recommend-emr-dataprocess/src/main/java/examples/sparksql/SparkAdCVRSampleTester.java

package examples.sparksql;

import com.aliyun.odps.TableSchema;
import com.aliyun.odps.data.Record;
import com.tzld.piaoquan.recommend.feature.domain.ad.base.*;
import com.tzld.piaoquan.recommend.feature.domain.ad.feature.VlogAdCtrLRFeatureExtractor;
import com.tzld.piaoquan.recommend.feature.model.sample.BaseFeature;
import com.tzld.piaoquan.recommend.feature.model.sample.GroupedFeature;
import com.tzld.piaoquan.recommend.feature.model.sample.LRSamples;
import examples.dataloader.AdSampleConstructor;
import org.apache.spark.SparkConf;
import org.apache.spark.aliyun.odps.OdpsOps;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function2;

import java.util.ArrayList;


public class SparkAdCVRSampleTester {

    public static void main(String[] args) {

        String partition = args[0];
        String accessId = "LTAIWYUujJAm7CbH";
        String accessKey = "RfSjdiWwED1sGFlsjXv0DlfTnZTG1P";
        String odpsUrl = "http://service.odps.aliyun.com/api";
        String tunnelUrl = "http://dt.cn-hangzhou.maxcompute.aliyun-inc.com";
        String project = "loghubods";
        String table = "alg_ad_view_sample";
        String hdfsPath = "/dw/recommend/model/ad_cvr_samples_test/" + partition;

        SparkConf sparkConf = new SparkConf().setAppName("E-MapReduce Demo 3-2: Spark MaxCompute Demo (Java)");
        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        OdpsOps odpsOps = new OdpsOps(jsc.sc(), accessId, accessKey, odpsUrl, tunnelUrl);
        System.out.println("Read odps table...");

        JavaRDD<Record> readData = odpsOps.readTableWithJava(project, table, partition, new RecordsToSamples(), Integer.valueOf(30));
        readData.filter(row -> row.getString("type").equals("VlogAdCtrLRScorer"))
                .map(line -> singleParse(line))
                .saveAsTextFile(hdfsPath);
    }


    static class RecordsToSamples implements Function2<Record, TableSchema, Record> {
        @Override
        public Record call(Record record, TableSchema schema) throws Exception {
            return record;
        }
    }


    // 单条日志处理逻辑
    public static String singleParse(Record record) {
        // 数据解析
        String label = record.getString("pcvr");

        // 从sql的 record中 初始化对象内容
        AdRequestContext requestContext = AdSampleConstructor.constructRequestContext(record);
        UserAdFeature userFeature = AdSampleConstructor.constructUserFeature(record);
        AdItemFeature itemFeature = AdSampleConstructor.constructItemFeature(record);

        // 转化成bytes
        AdRequestContextBytesFeature adRequestContextBytesFeature = new AdRequestContextBytesFeature(requestContext);
        UserAdBytesFeature userBytesFeature = new UserAdBytesFeature(userFeature);
        AdItemBytesFeature adItemBytesFeature = new AdItemBytesFeature(itemFeature);

        // 特征抽取
        VlogAdCtrLRFeatureExtractor bytesFeatureExtractor;
        bytesFeatureExtractor = new VlogAdCtrLRFeatureExtractor();

        LRSamples lrSamples = bytesFeatureExtractor.single(userBytesFeature, adItemBytesFeature, adRequestContextBytesFeature);

        return parseSamplesToString2(label, lrSamples);
    }


    // 构建样本的字符串
    public static String parseSamplesToString2(String label, LRSamples lrSamples) {
        ArrayList<String> featureList = new ArrayList<String>();
        for (int i = 0; i < lrSamples.getFeaturesCount(); i++) {
            GroupedFeature groupedFeature = lrSamples.getFeatures(i);
            if (groupedFeature != null && groupedFeature.getFeaturesCount() != 0) {
                for (int j = 0; j < groupedFeature.getFeaturesCount(); j++) {
                    BaseFeature baseFeature = groupedFeature.getFeatures(j);
                    if (baseFeature != null) {
                        featureList.add(String.valueOf(baseFeature.getIdentifier()) + ":1");
                    }
                }
            }
        }
        return label + "\t" + String.join("\t", featureList);
    }


}