I've tried this now. Spark can load multiple avro files from the same directory by passing a path to a directory. However, passing multiple paths separated with commas didn't work.
Is there any way to load all avro files in multiple directories using sqlContext.avroFile? On Wed, Jan 14, 2015 at 3:53 PM, David Jones <letsnumsperi...@gmail.com> wrote: > Should I be able to pass multiple paths separated by commas? I haven't > tried but didn't think it'd work. I'd expected a function that accepted a > list of strings. > > On Wed, Jan 14, 2015 at 3:20 PM, Yana Kadiyska <yana.kadiy...@gmail.com> > wrote: > >> If the wildcard path you have doesn't work you should probably open a bug >> -- I had a similar problem with Parquet and it was a bug which recently got >> closed. Not sure if sqlContext.avroFile shares a codepath with >> .parquetFile...you >> can try running with bits that have the fix for .parquetFile or look at the >> source... >> Here was my question for reference: >> >> http://mail-archives.apache.org/mod_mbox/spark-user/201412.mbox/%3ccaaswr-5rfmu-y-7htluj2eqqaecwjs8jh+irrzhm7g1ex7v...@mail.gmail.com%3E >> >> On Wed, Jan 14, 2015 at 4:34 AM, David Jones <letsnumsperi...@gmail.com> >> wrote: >> >>> Hi, >>> >>> I have a program that loads a single avro file using spark SQL, queries >>> it, transforms it and then outputs the data. The file is loaded with: >>> >>> val records = sqlContext.avroFile(filePath) >>> val data = records.registerTempTable("data") >>> ... >>> >>> >>> Now I want to run it over tens of thousands of Avro files (all with >>> schemas that contain the fields I'm interested in). >>> >>> Is it possible to load multiple avro files recursively from a top-level >>> directory using wildcards? All my avro files are stored under >>> s3://my-bucket/avros/*/DATE/*.avro, and I want to run my task across all of >>> these on EMR. >>> >>> If that's not possible, is there some way to load multiple avro files >>> into the same table/RDD so the whole dataset can be processed (and in that >>> case I'd supply paths to each file concretely, but I *really* don't want to >>> have to do that). >>> >>> Thanks >>> David >>> >> >> >
