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Registered: ‎03-28-2017

Unable to map the data properly from a CSV file to a Hive table on HDFS

[ Edited ]

I am trying to load a dataframe into a Hive table by following the below steps:

  • Read the source table and save the dataframe as a CSV file on HDFS
    val yearDF = spark.read.format("jdbc").option("url", connectionUrl).option("dbtable", s"(${execQuery}) as year2016").option("user", devUserName).option("password", devPassword).option("partitionColumn","header_id").option("lowerBound", 199199).option("upperBound", 284058).option("numPartitions",10).load()
  • Order the columns as per my Hive table columns My hive table columns are present in a string in the format of:
    val hiveCols          = "col1:coldatatype|col2:coldatatype|col3:coldatatype|col4:coldatatype...col200:datatype"
    val schemaList        = hiveCols.split("\\|")
    val hiveColumnOrder   = schemaList.map(e => e.split("\\:")).map(e => e(0)).toSeq
    val finalDF           = yearDF.selectExpr(hiveColumnOrder:_*)
    The order of columns that I read in "execQuery" are same as "hiveColumnOrder" and just to make sure of the order, I select the columns in yearDF once again using selectExpr
  • Saving the dataframe as a CSV file on HDFS:

    newDF.write.format("CSV").save("hdfs://username/apps/hive/warehouse/database.db/lines_test_data56/")

     

Once I save the dataframe, I take the same columns from "hiveCols", prepare a DDL to create a hive table on the same location with values being comma separated as given below:

    create table if not exists schema.tablename(col1 coldatatype,col2 coldatatype,col3 coldatatype,col4 coldatatype...col200 datatype)
    ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
    STORED AS TEXTFILE
    LOCATION 'hdfs://username/apps/hive/warehouse/database.db/lines_test_data56/';

After I load the dataframe into the table created, the problem I am facing here is when I query the table, I am getting improper output in the query. For ex: If I apply the below query on the dataframe before saving it as a file:

finalDF.createOrReplaceTempView("tmpTable")
select header_id,line_num,debit_rate,debit_rate_text,credit_rate,credit_rate_text,activity_amount,activity_amount_text,exchange_rate,exchange_rate_text,amount_cr,amount_cr_text from tmpTable where header_id=19924598 and line_num=2

I get the output properly. All the values are properly aligned to the columns:

[19924598,2,null,null,381761.40000000000000000000,381761.4,-381761.40000000000000000000,-381761.4,0.01489610000000000000,0.014896100000000,5686.76000000000000000000,5686.76]

But after saving the dataframe in a CSV file, create a table on top of it (step4) and apply the same query on the created table I see the data is jumbled and improperly mapped with the columns:

select header_id,line_num,debit_rate,debit_rate_text,credit_rate,credit_rate_text,activity_amount,activity_amount_text,exchange_rate,exchange_rate_text,amount_cr,amount_cr_text from schema.tablename where header_id=19924598 and line_num=2

+---------------+--------------+-------------+------------------+-------------+------------------+--------------------------+-------------------------------+------------------------+-----------------------------+--------------------+-------------------------+--+
| header_id     | line_num     | debit_rate  | debit_rate_text  | credit_rate  | credit_rate_text  | activity_amount  | activity_amount_text  | exchange_rate  | exchange_rate_text  | amount_cr  | amount_cr_text  |
+---------------+--------------+-------------+------------------+-------------+------------------+--------------------------+-------------------------------+------------------------+-----------------------------+--------------------+-------------------------+--+
| 19924598      | 2            | NULL        |                  | 381761.4    |                    | 5686.76          | 5686.76               | NULL           | -5686.76            | NULL       |                 |


So I tried use a different approach where I created the hive table upfront and insert data into it from:

  • dataframe:Running the DDL in step4 above
  • finalDF.createOrReplaceTempView("tmpTable")
  • spark.sql("insert into schema.table select * from tmpTable")

And even this way fails if I run the aforementioned select query once the job is completed. I tried to refresh the table using

refresh table schema.table and msckrepair table schema.table

just to see if there is any problem with the metadata but nothing seems to workout.

 

Could anyone let me know what is causing this phenomenon, is there is any problem with the way I operating the data here ?

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