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.
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
