[ https://issues.apache.org/jira/browse/SPARK-22307?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Andrey Yakovenko updated SPARK-22307: ------------------------------------- Description: Suggest test case: table with x record filtered by expression expr returns y records (< x), not(expr) does not returns x-y records. Work around: when(expr, false).otherwise(true) is working fine. {code} val ctg = spark.sqlContext.read.json("/user/Catalog.json") scala> ctg.printSchema root |-- Id: string (nullable = true) |-- Name: string (nullable = true) |-- Parent: struct (nullable = true) | |-- Id: string (nullable = true) | |-- Name: string (nullable = true) | |-- Parent: struct (nullable = true) | | |-- Id: string (nullable = true) | | |-- Name: string (nullable = true) | | |-- Parent: struct (nullable = true) | | | |-- Id: string (nullable = true) | | | |-- Name: string (nullable = true) | | | |-- Parent: string (nullable = true) | | | |-- SKU: string (nullable = true) | | |-- SKU: string (nullable = true) | |-- SKU: string (nullable = true) |-- SKU: string (nullable = true) val col1 = expr("((((Id IN ('13MXIIAA4', '13MXIBAA4')) OR (Parent.Id IN ('13MXIIAA4', '13MXIBAA4'))) OR (Parent.Parent.Id IN ('13MXIIAA4', '13MXIBAA4'))) OR (Parent.Parent.Parent.Id IN ('13MXIIAA4', '13MXIBAA4')))") col1: org.apache.spark.sql.Column = ((((Id IN (13MXIIAA4, 13MXIBAA4)) OR (Parent.Id IN (13MXIIAA4, 13MXIBAA4))) OR (Parent.Parent.Id IN (13MXIIAA4, 13MXIBAA4))) OR (Parent.Parent.Parent.Id IN (13MXIIAA4, 13MXIBAA4))) scala> ctg.count res5: Long = 623 scala> ctg.filter(col1).count res2: Long = 2 scala> ctg.filter(not(col1)).count res3: Long = 4 scala> ctg.filter(when(col1, false).otherwise(true)).count res4: Long = 621 {code} Table is hierarchy like structure and has a records with different number of levels filled up. I have a suspicion that due to partly filled hierarchy condition return null/undefined/failed/nan some times (neither true or false). was: Suggest test case: table with x record filtered by expression expr returns y records (< x), not(expr) does not returns x-y records. Work around: when(expr, false).otherwise(true) is working fine. {code} val ctg = spark.sqlContext.read.json("/user/Catalog.json") scala> ctg.printSchema root[^attachment-name.zip] |-- Id: string (nullable = true) |-- Name: string (nullable = true) |-- Parent: struct (nullable = true) | |-- Id: string (nullable = true) | |-- Name: string (nullable = true) | |-- Parent: struct (nullable = true) | | |-- Id: string (nullable = true) | | |-- Name: string (nullable = true) | | |-- Parent: struct (nullable = true) | | | |-- Id: string (nullable = true) | | | |-- Name: string (nullable = true) | | | |-- Parent: string (nullable = true) | | | |-- SKU: string (nullable = true) | | |-- SKU: string (nullable = true) | |-- SKU: string (nullable = true) |-- SKU: string (nullable = true) val col1 = expr("((((Id IN ('13MXIIAA4', '13MXIBAA4')) OR (Parent.Id IN ('13MXIIAA4', '13MXIBAA4'))) OR (Parent.Parent.Id IN ('13MXIIAA4', '13MXIBAA4'))) OR (Parent.Parent.Parent.Id IN ('13MXIIAA4', '13MXIBAA4')))") col1: org.apache.spark.sql.Column = ((((Id IN (13MXIIAA4, 13MXIBAA4)) OR (Parent.Id IN (13MXIIAA4, 13MXIBAA4))) OR (Parent.Parent.Id IN (13MXIIAA4, 13MXIBAA4))) OR (Parent.Parent.Parent.Id IN (13MXIIAA4, 13MXIBAA4))) scala> ctg.count res5: Long = 623 scala> ctg.filter(col1).count res2: Long = 2 scala> ctg.filter(not(col1)).count res3: Long = 4 scala> ctg.filter(when(col1, false).otherwise(true)).count res4: Long = 621 {code} Table is hierarchy like structure and has a records with different number of levels filled up. I have a suspicion that due to partly filled hierarchy condition return null/undefined/failed/nan some times (neither true or false). > NOT condition working incorrectly > --------------------------------- > > Key: SPARK-22307 > URL: https://issues.apache.org/jira/browse/SPARK-22307 > Project: Spark > Issue Type: Bug > Components: Spark Core > Affects Versions: 2.1.0, 2.1.1 > Reporter: Andrey Yakovenko > Attachments: Catalog.json.gz > > > Suggest test case: table with x record filtered by expression expr returns y > records (< x), not(expr) does not returns x-y records. Work around: > when(expr, false).otherwise(true) is working fine. > {code} > val ctg = spark.sqlContext.read.json("/user/Catalog.json") > scala> ctg.printSchema > root > |-- Id: string (nullable = true) > |-- Name: string (nullable = true) > |-- Parent: struct (nullable = true) > | |-- Id: string (nullable = true) > | |-- Name: string (nullable = true) > | |-- Parent: struct (nullable = true) > | | |-- Id: string (nullable = true) > | | |-- Name: string (nullable = true) > | | |-- Parent: struct (nullable = true) > | | | |-- Id: string (nullable = true) > | | | |-- Name: string (nullable = true) > | | | |-- Parent: string (nullable = true) > | | | |-- SKU: string (nullable = true) > | | |-- SKU: string (nullable = true) > | |-- SKU: string (nullable = true) > |-- SKU: string (nullable = true) > val col1 = expr("((((Id IN ('13MXIIAA4', '13MXIBAA4')) OR (Parent.Id IN > ('13MXIIAA4', '13MXIBAA4'))) OR (Parent.Parent.Id IN ('13MXIIAA4', > '13MXIBAA4'))) OR (Parent.Parent.Parent.Id IN ('13MXIIAA4', '13MXIBAA4')))") > col1: org.apache.spark.sql.Column = ((((Id IN (13MXIIAA4, 13MXIBAA4)) OR > (Parent.Id IN (13MXIIAA4, 13MXIBAA4))) OR (Parent.Parent.Id IN (13MXIIAA4, > 13MXIBAA4))) OR (Parent.Parent.Parent.Id IN (13MXIIAA4, 13MXIBAA4))) > scala> ctg.count > res5: Long = 623 > scala> ctg.filter(col1).count > res2: Long = 2 > scala> ctg.filter(not(col1)).count > res3: Long = 4 > scala> ctg.filter(when(col1, false).otherwise(true)).count > res4: Long = 621 > {code} > Table is hierarchy like structure and has a records with different number of > levels filled up. I have a suspicion that due to partly filled hierarchy > condition return null/undefined/failed/nan some times (neither true or false). -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org