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https://issues.apache.org/jira/browse/SPARK-47193?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17821393#comment-17821393
]
Bruce Robbins edited comment on SPARK-47193 at 2/27/24 8:48 PM:
----------------------------------------------------------------
Running this in Spark 3.5.0 in local mode on my laptop, I get
{noformat}
df count = 8
...
rdd count = 8
{noformat}
What is your environment and Spark configuration?
By the way, the "{{...}}" above are messages like
{noformat}
24/02/27 11:34:51 WARN CSVHeaderChecker: CSV header does not conform to the
schema.
Header: UserId, LocationId, LocationName, CreatedDate, Status
Schema: UserId, LocationId, LocationName, Status, CreatedDate
Expected: Status but found: CreatedDate
CSV file: file:///<path>/userLocation.csv
{noformat}
was (Author: bersprockets):
Running this in Spark 3.5.0 in local mode on my laptop, I get
{noformat}
df count = 8
...
rdd count = 8
{noformat}
What is your environment and Spark configuration?
By the way, the {{...}} above are messages like
{noformat}
24/02/27 11:34:51 WARN CSVHeaderChecker: CSV header does not conform to the
schema.
Header: UserId, LocationId, LocationName, CreatedDate, Status
Schema: UserId, LocationId, LocationName, Status, CreatedDate
Expected: Status but found: CreatedDate
CSV file: file:///<path>/userLocation.csv
{noformat}
> Converting dataframe to rdd results in data loss
> ------------------------------------------------
>
> Key: SPARK-47193
> URL: https://issues.apache.org/jira/browse/SPARK-47193
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.5.0, 3.5.1
> Reporter: Ivan Bova
> Priority: Critical
> Labels: correctness
> Attachments: device.csv, deviceClass.csv, deviceType.csv,
> language.csv, location.csv, location1.csv, timeZoneLookup.csv, user.csv,
> userLocation.csv, userProfile.csv
>
>
> I have 10 csv files and need to create mapping from them. After all of the
> joins dataframe contains all expected rows but rdd from this dataframe
> contains only half of them.
> {code:java}
> case class MyUserProfileMessage(UserId: Int, Email: String, FirstName:
> String, LastName: String, LanguageId: Option[Int])
> case class MyLanguageMessage(LanguageId: Int, LanguageLocaleId: String)
> case class MyDeviceMessage(DeviceId1: String, Created: Option[Timestamp],
> UpdatedDate: Timestamp, DeviceId2: String, DeviceName: String, LocationId:
> Option[Int], DeviceTypeId: Option[Int], DeviceClassId: Int, UserId1:
> Option[Int])
> case class MyDeviceClassMessage(DeviceClassId: Int, DeviceClassName: String)
> case class MyDeviceTypeMessage(DeviceTypeId: Int, DeviceTypeName: String)
> case class MyLocation1(LocationId1: Int, LocationId: Int, Latitude:
> Option[Double], Longitude: Option[Double], Radius: Option[Double],
> CreatedDate: Timestamp)
> case class MyTimeZoneLookupMessage(TimeZoneId: Int, ZoneName: String)
> case class MyUserLocationMessage(UserId: Int, LocationId: Int, LocationName:
> String, Status: Int, CreatedDate: Timestamp)
> case class MyUserMessage(UserId: Int, Created: Option[Timestamp], Deleted:
> Option[Timestamp], Active: Option[Boolean], ActivatedDate: Option[Timestamp])
> case class MyLocationMessage(LocationId: Int, IsDeleted: Option[Boolean],
> Address1: String, Address2: String, City: String, State: String, Country:
> String, ZipCode: String, Feature2Enabled: Option[Boolean], LocationStatus:
> Option[Int], Location1Enabled: Option[Boolean], LocationKey: String,
> UpdatedDateTime: Timestamp, CreatedDate: Timestamp, Feature1Enabled:
> Option[Boolean], Level: Option[Int], TimeZone: Option[Int])
> val userProfile = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyUserProfileMessage].schema).csv("userProfile.csv").as[MyUserProfileMessage]
> val language = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyLanguageMessage].schema).csv("language.csv").as[MyLanguageMessage]
> val device = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyDeviceMessage].schema).csv("device.csv").as[MyDeviceMessage]
> val deviceClass = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyDeviceClassMessage].schema).csv("deviceClass.csv").as[MyDeviceClassMessage]
> val deviceType = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyDeviceTypeMessage].schema).csv("deviceType.csv").as[MyDeviceTypeMessage]
> val location1 = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyLocation1].schema).csv("location1.csv").as[MyLocation1]
> val timeZoneLookup = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyTimeZoneLookupMessage].schema).csv("timeZoneLookup.csv").as[MyTimeZoneLookupMessage]
> val userLocation = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyUserLocationMessage].schema).csv("userLocation.csv").as[MyUserLocationMessage]
> val user = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyUserMessage].schema).csv("user.csv").as[MyUserMessage]
> val location = spark.read.option("header", "true").option("comment",
> "#").option("nullValue",
> "null").schema(Encoders.product[MyLocationMessage].schema).csv("location.csv").as[MyLocationMessage]
> val result = user
> .join(userProfile, user("UserId") === userProfile("UserId"), "inner")
> .join(language, userProfile("LanguageId") === language("LanguageId"),
> "left")
> .join(userLocation, user("UserId") === userLocation("UserId"), "inner")
> .join(location, userLocation("LocationId") === location("LocationId"),
> "inner")
> .join(device, location("LocationId") === device("LocationId"), "inner")
> .join(deviceType, device("DeviceTypeId") === deviceType("DeviceTypeId"),
> "inner")
> .join(
> deviceClass,
> device("DeviceClassId") === deviceClass("DeviceClassId"),
> "inner")
> .join(
> timeZoneLookup,
> timeZoneLookup("TimeZoneId") === location("TimeZone"),
> "left")
> .join(location1, location("LocationId") === location1("LocationId"), "left")
> .where(
> device("UserId1").isNull
> && (user("Active") === lit(true) || user("ActivatedDate").isNotNull)
> )
> .dropDuplicates()
> println("df count = " + result.count())
> println("rdd count = "+ result.rdd.count())
> result.show(false)
> println("------")
> result.rdd.foreach(println) {code}
> output:
> {code:java}
> df count = 8
> rdd count = 4
> +------+-------------------+-------------------+------+-------------------+------+------+----------+---------+----------+----------+----------------+------+----------+------------+------+-----------+----------+---------+----------+----------+-----+------+--------+-------+---------------+--------------+----------------+-----------+-------------------+-------------------+---------------+-----+--------+---------+-------------------+-------------------+----------+-----------+----------+------------+-------------+-------+------------+--------------+-------------+---------------+----------+--------+-----------+----------+--------+---------+------+-------------------+
> |UserId|Created |Deleted |Active|ActivatedDate
> |UserId|Email |FirstName |LastName
> |LanguageId|LanguageId|LanguageLocaleId|UserId|LocationId|LocationName|Status|CreatedDate|LocationId|IsDeleted|Address1
> |Address2 |City |State |Country
> |ZipCode|Feature2Enabled|LocationStatus|Location1Enabled|LocationKey|UpdatedDateTime
> |CreatedDate |Feature1Enabled|Level|TimeZone|DeviceId1|Created
> |UpdatedDate |DeviceId2 |DeviceName
> |LocationId|DeviceTypeId|DeviceClassId|UserId1|DeviceTypeId|DeviceTypeName|DeviceClassId|DeviceClassName|TimeZoneId|ZoneName|LocationId1|LocationId|Latitude|Longitude|Radius|CreatedDate
> |
> +------+-------------------+-------------------+------+-------------------+------+------+----------+---------+----------+----------+----------------+------+----------+------------+------+-----------+----------+---------+----------+----------+-----+------+--------+-------+---------------+--------------+----------------+-----------+-------------------+-------------------+---------------+-----+--------+---------+-------------------+-------------------+----------+-----------+----------+------------+-------------+-------+------------+--------------+-------------+---------------+----------+--------+-----------+----------+--------+---------+------+-------------------+
> |1 |2021-11-22 11:27:27|2021-11-25 11:27:27|false |2021-11-22 11:27:27|1
> |email1|firstName1|lastName1|1 |1 |It |1
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device3 |2021-11-18
> 11:27:27|2021-11-19 11:27:27|DeviceId23|DeviceName3|1 |3 |3
> |NULL |3 |type3 |3 |class3
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> |1 |2021-11-22 11:27:27|2021-11-25 11:27:27|false |2021-11-22 11:27:27|1
> |email1|firstName1|lastName1|1 |1 |It |1
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device1 |2021-11-16
> 11:27:27|2021-11-17 11:27:27|DeviceId21|DeviceName1|1 |1 |1
> |NULL |1 |type1 |1 |class1
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> |2 |2021-11-22 11:27:27|NULL |true |2021-11-22 11:27:27|2
> |email2|firstName2|lastName2|2 |2 |En |2
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device3 |2021-11-18
> 11:27:27|2021-11-19 11:27:27|DeviceId23|DeviceName3|1 |3 |3
> |NULL |3 |type3 |3 |class3
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> |2 |2021-11-22 11:27:27|NULL |true |2021-11-22 11:27:27|2
> |email2|firstName2|lastName2|2 |2 |En |2
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device1 |2021-11-16
> 11:27:27|2021-11-17 11:27:27|DeviceId21|DeviceName1|1 |1 |1
> |NULL |1 |type1 |1 |class1
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> |3 |2021-11-22 11:27:27|NULL |true |2021-11-22 11:27:27|3
> |email3|firstName3|lastName3|3 |3 |DE |3
> |2 |Location2 |NULL |NULL |2 |false
> |address1_2|address2_2|City2|State2|Country2|code2 |true |2
> |true |LocKey2 |2021-11-17 11:27:27|2021-11-17
> 11:27:27|false |1 |1 |device4 |2021-11-25 11:27:27|NULL
> |DeviceId24|DeviceName4|2 |1 |2 |NULL
> |1 |type1 |2 |class2 |1 |Zone1
> |3 |2 |14.32 |45.23 |16.2 |2021-11-23 11:27:27|
> |3 |2021-11-22 11:27:27|NULL |true |2021-11-22 11:27:27|3
> |email3|firstName3|lastName3|3 |3 |DE |3
> |2 |Location2 |NULL |NULL |2 |false
> |address1_2|address2_2|City2|State2|Country2|code2 |true |2
> |true |LocKey2 |2021-11-17 11:27:27|2021-11-17
> 11:27:27|false |1 |1 |device2 |2021-11-17
> 11:27:27|2021-11-18 11:27:27|DeviceId22|DeviceName2|2 |2 |2
> |NULL |2 |type2 |2 |class2
> |1 |Zone1 |3 |2 |14.32 |45.23 |16.2
> |2021-11-23 11:27:27|
> |4 |2021-11-22 11:27:27|NULL |NULL |2021-11-22 11:27:27|4
> |email4|firstName4|lastName4|NULL |NULL |NULL |4
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device3 |2021-11-18
> 11:27:27|2021-11-19 11:27:27|DeviceId23|DeviceName3|1 |3 |3
> |NULL |3 |type3 |3 |class3
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> |4 |2021-11-22 11:27:27|NULL |NULL |2021-11-22 11:27:27|4
> |email4|firstName4|lastName4|NULL |NULL |NULL |4
> |1 |Location1 |NULL |NULL |1 |false
> |address1_1|address2_1|City1|State1|Country1|code1 |true |1
> |true |LocKey1 |2021-11-16 11:27:27|2021-11-16
> 11:27:27|false |1 |1 |device1 |2021-11-16
> 11:27:27|2021-11-17 11:27:27|DeviceId21|DeviceName1|1 |1 |1
> |NULL |1 |type1 |1 |class1
> |1 |Zone1 |1 |1 |12.32 |43.23 |14.2
> |2021-11-21 11:27:27|
> +------+-------------------+-------------------+------+-------------------+------+------+----------+---------+----------+----------+----------------+------+----------+------------+------+-----------+----------+---------+----------+----------+-----+------+--------+-------+---------------+--------------+----------------+-----------+-------------------+-------------------+---------------+-----+--------+---------+-------------------+-------------------+----------+-----------+----------+------------+-------------+-------+------------+--------------+-------------+---------------+----------+--------+-----------+----------+--------+---------+------+-------------------+------
> [2,null,null,true,null,2,email2,firstName2,lastName2,2,2,En,2,1,Location1,null,null,1,false,address1_1,address2_1,City1,State1,Country1,code1,true,1,true,LocKey1,null,null,false,1,1,device3,null,null,DeviceId23,DeviceName3,1,3,3,null,3,type3,3,class3,1,Zone1,1,1,12.32,43.23,14.2,null]
> [2,null,null,true,null,2,email2,firstName2,lastName2,2,2,En,2,1,Location1,null,null,1,false,address1_1,address2_1,City1,State1,Country1,code1,true,1,true,LocKey1,null,null,false,1,1,device1,null,null,DeviceId21,DeviceName1,1,1,1,null,1,type1,1,class1,1,Zone1,1,1,12.32,43.23,14.2,null]
> [3,null,null,true,null,3,email3,firstName3,lastName3,3,3,DE,3,2,Location2,null,null,2,false,address1_2,address2_2,City2,State2,Country2,code2,true,2,true,LocKey2,null,null,false,1,1,device4,null,null,DeviceId24,DeviceName4,2,1,2,null,1,type1,2,class2,1,Zone1,3,2,14.32,45.23,16.2,null]
> [3,null,null,true,null,3,email3,firstName3,lastName3,3,3,DE,3,2,Location2,null,null,2,false,address1_2,address2_2,City2,State2,Country2,code2,true,2,true,LocKey2,null,null,false,1,1,device2,null,null,DeviceId22,DeviceName2,2,2,2,null,2,type2,2,class2,1,Zone1,3,2,14.32,45.23,16.2,null]
> {code}
> Dataframe count and show work as expected, dut rdd does not fave first two
> and last two records
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