load csv file in sql table using scala - scala

Question

I have a task to read csv file and load the csv file to sql table but I am not sure of my code and facing "No suitable driver error" and tried with new driver.
val DBURL= "jdbc:sqlserver://servername:port;DatabaseName=DBname"
val srcfile=spark.read.text("filename")
val test =srcfile.write.format("jdbc")
.option("url", DBURL)
.option("dbtable", "tablename")
.option("user", "username")
.option("password", "password")
.save()
Any help highly appreciated.

Answer 1

You can add the corresponding driver also in the option, like
.option ("driver","org.postgresql.Driver")
or
.option("driver", "com.mysql.jdbc.Driver")

Answer 2

I hope the following answer will help you and it is tried one so it must not have any kind of error
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("Testing Transpose").setMaster("local[*]").set("spark.sql.crossJoin.enabled","true")
val sc = new SparkContext(conf)
val sparksession = SparkSession.builder().config("spark.sql.warehouse.dir","file:///c://tmp/spark-warehouse").getOrCreate()
val sqlContext = new SQLContext(sc)
import sqlContext.implicits._
val df = sparksession.read.format("com.databricks.spark.csv").option("header", "true").load(Path)
val prop : java.util.Properties = new Properties()
prop.setProperty("user","(temp_User)")
prop.setProperty("password","(temp_password)")
df
.write
.option("driver","com.microsoft.sqlserver.jdbc.SQLServerDriver")
.mode("append")
.jdbc("jdbc:sqlserver://(database_ip):(database_port_to_access)","(table_name)",prop)
sparksession.stop()
}
include this dependency if you want to use databricks.csv else you can replace it
val df = sparkSession.read.option("header","true").csv("src/main/resources/sales.csv")
this needed to be included in build.sbt
libraryDependencies += "com.databricks" % "spark-csv_2.10" % "0.1"
if your file doesn't have header then you can provide them header like the following
import sqlContext.implicits._
df.toDF("column_name_1","column_name_2",.....)
Note: The number of column names must be similar to the number of columns in the dataframe and one more thing to note you need to change the header option to false like follows
sparksession.read.format("com.databricks.spark.csv").option("header", "false").load(Path)