使用 scala 在 Flink 中进行实时流预测

Question

Flink 版本：1.2.0
Scala 版本：2.11.8

我想使用 DataStream 来预测使用 Scala 的 Flink 中的模型。我在使用 scala 的 flink 中有一个 DataStream[String]，它包含来自 kafka json 格式的数据 source.I 想使用这个数据流来预测已经训练过的 Flink-ml 模型。问题是所有的 flink-ml 示例都使用 DataSet api 来预测。我对 flink 和 scala 比较陌生，所以任何以代码解决方案形式提供的帮助都将不胜感激。

输入：

{"FC196":"Dormant","FC174":"Yolo","FC195":"Lol","FC176":"4","FC198":"BANKING","FC175":"ABDULMAJEED","FC197":"2017/04/04","FC178":"1","FC177":"CBS","FC199":"INDIVIDUAL","FC179":"SYSTEM","FC190":"OK","FC192":"osName","FC191":"Completed","FC194":"125","FC193":"7","FC203":"A10SBPUB000000000004439900053570","FC205":"1","FC185":"20","FC184":"Transfer","FC187":"2","FC186":"2121","FC189":"abcdef","FC200":"","FC188":"BR01","FC202":"INDIVIDUAL","FC201":"","FC181":"7:00PM","FC180":"2007/04/01","FC183":"11000000","FC182":"INR"}

代码：

package org.apache.flink.quickstart

//imports

import java.util.Properties

import org.apache.flink.api.scala._
import org.apache.flink.ml.recommendation.ALS
import org.apache.flink.ml.regression.MultipleLinearRegression
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment

import scala.util.parsing.json.JSON

//kafka consumer imports
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer09
import org.apache.flink.streaming.util.serialization.SimpleStringSchema

//kafka json table imports
import org.apache.flink.table.examples.scala.StreamTableExample
import org.apache.flink.table.api.TableEnvironment
import org.apache.flink.streaming.connectors.kafka.Kafka09JsonTableSource
import org.apache.flink.api.java.DataSet

//JSon4s imports
import org.json4s.native.JsonMethods



// Case class
case class CC(FC196:String,FC174:String,FC195:String,FC176:String,FC198:String,FC175:String,FC197:String,FC178:String,FC177:String,FC199:String,FC179:String,FC190:String,FC192:String,FC191:String,FC194:String,FC193:String,FC203:String,FC205:String,FC185:String,FC184:String,FC187:String,FC186:String,FC189:String,FC200:String,FC188:String,FC202:String,FC201:String,FC181:String,FC180:String,FC183:String,FC182:String)


object WordCount {

  implicit val formats = org.json4s.DefaultFormats

  def main(args: Array[String]) {

    // set up the execution environment
    implicit lazy val formats = org.json4s.DefaultFormats

    // kafka properties
    val properties = new Properties()
    properties.setProperty("bootstrap.servers", "***.**.*.***:9093")
    properties.setProperty("zookeeper.connect", "***.**.*.***:2181")
    properties.setProperty("group.id","grouop")
    properties.setProperty("auto.offset.reset", "earliest")
    val env = StreamExecutionEnvironment.getExecutionEnvironment
//    val tableEnv = TableEnvironment.getTableEnvironment(env)

    val st = env
      .addSource(new FlinkKafkaConsumer09("new", new SimpleStringSchema() , properties))
      .flatMap(raw => JsonMethods.parse(raw).toOption)


    val mapped = st.map(_.extract[CC])

    mapped.print()

    env.execute()

    }
}

Answer 1

解决这个问题的方法是编写一个 MapFunction，它在作业开始时读取模型。 MapFunction 然后将模型存储为其内部状态的一部分。这样它会在失败的情况下自动恢复：

public static void main(String[] args) throws Exception {
        // obtain execution environment, run this example in "ingestion time"
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime);

        DataStream<Value> input = ...; // read from Kafka for example

        DataStream<Prediction> prediction = input.map(new Predictor());

        prediction.print();

        env.execute();
    }

    public static class Predictor implements MapFunction<Value, Prediction>, CheckpointedFunction {

        private transient ListState<Model> modelState;

        private transient Model model;

        @Override
        public Prediction map(Value value) throws Exception {
            return model.predict(value);
        }

        @Override
        public void snapshotState(FunctionSnapshotContext context) throws Exception {
            // we don't have to do anything here because we assume the model to be constant
        }

        @Override
        public void initializeState(FunctionInitializationContext context) throws Exception {
            ListStateDescriptor<Model> listStateDescriptor = new ListStateDescriptor<>("model", Model.class);

            modelState = context.getOperatorStateStore().getUnionListState(listStateDescriptor);

            if (context.isRestored()) {
                // restore the model from state
                model = modelState.get().iterator().next();
            } else {
                modelState.clear();

                // read the model from somewhere, e.g. read from a file
                model = ...;

                // update the modelState so that it is checkpointed from now
                modelState.add(model);
            }
        }
    }

    public static class Model {}

    public static class Value{}

    public static class Prediction{}
}

使用 scala 在 Flink 中进行实时流预测

Real-Time streaming prediction in Flink using scala

scala

apache-flink

data-science

flink-streaming

flinkml