文章目录
0x00 文章内容- 行存储与列存储
- 编码实现Avro格式的读写
比如现在有一张表,数据如下:
分别用行存储于列存储。
1. 行存储
a. 行存储的存储方式
传统数据库就是行存储,如MySQL等。
2. 列存储
a. 列存储的存储方式
其中,这里对行进行了一个split,两行为一个split。思想与HBase的Region分区类似。
- HBase理论参考文章:浅显易懂入门大数据系列:四、HBase(超详细)的
五、HBase的存储结构
- 并且了解行存储与列存储的优缺点。
2. Avro与Parquet
a. Avro是行存储,Parquet是列存储。
b. 还需要清楚的是Avro与Parquet格式都是有Schema的,即结构。类似于我们传统数据库的字段,所以在写的时候需要指定。
1. 编码实现读写Avro文件
a. 引入Avro相关jar包
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>1.8.0</version>
</dependency>
b. 引入Avro的Schema文件,编辑,放于src/main/data
目录下,命名为:person.avsc
{"namespace": "com.shaonaiyi.hadoop.filetype.avro",
"type": "record",
"name": "Person",
"fields": [
{"name": "name", "type": "string"},
{"name": "age", "type": ["int", "null"]},
{"name": "favorite_number", "type": ["int", "null"]},
{"name": "favorite_color", "type": ["string", "null"]}
]
}
我们准备使用此定义好结构的文件生成一个对应的Java实体类,所以这里定义了实体类存放的位置,这里是:com.shaonaiyi.hadoop.filetype.avro
c. 我们准备使用Maven插件工具生成Java类,此处引入插件:
<plugin>
<groupId>org.apache.avro</groupId>
<artifactId>avro-maven-plugin</artifactId>
<version>1.7.7</version>
<executions>
<execution>
<phase>generate-sources</phase>
<goals>
<goal>schema</goal>
</goals>
<configuration>
<sourceDirectory>${project.basedir}/src/main/data</sourceDirectory>
<outputDirectory>${project.basedir}/src/main/java</outputDirectory>
</configuration>
</execution>
</executions>
</plugin>
d. 生成Java类(clean
->compile
)
执行完,会发现已经生成了一个Person
类,可能会报错,我们将@Override
注释掉即可,因为之前写过一些代码,所以报错了,不管它。
e. Person
类里面这行就是我们所需要的Schema
,对应着我们的person.avsc
public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Person\",\"namespace\":\"com.shaonaiyi.hadoop.filetype.avro\",\"fields\":[{\"name\":\"name\",\"type\":\"string\"},{\"name\":\"age\",\"type\":[\"int\",\"null\"]},{\"name\":\"favorite_number\",\"type\":[\"int\",\"null\"]},{\"name\":\"favorite_color\",\"type\":[\"string\",\"null\"]}]}");
g. 完整的写Avro文件代码
package com.shaonaiyi.hadoop.filetype.avro;
import org.apache.avro.file.DataFileWriter;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericDatumWriter;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.DatumWriter;
import java.io.File;
import java.io.IOException;
/**
* @Author shaonaiyi@163.com
* @Date 2019/12/17 16:17
* @Description 编码实现写Avro文件
*/
public class AvroFileWriter {
public static void main(String[] args) throws IOException {
GenericRecord record1 = new GenericData.Record(Person.SCHEMA$);
record1.put("name", "shaonaiyi");
record1.put("age", 18);
record1.put("favorite_number", 7);
record1.put("favorite_color", "red");
GenericRecord record2 = new GenericData.Record(Person.SCHEMA$);
record2.put("name", "shaonaier");
record2.put("age", 17);
record2.put("favorite_number", 1);
record2.put("favorite_color", "yellow");
File file = new File("person.avro");
DatumWriter<GenericRecord> writer = new GenericDatumWriter<>(Person.SCHEMA$);
DataFileWriter<GenericRecord> dataFileWriter = new DataFileWriter<>(writer);
dataFileWriter.create(Person.SCHEMA$, file);
dataFileWriter.append(record1);
dataFileWriter.append(record2);
dataFileWriter.close();
}
}
h. 完整的读Avro文件代码
package com.shaonaiyi.hadoop.filetype.avro;
import org.apache.avro.file.DataFileReader;
import org.apache.avro.generic.GenericDatumReader;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.DatumReader;
import java.io.File;
import java.io.IOException;
/**
* @Author shaonaiyi@163.com
* @Date 2019/12/17 16:48
* @Description 编码实现读Avro文件
*/
public class AvroFileReader {
public static void main(String[] args) throws IOException {
File file = new File("person.avro");
DatumReader<GenericRecord> reader = new GenericDatumReader<>();
DataFileReader<GenericRecord> dataFileReader = new DataFileReader<GenericRecord>(file, reader);
GenericRecord record = null;
while (dataFileReader.hasNext()) {
record = dataFileReader.next();
System.out.println("name:" + record.get("name").toString());
System.out.println("age:" + record.get("age").toString());
System.out.println("favorite_number:" + record.get("favorite_number").toString());
System.out.println("favorite_color:" + record.get("favorite_color"));
System.out.println("-----------------------------------");
}
}
}
2. 查看读写Avro文件结果
a. 写Avro文件
b. 读Avro文件
3. 编码实现读写Avro文件(HDFS)
a. 引入所需要的jar包
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro-mapred</artifactId>
<version>1.8.0</version>
</dependency>
b. 写Avro文件到HDFS完整代码
package com.shaonaiyi.hadoop.filetype.avro;
import org.apache.avro.mapred.AvroKey;
import org.apache.avro.mapreduce.AvroJob;
import org.apache.avro.mapreduce.AvroKeyOutputFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.task.JobContextImpl;
import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl;
import java.io.IOException;
/**
* @Author shaonaiyi@163.com
* @Date 2019/12/17 17:15
* @Description 编码实现写Avro文件到HDFS
*/
public class MRAvroFileWriter {
public static void main(String[] args) throws IOException, IllegalAccessException, InstantiationException, ClassNotFoundException, InterruptedException {
//1 构建一个job实例
Configuration hadoopConf = new Configuration();
Job job = Job.getInstance(hadoopConf);
//2 设置job的相关属性
// job.setOutputKeyClass(NullWritable.class);
// job.setOutputValueClass(Text.class);
// job.setOutputFormatClass(TextOutputFormat.class);
//job.setOutputKeyClass(AvroKey.class);
//job.setOutputValueClass(Person.class);
job.setOutputFormatClass(AvroKeyOutputFormat.class);
//AvroJob.setOutputKeySchema(job, Schema.create(Schema.Type.INT));
AvroJob.setOutputKeySchema(job, Person.SCHEMA$);
//3 设置输出路径
FileOutputFormat.setOutputPath(job, new Path("hdfs://master:9999/user/hadoop-sny/mr/filetype/avro"));
//FileOutputFormat.setCompressOutput(job, true);
//FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
//4 构建JobContext
JobID jobID = new JobID("jobId", 123);
JobContext jobContext = new JobContextImpl(job.getConfiguration(), jobID);
//5 构建taskContext
TaskAttemptID attemptId = new TaskAttemptID("jobTrackerId", 123, TaskType.REDUCE, 0, 0);
TaskAttemptContext hadoopAttemptContext = new TaskAttemptContextImpl(job.getConfiguration(), attemptId);
//6 构建OutputFormat实例
OutputFormat format = job.getOutputFormatClass().newInstance();
//7 设置OutputCommitter
OutputCommitter committer = format.getOutputCommitter(hadoopAttemptContext);
committer.setupJob(jobContext);
committer.setupTask(hadoopAttemptContext);
//8 获取writer写数据,写完关闭writer
RecordWriter<AvroKey, Person> writer = format.getRecordWriter(hadoopAttemptContext);
// writer.write(null, new Text("shao"));
// writer.write(null, new Text("nai"));
// writer.write(null, new Text("yi"));
// writer.write(null, new Text("bigdata-man"));
Person person = new Person();
person.setName("jeffy");
person.setAge(20);
person.setFavoriteNumber(10);
person.setFavoriteColor("red");
writer.write(new AvroKey(person), null);
writer.close(hadoopAttemptContext);
//9 committer提交job和task
committer.commitTask(hadoopAttemptContext);
committer.commitJob(jobContext);
}
}
与写Text格式(文章链接跳转:Hadoop支持的文件格式之Text)时类似,主要不同如下:
c. 从HDFS上读Avro文件完整代码
package com.shaonaiyi.hadoop.filetype.avro;
import org.apache.avro.mapred.AvroKey;
import org.apache.avro.mapreduce.AvroKeyInputFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.task.JobContextImpl;
import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl;
import java.io.IOException;
import java.util.List;
import java.util.function.Consumer;
/**
* @Author shaonaiyi@163.com
* @Date 2019/12/17 17:29
* @Description 编码实现从HDFS上读Avro文件
*/
public class MRAvroFileReader {
public static void main(String[] args) throws IOException, IllegalAccessException, InstantiationException {
//1 构建一个job实例
Configuration hadoopConf = new Configuration();
Job job = Job.getInstance(hadoopConf);
//2 设置需要读取的文件全路径
FileInputFormat.setInputPaths(job, "hdfs://master:9999/user/hadoop-sny/mr/filetype/avro");
//3 获取读取文件的格式
// TextInputFormat inputFormat = TextInputFormat.class.newInstance();
AvroKeyInputFormat inputFormat = AvroKeyInputFormat.class.newInstance();
//4 获取需要读取文件的数据块的分区信息
//4.1 获取文件被分成多少数据块了
JobID jobID = new JobID("jobId", 123);
JobContext jobContext = new JobContextImpl(job.getConfiguration(), jobID);
List<InputSplit> inputSplits = inputFormat.getSplits(jobContext);
//读取每一个数据块的数据
inputSplits.forEach(new Consumer<InputSplit>() {
@Override
public void accept(InputSplit inputSplit) {
TaskAttemptID attemptId = new TaskAttemptID("jobTrackerId", 123, TaskType.MAP, 0, 0);
TaskAttemptContext hadoopAttemptContext = new TaskAttemptContextImpl(job.getConfiguration(), attemptId);
// RecordReader reader = inputFormat.createRecordReader(inputSplit, hadoopAttemptContext);
RecordReader<AvroKey<Person>, NullWritable> reader = null;
try {
// reader.initialize(inputSplit, hadoopAttemptContext);
// System.out.println("<key,value>");
// System.out.println("-----------");
// while (reader.nextKeyValue()) {
// System.out.println("<"+reader.getCurrentKey() + "," + reader.getCurrentValue()+ ">" );
// }
reader = inputFormat.createRecordReader(inputSplit, hadoopAttemptContext);
reader.initialize(inputSplit, hadoopAttemptContext);
while (reader.nextKeyValue()) {
Person person = reader.getCurrentKey().datum();
System.out.println("key=>" + person);
System.out.println("value=>" + reader.getCurrentValue());
}
reader.close();
} catch (IOException e) {
e.printStackTrace();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
});
}
}
与读Text格式(文章链接跳转:Hadoop支持的文件格式之Text)时类似,主要不同如下:
4. 查看读写Avro文件结果(HDFS)
a. 写文件结果
b. 读文件结果,我们在代码里没有设置值
作者简介:邵奈一
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