IDEA 中使用 Hudi的示例代码
环境准备
创建 Maven 项目创建服务器远程连接
Tools------Delployment-----Browse Remote Host
设置如下内容：
在这里输入服务器的账号和密码
点击Test Connection，提示Successfully的话，就说明配置成功。
复制hadoop的 core-site.xml、hdfs-site.xml 以及 log4j.properties 三个文件复制到resources文件夹下。
设置 log4j.properties 为打印警告异常信息：
log4j.rootCateGory=WARN, console
4.添加 pom.xml 文件
<repositories>
<repository>
<id>aliyun</id>
<url>Http://maven.aliyun.com/nexus/content/groups/public/</url>
</repository>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
<repository>
<id>jboss</id>
<url>http://repository.jboss.com/nexus/content/groups/public</url>
</repository>
</repositories>
<properties>
<Scala.version>2.12.10</scala.version>
<scala.binary.version>2.12</scala.binary.version>
<spark.version>3.0.0</spark.version>
<hadoop.version>2.7.3</hadoop.version>
<hudi.version>0.9.0</hudi.version>
</properties>
<dependencies>
<!-- 依赖Scala语言 -->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<!-- Spark Core 依赖 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_${scala.binary.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- Spark sql 依赖 -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_${scala.binary.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- Hadoop Client 依赖 -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<!-- hudi-spark3 -->
<dependency>
<groupId>org.apache.hudi</groupId>
<artifactId>hudi-spark3-bundle_2.12</artifactId>
<version>${hudi.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-avro_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
</dependencies>
<build>
<outputDirectory>target/classes</outputDirectory>
<testOutputDirectory>target/test-classes</testOutputDirectory>
<resources>
<resource>
<directory>${project.basedir}/src/main/resources</directory>
</resource>
</resources>
<!-- Maven 编译的插件 -->
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
</configuration>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
要注释掉创建项目时的生成的下面的代码，不然依赖一直报错：
<!-- <properties>-->
<!-- <maven.compiler.source>8</maven.compiler.source>-->
<!-- <maven.compiler.target>8</maven.compiler.target>-->
<!-- </properties>-->
代码结构：
核心代码
import org.apache.hudi.QuickstartUtils.DataGenerator
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
object HudiSparkDemo {
def insertData(spark: SparkSession, table: String, path: String): Unit = {
import spark.implicits._
// 第1步、模拟乘车数据
import org.apache.hudi.QuickstartUtils._
val dataGen: DataGenerator = new DataGenerator()
val inserts = convertToStringList(dataGen.generateInserts(100))
import scala.collection.JavaConverters._
val insertDF: DataFrame = spark.read.JSON(
spark.sparkContext.parallelize(inserts.asScala, 2).toDS()
)
// insertDF.printSchema()
// insertDF.show(10, truncate = false)
//第二步： 插入数据到Hudi表
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
insertDF.write
.mode(SaveMode.Append)
.fORMat("hudi")
.option("hoodie.insert.shuffle.parallelism", 2)
.option("hoodie.insert.shuffle.parallelism", 2)
//Hudi表的属性设置
.option(PRECOMBINE_FIELD.key(), "ts")
.option(RECORDKEY_FIELD.key(), "uuid")
.option(PARTITIONPATH_FIELD.key(), "partitionpath")
.option(TBL_NAME.key(), table)
.save(path)
}
def queryData(spark: SparkSession, path: String): Unit = {
import spark.implicits._
val tripsDF: DataFrame = spark.read.format("hudi").load(path)
// tripsDF.printSchema()
// tripsDF.show(10, truncate = false)
//查询费用大于10，小于50的乘车数据
tripsDF
.filter($"fare" >= 20 && $"fare" <=50)
.select($"driver", $"rider", $"fare", $"begin_lat", $"begin_lon", $"partitionpath", $"_hoodie_commit_time")
.orderBy($"fare".desc, $"_hoodie_commit_time".desc)
.show(20, truncate = false)
}
def queryDataByTime(spark: SparkSession, path: String):Unit = {
import org.apache.spark.sql.functions._
//方式一：指定字符串，按照日期时间过滤获取数据
val df1 = spark.read
.format("hudi")
.option("as.of.instant", "20220610160908")
.load(path)
.sort(col("_hoodie_commit_time").desc)
df1.printSchema()
df1.show(numRows = 5, truncate = false)
//方式二：指定字符串，按照日期时间过滤获取数据
val df2 = spark.read
.format("hudi")
.option("as.of.instant", "2022-06-10 16:09:08")
.load(path)
.sort(col("_hoodie_commit_time").desc)
df2.printSchema()
df2.show(numRows = 5, truncate = false)
}
def insertData(spark: SparkSession, table: String, path: String, dataGen: DataGenerator): Unit = {
import spark.implicits._
// 第1步、模拟乘车数据
import org.apache.hudi.QuickstartUtils._
val inserts = convertToStringList(dataGen.generateInserts(100))
import scala.collection.JavaConverters._
val insertDF: DataFrame = spark.read.json(
spark.sparkContext.parallelize(inserts.asScala, 2).toDS()
)
// insertDF.printSchema()
// insertDF.show(10, truncate = false)
//第二步： 插入数据到Hudi表
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
insertDF.write
//更换为Overwrite模式
.mode(SaveMode.Overwrite)
.format("hudi")
.option("hoodie.insert.shuffle.parallelism", 2)
.option("hoodie.insert.shuffle.parallelism", 2)
//Hudi表的属性设置
.option(PRECOMBINE_FIELD.key(), "ts")
.option(RECORDKEY_FIELD.key(), "uuid")
.option(PARTITIONPATH_FIELD.key(), "partitionpath")
.option(TBL_NAME.key(), table)
.save(path)
}
def updateData(spark: SparkSession, table: String, path: String, dataGen: DataGenerator):Unit = {
import spark.implicits._
// 第1步、模拟乘车数据
import org.apache.hudi.QuickstartUtils._
//产生更新的数据
val updates = convertToStringList(dataGen.generateUpdates(100))
import scala.collection.JavaConverters._
val updateDF: DataFrame = spark.read.json(
spark.sparkContext.parallelize(updates.asScala, 2).toDS()
)
// TOOD: 第2步、插入数据到Hudi表
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
updateDF.write
//追加模式
.mode(SaveMode.Append)
.format("hudi")
.option("hoodie.insert.shuffle.parallelism", "2")
.option("hoodie.upsert.shuffle.parallelism", "2")
// Hudi 表的属性值设置
.option(PRECOMBINE_FIELD.key(), "ts")
.option(RECORDKEY_FIELD.key(), "uuid")
.option(PARTITIONPATH_FIELD.key(), "partitionpath")
.option(TBL_NAME.key(), table)
.save(path)
}
def incrementalQueryData(spark: SparkSession, path: String): Unit = {
import spark.implicits._
// 第1步、加载Hudi表数据，获取commit time时间，作为增量查询数据阈值
import org.apache.hudi.DataSourceReadOptions._
spark.read
.format("hudi")
.load(path)
.createOrReplaceTempView("view_temp_hudi_trips")
val commits: Array[String] = spark
.sql(
"""
|select
| distinct(_hoodie_commit_time) as commitTime
|from
| view_temp_hudi_trips
|order by
| commitTime DESC
|""".stripMargin
)
.map(row => row.getString(0))
.take(50)
val beginTime = commits(commits.length - 1) // commit time we are interested in
println(s"beginTime = ${beginTime}")
// 第2步、设置Hudi数据CommitTime时间阈值，进行增量数据查询
val tripsIncrementalDF = spark.read
.format("hudi")
// 设置查询数据模式为：incremental，增量读取
.option(QUERY_TYPE.key(), QUERY_TYPE_INCREMENTAL_OPT_VAL)
// 设置增量读取数据时开始时间
.option(BEGIN_INSTANTTIME.key(), beginTime)
.load(path)
// 第3步、将增量查询数据注册为临时视图，查询费用大于20数据
tripsIncrementalDF.createOrReplaceTempView("hudi_trips_incremental")
spark
.sql(
"""
|select
| `_hoodie_commit_time`, fare, begin_lon, begin_lat, ts
|from
| hudi_trips_incremental
|where
| fare > 20.0
|""".stripMargin
)
.show(10, truncate = false)
}
def deleteData(spark: SparkSession, table: String, path: String): Unit = {
import spark.implicits._
// 第1步、加载Hudi表数据，获取条目数
val tripsDF: DataFrame = spark.read.format("hudi").load(path)
println(s"Raw Count = ${tripsDF.count()}")
// 第2步、模拟要删除的数据，从Hudi中加载数据，获取几条数据，转换为要删除数据集合
val dataframe = tripsDF.limit(2).select($"uuid", $"partitionpath")
import org.apache.hudi.QuickstartUtils._
val dataGenerator = new DataGenerator()
val deletes = dataGenerator.generateDeletes(dataframe.collectAsList())
import scala.collection.JavaConverters._
val deleteDF = spark.read.json(spark.sparkContext.parallelize(deletes.asScala, 2))
// 第3步、保存数据到Hudi表中，设置操作类型：DELETE
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._
deleteDF.write
.mode(SaveMode.Append)
.format("hudi")
.option("hoodie.insert.shuffle.parallelism", "2")
.option("hoodie.upsert.shuffle.parallelism", "2")
// 设置数据操作类型为delete，默认值为upsert
.option(OPERATION.key(), "delete")
.option(PRECOMBINE_FIELD.key(), "ts")
.option(RECORDKEY_FIELD.key(), "uuid")
.option(PARTITIONPATH_FIELD.key(), "partitionpath")
.option(TBL_NAME.key(), table)
.save(path)
// 第4步、再次加载Hudi表数据，统计条目数，查看是否减少2条数据
val hudiDF: DataFrame = spark.read.format("hudi").load(path)
println(s"Delete After Count = ${hudiDF.count()}")
}
def main(args: Array[String]): Unit = {
System.setProperty("HADOOP_USER_NAME","hty")
//创建SparkSession示例对象，设置属性
val spark: SparkSession = {
SparkSession.builder()
.appName(this.getClass.getSimpleName.stripSuffix("$"))
.master("local[2]")
// 设置序列化方式：Kryo
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.getOrCreate()
}
//定义变量：表名称、保存路径
val tableName: String = "tbl_trips_cow"
val tablePath: String = "/hudi_warehouse/tbl_trips_cow"
//构建数据生成器，模拟产生业务数据
import org.apache.hudi.QuickstartUtils._
//任务一：模拟数据，插入Hudi表，采用COW模式
//insertData(spark, tableName, tablePath)
//任务二：快照方式查询（Snapshot Query）数据，采用DSL方式
//queryData(spark, tablePath)
//queryDataByTime(spark, tablePath)
// 任务三：更新（Update）数据，第1步、模拟产生数据，第2步、模拟产生数据，针对第1步数据字段值更新，
// 第3步、将数据更新到Hudi表中
val dataGen: DataGenerator = new DataGenerator()
//insertData(spark, tableName, tablePath, dataGen)
//updateData(spark, tableName, tablePath, dataGen)
//任务四：增量查询（Incremental Query）数据，采用SQL方式
//incrementalQueryData(spark, tablePath)
//任务五：删除（Delete）数据
deleteData(spark, tableName,tablePath)
//应用结束，关闭资源
spark.stop()
}
}测试
执行 insertData(spark, tableName, tablePath) 方法后对其用快照查询的方式进行查询：
queryData(spark, tablePath)
增量查询（Incremental Query）数据：
incrementalQueryData(spark, tablePath)
参考资料
https://www.bilibili.com/video/BV1sb4y1n7hK?p=21&vd_source=e21134e00867aeadc3c6b37bb38b9eee
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