CODEWITHSUNDEEP
sqlpysparkdatabricks
Carltonp
Carltonp

Reputation: 1344

Databricks Error in SQL statement: AnalysisException: cannot resolve '``' given input columns:

I'm not sure if I'm in the correct group for this question. Any I have created the following sql code in Databricks, however I'm getting the error message;

Error in SQL statement: AnalysisException: cannot resolve 'a.COUNTRY_ID' given input columns: [a."PK_LOYALTYACCOUNT";"COUNTRY_ID";"CDC_TYPE", b."PK_LOYALTYACCOUNT";"COUNTRY_ID";"CDC_TYPE"]; line 7 pos 7;

I know the code works as I have successfully run the code on my SQL Server The code is as follows:

tabled = spark.read.csv("adl://carlslake.azuredatalakestore.net/testfolder/dbo_tabled.csv",inferSchema=True,header=True)
tablee = spark.read.csv("adl://carlslake.azuredatalakestore.net/testfolder/dbo_tablee.csv",inferSchema=True,header=True)
tabled.createOrReplaceTempView('tabled') 
tablee.createOrReplaceTempView('tablee')
%sql
; with cmn as 
  ( SELECT a.CDC_TYPE,
           a. PK_LOYALTYACCOUNT, --Add these also in CTE result set 
           a.COUNTRY_ID --Add these also in CTE result set 
    FROM  tabled  a 
    INNER JOIN tablee b 
    ON a.COUNTRY_ID = b.COUNTRY_ID 
    AND a.PK_LOYALTYACCOUNT = b.PK_LOYALTYACCOUNT 
    AND a.CDC_TYPE = 'U'
    )
 SELECT 1 AS is_deleted, 
        a.* 
 FROM  tabled  a 
 INNER JOIN cmn 
 ON a.CDC_TYPE = cmn.CDC_TYPE 
 and  a.COUNTRY_ID = cmn.COUNTRY_ID 
 AND a.PK_LOYALTYACCOUNT = cmn.PK_LOYALTYACCOUNT
 UNION ALL 
 SELECT 0 AS is_deleted, 
        b.* 
 FROM tablee  b 
 INNER JOIN cmn 
 ON b.CDC_TYPE = cmn.CDC_TYPE 
 and b.COUNTRY_ID = cmn.COUNTRY_ID 
 AND b.PK_LOYALTYACCOUNT = cmn.PK_LOYALTYACCOUNT
UNION ALL 
SELECT NULL, 
       a.* 
FROM   tabled a 
WHERE  a.CDC_TYPE = 'N' 
UNION ALL 
SELECT NULL, 
       b.* 
FROM   tablee b 
WHERE  b.CDC_TYPE = 'N'

when I run the simple query...

example1 = 

spark.sql("""select * from tablee""")

or example2 = 

spark.sql("""select * from tabled""")

I get the following output, so I know the tables are there

output

Any suggestions will be well received.

Upvotes: 1

Views: 7775

Answers (2)

Krishna Sistla
Krishna Sistla

Reputation: 64

Use semicolon delimiter while reading from csv

tabled = spark.read.option("delimiter", ";").csv("adl://carlslake.azuredatalakestore.net/testfolder/dbo_tabled.csv",inferSchema=True,header=True)

or

tabled = spark.read.load("adl://carlslake.azuredatalakestore.net/testfolder/dbo_tabled.csv",
                 format="csv", sep=";", inferSchema="true", header="true")

ref: https://spark.apache.org/docs/2.3.0/sql-programming-guide.html#manually-specifying-options

Upvotes: 1

Carltonp
Carltonp

Reputation: 1344

The columns were not being identified properly since the delimiter used was a semicolon(;) and the job was looking for commas. Problem solved

Upvotes: 0

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