使用idea编写hadoop数据生成器生成1G~10G大小的数据，字段必须包括id,日期,手机号码、型号、操作系统字段。

好的，这个问题我可以回答。首先，你需要在IDEA中创建一个Java项目。然后，你需要下载并安装Hadoop。接下来，你可以使用Hadoop自带的数据生成器工具TeraGen来生成数据。

以下是一个示例代码，你可以根据自己的需求修改：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Random;

public class DataGenerator implements Tool {

    private Configuration conf;

    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new DataGenerator(), args);
        System.exit(res);
    }

    public int run(String[] args) throws Exception {
        Path outputPath = new Path(args[0]);
        int numRecords = Integer.parseInt(args[1]);
        int numMappers = Integer.parseInt(args[2]);
        int numReducers = Integer.parseInt(args[3]);

        Job job = Job.getInstance(getConf());
        job.setJobName("DataGenerator");
        job.setJarByClass(DataGenerator.class);

        // Set input/output format
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        // Set mapper/reducer class
        job.setMapperClass(DataGeneratorMapper.class);
        job.setReducerClass(DataGeneratorReducer.class);

        // Set number of mappers/reducers
        job.setNumReduceTasks(numReducers);

        // Set output key/value class
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // Set input/output path
        TextInputFormat.setInputPaths(job, new Path("input"));
        TextOutputFormat.setOutputPath(job, outputPath);

        // Set number of records to generate
        job.getConfiguration().setInt("numRecords", numRecords);

        // Set number of mappers
        job.getConfiguration().setInt("mapred.map.tasks", numMappers);

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

    public void setConf(Configuration conf) {
        this.conf = conf;
    }

    public Configuration getConf() {
        return conf;
    }

    public static class DataGeneratorMapper extends Mapper<Object, Text, Text, Text> {

        private static final String[] PHONES = {"135", "136", "137", "138", "139", "150", "151", "152", "157", "158", "159", "182", "183", "184", "187", "188"};
        private static final String[] MODELS = {"iPhone", "Samsung", "Huawei", "Xiaomi", "Oppo", "Vivo", "OnePlus", "Realme", "Lenovo", "Asus"};
        private static final String[] OS = {"iOS", "Android", "Windows", "macOS", "Linux", "Chrome OS", "Ubuntu"};

        private int numRecords;

        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            super.setup(context);
            numRecords = context.getConfiguration().getInt("numRecords", 1000000);
        }

        @Override
        protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            Random random = new Random();
            SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd");

            for (int i = 0; i < numRecords; i++) {
                int id = i;
                String date = dateFormat.format(new Date(random.nextLong()));
                String phone = PHONES[random.nextInt(PHONES.length)] + String.format("%08d", random.nextInt(100000000));
                String model = MODELS[random.nextInt(MODELS.length)];
                String os = OS[random.nextInt(OS.length)];

                context.write(new Text(String.valueOf(id)), new Text(date + "," + phone + "," + model + "," + os));
            }
        }
    }

    public static class DataGeneratorReducer extends Reducer<Text, Text, Text, Text> {

        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            for (Text value : values) {
                context.write(key, value);
            }
        }
    }
}

在这个代码中，我们使用了一个Mapper来生成数据。在Mapper中，我们使用了一个随机数生成器和SimpleDateFormat来生成日期和手机号码。我们还定义了一些模型和操作系统，用于生成“型号”和“操作系统”字段。

你可以通过以下命令来运行这个程序：

hadoop jar DataGenerator.jar /output 10000000 10 1

这会生成一个包含1000万行数据的文件，文件名为part-r-00000，位于/output目录下。其中，第二个参数表示要生成的记录数量，第三个参数表示要使用的Mapper数量，第四个参数表示要使用的Reducer数量。