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- 數據在HDFS提供Avro的格式。
- 上述avro數據的模式也可用。
- 這個Avro數據需要在map reduce中解析並生成具有相同模式的輸出avro數據（需要清理傳入的Avro數據）。
- 傳入的avro數據可以是任何模式。
因此，需求是編寫一個通用映射reduce，可以採用任何Avro數據，但生成與Avro格式相同的輸出。
代碼（多次嘗試後，這是我在多大程度上達到）
驅動
public class AvroDriver extends Configured implements Tool {
public int run(String[] args) throws Exception {
Job job = new Job(getConf());
job.setJarByClass(AvroMapper.class);
job.setJobName("Avro With Xml Mapper");
job.getConfiguration().setBoolean("mapreduce.input.fileinputformat.input.dir.recursive", true);
//This is required to use avro-1.7.6 and above
job.getConfiguration().set("mapreduce.job.user.classpath.first", "true");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setInputFormatClass(AvroKeyInputFormat.class);
job.setMapperClass(AvroMapper.class);
Schema schema = new Schema.Parser().parse(new File(args[2]));
AvroJob.setInputKeySchema(job, schema);
job.setOutputFormatClass(AvroKeyOutputFormat.class);
job.setMapOutputKeyClass(AvroKey.class);
AvroJob.setOutputKeySchema(job, schema);
job.setNumReduceTasks(0);
return (job.waitForCompletion(true) ? 0 : 1);
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new AvroDriver(), args);
System.exit(res);
}
}
映射
public static class AvroMapper extends Mapper<AvroKey<GenericData.Record>, NullWritable, AvroKey<GenericData>, NullWritable> {
@Override
public void map(AvroKey<GenericData.Record> key, NullWritable value, Context context) throws IOException, InterruptedException {
try {
System.out.println("Specific Record - " + key);
System.out.println("Datum :: " + key.datum());
System.out.println("Schema :: " + key.datum().getSchema());
List<Field> fields = key.datum().getSchema().getFields();
GenericRecord record = new GenericData.Record(key.datum().getSchema());
for(Field f : fields) {
System.out.println("Field Name - " + f.name());
record.put(f.name(), key.datum().get(f.name()));
}
System.out.println("Record - " + record);
GenericData d = new GenericData();
d.newRecord(record, key.datum().getSchema());
AvroKey<GenericData> outkey = new AvroKey<GenericData>(d);
System.out.println("Generic Record (Avro Key) - " + outkey);
context.write(outkey, NullWritable.get());
} catch (Exception e) {
e.printStackTrace();
throw new IOException(e.getMessage());
}
}
}
命令
Hadoop的罐子$ jar_name都$ input_avro_data_path $ output_path $ path_to_the_input_avro_schema
Avro的模式樣本
{ "type" : "record", "name" : "Entity", "namespace" : "com.sample.avro", "fields".......
問題，當我跑地圖，我得到降低
錯誤運行孩子：顯示java.lang.NullPointerException：在com.sample.avro.Entity
org.apache.avro.file.DataFileWriter $ AppendWriteException的 com.sample.avro.Entity空： 的java.lang。 NullPointerException異常：在 com.sample.avro.Entity
環境
HDP 2.3沙盒
任何com.sample.avro.Entity空想法？
修訂
我嘗試以下，但相同的結果
public static class AvroMapper extends Mapper<AvroKey<GenericData.Record>, NullWritable, AvroKey<GenericData>, NullWritable> {
@Override
public void map(AvroKey<GenericData.Record> key, NullWritable value, Context context) throws IOException, InterruptedException {
try {
System.out.println("Specific Record - " + key);
System.out.println("Datum :: " + key.datum());
System.out.println("Schema :: " + key.datum().getSchema());
List<Field> fields = key.datum().getSchema().getFields();
Schema s = Schema.createRecord(key.datum().getSchema().getName(), null, key.datum().getSchema().getNamespace(), false);
List<Field> outFields = new ArrayList<Field>();
for(Field f : fields) {
System.out.println("Field Name - " + f.name());
Schema.Field f1 = new Schema.Field(f.name(),Schema.create(Schema.Type.STRING), null,null);
outFields.add(f1);
}
s.setFields(outFields);
System.out.println("Out Schema - " + s);
GenericRecord record = new GenericData.Record(s);
for(Field f : fields) {
record.put(f.name(), key.datum().get(f.name()));
}
System.out.println("Record - " + record);
GenericData d = new GenericData();
d.newRecord(record, s);
AvroKey<GenericData> outkey = new AvroKey<GenericData>(d);
System.out.println("Generic Record (Avro Key) - " + outkey.datum());
context.write(outkey, NullWritable.get());
} catch (Exception e) {
e.printStackTrace();
}
}
}
請注意，Avro的輸入地圖降低工作正常，但在Avro的格式輸出是這裏的問題。