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https://issues.apache.org/jira/browse/SPARK-36277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17386801#comment-17386801
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Hyukjin Kwon commented on SPARK-36277:
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Can you provide a self-contained reproducer?
> Issue with record count of data frame while reading in DropMalformed mode
> -------------------------------------------------------------------------
>
> Key: SPARK-36277
> URL: https://issues.apache.org/jira/browse/SPARK-36277
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.3
> Reporter: anju
> Priority: Major
> Attachments: 111.PNG
>
>
> While reading the dataframe in malformed mode ,I am not getting right record
> count. dataframe.count() is giving me the record count of actual file
> including malformed records, eventhough data frame is read in "dropmalformed"
> mode. Is there a way to overcome this in pyspark
> here is the high level overview of what i am doing. I am trying to read the
> two dataframes from one file using with/without predefined schema. Issue is
> when i read a DF with a predefined schema and with mode as "dropmalformed",
> the record count in df is not dropping the records. The record count is same
> as actual file where i am expecting less record count,as there are few
> malformed records . But when i try to select and display the records in df
> ,it is not showing malformed records. So display is correct. output is
> attached in the aattchment
> code
>
> {code}
> s3_obj =boto3.client('s3')
> s3_clientobj = s3_obj.get_object(Bucket='xyz',
> Key='data/test_files/schema_xyz.json')
> s3_clientobj
> s3_clientdata =
> s3_clientobj['Body'].read().decode('utf-8')#print(s3_clientdata)schemaSource=json.loads(s3_clientdata)
> schemaFromJson =StructType.fromJson(json.loads(s3_clientdata))
> extract_with_schema_df =
> spark.read.csv("s3:few_columns.csv",header=True,sep=",",schema=schemaFromJson,mode="DROPMALFORMED")
> extract_without_schema_df =
> spark.read.csv("s3:few_columns.csv",header=True,sep=",",mode="permissive")
> extract_with_schema_df.select("col1","col2").show()
> cnt1=extract_with_schema_df.select("col1","col2").count()print("count of the
> records with schema "+ str(cnt1))
> cnt2=extract_without_schema_df.select("col1","col2").count()print("count of
> the records without schema "+str(cnt2))
> cnt2=extract_without_schema_df.select("col1","col2").show()}}
> {code}
>
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