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Subject [GitHub] [flink] wuchong commented on a change in pull request #8244: [FLINK-11945] [table-runtime-blink] Support over aggregation for blink streaming runtime
Date Sun, 05 May 2019 13:53:08 GMT
wuchong commented on a change in pull request #8244: [FLINK-11945] [table-runtime-blink] Support
over aggregation for blink streaming runtime
URL: https://github.com/apache/flink/pull/8244#discussion_r281022682
 
 

 ##########
 File path: flink-table/flink-table-runtime-blink/src/main/java/org/apache/flink/table/runtime/aggregate/RowTimeUnboundedOver.java
 ##########
 @@ -0,0 +1,266 @@
+/*
+ * 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.table.runtime.aggregate;
+
+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.typeinfo.Types;
+import org.apache.flink.api.java.typeutils.ListTypeInfo;
+import org.apache.flink.configuration.Configuration;
+import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
+import org.apache.flink.table.api.TableConfig;
+import org.apache.flink.table.dataformat.BaseRow;
+import org.apache.flink.table.dataformat.JoinedRow;
+import org.apache.flink.table.dataview.PerKeyStateDataViewStore;
+import org.apache.flink.table.generated.AggsHandleFunction;
+import org.apache.flink.table.generated.GeneratedAggsHandleFunction;
+import org.apache.flink.table.type.InternalType;
+import org.apache.flink.table.typeutils.BaseRowTypeInfo;
+import org.apache.flink.util.Collector;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.ListIterator;
+import java.util.Map;
+
+/**
+ * A ProcessFunction to support unbounded event-time over-window.
+ */
+public class RowTimeUnboundedOver<K> extends
+	ProcessFunctionWithCleanupState<K, BaseRow, BaseRow> {
+	private static final Logger LOG = LoggerFactory.getLogger(RowTimeUnboundedOver.class);
+
+	private GeneratedAggsHandleFunction genAggsHandler;
+	private InternalType[] accTypes;
+	private InternalType[] inputFieldTypes;
+	private int rowTimeIdx;
+
+	protected JoinedRow output;
+	// state to hold the accumulators of the aggregations
+	private ValueState<BaseRow> accState;
+	// state to hold rows until the next watermark arrives
+	private MapState<Long, List<BaseRow>> inputState;
+	// list to sort timestamps to access rows in timestamp order
+	private LinkedList<Long> sortedTimestamps;
+
+	protected AggsHandleFunction function;
+
+	public RowTimeUnboundedOver(
+		GeneratedAggsHandleFunction genAggsHandler,
+		InternalType[] accTypes,
+		InternalType[] inputFieldTypes,
+		int rowTimeIdx,
+		TableConfig tableConfig) {
+		super(tableConfig);
+		this.genAggsHandler = genAggsHandler;
+		this.accTypes = accTypes;
+		this.inputFieldTypes = inputFieldTypes;
+		this.rowTimeIdx = rowTimeIdx;
+	}
+
+	@Override
+	public void open(Configuration parameters) throws Exception {
+		LOG.debug("Compiling AggregateHelper: " + genAggsHandler.getClassName() + " \n\n" +
+			"Code:\n" + genAggsHandler.getCode());
+		function = genAggsHandler.newInstance(getRuntimeContext().getUserCodeClassLoader());
+		function.open(new PerKeyStateDataViewStore(getRuntimeContext()));
+
+		output = new JoinedRow();
+
+		sortedTimestamps = new LinkedList<Long>();
+
+		// initialize accumulator state
+		BaseRowTypeInfo accTypeInfo = new BaseRowTypeInfo(accTypes);
+		ValueStateDescriptor<BaseRow> accStateDesc =
+			new ValueStateDescriptor<BaseRow>("accState", accTypeInfo);
+		accState = getRuntimeContext().getState(accStateDesc);
+
+		// input element are all binary row as they are came from network
+		BaseRowTypeInfo inputType = new BaseRowTypeInfo(inputFieldTypes);
+		ListTypeInfo<BaseRow> rowListTypeInfo = new ListTypeInfo<BaseRow>(inputType);
+		MapStateDescriptor<Long, List<BaseRow>> inputStateDesc = new MapStateDescriptor<Long,
List<BaseRow>>(
+			"inputState",
+			Types.LONG,
+			rowListTypeInfo);
+		inputState = getRuntimeContext().getMapState(inputStateDesc);
+
+		initCleanupTimeState("RowTimeUnboundedOverCleanupTime");
+	}
+
+	/**
+	 * Puts an element from the input stream into state if it is not late.
+	 * Registers a timer for the next watermark.
+	 *
+	 * @param input The input value.
+	 * @param ctx   A {@link Context} that allows querying the timestamp of the element and
getting
+	 *              a {@link TimerService} for registering timers and querying the time. The
+	 *              context is only valid during the invocation of this method, do not store
it.
+	 * @param out   The collector for returning result values.
+	 * @throws Exception
+	 */
+	@Override
+	public void processElement(
+		BaseRow input,
+		KeyedProcessFunction<K, BaseRow, BaseRow>.Context ctx,
+		Collector<BaseRow> out) throws Exception {
+		// register state-cleanup timer
+		registerProcessingCleanupTimer(ctx, ctx.timerService().currentProcessingTime());
+
+		Long timestamp = input.getLong(rowTimeIdx);
+		Long curWatermark = ctx.timerService().currentWatermark();
+
+		// discard late record
+		if (timestamp > curWatermark) {
+			// ensure every key just registers one timer
+			// default watermark is Long.Min, avoid overflow we use zero when watermark < 0
+			Long triggerTs = curWatermark < 0 ? 0 : curWatermark + 1;
+			ctx.timerService().registerEventTimeTimer(triggerTs);
+
+			// put row into state
+			List<BaseRow> rowList = inputState.get(timestamp);
+			if (rowList == null) {
+				rowList = new ArrayList<BaseRow>();
+			}
+			rowList.add(input);
+			inputState.put(timestamp, rowList);
+		}
+	}
+
+	@Override
+	public void onTimer(
+		long timestamp,
+		KeyedProcessFunction<K, BaseRow, BaseRow>.OnTimerContext ctx,
+		Collector<BaseRow> out) throws Exception {
+		if (isProcessingTimeTimer(ctx)) {
+			if (needToCleanupState(timestamp)) {
+
+				// we check whether there are still records which have not been processed yet
+				Boolean noRecordsToProcess = !inputState.contains(timestamp);
+				if (noRecordsToProcess) {
+					// we clean the state
+					cleanupState(inputState, accState);
+					function.cleanup();
+				} else {
+					// There are records left to process because a watermark has not been received yet.
+					// This would only happen if the input stream has stopped. So we don't need to clean
up.
+					// We leave the state as it is and schedule a new cleanup timer
+					registerProcessingCleanupTimer(ctx, ctx.timerService().currentProcessingTime());
+				}
+			}
+			return;
+		}
+
+		Iterator<Map.Entry<Long, List<BaseRow>>> keyIterator = inputState.iterator();
+		if (keyIterator != null && keyIterator.hasNext()) {
+			Long curWatermark = ctx.timerService().currentWatermark();
+			Boolean existEarlyRecord = false;
 
 Review comment:
   type can be primitive `boolean`.

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