Using Hudi in idea

Environmental preparation

1. establish Maven project
2. Create a server remote connection
   Tools------Delployment-----Browse Remote Host
   
   Set the following ：
   
   Enter the account and password of the server here
   
   Click on Test Connection, Tips Successfully Words , The configuration is successful .
   
3. Copy Hadoop Of core-site.xml、hdfs-site.xml as well as log4j.properties Three files copied to resources Under the folder .
   
   Set up log4j.properties For printing warning exception information ：

log4j.rootCategory=WARN, console

4. add to pom.xml file

<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>
        <!--  rely on Scala Language  -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!-- Spark Core  rely on  -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- Spark SQL  rely on  -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- Hadoop Client  rely on  -->
        <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  Compiled plug-ins  -->
        <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>

To comment out the following code generated when creating the project , Otherwise, the dependency keeps reporting errors ：

<!-- <properties>-->
<!-- <maven.compiler.source>8</maven.compiler.source>-->
<!-- <maven.compiler.target>8</maven.compiler.target>-->
<!-- </properties>-->

The code structure ：

Core code

import org.apache.hudi.QuickstartUtils.DataGenerator
import org.apache.spark.sql.{
    DataFrame, SaveMode, SparkSession}

/** * Hudi  Framework of data Lake , be based on Spark Calculation engine , On data CURD operation , Use the taxi travel data generated by the official simulation  * *  Task a ： Analog data , Insert Hudi surface , use COW Pattern  *  Task 2 ： Snapshot query （Snapshot Query） data , use DSL The way  *  Task three ： to update （Update） data  *  Task 4 ： Incremental query （Incremental Query） data , use SQL The way  *  Task five ： Delete （Delete） data  */
object HudiSparkDemo {
    

  /** *  Official case : Simulation generates data , Insert Hudi surface , The type of the table is COW */
  def insertData(spark: SparkSession, table: String, path: String): Unit = {
    
    import spark.implicits._

    //  The first 1 Step 、 Simulated ride data 
    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)

    // The second step ：  Insert data into Hudi surface 
    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 Property settings of the table 
      .option(PRECOMBINE_FIELD.key(), "ts")
      .option(RECORDKEY_FIELD.key(), "uuid")
      .option(PARTITIONPATH_FIELD.key(), "partitionpath")
      .option(TBL_NAME.key(), table)
      .save(path)
  }

  /** *  use Snapshot Query Query table data in snapshot mode  */
  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)

    // The query cost is greater than 10, Less than 50 Ride data 
    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._

    // Mode one ： Specified string , Filter and obtain data according to date and time 
    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)

    // Mode two ： Specified string , Filter and obtain data according to date and time 
    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)
  }

  
  /** *  take DataGenerator Pass in the generated data as a parameter  */
  def insertData(spark: SparkSession, table: String, path: String, dataGen: DataGenerator): Unit = {
    
    import spark.implicits._

    //  The first 1 Step 、 Simulated ride data 
    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)

    // The second step ：  Insert data into Hudi surface 
    import org.apache.hudi.DataSourceWriteOptions._
    import org.apache.hudi.config.HoodieWriteConfig._
    insertDF.write
      // Replace with Overwrite Pattern 
      .mode(SaveMode.Overwrite)
      .format("hudi")
      .option("hoodie.insert.shuffle.parallelism", 2)
      .option("hoodie.insert.shuffle.parallelism", 2)
      //Hudi Property settings of the table 
      .option(PRECOMBINE_FIELD.key(), "ts")
      .option(RECORDKEY_FIELD.key(), "uuid")
      .option(PARTITIONPATH_FIELD.key(), "partitionpath")
      .option(TBL_NAME.key(), table)
      .save(path)
  }

  /** *  Simulation produces Hudi Update data in table , Update it to Hudi In the table  */
  def updateData(spark: SparkSession, table: String, path: String, dataGen: DataGenerator):Unit = {
    
    import spark.implicits._

    //  The first 1 Step 、 Simulated ride data 
    import org.apache.hudi.QuickstartUtils._
    // Generate updated data 
    val updates = convertToStringList(dataGen.generateUpdates(100))

    import scala.collection.JavaConverters._
    val updateDF: DataFrame = spark.read.json(
      spark.sparkContext.parallelize(updates.asScala, 2).toDS()
    )

    // TOOD:  The first 2 Step 、 Insert data into Hudi surface 
    import org.apache.hudi.DataSourceWriteOptions._
    import org.apache.hudi.config.HoodieWriteConfig._
    updateDF.write
      // Append mode 
      .mode(SaveMode.Append)
      .format("hudi")
      .option("hoodie.insert.shuffle.parallelism", "2")
      .option("hoodie.upsert.shuffle.parallelism", "2")
      // Hudi  Set the attribute value of the table 
      .option(PRECOMBINE_FIELD.key(), "ts")
      .option(RECORDKEY_FIELD.key(), "uuid")
      .option(PARTITIONPATH_FIELD.key(), "partitionpath")
      .option(TBL_NAME.key(), table)
      .save(path)

  }

  /** *  use Incremental Query Query data incrementally , You need to specify a timestamp  */
  def incrementalQueryData(spark: SparkSession, path: String): Unit = {
    
    import spark.implicits._

    //  The first 1 Step 、 load Hudi Table data , obtain commit time Time , As an incremental query data threshold 
    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}")

    //  The first 2 Step 、 Set up Hudi data CommitTime Time threshold , Perform incremental data query 
    val tripsIncrementalDF = spark.read
      .format("hudi")
      //  Set the query data mode to ：incremental, Incremental read 
      .option(QUERY_TYPE.key(), QUERY_TYPE_INCREMENTAL_OPT_VAL)
      //  Set the start time when incrementally reading data 
      .option(BEGIN_INSTANTTIME.key(), beginTime)
      .load(path)

    //  The first 3 Step 、 Register the incremental query data as a temporary view , The query cost is greater than 20 data 
    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)
  }

  /** *  Delete Hudi Table data , According to the primary key uuid To delete , If it's a partition table , Specify partition path  */
  def deleteData(spark: SparkSession, table: String, path: String): Unit = {
    
    import spark.implicits._

    //  The first 1 Step 、 load Hudi Table data , Get the number of entries 
    val tripsDF: DataFrame = spark.read.format("hudi").load(path)
    println(s"Raw Count = ${
      tripsDF.count()}")

    //  The first 2 Step 、 Simulate the data to be deleted , from Hudi Load data in , Get a few pieces of data , Convert to the data set to be deleted 
    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))

    //  The first 3 Step 、 Save data to Hudi In the table , Set the operation type ：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")
      //  Set the data operation type to delete, The default value is 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)

    //  The first 4 Step 、 Load again Hudi Table data , Count the number of entries , See if you can reduce 2 Data 
    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")

    // establish SparkSession Sample object , Set properties 
    val spark: SparkSession = {
    
      SparkSession.builder()
        .appName(this.getClass.getSimpleName.stripSuffix("$"))
        .master("local[2]")
        //  Set serialization method ：Kryo
        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
        .getOrCreate()
    }

    // Defining variables ： The name of the table 、 Save the path 
    val tableName: String = "tbl_trips_cow"
    val tablePath: String = "/hudi_warehouse/tbl_trips_cow"

    // Build a data generator , Simulate and generate business data 
    import org.apache.hudi.QuickstartUtils._

    // Task a ： Analog data , Insert Hudi surface , use COW Pattern 
    //insertData(spark, tableName, tablePath)

     // Task 2 ： Snapshot query （Snapshot Query） data , use DSL The way 
      //queryData(spark, tablePath)
    //queryDataByTime(spark, tablePath)

    //  Task three ： to update （Update） data , The first 1 Step 、 Simulation generates data , The first 2 Step 、 Simulation generates data , For 1 Step data field value update ,
    //  The first 3 Step 、 Update data to Hudi In the table 
    val dataGen: DataGenerator = new DataGenerator()
    //insertData(spark, tableName, tablePath, dataGen)
    //updateData(spark, tableName, tablePath, dataGen)

    // Task 4 ： Incremental query （Incremental Query） data , use SQL The way 
    //incrementalQueryData(spark, tablePath)

    // Task five ： Delete （Delete） data 
    deleteData(spark, tableName,tablePath)

    // End of application , close resource 
    spark.stop()
  }
}

test

perform insertData(spark, tableName, tablePath) Method, and then query it in the way of snapshot query ：

queryData(spark, tablePath)

Incremental query （Incremental Query） data ：

incrementalQueryData(spark, tablePath)

Reference material

https://www.bilibili.com/video/BV1sb4y1n7hK?p=21&vd_source=e21134e00867aeadc3c6b37bb38b9eee