I am assuming that you have written your own BigQuerySource (i dont see that code in the link you posted). In that source, you must have implemented getBatch which uses offsets to return the Dataframe having the data of a batch. Can you double check when this DataFrame returned by getBatch, has the expected schema?
On Wed, Feb 1, 2017 at 3:33 PM, Sam Elamin <hussam.ela...@gmail.com> wrote: > Thanks for the quick response TD! > > Ive been trying to identify where exactly this transformation happens > > The readStream returns a dataframe with the correct schema > > The minute I call writeStream, by the time I get to the addBatch method, > the dataframe there has an incorrect Schema > > So Im skeptical about the issue being prior to the readStream since the > output dataframe has the correct Schema > > > Am I missing something completely obvious? > > Regards > Sam > > On Wed, Feb 1, 2017 at 11:29 PM, Tathagata Das < > tathagata.das1...@gmail.com> wrote: > >> You should make sure that schema of the streaming Dataset returned by >> `readStream`, and the schema of the DataFrame returned by the sources >> getBatch. >> >> On Wed, Feb 1, 2017 at 3:25 PM, Sam Elamin <hussam.ela...@gmail.com> >> wrote: >> >>> Hi All >>> >>> I am writing a bigquery connector here >>> <http://github.com/samelamin/spark-bigquery> and I am getting a strange >>> error with schemas being overwritten when a dataframe is passed over to the >>> Sink >>> >>> >>> for example the source returns this StructType >>> WARN streaming.BigQuerySource: StructType(StructField(custome >>> rid,LongType,true), >>> >>> and the sink is recieving this StructType >>> WARN streaming.BigQuerySink: StructType(StructField(custome >>> rid,StringType,true) >>> >>> >>> Any idea why this might be happening? >>> I dont have infering schema on >>> >>> spark.conf.set("spark.sql.streaming.schemaInference", "false") >>> >>> I know its off by default but I set it just to be sure >>> >>> So completely lost to what could be causing this >>> >>> Regards >>> Sam >>> >> >> >
