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：
java
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
