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 * 
https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
<https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*



http://talebzadehmich.wordpress.com


*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
such loss, damage or destruction.




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
> >*
> >
> >
> >
> > http://talebzadehmich.wordpress.com
> >
> >
> > *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
> > such loss, damage or destruction.
>

Reply via email to