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关于java:列式存储格式之parquet读写

jiezi

概述

Apache Parquet 是 Hadoop 生态系统中任何我的项目均可应用的列式存储格局，更高压缩比以及更小 IO 操作。网上许多写入 parquet 须要在本地装置 haddop 环境，上面介绍一种不须要装置 haddop 即可写入 parquet 文件的形式，以及通过两种形式来读取 parquet 文件。上面开始入坑了 …

parquet 写入

1.pom 依赖

 <dependency>
     <groupId>org.apache.avro</groupId>
     <artifactId>avro</artifactId>
     <version>1.8.2</version>
 </dependency>
 <dependency>
     <groupId>org.apache.hadoop</groupId>
     <artifactId>hadoop-core</artifactId>
     <version>1.2.1</version>
 </dependency>
 <dependency>
     <groupId>org.apache.parquet</groupId>
     <artifactId>parquet-hadoop</artifactId>
     <version>1.8.1</version>
 </dependency>
 <!-- https://mvnrepository.com/artifact/org.apache.parquet/parquet-avro -->
 <dependency>
     <groupId>org.apache.parquet</groupId>
     <artifactId>parquet-avro</artifactId>
     <version>1.8.1</version>
 </dependency>

2. 定义 schema（实体类）

package com.kestrel;

public class User {
    private String id;
    private String name;
    private String password;

    public User() {}

    public User(String id, String name, String password) {
        this.id = id;
        this.name = name;
        this.password = password;
    }

    public String getId() {return id;}



    public String getName() {return name;}

    public void setName(String name) {this.name = name;}

    public String getPassword() {return password;}

    public void setPassword(String password) {this.password = password;}

    @Override
    public String toString() {
        return "User{" +
                "id='" + id + '\'' +
                ", name='" + name + '\'' +
                ", password='" + password + '\'' +
                '}';
    }
}

  1. AvroParquetWriter 写入

    List<User> users = new ArrayList<>();

User user1 = new User("1","huangchixin","123123");
User user2 = new User("2","huangchixin2","123445");
users.add(user1);
users.add(user2);
Path dataFile = new Path("./tmp/demo.snappy.parquet");

// Write as Parquet file.
try (ParquetWriter<User> writer = AvroParquetWriter.<User>builder(dataFile)
     .withSchema(ReflectData.AllowNull.get().getSchema(User.class))
     .withDataModel(ReflectData.get())
     .withConf(new Configuration())
     .withCompressionCodec(SNAPPY)
     .withWriteMode(OVERWRITE)
     .build()) {for (User user : users) {writer.write(user);
    }
}

    parquet 读取

    1. AvroParquetReader 读取，须要指定对象实例，或者也可自定义 json 字符串
    // Read from Parquet file.
try (ParquetReader<User> reader = AvroParquetReader.<User>builder(dataFile)
    .withDataModel(new ReflectData(User.class.getClassLoader()))
    .disableCompatibility()
    .withConf(new Configuration())
    .build()) {
    User user;

    while ((user = reader.read()) != null) {System.out.println(user);
    }
}

    1. ParquetFileReader 读取，不须要

      • 列实体
      package com.kestrel;

/**
 * @Auther: 12640
 * @Date: 2021/1/1 15:13
 * @Description:
 */
public class TableHead {

    /**
     * 列名
     */
    private String name;
    /**
     * 存储 列的 数据类型
     */
    private String type;
    /**
     * 所在列
     */
    private Integer index;
    public String getType() {return type;}

    public void setType(String type) {this.type = type;}

    public String getName() {return name;}

    public void setName(String name) {this.name = name;}

    public Integer getIndex() {return index;}

    public void setIndex(Integer index) {this.index = index;}
}
      • parquet 实体类
      package com.kestrel;

import java.util.List;

/**
 * @Auther: 12640
 * @Date: 2021/1/1 15:14
 * @Description:
 */
public class TableResult {
    /**
     * 解析文件的表头信息 临时只对 arrow，csv 文件无效
     */
    private List< TableHead> columns;
    /**
     * 数据内容
     */
    private List<?> data;

    public List< TableHead> getColumns() {return columns;}

    public void setColumns(List< TableHead> columns) {this.columns = columns;}

    public List<?> getData() {return data;}

    public void setData(List<?> data) {this.data = data;}
}
      • 读取 parquet 文件
      import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.common.collect.Lists;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.parquet.column.page.PageReadStore;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.simple.convert.GroupRecordConverter;
import org.apache.parquet.format.converter.ParquetMetadataConverter;
import org.apache.parquet.hadoop.ParquetFileReader;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.example.GroupReadSupport;
import org.apache.parquet.hadoop.metadata.ParquetMetadata;
import org.apache.parquet.io.ColumnIOFactory;
import org.apache.parquet.io.MessageColumnIO;
import org.apache.parquet.io.RecordReader;
import org.apache.parquet.schema.GroupType;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.OriginalType;
import org.apache.parquet.schema.Type;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public class ReadParquet {public static void main(String[] args) throws Exception {TableResult tableResult = parquetReaderV2(new File("./tmp/demo.snappy.parquet"));
        ObjectMapper mapper = new ObjectMapper();
        String jsonString = mapper.writerWithDefaultPrettyPrinter()
                .writeValueAsString(tableResult);
        System.out.println(jsonString);
    }


    public static TableResult parquetReaderV2(File file) throws Exception {long start = System.currentTimeMillis();
        haddopEnv();
        Path path = new Path(file.getAbsolutePath());

        Configuration conf = new Configuration();
        TableResult table = new TableResult();

        // 二位数据列表
        List<List<Object>> dataList = Lists.newArrayList();

        ParquetMetadata readFooter = ParquetFileReader.readFooter(conf, path, ParquetMetadataConverter.NO_FILTER);

        MessageType schema = readFooter.getFileMetaData().getSchema();
        ParquetFileReader r = new ParquetFileReader(conf, readFooter.getFileMetaData(), path, readFooter.getBlocks(), schema.getColumns());
//        1.9.0 应用以下创建对象
//        ParquetFileReader r = new ParquetFileReader(conf, path, readFooter);

        PageReadStore pages = null;
        try {while (null != (pages = r.readNextRowGroup())) {final long rows = pages.getRowCount();
//                logger.info(file.getName()+"行数:" + rows);

                final MessageColumnIO columnIO = new ColumnIOFactory().getColumnIO(schema);
                final RecordReader<Group> recordReader = columnIO.getRecordReader(pages,
                        new GroupRecordConverter(schema));
                for (int i = 0; i <= rows; i++) {//                    System.out.println(recordReader.shouldSkipCurrentRecord());
                    final Group g = recordReader.read();
                    if (i == 0) {
                        // 设置表头列名
                        table.setColumns(parquetColumn(g));
                        i++;
                    }
                    // 获取行数据
                    List<Object> row = getparquetData(table.getColumns(), g);
                    dataList.add(row);
                    // printGroup(g);

                }
            }
        } finally {r.close();
        }
//        logger.info(file.getName()+"加载工夫:"+(System.currentTimeMillis() - start));


        table.setData(dataList);


        return table;
    }


    // 新版本中 new ParquetReader()所有构造方法如同都弃用了, 用下面的 builder 去结构对象
    static void parquetReader(String inPath) throws Exception{GroupReadSupport readSupport = new GroupReadSupport();
        ParquetReader<Group> reader = new ParquetReader<Group>(new Path(inPath),readSupport);
        Group line=null;
        while((line=reader.read())!=null){System.out.println(line.toString());
        }
        System.out.println("读取完结");

    }





    private static List<Object> getparquetData(List<TableHead> columns, Group line) {List<Object> row = new ArrayList<>();
        Object cellStr = null;
        for (int i = 0; i < columns.size(); i++) {
            try {switch (columns.get(i).getType()) {
                    case "DOUBLE":
                        cellStr = line.getDouble(i, 0);
                        break;
                    case "FLOAT":
                        cellStr = line.getFloat(i, 0);
                        break;
                    case "BOOLEAN":
                        cellStr = line.getBoolean(i, 0);
                        break;
                    case "INT96":
                        cellStr = line.getInt96(i, 0);
                        break;
                    case "LONG":
                        cellStr = line.getLong(i, 0);
                        break;
                    default:
                        cellStr = line.getValueToString(i, 0);
                }

            } catch (RuntimeException e) { } finally {row.add(cellStr);
            }

        }
        return row;
    }

    /**
     * 获取 arrow 文件 表头信息
     *
     * @param
     * @return
     */
    private static List<TableHead> parquetColumn(Group line) {List<TableHead> columns = Lists.newArrayList();
        TableHead dto = null;

        GroupType groupType = line.getType();

        int fieldCount = groupType.getFieldCount();
        for (int i = 0; i < fieldCount; i++) {dto = new TableHead();
            Type type = groupType.getType(i);
            String fieldName = type.getName();
            OriginalType originalType = type.getOriginalType();
            String typeName = null;
            if (originalType != null) {typeName = originalType.name();
            } else {typeName = type.asPrimitiveType().getPrimitiveTypeName().name();
            }

            dto.setIndex(i);
            dto.setName(fieldName);
            dto.setType(typeName);
            columns.add(dto);
        }

        return columns;
    }


    public static void haddopEnv() throws IOException {File workaround = new File(".");
        System.getProperties().put("hadoop.home.dir", workaround.getAbsolutePath());
        new File("./bin").mkdirs();
        new File("./bin/winutils.exe").createNewFile();}


}

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