【Hadoop】3、Hadoop-MapReduce使用avro进行数据的序列化与反序列化

package cn.cutter.demo.hadoop.avro;

import org.apache.hadoop.io.Text;

import java.text.DateFormat;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;

/**
 * @ClassName NcdcRecordParser
 * @Description TODO
 * @Author xiaof
 * @Date 2019/2/17 16:36
 * @Version 1.0
 **/
public class NcdcRecordParser {
    private static final int MISSING_TEMPERATURE = 9999;

    private static final DateFormat DATE_FORMAT =
            new SimpleDateFormat("yyyyMMddHHmm");

    private String stationId;
    private String observationDateString;
    private String year;
    private String airTemperatureString;
    private int airTemperature;
    private boolean airTemperatureMalformed;
    private String quality;

    public void parse(String record) {
        stationId = record.substring(4, 10) + "-" + record.substring(10, 15);
        observationDateString = record.substring(15, 27);
        year = record.substring(15, 19);
        airTemperatureMalformed = false;
        // Remove leading plus sign as parseInt doesn't like them
        if (record.charAt(87) == '+') {
            airTemperatureString = record.substring(88, 92);
            airTemperature = Integer.parseInt(airTemperatureString);
        } else if (record.charAt(87) == '-') {
            airTemperatureString = record.substring(87, 92);
            airTemperature = Integer.parseInt(airTemperatureString);
        } else {
            airTemperatureMalformed = true;
        }
        airTemperature = Integer.parseInt(airTemperatureString);
        quality = record.substring(92, 93);
    }

    public void parse(Text record) {
        parse(record.toString());
    }

    public boolean isValidTemperature() {
        return !airTemperatureMalformed && airTemperature != MISSING_TEMPERATURE
                && quality.matches("[01459]");
    }

    public boolean isMalformedTemperature() {
        return airTemperatureMalformed;
    }

    public boolean isMissingTemperature() {
        return airTemperature == MISSING_TEMPERATURE;
    }

    public String getStationId() {
        return stationId;
    }

    public Date getObservationDate() {
        try {
            System.out.println(observationDateString);
            return DATE_FORMAT.parse(observationDateString);
        } catch (ParseException e) {
            throw new IllegalArgumentException(e);
        }
    }

    public String getYear() {
        return year;
    }

    public int getYearInt() {
        return Integer.parseInt(year);
    }

    public int getAirTemperature() {
        return airTemperature;
    }

    public String getAirTemperatureString() {
        return airTemperatureString;
    }

    public String getQuality() {
        return quality;
    }

}

通过avro输出数据，我们的数据集是：

0067011990999991950051507004+68750+023550FM-12+038299999V0203301N00671220001CN9999999N9+00001+99999999999
0043011990999991950051512004+68750+023550FM-12+038299999V0203201N00671220001CN9999999N9+00221+99999999999
0043011990999991950051518004+68750+023550FM-12+038299999V0203201N00261220001CN9999999N9-00111+99999999999
0043012650999991949032412004+62300+010750FM-12+048599999V0202701N00461220001CN0500001N9+01111+99999999999
0043012650999991949032418004+62300+010750FM-12+048599999V0202701N00461220001CN0500001N9+00781+99999999999

package cn.cutter.demo.hadoop.avro;

import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.mapred.*;
import org.apache.avro.mapreduce.AvroJob;
import org.apache.avro.mapreduce.AvroKeyOutputFormat;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.io.FileInputStream;
import java.io.IOException;

/**
 * @ClassName AvroGenericMaxTemperature
 * @Description 通过avro记录的数据文件，使用MapReduce进行解析读取数据
 * @Author xiaof
 * @Date 2019/2/17 15:23
 * @Version 1.0
 **/
public class AvroGenericMaxTemperature extends Configured implements Tool {

    //转换json数据,avro的模式解析
    private static final Schema SCHEMA = new Schema.Parser().parse("{\"name\":\"WeatherRecord\",\"doc\":\"A weather reading\",\"type\":\"record\",\"fields\":[{\"name\":\"year\",\"type\":\"int\"},{\"name\":\"temperature\",\"type\":\"int\"},{\"name\":\"stationId\",\"type\":\"string\"}]}");

    public static class MaxTemperatureMapper extends Mapper<LongWritable, Text, AvroKey<Integer>, AvroValue<GenericRecord>> {
        //创建天气解析类对象，数据记录对象
        private NcdcRecordParser ncdcRecordParser = new NcdcRecordParser();
        private GenericRecord genericRecord = new GenericData.Record(SCHEMA);

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //1.解析数据
            ncdcRecordParser.parse(value.toString());

            //2.判断数据有效性
            if(ncdcRecordParser.isValidTemperature()) {
                //3.获取对应的数据进入记录record中，然后输出到上下文对象
                genericRecord.put("year", ncdcRecordParser.getYearInt());
                genericRecord.put("temperature", ncdcRecordParser.getAirTemperature());
                genericRecord.put("stationId", ncdcRecordParser.getStationId());
                context.write(new AvroKey<Integer>(ncdcRecordParser.getYearInt()), new AvroValue<GenericRecord>(genericRecord));
            }
        }
    }

    public static class MaxTemperatureReducer extends Reducer<AvroKey<Integer>, AvroValue<GenericRecord>, AvroKey<GenericRecord>, NullWritable> {
        @Override
        protected void reduce(AvroKey<Integer> key, Iterable<AvroValue<GenericRecord>> values, Context context) throws IOException, InterruptedException {
            //遍历所有的数据，获取最大的数据
            GenericRecord max = null;
            for(AvroValue<GenericRecord> value : values) {
                //获取数据的值，判断max等于空，或者当前温度大于max记录的温度，那么就更新max
                GenericRecord record = value.datum();
                if(max == null || (Integer) record.get("temperature") > (Integer) max.get("temperature")) {
                    max = newWeatherRecord(record);
                }
            }

            context.write(new AvroKey(max), NullWritable.get());
        }

        private GenericRecord newWeatherRecord(GenericRecord value) {
            GenericRecord record = new GenericData.Record(SCHEMA);
            record.put("year", value.get("year"));
            record.put("temperature", value.get("temperature"));
            record.put("stationId", value.get("stationId"));

            return record;
        }
    }

    @Override
    public int run(String[] strings) throws Exception {
        if (strings.length != 2) {
            System.err.printf("Usage: %s [generic options] <input> <output>\n",
                    getClass().getSimpleName());
            ToolRunner.printGenericCommandUsage(System.err);
            return -1;
        }
        Job job = Job.getInstance(this.getConf(), "Max temperature");
        job.setJarByClass(this.getClass());

        job.getConfiguration().setBoolean(Job.MAPREDUCE_JOB_USER_CLASSPATH_FIRST, true);

        FileInputFormat.addInputPath(job, new Path(strings[0]));
        FileOutputFormat.setOutputPath(job, new Path(strings[1]));

        AvroJob.setMapOutputKeySchema(job, Schema.create(Schema.Type.INT));
        AvroJob.setMapOutputValueSchema(job, SCHEMA);
        AvroJob.setOutputKeySchema(job, SCHEMA);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(AvroKeyOutputFormat.class);

        job.setMapperClass(MaxTemperatureMapper.class);
        job.setReducerClass(MaxTemperatureReducer.class);

        return job.waitForCompletion(true) ? 0: 1;

    }

    public static void main(String[] args) throws Exception {

        System.setProperty("hadoop.home.dir", "F:\\hadoop-2.7.7");
        String paths[] = {"H:\\ideaworkspace\\1-tmp\\input\\1.txt", "H:\\ideaworkspace\\1-tmp\\output1"};

        int exitCode = ToolRunner.run(new AvroGenericMaxTemperature(), paths);
        System.exit(exitCode);

    }
}

结果使用avro-tool进行查看：

H:\>java -jar avro-tools-1.8.2.jar tojson H:\ideaworkspace\1-tmp\output1\part-r-
00000.avro