Hi Kwon,
Thanks for this but Isn't this what Michael suggested?
Thanks,
kant
On Mon, Dec 5, 2016 at 4:45 AM, Hyukjin Kwon <gurwls...@gmail.com> wrote:
> Hi Kant,
>
> How about doing something like this?
>
> import org.apache.spark.sql.functions._
>
> // val df2 = df.select(df("body").cast(StringType).as("body"))
> val df2 = Seq("""{"a": 1}""").toDF("body")
> val schema = spark.read.json(df2.as[String].rdd).schema
> df2.select(from_json(col("body"), schema)).show()
>
>
>
> 2016-12-05 19:51 GMT+09:00 kant kodali <kanth...@gmail.com>:
>
>> Hi Michael,
>>
>> " Personally, I usually take a small sample of data and use schema
>> inference on that. I then hardcode that schema into my program. This
>> makes your spark jobs much faster and removes the possibility of the schema
>> changing underneath the covers."
>>
>> This may or may not work for us. Not all rows have the same schema. The
>> number of distinct schemas we have now may be small but going forward this
>> can go to any number moreover a distinct call can lead to a table scan
>> which can be billions of rows for us.
>>
>> I also would agree to keep the API consistent than making an exception
>> however I wonder if it make sense to provide an action call to infer the
>> schema which would return a new dataframe after the action call finishes
>> (after schema inference)? For example, something like below ?
>>
>> val inferedDF = df.inferSchema(col1);
>>
>> Thanks,
>>
>>
>>
>>
>> On Mon, Nov 28, 2016 at 6:12 PM, Michael Armbrust <mich...@databricks.com
>> > wrote:
>>
>>> You could open up a JIRA to add a version of from_json that supports
>>> schema inference, but unfortunately that would not be super easy to
>>> implement. In particular, it would introduce a weird case where only this
>>> specific function would block for a long time while we infer the schema
>>> (instead of waiting for an action). This blocking would be kind of odd for
>>> a call like df.select(...). If there is enough interest, though, we
>>> should still do it.
>>>
>>> To give a little more detail, your version of the code is actually doing
>>> two passes over the data: one to infer the schema and a second for whatever
>>> processing you are asking it to do. We have to know the schema at each
>>> step of DataFrame construction, so we'd have to do this even before you
>>> called an action.
>>>
>>> Personally, I usually take a small sample of data and use schema
>>> inference on that. I then hardcode that schema into my program. This
>>> makes your spark jobs much faster and removes the possibility of the schema
>>> changing underneath the covers.
>>>
>>> Here's some code I use to build the static schema code automatically
>>> <https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1023043053387187/1128172975083446/2840265927289860/latest.html>
>>> .
>>>
>>> Would that work for you? If not, why not?
>>>
>>> On Wed, Nov 23, 2016 at 2:48 AM, kant kodali <kanth...@gmail.com> wrote:
>>>
>>>> Hi Michael,
>>>>
>>>> Looks like all from_json functions will require me to pass schema and
>>>> that can be little tricky for us but the code below doesn't require me to
>>>> pass schema at all.
>>>>
>>>> import org.apache.spark.sql._
>>>> val rdd = df2.rdd.map { case Row(j: String) => j }
>>>> spark.read.json(rdd).show()
>>>>
>>>>
>>>> On Tue, Nov 22, 2016 at 2:42 PM, Michael Armbrust <
>>>> mich...@databricks.com> wrote:
>>>>
>>>>> The first release candidate should be coming out this week. You can
>>>>> subscribe to the dev list if you want to follow the release schedule.
>>>>>
>>>>> On Mon, Nov 21, 2016 at 9:34 PM, kant kodali <kanth...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Michael,
>>>>>>
>>>>>> I only see spark 2.0.2 which is what I am using currently. Any idea
>>>>>> on when 2.1 will be released?
>>>>>>
>>>>>> Thanks,
>>>>>> kant
>>>>>>
>>>>>> On Mon, Nov 21, 2016 at 5:12 PM, Michael Armbrust <
>>>>>> mich...@databricks.com> wrote:
>>>>>>
>>>>>>> In Spark 2.1 we've added a from_json
>>>>>>> <https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/functions.scala#L2902>
>>>>>>> function that I think will do what you want.
>>>>>>>
>>>>>>> On Fri, Nov 18, 2016 at 2:29 AM, kant kodali <kanth...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> This seem to work
>>>>>>>>
>>>>>>>> import org.apache.spark.sql._
>>>>>>>> val rdd = df2.rdd.map { case Row(j: String) => j }
>>>>>>>> spark.read.json(rdd).show()
>>>>>>>>
>>>>>>>> However I wonder if this any inefficiency here ? since I have to
>>>>>>>> apply this function for billion rows.
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
