谈谈怎么Apache Avro数据展示
场景介绍
-
如何序列化生成Avro数据
-
如何反序列化解析Avro数据
-
如何使用FlinkSQL解析Avro数据
前提条件
-
了解avro是什么,可参考apache avro官网快速入门指南
-
了解avro应用场景
操作步骤
1、新建avro maven工程项目,配置pom依赖
pom文件内容如下:
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.huawei.bigdata</groupId>
<artifactId>avrodemo</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.avro</groupId>
<artifactId>avro</artifactId>
<version>1.8.1</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.avro</groupId>
<artifactId>avro-maven-plugin</artifactId>
<version>1.8.1</version>
<executions>
<execution>
<phase>generate-sources</phase>
<goals>
<goal>schema</goal>
</goals>
<configuration>
<sourceDirectory>${project.basedir}/src/main/avro/</sourceDirectory>
<outputDirectory>${project.basedir}/src/main/java/</outputDirectory>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>1.6</source>
<target>1.6</target>
</configuration>
</plugin>
</plugins>
</build>
</project>
注意:以上pom文件配置了自动生成类的路径,即project.basedir/src/main/avro/和{project.basedir}/src/main/avro/和
{project.basedir}/src/main/java/,这样配置之后,在执行mvn命令的时候,这个插件就会自动将此目录下的avsc schema生成类文件,并放到后者这个目录下。如果没有生成avro目录,手动创建一下即可。2、定义schema
使用JSON为Avro定义schema。schema由基本类型(null,boolean, int, long, float, double, bytes 和string)和复杂类型(record, enum, array, map, union, 和fixed)组成。例如,以下定义一个user的schema,在main目录下创建一个avro目录,然后在avro目录下新建文件 user.avsc :
{"namespace": "lancoo.ecbdc.pre",
"type": "record",
"name": "User",
"fields": [
{"name": "name", "type": "string"},
{"name": "favorite_number", "type": ["int", "null"]},
{"name": "favorite_color", "type": ["string", "null"]}
]
}
3、编译schema
点击maven projects项目的compile进行编译,会自动在创建namespace路径和User类代码
4、序列化
创建TestUser类,用于序列化生成数据
User user1 = new User();
user1.setName("Alyssa");
user1.setFavoriteNumber(256);
// Leave favorite col or null
// Alternate constructor
User user2 = new User("Ben", 7, "red");
// Construct via builder
User user3 = User.newBuilder()
.setName("Charlie")
.setFavoriteColor("blue")
.setFavoriteNumber(null)
.build();
// Serialize user1, user2 and user3 to disk
DatumWriter<User> userDatumWriter = new SpecificDatumWriter<User>(User.class);
DataFileWriter<User> dataFileWriter = new DataFileWriter<User>(userDatumWriter);
dataFileWriter.create(user1.getSchema(), new File("user_generic.avro"));
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.append(user3);
dataFileWriter.close();
执行序列化程序后,会在项目的同级目录下生成avro数据
user_generic.avro内容如下:
Objavro.schema�{"type":"record","name":"User","namespace":"lancoo.ecbdc.pre","fields":[{"name":"name","type":"string"},{"name":"favorite_number","type":["int","null"]},{"name":"favorite_color","type":["string","null"]}]}
至此avro数据已经生成。
5、反序列化
通过反序列化代码解析avro数据
// Deserialize Users from disk
DatumReader<User> userDatumReader = new SpecificDatumReader<User>(User.class);
DataFileReader<User> dataFileReader = new DataFileReader<User>(new File("user_generic.avro"), userDatumReader);
User user = null;
while (dataFileReader.hasNext()) {
// Reuse user object by passing it to next(). This saves us from
// allocating and garbage collecting many objects for files with
// many items.
user = dataFileReader.next(user);
System.out.println(user);
}
执行反序列化代码解析user_generic.avro
avro数据解析成功。
6、将user_generic.avro上传至hdfs路径
hdfs dfs -mkdir -p /tmp/lztest/
hdfs dfs -put user_generic.avro /tmp/lztest/
7、配置flinkserver
- 准备avro jar包
将flink-sql-avro-*.jar、flink-sql-avro-confluent-registry-*.jar放入flinkserver lib,将下面的命令在所有flinkserver节点执行
cp /opt/huawei/Bigdata/FusionInsight_Flink_8.1.2/install/FusionInsight-Flink-1.12.2/flink/opt/flink-sql-avro*.jar /opt/huawei/Bigdata/FusionInsight_Flink_8.1.3/install/FusionInsight-Flink-1.12.2/flink/lib
chmod 500 flink-sql-avro*.jar
chown omm:wheel flink-sql-avro*.jar
-
同时重启FlinkServer实例,重启完成后查看avro包是否被上传
hdfs dfs -ls /FusionInsight_FlinkServer/8.1.2-312005/lib
8、编写FlinkSQL
CREATE TABLE testHdfs(
name String,
favorite_number int,
favorite_color String
) WITH(
'connector' = 'filesystem',
'path' = 'hdfs:///tmp/lztest/user_generic.avro',
'format' = 'avro'
);CREATE TABLE KafkaTable (
name String,
favorite_number int,
favorite_color String
) WITH (
'connector' = 'kafka',
'topic' = 'testavro',
'properties.bootstrap.servers' = '96.10.2.1:21005',
'properties.group.id' = 'testGroup',
'scan.startup.mode' = 'latest-offset',
'format' = 'avro'
);
insert into
KafkaTable
select
*
from
testHdfs;
保存提交任务
9、查看对应topic中是否有数据
FlinkSQL解析avro数据成功。
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