Ok. Thanks.
On 7/12/18, 11:12 AM, "Thakrar, Jayesh" <[email protected]> wrote:
Unless the tables are very small (< 1000 rows), the impact of hitting the
catalog tables is negligible.
Furthermore, normally the catalog tables (or views) are usually in memory
because they are needed for query compilation, query execution (for triggers,
referential integrity, etc) and even to establish a connection.
On 7/12/18, 9:53 AM, "Kadam, Gangadhar (GE Aviation, Non-GE)"
<[email protected]> wrote:
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" <[email protected]>
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)"
<[email protected]> 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
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