// Imports
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SaveMode
import scala.concurrent.ExecutionContext.Implicits.global
import java.util.Properties
import scala.concurrent.Future

// Set up spark on local with 2 threads
val conf = new SparkConf().setMaster("local[2]").setAppName("app").set("spark.sql.parquet.cacheMetadata", "false")
val sc = new SparkContext(conf)
val sqlCtx = new HiveContext(sc)
sqlCtx.setConf("spark.sql.parquet.cacheMetadata", "false")

// Create fake dataframe
import sqlCtx.implicits._
var df = sc.parallelize(1 to 50000).map { i => (i, i, i, i, i, i, i) }.toDF("a", "b", "c", "d", "e", "f", "g").repartition(2)
// Write it as a parquet file
df.write.parquet("/tmp/parquet1")
df = sqlCtx.read.parquet("/tmp/parquet1")

// JDBC connection
val url = s"jdbc:postgresql://localhost:5432/temple"
val prop = new Properties()
prop.setProperty("user", "admin")
prop.setProperty("password", "")

// 4 futures - at least one of them has been consistently failing for
val x1 = Future { df.write.mode(SaveMode.Overwrite).jdbc(url, "tmp5", prop)}
val x2 = Future { df.write.mode(SaveMode.Overwrite).jdbc(url, "tmp5", prop)}
val x3 = Future { df.write.mode(SaveMode.Overwrite).jdbc(url, "tmp5", prop)}
val x4 = Future { df.write.mode(SaveMode.Overwrite).jdbc(url, "tmp5", prop)}