Delta lake with Java--数据增删改查

之前写的关于spark sql 操作delta lake表的，总觉得有点混乱，今天用Java以真实的数据来进行一次数据的CRUD操作，所涉及的数据来源于Delta lake up and running配套的 GitGitHub - benniehaelen/delta-lake-up-and-running: Companion repository for the book 'Delta Lake Up and Running'

要实现的效果是新建表，导入数据，然后对表进行增删改查操作，具体代码如下：

package detal.lake.java;

import io.delta.tables.DeltaTable;
import org.apache.spark.sql.SparkSession;

import java.text.SimpleDateFormat;
import io.delta.tables.DeltaTable;
import org.apache.spark.sql.*;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.text.SimpleDateFormat;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.HashMap;

public class DeltaLakeCURD {

    //将字符串转换成java.sql.Timestamp
    public static java.sql.Timestamp strToSqlDate(String strDate, String dateFormat) {
        SimpleDateFormat sf = new SimpleDateFormat(dateFormat);
        java.util.Date date = null;
        try {
            date = sf.parse(strDate);
        } catch (Exception e) {
            e.printStackTrace();
        }
        java.sql.Timestamp dateSQL = new java.sql.Timestamp(date.getTime());
        return dateSQL;
    }

    public static void main(String[] args) {

        SparkSession spark = SparkSession.builder()
                .master("local[*]")
                .appName("delta_lake")
                .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
                .config("spark.databricks.delta.autoCompact.enabled", "true")
                .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")
                .getOrCreate();


        SimpleDateFormat sdf=new SimpleDateFormat("yyyy-MM-dd HH:mm:ss.SSS");
        String savePath="file:///D:\\\\bigdata\\\\detla-lake-with-java\\\\YellowTaxi";
        String csvPath="D:\\bookcode\\delta-lake-up-and-running-main\\data\\YellowTaxisLargeAppend.csv";
        String tableName = "taxidb.YellowTaxis";

        spark.sql("CREATE DATABASE IF NOT EXISTS taxidb");

        //定义表
        DeltaTable.createIfNotExists(spark)
                .tableName(tableName)
                .addColumn("RideId","INT")
                .addColumn("VendorId","INT")
                .addColumn("PickupTime","TIMESTAMP")
                .addColumn("DropTime","TIMESTAMP")
                .location(savePath)
                .execute();


        //加载csv数据并导入delta表
        var df=spark.read().format("delta").table(tableName);
        var schema=df.schema();
        System.out.println(schema.simpleString());
        var df_for_append=spark.read().option("header","true").schema(schema).csv(csvPath);
        System.out.println("记录总行数："+df_for_append.count());
        System.out.println("导入数据,开始时间"+  sdf.format(new Date()));
        df_for_append.write().format("delta").mode(SaveMode.Overwrite).saveAsTable(tableName);
        System.out.println("导入数据，结束时间" + sdf.format(new Date()));


        DeltaTable deltaTable = DeltaTable.forName(spark,tableName);


        //插入数据
        List<Row> list = new ArrayList<Row>();
        list.add(RowFactory.create(-1,-1,strToSqlDate("2023-01-01 10:00:00","yyyy-MM-dd HH:mm:ss"),strToSqlDate("2023-01-01 10:00:00","yyyy-MM-dd HH:mm:ss")));
        List<StructField> structFields = new ArrayList<>();
        structFields.add(DataTypes.createStructField("RideId", DataTypes.IntegerType, true));
        structFields.add(DataTypes.createStructField("VendorId", DataTypes.IntegerType, true));
        structFields.add(DataTypes.createStructField("PickupTime", DataTypes.TimestampType, true));
        structFields.add(DataTypes.createStructField("DropTime", DataTypes.TimestampType, true));
        StructType structType = DataTypes.createStructType(structFields);
        var yellowTaxipDF=spark.createDataFrame(list,structType); //建立需要新增数据并转换成dataframe

        System.out.println("插入数据,开始时间"+  sdf.format(new Date()));
        yellowTaxipDF.write().format("delta").mode(SaveMode.Append).saveAsTable(tableName);
        System.out.println("插入数据,结束时间"+  sdf.format(new Date()));
        System.out.println("插入后数据");
        deltaTable.toDF().select("*").where("RideId=-1").show(false);


        //更新数据
        System.out.println("更新前数据");
        deltaTable.toDF().select("*").where("RideId=999994").show(false);
        System.out.println("更新数据,开始时间"+  sdf.format(new Date()));
        deltaTable.updateExpr(
                "RideId = 999994",
                new HashMap<String, String>() {{
                    put("VendorId", "250");
                }}
        );
        System.out.println("更新数据,结束时间"+  sdf.format(new Date()));
        System.out.println("更新后数据");
        deltaTable.toDF().select("*").where("RideId=999994").show(false);


        //查询数据
        System.out.println("查询数据,开始时间"+  sdf.format(new Date()));
        var selectDf= deltaTable.toDF().select("*").where("RideId=1");
        selectDf.show(false);
        System.out.println("查询数据，结束时间" + sdf.format(new Date()));


        //删除数据
        System.out.println("删除数据,开始时间"+  sdf.format(new Date()));
        deltaTable.delete("RideId=1");
        System.out.println("删除数据,结束时间"+  sdf.format(new Date()));
        deltaTable.toDF().select("*").where("RideId=1").show(false);

    }
}

里面涉及spark的TimestampType类型，如何将字符串输入到TimestampType列，找了几个小时才找到答案，具体参考了如下连接，原来直接将string转成java.sql.Timestamp即可，于是在网上找了一个方法，实现了转换，转换代码非原创，也是借鉴其他大牛的。

scala - How to create TimestampType column in spark from string - Stack Overflow

最后运行结果