Maybe create a column with "lit" function for the variables you are comparing
against.Bentzi
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On Wed, Apr 29, 2020 at 18:40, Mich Talebzadeh<mich.talebza...@gmail.com>
wrote:
The below line works
valc =
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'")
But not the following when the values are passed as parameters
valrejectedDF =
newDF.withColumn("target_mobile_no",col("target_mobile_no").cast(StringType)).
filter("length(target_mobile_no) != broadcastStagingConfig.mobileNoLengthOR
substring(target_mobile_no,1,1) != broadcastStagingConfig.ukMobileNoStart")
Ithink it cannot interpret them
Dr Mich Talebzadeh
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On Wed, 29 Apr 2020 at 13:25, Mich Talebzadeh <mich.talebza...@gmail.com> wrote:
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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damage or destructionof 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 suchloss, damage or
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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
>
>
>
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>
>
>
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>
>
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