venu k tangirala created SPARK-14425:
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Summary: spark SQL/dataframe join error: mixes the columns up
Key: SPARK-14425
URL: https://issues.apache.org/jira/browse/SPARK-14425
Project: Spark
Issue Type: Bug
Components: PySpark, SQL
Affects Versions: 1.6.1
Environment: databricks cloud
Reporter: venu k tangirala
I am running this on databricks cloud.
I am running a join operation and the result has a the columns mixed up.
Here is an example:
the original df:
>>>df.take(3)
[Row(idSite=u'100', servertimestamp=u'1455219299',
visitorId=u'8391f66992d536e5', sessionId=u'725873', serverTimePretty=u'Feb 11,
2016 11:20:39 AM', productId=u'374695023', pageId=u'2617232'),
Row(idSite=u'100', servertimestamp=u'1455219299',
visitorId=u'8391f66992d536e5', sessionId=u'725873', serverTimePretty=u'Feb 11,
2016 11:21:07 AM', productId=u'374694787', pageId=u'2617240'),
Row(idSite=u'100', servertimestamp=u'1455219299',
visitorId=u'8391f66992d536e5', sessionId=u'725873', serverTimePretty=u'Feb 11,
2016 11:21:25 AM', productId=u'374694787', pageId=u'2617247')]
As I am trying to build a recommendation system, and ALS Ratings has to be
user_is and product_id has to be int, I am mapping them as follows:
# mapping string to int for visitors
visitorId_toInt = f_df.map(lambda
x:x["visitorId"]).distinct().zipWithUniqueId().toDF(schema=["visitorId","int_visitorId"])
# print visitorId_toInt.take(3)
visitorId_toInt.registerTempTable("visitorId_toInt") #doing this only for the
SQL
# mapping long to int for products
productId_toInt= f_df.map(lambda
x:x["productId"]).distinct().zipWithUniqueId().toDF(schema=["productId","int_productId"])
# print productId_toInt.take(3)
productId_toInt.registerTempTable("productId_toInt") #doing this only for the
SQL
f_df.registerTempTable("f_df") #doing this only for the SQL
Now I do the join and get the int versions of user_id and product_id as
follows, I tried it with both dataFrame join and SQL join, both have the same
error:
tmp = f_df\
.join(visitorId_toInt, f_df["visitorId"]==visitorId_toInt["visitorId"],'inner')\
.select(f_df["idSite"], f_df["servertimestamp"],
visitorId_toInt["int_visitorId"],\
f_df["sessionId"],f_df["serverTimePretty"], f_df["productId"],
f_df["pageId"] )
ratings_df =
tmp.join(productId_toInt,tmp["productId"]==productId_toInt["productId"],'inner')\
.select(tmp["idSite"], tmp["servertimestamp"], tmp["int_visitorId"],\
tmp["sessionId"],tmp["serverTimePretty"],
productId_toInt["int_productId"], tmp["pageId"] )
The SQL version:
ratings_df = sqlContext.sql("SELECT idSite, servertimestamp, int_visitorId,
sessionId, serverTimePretty, int_productId, pageId \
FROM f_df \
INNER JOIN visitorId_toInt \
ON f_df.visitorId = visitorId_toInt.visitorId \
INNER JOIN productId_toInt \
ON f_df.productId = productId_toInt.productId \
")
Here is the result of the join:
>>>ratings_df.take(3)
[Row(idSite=1453723983, servertimestamp=None, int_visitorId=5774,
sessionId=None, serverTimePretty=u'377347895', int_productId=7936, pageId=100),
Row(idSite=1453723983, servertimestamp=None, int_visitorId=5774,
sessionId=None, serverTimePretty=u'377347895', int_productId=7936, pageId=100),
Row(idSite=1453724668, servertimestamp=None, int_visitorId=4271,
sessionId=None, serverTimePretty=u'375989339', int_productId=1060, pageId=100)]
The idSite in my dataset is 100 for all the rows, and some how thats being
assigned to pageId.
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