypeSerializer;
+ this.rightTypeSerializer = rightTypeSerializer;
+ }
+
+ @Override
+ public void open() throws Exception {
+ super.open();
+ collector = new TimestampedCollector<>(output);
+ context = new ContextImpl(userFunction);
+
+ Class<Tuple3<T1, Long, Boolean>> leftTypedTuple =
+ (Class<Tuple3<T1, Long, Boolean>>) (Class<?>)
Tuple3.class;
+
+ TupleSerializer<Tuple3<T1, Long, Boolean>> leftTupleSerializer
= new TupleSerializer<>(
+ leftTypedTuple,
+ new TypeSerializer[]{
+ leftTypeSerializer,
+ LongSerializer.INSTANCE,
+ BooleanSerializer.INSTANCE
+ }
+ );
+
+ Class<Tuple3<T2, Long, Boolean>> rightTypedTuple =
+ (Class<Tuple3<T2, Long, Boolean>>) (Class<?>)
Tuple3.class;
+
+ TupleSerializer<Tuple3<T2, Long, Boolean>> rightTupleSerializer
= new TupleSerializer<>(
+ rightTypedTuple,
+ new TypeSerializer[]{
+ rightTypeSerializer,
+ LongSerializer.INSTANCE,
+ BooleanSerializer.INSTANCE
+ }
+ );
+
+ this.leftBuffer = getRuntimeContext().getMapState(new
MapStateDescriptor<>(
+ LEFT_BUFFER,
+ LongSerializer.INSTANCE,
+ new ListSerializer<>(leftTupleSerializer)
+ ));
+
+ this.rightBuffer = getRuntimeContext().getMapState(new
MapStateDescriptor<>(
+ RIGHT_BUFFER,
+ LongSerializer.INSTANCE,
+ new ListSerializer<>(rightTupleSerializer)
+ ));
+
+ this.lastCleanupRightBuffer = getRuntimeContext().getState(new
ValueStateDescriptor<>(
+ LAST_CLEANUP_RIGHT,
+ LONG_TYPE_INFO
+ ));
+
+ this.lastCleanupLeftBuffer = getRuntimeContext().getState(new
ValueStateDescriptor<>(
+ LAST_CLEANUP_LEFT,
+ LONG_TYPE_INFO
+ ));
+ }
+
+ /**
+ * Process a {@link StreamRecord} from the left stream. Whenever an
{@link StreamRecord}
+ * arrives at the left stream, it will get added to the left buffer.
Possible join candidates
+ * for that element will be looked up from the right buffer and if the
pair lies within the
+ * user defined boundaries, it gets collected.
+ *
+ * @param record An incoming record to be joined
+ * @throws Exception Can throw an Exception during state access
+ */
+ @Override
+ public void processElement1(StreamRecord<T1> record) throws Exception {
+
+ long leftTs = record.getTimestamp();
+ T1 leftValue = record.getValue();
+
+ addToLeftBuffer(leftValue, leftTs);
+
+ long min = leftTs + lowerBound;
+ long max = leftTs + upperBound;
+
+ // TODO: Adapt to different bucket sizes here
+ // Go over all buckets that are within the time bounds
+ for (long i = min; i <= max; i++) {
+ List<Tuple3<T2, Long, Boolean>> fromBucket =
rightBuffer.get(i);
+ if (fromBucket != null) {
+
+ // check for each element in current bucket if
it should be joined
+ for (Tuple3<T2, Long, Boolean> tuple :
fromBucket) {
+ if (shouldBeJoined(leftTs, tuple.f1)) {
+
+ // collect joined tuple with
left timestamp
+ collect(leftValue, tuple.f0,
leftTs, tuple.f1);
+ }
+ }
+ }
+ }
+ }
+
+ /**
+ * Process a {@link StreamRecord} from the right stream. Whenever a
{@link StreamRecord}
+ * arrives at the right stream, it will get added to the right buffer.
Possible join candidates
+ * for that element will be looked up from the left buffer and if the
pair lies within the user
+ * defined boundaries, it gets collected.
+ *
+ * @param record An incoming record to be joined
+ * @throws Exception Can throw an exception during state access
+ */
+ @Override
+ public void processElement2(StreamRecord<T2> record) throws Exception {
+
+ long rightTs = record.getTimestamp();
+ T2 rightElem = record.getValue();
+
+ addToRightBuffer(rightElem, rightTs);
+
+ long min = rightTs + inverseLowerBound;
+ long max = rightTs + inverseUpperBound;
+
+ // TODO: Adapt to different bucket sizes here
+ // Go over all buckets that are within the time bounds
+ for (long i = min; i <= max; i++) {
+ List<Tuple3<T1, Long, Boolean>> fromBucket =
leftBuffer.get(i);
+ if (fromBucket != null) {
+
+ // check for each element in current bucket if
it should be joined
+ for (Tuple3<T1, Long, Boolean> tuple :
fromBucket) {
+ if (shouldBeJoined(tuple.f1, rightTs)) {
+
+ // collect joined tuple with
left timestamp
+ collect(tuple.f0, rightElem,
tuple.f1, rightTs);
+ }
+ }
+ }
+ }
+ }
+
+ @Override
+ public void processWatermark(Watermark mark) throws Exception {
+
+ // remove from both sides all those elements where the
timestamp is less than the lower
+ // bound, because they are not considered for joining anymore
+ removeFromLhsUntil(mark.getTimestamp() + inverseLowerBound);
+ removeFromRhsUntil(mark.getTimestamp() + lowerBound);
+
+ if (timeServiceManager != null) {
+ timeServiceManager.advanceWatermark(mark);
+ }
+
+ output.emitWatermark(new Watermark(mark.getTimestamp() -
getWatermarkDelay()));
+ }
+
+ private void collect(T1 left, T2 right, long leftTs, long rightTs)
throws Exception {
+ collector.setAbsoluteTimestamp(leftTs);
+ context.leftTs = leftTs;
+ context.rightTs = rightTs;
+ userFunction.processElement(left, right, context,
this.collector);
+ }
+
+ private void removeFromLhsUntil(long maxCleanup) throws Exception {
+
+ // setup state
+ if (this.lastCleanupRightBuffer.value() == null) {
+ this.lastCleanupRightBuffer.update(0L);
+ }
+
+ // remove elements from leftBuffer in range [lastValue,
maxCleanup]
+ Long lastCleanUpRight = lastCleanupRightBuffer.value();
+ for (long i = lastCleanUpRight; i <= maxCleanup; i++) {
+ leftBuffer.remove(i);
+ }
+
+ lastCleanupRightBuffer.update(maxCleanup);
+ }
+
+ private void removeFromRhsUntil(long maxCleanup) throws Exception {
+
+ // setup state
+ if (this.lastCleanupLeftBuffer.value() == null) {
+ this.lastCleanupLeftBuffer.update(0L);
+ }
+
+ // remove elements from rightBuffer in range [lastValue,
maxCleanup]
+ Long lastCleanupLeft = lastCleanupLeftBuffer.value();
+ for (long i = lastCleanupLeft; i <= maxCleanup; i++) {
+ rightBuffer.remove(i);
+ }
+
+ lastCleanupLeftBuffer.update(maxCleanup);
+ }
+
+ private boolean shouldBeJoined(long leftTs, long rightTs) {
+ long elemLowerBound = leftTs + lowerBound;
+ long elemUpperBound = leftTs + upperBound;
+
+ boolean lowerBoundOk = (lowerBoundInclusive)
+ ? (elemLowerBound <= rightTs)
+ : (elemLowerBound < rightTs);
+
+ boolean upperBoundOk = (upperBoundInclusive)
+ ? (rightTs <= elemUpperBound)
+ : (rightTs < elemUpperBound);
+
+ return lowerBoundOk && upperBoundOk;
+ }
+
+ private void addToLeftBuffer(T1 value, long ts) throws Exception {
+ long bucket = calculateBucket(ts);
+ Tuple3<T1, Long, Boolean> elem = Tuple3.of(
+ value, // actual value
+ ts, // actual timestamp
+ false // has been joined
+ );
+
+ List<Tuple3<T1, Long, Boolean>> elemsInBucket =
leftBuffer.get(bucket);
+ if (elemsInBucket == null) {
+ elemsInBucket = new ArrayList<>();
+ }
+ elemsInBucket.add(elem);
+ leftBuffer.put(bucket, elemsInBucket);
+ }
+
+ private void addToRightBuffer(T2 value, long ts) throws Exception {
+ long bucket = calculateBucket(ts);
+ Tuple3<T2, Long, Boolean> elem = Tuple3.of(
+ value, // actual value
+ ts, // actual timestamp
+ false // has been joined
+ );
+
+ List<Tuple3<T2, Long, Boolean>> elemsInBucket =
rightBuffer.get(bucket);
+ if (elemsInBucket == null) {
+ elemsInBucket = new ArrayList<>();
+ }
+ elemsInBucket.add(elem);
+ rightBuffer.put(bucket, elemsInBucket);
+ }
+
+ private long calculateBucket(long ts) {
+ return Math.floorDiv(ts, bucketGranularity);
+ }
+
+ public long getWatermarkDelay() {
+ // TODO: Adapt this to when we use the rightBuffer or min
timestamp
+ return (upperBound < 0) ? 0 : upperBound;
--- End diff --
Watermark delay should be max(abs(lower_bond), abs(upper_bond)), because
both left and right may output watermark to join.
> Implement time-bounded inner join of streams as a TwoInputStreamOperator
> ------------------------------------------------------------------------
>
> Key: FLINK-8479
> URL: https://issues.apache.org/jira/browse/FLINK-8479
> Project: Flink
> Issue Type: Sub-task
> Reporter: Florian Schmidt
> Priority: Major
>
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