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

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

  1. Read the source table and save the dataframe as a CSV file on HDFS

    val yearDF ="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()
  2. 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   = => 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

  3. Saving the dataframe as a CSV file on HDFS:
  4. 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)

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:

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:


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 ?


With Hive 3 pushing hard with fully managed tables with native file formats as transactional tables, see for more info, this "direct from Spark to Hive" approach will get much harder do to the underlying "delta files" that get created when data is added/modified/removed from a Hive table. The Spark LLAP Connector will aid in this integration. That said, historically, the better answer is often to simple save your DF from Spark to HDFS, wrap it with an External Hive table and then do and INSERT INTO your existing Hive table with a SELECT * FROM your new external table. This lets Hive do all the heavy lifting and file conversions as needed and takes care of any partitioning and/or bucketing that you have in place. Good luck and happy Hadooping!

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