Static and dynamic partition loads in Hive table through Spark

[Mich Talebzadeh]

Hi,

This is something that I am banging my head to explain someone so hence
this question

There is an underlying Hive that which is populated reading an XML file
through Spark.

The table in Hive is defined as follows with two partition columns

      var sqltext =
      s"""
        CREATE TABLE IF NOT EXISTS ${broadcastStagingConfig.broadcastTable}
(
           partyId STRING
         , phoneNumber STRING
        )
        PARTITIONED BY (
           broadcastId STRING
        ,  brand STRING
        )
        STORED AS PARQUET
        """
    spark.sql(sqltext)

Then we populate table as follows

    sqltext =
      s"""
        INSERT INTO TABLE ${broadcastStagingConfig.broadcastTable}
PARTITION (broadcastId = ${broadcastStagingConfig.broadcastValue},  brand)
        SELECT
          ocis_mrg_pty_id AS partyId
        , target_mobile_no AS phoneNumber
                      , brand
        FROM tmp
       WHERE
          length(target_mobile_no) = 10
       AND
          substring(target_mobile_no,1,1) = "7"
        """
    spark.sql(sqltext)

So back to partitioning, Partition column *broascastId *is a constant value
for this particular insert of rows (all rows share the same value). That is
its value is known before actually reading the XML rows so I qualify
it as *static
partition*. However, the column brand is  a dynamic partition as
potentially it can have a range of values and which *brand partition* it
will belong to will only be known after the row is inserted.

Is this assertion correct?

Thanks,

 Dr Mich Talebzadeh



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