Converting mysql table to spark dataset is very slow compared to same from csv file

I have csv file in Amazon s3 with is 62mb in size (114 000 rows). I am converting it into spark dataset, and taking first 500 rows from it. Code is as follow;

DataFrameReader df = new DataFrameReader(spark).format("csv").option("header", true);
Dataset<Row> set=df.load("s3n://"+this.accessId.replace("\"", "")+":"+this.accessToken.replace("\"", "")+"@"+this.bucketName.replace("\"", "")+"/"+this.filePath.replace("\"", "")+"");

 set.take(500)

The whole operation takes 20 to 30 sec.

Now I am trying the same but rather using csv I am using mySQL table with 119 000 rows. MySQL server is in amazon ec2. Code is as follow;

String url ="jdbc:mysql://"+this.hostName+":3306/"+this.dataBaseName+"?user="+this.userName+"&password="+this.password;

SparkSession spark=StartSpark.getSparkSession();

SQLContext sc = spark.sqlContext();

DataFrameReader df = new DataFrameReader(spark).format("csv").option("header", true);
Dataset<Row> set = sc
            .read()
            .option("url", url)
            .option("dbtable", this.tableName)
            .option("driver","com.mysql.jdbc.Driver")
            .format("jdbc")
            .load();
set.take(500);

This is taking 5 to 10 minutes. I am running spark inside jvm. Using same configuration in both cases.

I can use partitionColumn,numParttition etc but I don't have any numeric column and one more issue is the schema of the table is unknown to me.

My issue is not how to decrease the required time as I know in ideal case spark will run in cluster but what I can not understand is why this big time difference in the above two case?

This problem has been covered multiple times on StackOverflow:

• How to improve performance for slow Spark jobs using DataFrame and JDBC connection?
• spark jdbc df limit... what is it doing?
• How to use JDBC source to write and read data in (Py)Spark?

and in external sources:

• https://github.com/awesome-spark/spark-gotchas/blob/master/05_spark_sql_and_dataset_api.md#parallelizing-reads

so just to reiterate - by default DataFrameReader.jdbc doesn't distribute data or reads. It uses single thread, single exectuor.

To distribute reads:

• use ranges with lowerBound / upperBound:

  Properties properties;
  Lower
  
  Dataset<Row> set = sc
      .read()
      .option("partitionColumn", "foo")
      .option("numPartitions", "3")
      .option("lowerBound", 0)
      .option("upperBound", 30)
      .option("url", url)
      .option("dbtable", this.tableName)
      .option("driver","com.mysql.jdbc.Driver")
      .format("jdbc")
      .load();
  

• predicates

  Properties properties;
  Dataset<Row> set = sc
      .read()
      .jdbc(
          url, this.tableName,
          {"foo < 10", "foo BETWWEN 10 and 20", "foo > 20"},
          properties
      )