You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "venu k tangirala (JIRA)" <ji...@apache.org> on 2016/04/06 07:40:25 UTC
[jira] [Updated] (SPARK-14425) spark SQL/dataframe join error:
mixes the columns up
[ https://issues.apache.org/jira/browse/SPARK-14425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
venu k tangirala updated SPARK-14425:
-------------------------------------
Priority: Blocker (was: Major)
> 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
> Priority: Blocker
>
> 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.
>
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org