Hey Sam,
Nope – it does not work the way I want. I guess it is only working with one
type…
Trying to convert:
{"releaseDate":1479448800000,"link":"http://amzn.to/2kup94P","id":1,"authorId":1,"title":"Fantastic
Beasts and Where to Find Them: The Original Screenplay"}
I get:
[Executor task launch worker for task 3:ERROR] Logging$class: Exception in task
0.0 in stage 3.0 (TID 3)
java.lang.IllegalArgumentException: Failed to convert the JSON string
'{"releaseDate":1479448800000,"link":"http://amzn.to/2kup94P","id":1,"authorId":1,"title":"Fantastic
Beasts and Where to Find Them: The Original Screenplay"}' to a data type.
at org.apache.spark.sql.types.DataType$.parseDataType(DataType.scala:176)
at org.apache.spark.sql.types.DataType$.fromJson(DataType.scala:108)
at org.apache.spark.sql.types.DataType.fromJson(DataType.scala)
at
net.jgp.labs.spark.l250_map.l031_dataset_book_json_in_progress.CsvToDatasetBookAsJson$BookMapper.call(CsvToDatasetBookAsJson.java:44)
at
net.jgp.labs.spark.l250_map.l031_dataset_book_json_in_progress.CsvToDatasetBookAsJson$BookMapper.call(CsvToDatasetBookAsJson.java:1)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source)
at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
From: JG Perrin [mailto:jper...@lumeris.com]
Sent: Monday, August 28, 2017 1:29 PM
To: Sam Elamin <hussam.ela...@gmail.com>
Cc: user@spark.apache.org
Subject: RE: from_json()
Thanks Sam – this might be the solution. I will investigate!
From: Sam Elamin [mailto:hussam.ela...@gmail.com]
Sent: Monday, August 28, 2017 1:14 PM
To: JG Perrin <jper...@lumeris.com<mailto:jper...@lumeris.com>>
Cc: user@spark.apache.org<mailto:user@spark.apache.org>
Subject: Re: from_json()
Hi jg,
Perhaps I am misunderstanding you, but if you just want to create a new schema
from a df its fairly simple, assuming you have a schema already predefined or
in a string. i.e.
val newSchema = DataType.fromJson(json_schema_string)
then all you need to do is re-create the dataframe using this new dataframe
sqlContext.createDataFrame(oldDF.rdd,newSchema)
Regards
Sam
On Mon, Aug 28, 2017 at 5:57 PM, JG Perrin
<jper...@lumeris.com<mailto:jper...@lumeris.com>> wrote:
Is there a way to not have to specify a schema when using from_json() or infer
the schema? When you read a JSON doc from disk, you can infer the schema.
Should I write it to disk before (ouch)?
jg
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