在Java中将Avro转换为ORC？

在Java中将Avro转换为ORC可以通过使用Apache Hive来实现。Apache Hive是一个基于Hadoop的数据仓库基础设施，它提供了类似于SQL的查询语言HiveQL，可以将Avro数据转换为ORC格式。

下面是一个简单的示例代码，展示了如何在Java中使用Apache Hive将Avro转换为ORC：

import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.io.orc.OrcFile;
import org.apache.hadoop.hive.ql.io.orc.OrcStruct;
import org.apache.hadoop.hive.ql.io.orc.Writer;
import org.apache.hadoop.hive.serde2.avro.AvroDeserializer;
import org.apache.hadoop.hive.serde2.avro.AvroSerdeUtils;
import org.apache.hadoop.hive.serde2.avro.AvroTableUtils;
import org.apache.hadoop.hive.serde2.avro.AvroUtilities;
import org.apache.hadoop.hive.serde2.avro.AvroWrapper;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.orc.TypeDescription;

import java.io.IOException;

public class AvroToOrcConverter implements Tool {
    private Configuration conf;

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

    @Override
    public int run(String[] args) throws Exception {
        JobConf jobConf = new JobConf(getConf(), AvroToOrcConverter.class);
        String[] remainingArgs = new GenericOptionsParser(jobConf, args).getRemainingArgs();

        if (remainingArgs.length != 2) {
            System.err.println("Usage: AvroToOrcConverter <input path> <output path>");
            return 1;
        }

        Job job = Job.getInstance(jobConf);
        job.setJarByClass(AvroToOrcConverter.class);
        job.setJobName("Avro to ORC Converter");

        // 设置输入路径和格式
        FileInputFormat.addInputPath(job, new Path(remainingArgs[0]));
        job.setInputFormatClass(TextInputFormat.class);

        // 设置输出路径和格式
        FileOutputFormat.setOutputPath(job, new Path(remainingArgs[1]));
        job.setOutputFormatClass(TextOutputFormat.class);

        // 设置Mapper和Reducer类
        job.setMapperClass(AvroToOrcMapper.class);
        job.setReducerClass(AvroToOrcReducer.class);

        // 设置Mapper输出键值对类型
        job.setMapOutputKeyClass(NullWritable.class);
        job.setMapOutputValueClass(OrcStruct.class);

        // 设置Reducer输出键值对类型
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(OrcStruct.class);

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

    @Override
    public Configuration getConf() {
        return conf;
    }

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

    public static class AvroToOrcMapper extends Mapper<LongWritable, Text, NullWritable, OrcStruct> {
        private AvroDeserializer avroDeserializer;
        private ObjectInspector avroInspector;
        private OrcStruct orcStruct;
        private VectorizedRowBatch batch;
        private Writer writer;

        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            Configuration conf = context.getConfiguration();

            // 从Avro模式文件中获取Avro模式
            String avroSchemaFile = conf.get("avro.schema.file");
            Schema avroSchema = new Schema.Parser().parse(new File(avroSchemaFile));

            // 从ORC模式文件中获取ORC模式
            String orcSchemaFile = conf.get("orc.schema.file");
            TypeDescription orcSchema = TypeDescription.fromString(FileUtils.readFileToString(new File(orcSchemaFile), "UTF-8"));

            // 创建Avro反序列化器和对象检查器
            avroDeserializer = new AvroDeserializer(avroSchema);
            avroInspector = AvroTableUtils.getAvroDeserializerObjectInspector(avroSchema);

            // 创建ORC结构和批处理
            orcStruct = (OrcStruct) OrcStruct.createValue(orcSchema);
            batch = orcSchema.createRowBatch();

            // 创建ORC写入器
            Path outputPath = FileOutputFormat.getOutputPath(context);
            writer = OrcFile.createWriter(outputPath, OrcFile.writerOptions(conf).setSchema(orcSchema));
        }

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 将Avro记录转换为ORC记录
            GenericRecord avroRecord = avroDeserializer.deserialize(value.toString());
            AvroWrapper<GenericRecord> avroWrapper = new AvroWrapper<>(avroRecord);
            Object orcObject = AvroUtilities.ewToOrc(avroWrapper, avroInspector, orcStruct, batch);

            // 将ORC记录写入ORC文件
            int rowIndex = batch.size++;
            for (int fieldIndex = 0; fieldIndex < orcStruct.getNumFields(); fieldIndex++) {
                ((OrcStruct) batch.cols[fieldIndex]).setFieldValue(rowIndex, ((OrcStruct) orcObject).getFieldValue(fieldIndex));
            }

            if (batch.size == batch.getMaxSize()) {
                writer.addRowBatch(batch);
                batch.reset();
            }
        }

        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            if (batch.size > 0) {
                writer.addRowBatch(batch);
            }

            writer.close();
        }
    }

    public static class AvroToOrcReducer extends Reducer<NullWritable, OrcStruct, NullWritable, OrcStruct> {
        @Override
        protected void reduce(NullWritable key, Iterable<OrcStruct> values, Context context) throws IOException, InterruptedException {
            for (OrcStruct value : values) {
                context.write(key, value);
            }
        }
    }
}

在上述代码中，我们使用了Apache Hive提供的AvroDeserializer和AvroTableUtils来处理Avro数据的反序列化和对象检查。我们还使用了Apache ORC提供的Writer和TypeDescription来创建ORC文件和ORC模式。

要使用该代码，您需要将Avro模式文件和ORC模式文件作为输入参数传递给程序。Avro模式文件应该是一个JSON文件，描述了Avro数据的结构。ORC模式文件应该是一个文本文件，描述了ORC数据的结构。

您可以使用以下命令来运行该程序：

hadoop jar avro-to-orc-converter.jar AvroToOrcConverter <input path> <output path> -Davro.schema.file=<avro schema file> -Dorc.schema.file=<orc schema file>

其中，<input path>是包含Avro数据的输入路径，<output path>是输出ORC文件的路径，<avro schema file>是Avro模式文件的路径，<orc schema file>是ORC模式文件的路径。

请注意，这只是一个简单的示例代码，您可能需要根据您的实际需求进行修改和调整。此外，您还需要安装和配置Apache Hive和Apache ORC来运行此代码。

希望这个答案能够满足您的需求。如果您有任何其他问题，请随时提问。