RE: DataFrame select non-existing column

[Mendelson, Assaf]

The nested columns are in fact a syntactic sugar.
You basically have a column called pass. The type of this column is a struct 
which has a field called mobile.
After you read the parquet file you can check the schema (df.schema) and 
looking at what it has. Basically loop through the types and see if you have a 
pass column. Then take the type of that and check if it has a mobile element. 
This would be something like this (writing it without testing so it probably 
will have small mistakes):

df_schema = df.schema
found_pass = False
found_mobile = False
for f in df_schema:
if f.name == 'pass':
found_pass = True
for g in f.dataType:
 if g.name == 'mobile':
 found_mobile = True
break

Assaf
-Original Message-
From: Kristoffer Sjögren [mailto:sto...@gmail.com] 
Sent: Sunday, November 20, 2016 4:13 PM
To: Mendelson, Assaf
Cc: user
Subject: Re: DataFrame select non-existing column

The problem is that I do not know which data frames has the pass.mobile column. 
I just list a HDFS directory which contain the parquet files and some files has 
the column and some don't. I really don't want to have conditional logic that 
inspect the schema. But maybe that's the only option?

Maybe I misunderstand you, but the following code fails with the same error as 
before.

DataFrame dataFrame = ctx.read().parquet(localPath)
.select("pass")
.withColumn("mobile", col("pass.mobile"));


The flatten option works for my use case. But the problem is that there seems 
to be no way of dropping nested columns, i.e.
drop("pass.auction")


On Sun, Nov 20, 2016 at 10:55 AM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
wrote:
> The issue is that you already have a struct called pass. What you did was add 
> a new columned called "pass.mobile" instead of adding the element to pass - 
> The schema for pass element is the same as before.
> When you do select pass.mobile, it finds the pass structure and checks for 
> mobile in it.
>
> You can do it the other way around: set the name to be: pass_mobile. Add it 
> as before with lit(0) for those that dataframes that do not have the mobile 
> field and do something like withColumn("pass_mobile", df["pass.modile"]) for 
> those that do.
> Another option is to use do something like df.select("pass.*") to flatten the 
> pass structure and work on that (then you can do withColumn("mobile",...) 
> instead of "pass.mobile") but this would change the schema.
>
>
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Saturday, November 19, 2016 4:57 PM
> To: Mendelson, Assaf
> Cc: user
> Subject: Re: DataFrame select non-existing column
>
> Thanks. Here's my code example [1] and the printSchema() output [2].
>
> This code still fails with the following message: "No such struct field 
> mobile in auction, geo"
>
> By looking at the schema, it seems that pass.mobile did not get nested, which 
> is the way it needs to be for my use case. Is nested columns not supported by 
> withColumn()?
>
> [1]
>
> DataFrame df = ctx.read().parquet(localPath).withColumn("pass.mobile", 
> lit(0L)); dataFrame.printSchema(); dataFrame.select("pass.mobile");
>
> [2]
>
> root
>  |-- pass: struct (nullable = true)
>  ||-- auction: struct (nullable = true)
>  |||-- id: integer (nullable = true)
>  ||-- geo: struct (nullable = true)
>  |||-- postalCode: string (nullable = true)
>  |-- pass.mobile: long (nullable = false)
>
> On Sat, Nov 19, 2016 at 7:45 AM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
> wrote:
>> In pyspark for example you would do something like:
>>
>> df.withColumn("newColName",pyspark.sql.functions.lit(None))
>>
>> Assaf.
>> -Original Message-
>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>> Sent: Friday, November 18, 2016 9:19 PM
>> To: Mendelson, Assaf
>> Cc: user
>> Subject: Re: DataFrame select non-existing column
>>
>> Thanks for your answer. I have been searching the API for doing that but I 
>> could not find how to do it?
>>
>> Could you give me a code snippet?
>>
>> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
>> wrote:
>>> You can always add the columns to old dataframes giving them null (or some 
>>> literal) as a preprocessing.
>>>
>>> -Original Message-
>>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>>> Sent: Friday, November 18, 2016 4:32 PM
>>> To: user
>>> Subject: DataFrame select non-existing column
>>>
>>>

Re: DataFrame select non-existing column

[Kristoffer Sjögren]

The problem is that I do not know which data frames has the
pass.mobile column. I just list a HDFS directory which contain the
parquet files and some files has the column and some don't. I really
don't want to have conditional logic that inspect the schema. But
maybe that's the only option?

Maybe I misunderstand you, but the following code fails with the same
error as before.

DataFrame dataFrame = ctx.read().parquet(localPath)
.select("pass")
.withColumn("mobile", col("pass.mobile"));


The flatten option works for my use case. But the problem is that
there seems to be no way of dropping nested columns, i.e.
drop("pass.auction")


On Sun, Nov 20, 2016 at 10:55 AM, Mendelson, Assaf
<assaf.mendel...@rsa.com> wrote:
> The issue is that you already have a struct called pass. What you did was add 
> a new columned called "pass.mobile" instead of adding the element to pass - 
> The schema for pass element is the same as before.
> When you do select pass.mobile, it finds the pass structure and checks for 
> mobile in it.
>
> You can do it the other way around: set the name to be: pass_mobile. Add it 
> as before with lit(0) for those that dataframes that do not have the mobile 
> field and do something like withColumn("pass_mobile", df["pass.modile"]) for 
> those that do.
> Another option is to use do something like df.select("pass.*") to flatten the 
> pass structure and work on that (then you can do withColumn("mobile",...) 
> instead of "pass.mobile") but this would change the schema.
>
>
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Saturday, November 19, 2016 4:57 PM
> To: Mendelson, Assaf
> Cc: user
> Subject: Re: DataFrame select non-existing column
>
> Thanks. Here's my code example [1] and the printSchema() output [2].
>
> This code still fails with the following message: "No such struct field 
> mobile in auction, geo"
>
> By looking at the schema, it seems that pass.mobile did not get nested, which 
> is the way it needs to be for my use case. Is nested columns not supported by 
> withColumn()?
>
> [1]
>
> DataFrame df = ctx.read().parquet(localPath).withColumn("pass.mobile", 
> lit(0L)); dataFrame.printSchema(); dataFrame.select("pass.mobile");
>
> [2]
>
> root
>  |-- pass: struct (nullable = true)
>  ||-- auction: struct (nullable = true)
>  |||-- id: integer (nullable = true)
>  ||-- geo: struct (nullable = true)
>  |||-- postalCode: string (nullable = true)
>  |-- pass.mobile: long (nullable = false)
>
> On Sat, Nov 19, 2016 at 7:45 AM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
> wrote:
>> In pyspark for example you would do something like:
>>
>> df.withColumn("newColName",pyspark.sql.functions.lit(None))
>>
>> Assaf.
>> -Original Message-
>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>> Sent: Friday, November 18, 2016 9:19 PM
>> To: Mendelson, Assaf
>> Cc: user
>> Subject: Re: DataFrame select non-existing column
>>
>> Thanks for your answer. I have been searching the API for doing that but I 
>> could not find how to do it?
>>
>> Could you give me a code snippet?
>>
>> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
>> wrote:
>>> You can always add the columns to old dataframes giving them null (or some 
>>> literal) as a preprocessing.
>>>
>>> -Original Message-
>>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>>> Sent: Friday, November 18, 2016 4:32 PM
>>> To: user
>>> Subject: DataFrame select non-existing column
>>>
>>> Hi
>>>
>>> We have evolved a DataFrame by adding a few columns but cannot write select 
>>> statements on these columns for older data that doesn't have them since 
>>> they fail with a AnalysisException with message "No such struct field".
>>>
>>> We also tried dropping columns but this doesn't work for nested columns.
>>>
>>> Any non-hacky ways to get around this?
>>>
>>> Cheers,
>>> -Kristoffer
>>>
>>> -
>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>>

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

RE: DataFrame select non-existing column

[Mendelson, Assaf]

The issue is that you already have a struct called pass. What you did was add a 
new columned called "pass.mobile" instead of adding the element to pass - The 
schema for pass element is the same as before.
When you do select pass.mobile, it finds the pass structure and checks for 
mobile in it.

You can do it the other way around: set the name to be: pass_mobile. Add it as 
before with lit(0) for those that dataframes that do not have the mobile field 
and do something like withColumn("pass_mobile", df["pass.modile"]) for those 
that do.
Another option is to use do something like df.select("pass.*") to flatten the 
pass structure and work on that (then you can do withColumn("mobile",...) 
instead of "pass.mobile") but this would change the schema.


-Original Message-
From: Kristoffer Sjögren [mailto:sto...@gmail.com] 
Sent: Saturday, November 19, 2016 4:57 PM
To: Mendelson, Assaf
Cc: user
Subject: Re: DataFrame select non-existing column

Thanks. Here's my code example [1] and the printSchema() output [2].

This code still fails with the following message: "No such struct field mobile 
in auction, geo"

By looking at the schema, it seems that pass.mobile did not get nested, which 
is the way it needs to be for my use case. Is nested columns not supported by 
withColumn()?

[1]

DataFrame df = ctx.read().parquet(localPath).withColumn("pass.mobile", 
lit(0L)); dataFrame.printSchema(); dataFrame.select("pass.mobile");

[2]

root
 |-- pass: struct (nullable = true)
 ||-- auction: struct (nullable = true)
 |||-- id: integer (nullable = true)
 ||-- geo: struct (nullable = true)
 |||-- postalCode: string (nullable = true)
 |-- pass.mobile: long (nullable = false)

On Sat, Nov 19, 2016 at 7:45 AM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
wrote:
> In pyspark for example you would do something like:
>
> df.withColumn("newColName",pyspark.sql.functions.lit(None))
>
> Assaf.
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Friday, November 18, 2016 9:19 PM
> To: Mendelson, Assaf
> Cc: user
> Subject: Re: DataFrame select non-existing column
>
> Thanks for your answer. I have been searching the API for doing that but I 
> could not find how to do it?
>
> Could you give me a code snippet?
>
> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
> wrote:
>> You can always add the columns to old dataframes giving them null (or some 
>> literal) as a preprocessing.
>>
>> -Original Message-
>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>> Sent: Friday, November 18, 2016 4:32 PM
>> To: user
>> Subject: DataFrame select non-existing column
>>
>> Hi
>>
>> We have evolved a DataFrame by adding a few columns but cannot write select 
>> statements on these columns for older data that doesn't have them since they 
>> fail with a AnalysisException with message "No such struct field".
>>
>> We also tried dropping columns but this doesn't work for nested columns.
>>
>> Any non-hacky ways to get around this?
>>
>> Cheers,
>> -Kristoffer
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>

Re: DataFrame select non-existing column

[Kristoffer Sjögren]

Thanks. Here's my code example [1] and the printSchema() output [2].

This code still fails with the following message: "No such struct
field mobile in auction, geo"

By looking at the schema, it seems that pass.mobile did not get
nested, which is the way it needs to be for my use case. Is nested
columns not supported by withColumn()?

[1]

DataFrame df = ctx.read().parquet(localPath).withColumn("pass.mobile", lit(0L));
dataFrame.printSchema();
dataFrame.select("pass.mobile");

[2]

root
 |-- pass: struct (nullable = true)
 ||-- auction: struct (nullable = true)
 |||-- id: integer (nullable = true)
 ||-- geo: struct (nullable = true)
 |||-- postalCode: string (nullable = true)
 |-- pass.mobile: long (nullable = false)

On Sat, Nov 19, 2016 at 7:45 AM, Mendelson, Assaf
<assaf.mendel...@rsa.com> wrote:
> In pyspark for example you would do something like:
>
> df.withColumn("newColName",pyspark.sql.functions.lit(None))
>
> Assaf.
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Friday, November 18, 2016 9:19 PM
> To: Mendelson, Assaf
> Cc: user
> Subject: Re: DataFrame select non-existing column
>
> Thanks for your answer. I have been searching the API for doing that but I 
> could not find how to do it?
>
> Could you give me a code snippet?
>
> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
> wrote:
>> You can always add the columns to old dataframes giving them null (or some 
>> literal) as a preprocessing.
>>
>> -Original Message-
>> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
>> Sent: Friday, November 18, 2016 4:32 PM
>> To: user
>> Subject: DataFrame select non-existing column
>>
>> Hi
>>
>> We have evolved a DataFrame by adding a few columns but cannot write select 
>> statements on these columns for older data that doesn't have them since they 
>> fail with a AnalysisException with message "No such struct field".
>>
>> We also tried dropping columns but this doesn't work for nested columns.
>>
>> Any non-hacky ways to get around this?
>>
>> Cheers,
>> -Kristoffer
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

RE: DataFrame select non-existing column

[Mendelson, Assaf]

In pyspark for example you would do something like:

df.withColumn("newColName",pyspark.sql.functions.lit(None))

Assaf.
-Original Message-
From: Kristoffer Sjögren [mailto:sto...@gmail.com] 
Sent: Friday, November 18, 2016 9:19 PM
To: Mendelson, Assaf
Cc: user
Subject: Re: DataFrame select non-existing column

Thanks for your answer. I have been searching the API for doing that but I 
could not find how to do it?

Could you give me a code snippet?

On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf <assaf.mendel...@rsa.com> 
wrote:
> You can always add the columns to old dataframes giving them null (or some 
> literal) as a preprocessing.
>
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Friday, November 18, 2016 4:32 PM
> To: user
> Subject: DataFrame select non-existing column
>
> Hi
>
> We have evolved a DataFrame by adding a few columns but cannot write select 
> statements on these columns for older data that doesn't have them since they 
> fail with a AnalysisException with message "No such struct field".
>
> We also tried dropping columns but this doesn't work for nested columns.
>
> Any non-hacky ways to get around this?
>
> Cheers,
> -Kristoffer
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>

Re: DataFrame select non-existing column

[Muthu Jayakumar]

Depending on your use case, 'df.withColumn("my_existing_or_new_col",
lit(0l))' could work?

On Fri, Nov 18, 2016 at 11:18 AM, Kristoffer Sjögren 
wrote:

> Thanks for your answer. I have been searching the API for doing that
> but I could not find how to do it?
>
> Could you give me a code snippet?
>
> On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf
>  wrote:
> > You can always add the columns to old dataframes giving them null (or
> some literal) as a preprocessing.
> >
> > -Original Message-
> > From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> > Sent: Friday, November 18, 2016 4:32 PM
> > To: user
> > Subject: DataFrame select non-existing column
> >
> > Hi
> >
> > We have evolved a DataFrame by adding a few columns but cannot write
> select statements on these columns for older data that doesn't have them
> since they fail with a AnalysisException with message "No such struct
> field".
> >
> > We also tried dropping columns but this doesn't work for nested columns.
> >
> > Any non-hacky ways to get around this?
> >
> > Cheers,
> > -Kristoffer
> >
> > -
> > To unsubscribe e-mail: user-unsubscr...@spark.apache.org
> >
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

Re: DataFrame select non-existing column

[Kristoffer Sjögren]

Thanks for your answer. I have been searching the API for doing that
but I could not find how to do it?

Could you give me a code snippet?

On Fri, Nov 18, 2016 at 8:03 PM, Mendelson, Assaf
 wrote:
> You can always add the columns to old dataframes giving them null (or some 
> literal) as a preprocessing.
>
> -Original Message-
> From: Kristoffer Sjögren [mailto:sto...@gmail.com]
> Sent: Friday, November 18, 2016 4:32 PM
> To: user
> Subject: DataFrame select non-existing column
>
> Hi
>
> We have evolved a DataFrame by adding a few columns but cannot write select 
> statements on these columns for older data that doesn't have them since they 
> fail with a AnalysisException with message "No such struct field".
>
> We also tried dropping columns but this doesn't work for nested columns.
>
> Any non-hacky ways to get around this?
>
> Cheers,
> -Kristoffer
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

RE: DataFrame select non-existing column

[Mendelson, Assaf]

You can always add the columns to old dataframes giving them null (or some 
literal) as a preprocessing.

-Original Message-
From: Kristoffer Sjögren [mailto:sto...@gmail.com] 
Sent: Friday, November 18, 2016 4:32 PM
To: user
Subject: DataFrame select non-existing column

Hi

We have evolved a DataFrame by adding a few columns but cannot write select 
statements on these columns for older data that doesn't have them since they 
fail with a AnalysisException with message "No such struct field".

We also tried dropping columns but this doesn't work for nested columns.

Any non-hacky ways to get around this?

Cheers,
-Kristoffer

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org