OK how do you pass variables for 10 and '7'
val rejectedDF = newDF.withColumn("target_mobile_no",
col("target_mobile_no").cast(StringType)).
filter("length(target_mobile_no) != 10 OR
substring(target_mobile_no,1,1) != '7'")
in above in Scala. Neither $ value below or lit() are working!
val rejectedDF = newDF.withColumn("target_mobile_no",
col("target_mobile_no").cast(StringType)).
filter("length(target_mobile_no) !=
${broadcastStagingConfig.mobileNoLength} OR substring(target_mobile_no,1,1)
!= ${broadcastStagingConfig.ukMobileNoStart}")
Thanks
Dr Mich Talebzadeh
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On Wed, 29 Apr 2020 at 10:15, Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
> Hi Zhang,
>
> Yes the SQL way worked fine
>
> val rejectedDF = newDF.withColumn("target_mobile_no",
> col("target_mobile_no").cast(StringType)).
>
> filter("length(target_mobile_no) != 10 OR
> substring(target_mobile_no,1,1) != '7'")
>
> Many thanks,
>
> Dr Mich Talebzadeh
>
>
>
> LinkedIn *
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> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
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> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
> The author will in no case be liable for any monetary damages arising from
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>
> On Wed, 29 Apr 2020 at 09:51, ZHANG Wei <wezh...@outlook.com> wrote:
>
>> AFAICT, maybe Spark SQL built-in functions[1] can help as below:
>>
>> scala> df.show()
>> +----+-------+
>> | age| name|
>> +----+-------+
>> |null|Michael|
>> | 30| Andy|
>> | 19| Justin|
>> +----+-------+
>>
>>
>> scala> df.filter("length(name) == 4 or substring(name, 1, 1) ==
>> 'J'").show()
>> +---+------+
>> |age| name|
>> +---+------+
>> | 30| Andy|
>> | 19|Justin|
>> +---+------+
>>
>>
>> --
>> Cheers,
>> -z
>> [1] https://spark.apache.org/docs/latest/api/sql/index.html
>>
>> On Wed, 29 Apr 2020 08:45:26 +0100
>> Mich Talebzadeh <mich.talebza...@gmail.com> wrote:
>>
>> > Hi,
>> >
>> >
>> >
>> > Trying to filter a dataframe with multiple conditions using OR "||" as
>> below
>> >
>> >
>> >
>> > val rejectedDF = newDF.withColumn("target_mobile_no",
>> > col("target_mobile_no").cast(StringType)).
>> >
>> > filter(length(col("target_mobile_no")) !== 10 ||
>> > substring(col("target_mobile_no"),1,1) !== "7")
>> >
>> >
>> >
>> > This throws this error
>> >
>> >
>> >
>> > res12: org.apache.spark.sql.DataFrame = []
>> >
>> > <console>:49: error: value || is not a member of Int
>> >
>> > filter(length(col("target_mobile_no")) !== 10
>> ||
>> > substring(col("target_mobile_no"),1,1) !== "7")
>> >
>> >
>> >
>> > Try another way
>> >
>> >
>> >
>> > val rejectedDF = newDF.withColumn("target_mobile_no",
>> > col("target_mobile_no").cast(StringType)).
>> >
>> > filter(length(col("target_mobile_no")) !=== 10 ||
>> > substring(col("target_mobile_no"),1,1) !=== "7")
>> >
>> > rejectedDF.createOrReplaceTempView("tmp")
>> >
>> >
>> >
>> > Tried few options but I am still getting this error
>> >
>> >
>> >
>> > <console>:49: error: value !=== is not a member of
>> > org.apache.spark.sql.Column
>> >
>> > filter(length(col("target_mobile_no")) !===
>> 10 ||
>> > substring(col("target_mobile_no"),1,1) !=== "7")
>> >
>> > ^
>> >
>> > <console>:49: error: value || is not a member of Int
>> >
>> > filter(length(col("target_mobile_no")) !===
>> 10 ||
>> > substring(col("target_mobile_no"),1,1) !=== "7")
>> >
>> >
>> >
>> > I can create a dataframe for each filter but that does not look
>> efficient
>> > to me?
>> >
>> >
>> >
>> > Thanks
>> >
>> >
>> >
>> > Dr Mich Talebzadeh
>> >
>> >
>> >
>> > LinkedIn *
>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> > <
>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> >*
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>> > loss, damage or destruction of data or any other property which may
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>
