基于scala的flink流媒体实时预测

ggazkfy8 于 2021-06-25 发布在 Flink

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flink版本：1.2.0
scala版本：2.11.8
我想用一个数据流来预测使用scala在flink中的一个模型。我在flink中有一个使用scala的datastream[string]，其中包含来自kafka源的json格式的数据。我想使用这个datastream在已经训练过的flink ml模型上进行预测。问题是所有的flink-ml示例都使用datasetapi来预测。我对flink和scala还比较陌生，所以任何代码解决方案形式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()

    }
}

scala apache-flink flink-streaming flinkml data-science

来源：https://stackoverflow.com/questions/43759372/real-time-streaming-prediction-in-flink-using-scala

1条答案

按热度按时间

x33g5p2x1#

解决这个问题的方法是写一个 MapFunction 在作业开始时读取模型。这个 MapFunction 然后将模型存储为其内部状态的一部分。这样，在出现故障时将自动恢复：

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{}
}

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基于scala的flink流媒体实时预测

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