Re: Creating JDBC source table schema(DDL) dynamically

[Kadam, Gangadhar (GE Aviation, Non-GE)]

Thanks Jayesh.

I was aware of the catalog table approach but I was avoiding that  because I 
will hit the database twice for one table, one to create DDL and other to read 
the data. I have lots of table to transport from one environment to other and I 
don’t want to create unnecessary load on the DB.


On 7/12/18, 10:09 AM, "Thakrar, Jayesh" <jthak...@conversantmedia.com> wrote:

    One option is to use plain JDBC to interrogate Postgresql catalog for the 
source table and generate the DDL to create the destination table.
    Then using plain JDBC again, create the table at the destination.
    
    See the link below for some pointers…..
    
    
https://stackoverflow.com/questions/2593803/how-to-generate-the-create-table-sql-statement-for-an-existing-table-in-postgr
    
    
    On 7/11/18, 9:55 PM, "Kadam, Gangadhar (GE Aviation, Non-GE)" 
<gangadhar.ka...@ge.com> wrote:
    
        Hi All,
        
        I am trying to build a spark application which will  read the data from 
Postgresql (source)  one environment  and write it to  postgreSQL, Aurora 
(target)  on a dfiffernt environment  (like to PROD to QA or QA to PROD etc) 
using spark JDBC.
        
        When I am loading the dataframe back to target DB, I would like to 
ensure the same schema as the source table schema using
        
        val targetTableSchema: String =
          """
            |  operating_unit_nm character varying(20),
            |  organization_id integer,
            |  organization_cd character varying(30),
            |  requesting_organization_id integer,
            |  requesting_organization_cd character varying(50),
            |  owning_organization_id integer,
            |  owning_organization_cd character varying(50)
        """.stripMargin
        
        
        .option("createTableColumnTypes", targetTableSchema )
        
        I would like to know if there is way I can create this 
targetTableSchema (source table DDL) variable directly from source table or 
from a csv file. I don’t want spark to enforce its default schema.  Based on 
the table name, How do I  get the DDL created dynamically to pass it to 
targetTableSchema variable as a string.
        
        Currently I am updating targetTableSchema manually  and looking for 
some pointer to automate it.
        
        
        Below is my code
        
        // Define the parameter
        val sourceDb: String = args(0)
        val targetDb: String = args(1)
        val sourceTable: String = args(2)
        val targetTable: String = args(3)
        val sourceEnv: String = args(4)
        val targetEnv: String = args(5)
        
        println("Arguments Provided: " + sourceDb, targetDb,sourceTable, 
targetTable, sourceEnv, targetEnv)
        
        // Define the spark session
        val spark: SparkSession = SparkSession
          .builder()
          .appName("Ca-Data-Transporter")
          .master("local")
          .config("driver", "org.postgresql.Driver")
          .getOrCreate()
        
        // define the input directory
        val inputDir: String = 
"/Users/gangadharkadam/projects/ca-spark-apps/src/main/resources/"
        
        // Define the source DB properties
        val sourceParmFile: String = if (sourceDb == "RDS") {
            "rds-db-parms-" + sourceEnv + ".txt"
          }
          else if (sourceDb == "AURORA") {
            "aws-db-parms-" + sourceEnv + ".txt"
          }
          else if (sourceDb == "GP") {
            "gp-db-parms-" + sourceEnv + ".txt"
          }
          else "NA"
        
        println(sourceParmFile)
        
        val sourceDbParms: Properties = new Properties()
        sourceDbParms.load(new FileInputStream(new File(inputDir + 
sourceParmFile)))
        val sourceDbJdbcUrl: String = sourceDbParms.getProperty("jdbcUrl")
        
        println(s"$sourceDb")
        println(s"$sourceDbJdbcUrl")
        
        // Define the target DB properties
        val targetParmFile: String = if (targetDb == "RDS") {
            s"rds-db-parms-" + targetEnv + ".txt"
          }
          else if (targetDb == "AURORA") {
            s"aws-db-parms-" + targetEnv + ".txt"
          }
          else if (targetDb == "GP") {
            s"gp-db-parms-" + targetEnv + ".txt"
          } else "aws-db-parms-$targetEnv.txt"
        
        println(targetParmFile)
        
        val targetDbParms: Properties = new Properties()
        targetDbParms.load(new FileInputStream(new File(inputDir + 
targetParmFile)))
        val targetDbJdbcUrl: String = targetDbParms.getProperty("jdbcUrl")
        
        println(s"$targetDb")
        println(s"$targetDbJdbcUrl")
        
        // Read the source table as dataFrame
        val sourceDF: DataFrame = spark
          .read
          .jdbc(url = sourceDbJdbcUrl,
            table = sourceTable,
            sourceDbParms
          )
          //.filter("site_code is not null")
        
        sourceDF.printSchema()
        sourceDF.show()
        
        val sourceDF1 = sourceDF.repartition(
          sourceDF("organization_id")
          //sourceDF("plan_id")
        )
        
        
        val targetTableSchema: String =
          """
            |  operating_unit_nm character varying(20),
            |  organization_id integer,
            |  organization_cd character varying(30),
            |  requesting_organization_id integer,
            |  requesting_organization_cd character varying(50),
            |  owning_organization_id integer,
            |  owning_organization_cd character varying(50)
          """.stripMargin
        
        
        // write the dataFrame
        sourceDF1
          .write
          .option("createTableColumnTypes", targetTableSchema )
          .mode(saveMode = "Overwrite")
          .option("truncate", "true")
          .jdbc(targetDbJdbcUrl, targetTable, targetDbParms)
        
        
        Thanks!
        Gangadhar Kadam
        Sr. Data Engineer
        M + 1 (401) 588 2269
        
    
    
    ---------------------------------------------------------------------
    To unsubscribe e-mail: dev-unsubscr...@spark.apache.org