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From fhueske <...@git.apache.org>
Subject [GitHub] flink pull request #2761: [FLINK-3551] [examples] Sync Scala Streaming Examp...
Date Tue, 24 Jan 2017 21:49:46 GMT
Github user fhueske commented on a diff in the pull request:

    https://github.com/apache/flink/pull/2761#discussion_r97207217
  
    --- Diff: flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/ml/IncrementalLearningSkeleton.scala
---
    @@ -0,0 +1,169 @@
    +/*
    + * 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.scala.examples.ml
    +
    +import java.util.concurrent.TimeUnit
    +
    +import org.apache.flink.api.java.utils.ParameterTool
    +import org.apache.flink.api.scala._
    +import org.apache.flink.streaming.api.TimeCharacteristic
    +import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks
    +import org.apache.flink.streaming.api.functions.source.SourceFunction
    +import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext
    +import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
    +import org.apache.flink.streaming.api.scala.function.AllWindowFunction
    +import org.apache.flink.streaming.api.watermark.Watermark
    +import org.apache.flink.streaming.api.windowing.time.Time
    +import org.apache.flink.streaming.api.windowing.windows.TimeWindow
    +import org.apache.flink.util.Collector
    +
    +/**
    +  * Skeleton for incremental machine learning algorithm consisting of a
    +  * pre-computed model, which gets updated for the new inputs and new input data
    +  * for which the job provides predictions.
    +  *
    +  * <p>
    +  * This may serve as a base of a number of algorithms, e.g. updating an
    +  * incremental Alternating Least Squares model while also providing the
    +  * predictions.
    +  *
    +  * <p>
    +  * This example shows how to use:
    +  * <ul>
    +  * <li>Connected streams
    +  * <li>CoFunctions
    +  * <li>Tuple data types
    +  * </ul>
    +  */
    +object IncrementalLearningSkeleton {
    +
    +  // *************************************************************************
    +  // PROGRAM
    +  // *************************************************************************
    +
    +  def main(args: Array[String]): Unit = {
    +    // Checking input parameters
    +    val params = ParameterTool.fromArgs(args)
    +
    +    // set up the execution environment
    +    val env = StreamExecutionEnvironment.getExecutionEnvironment
    +    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    +
    +    // build new model on every second of new data
    +    val trainingData = env.addSource(new FiniteTrainingDataSource)
    +    val newData = env.addSource(new FiniteNewDataSource)
    +
    +    val model = trainingData
    +      .assignTimestampsAndWatermarks(new LinearTimestamp)
    +      .timeWindowAll(Time.of(5000, TimeUnit.MILLISECONDS))
    +      .apply(new PartialModelBuilder)
    +
    +    // use partial model for newData
    +    val prediction = newData.connect(model).map(
    +      (_: Int) => 0,
    --- End diff --
    
    I agree with @thvasilo. We should copy the code of the Java job. 
    
    Otherwise, this example just demonstrates how to use `connect()` and `CoMapFunction`.

    For that we would not need custom sources and window aggregation.


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