How to solve analysis exception error in Pyspark - apache-spark

Question

I got an error in Pyspark:
AnalysisException: u'Resolved attribute(s) week#5230 missing from
longitude#4976,address#4982,minute#4986,azimuth#4977,province#4979,
action_type#4972,user_id#4969,week#2548,month#4989,postcode#4983,location#4981
in operator !Aggregate [user_id#4969, week#5230], [user_id#4969,
week#5230, count(distinct day#4987) AS days_per_week#3605L].
Attribute(s) with the same name appear in the operation: week.
Please check if the right attribute(s) are used
This seems to come from a snippet of code where the agg function is used:
df_rs = df_n.groupBy('user_id', 'week')
.agg(countDistinct('day').alias('days_per_week'))
.where('days_per_week >= 1')
.groupBy('user_id')
.agg(count('week').alias('weeks_per_user'))
.where('weeks_per_user >= 5').cache()
However I do not see the issue here. And I have previously used this line of code on the same data, many times.
EDIT: I have been looking through the code and the type of error seems to come from joins of this sort:
df = df1.join(df2, 'user_id', 'inner')
df3 = df4.join(df1, 'user_id', 'left_anti).
but still have not solved the problem yet.
EDIT2: Unfortunately the suggested question is not similar to mine, as this is not a question of column name ambiguity but of missing attribute, which seems not to be missing upon inspecting the actual dataframes.

Answer 1

I faced same problem and solved it using renaming the Resolved attributes missing columns to some temp name before join, its a workaround for me , hope it helps you too. Dont know the real reason behind this issue , its still going on since spark 1.6 SPARK-10925

Answer 2

I also faced this issue multiple times and came across this
here it's mentioned that this it's spark related bug.
Based on this article I came up with below code which resolved my issue.
The code can handle LEFT, RIGHT, INNER and OUTER Joins, though OUTER join works as FULL OUTER here.
def join_spark_dfs_sqlbased(sparkSession,left_table_sdf,right_table_sdf,common_join_cols_list=[],join_type="LEFT"):
temp_join_afix="_tempjoincolrenames"
join_type=join_type.upper()
left=left_table_sdf.select(left_table_sdf.columns)
right=right_table_sdf.select(right_table_sdf.columns)
if len(common_join_cols_list)>0:
common_join_cols_list=[col+temp_join_afix for col in common_join_cols_list]
else:
common_join_cols_list = list(set(left.columns).intersection(right.columns))
common_join_cols_list=[col+temp_join_afix for col in common_join_cols_list]
for col in left.columns:
left = left.withColumnRenamed(col, col + temp_join_afix)
left.createOrReplaceTempView('left')
for col in right.columns:
right = right.withColumnRenamed(col, col + temp_join_afix)
right.createOrReplaceTempView('right')
non_common_cols_left_list=list(set(left.columns)-set(common_join_cols_list))
non_common_cols_right_list=list(set(right.columns)-set(common_join_cols_list))
unidentified_common_cols=list(set(non_common_cols_left_list)-set(non_common_cols_right_list))
if join_type in ['LEFT','INNER','OUTER']:
non_common_cols_right_list=list(set(non_common_cols_right_list)-set(unidentified_common_cols))
common_join_cols_list_with_table=['a.'+col +' as '+col for col in common_join_cols_list]
else:
non_common_cols_left_list=list(set(non_common_cols_left_list)-set(unidentified_common_cols))
common_join_cols_list_with_table=['b.'+col +' as '+col for col in common_join_cols_list]
non_common_cols_left_list_with_table=['a.'+col +' as '+col for col in non_common_cols_left_list]
non_common_cols_right_list_with_table=['b.'+col +' as '+col for col in non_common_cols_right_list]
non_common_cols_list_with_table=non_common_cols_left_list_with_table + non_common_cols_right_list_with_table
if join_type=="OUTER":
join_type="FULL OUTER"
join_type=join_type+" JOIN"
select_cols=common_join_cols_list_with_table+non_common_cols_list_with_table
common_join_cols_list_with_table_join_query=['a.'+col+ '='+'b.'+col for col in common_join_cols_list]
query= "SELECT "+ ",".join(select_cols) + " FROM " + "left" + " a " + join_type + " " + "right" + " b" +" ON "+ " AND ".join(common_join_cols_list_with_table_join_query)
print("query:",query)
joined_sdf= sparkSession.sql(query)
for col in joined_sdf.columns:
if temp_join_afix in col:
joined_sdf = joined_sdf.withColumnRenamed(col, col.replace(temp_join_afix,''))
return joined_sdf