Hi all, It’s taken us a while, but one of my colleagues has made the pull request on github for our proposed solution to this,
https://issues.apache.org/jira/browse/SPARK-10947 https://github.com/apache/spark/pull/9249 It adds a parameter to the Json read otpions to force all primitives as a String type: val jsonDf = sqlContext.read.option("primitivesAsString", "true").json(sampleJsonFile) scala> jsonDf.printSchema() root |-- bigInteger: string (nullable = true) |-- boolean: string (nullable = true) |-- double: string (nullable = true) |-- integer: string (nullable = true) |-- long: string (nullable = true) |-- null: string (nullable = true) |-- string: string (nullable = true) Thanks, Ewan From: Yin Huai [mailto:yh...@databricks.com] Sent: 01 October 2015 23:54 To: Ewan Leith <ewan.le...@realitymine.com> Cc: r...@databricks.com; dev@spark.apache.org Subject: Re: Dataframe nested schema inference from Json without type conflicts Hi Ewan, For your use case, you only need the schema inference to pick up the structure of your data (basically you want spark sql to infer the type of complex values like arrays and structs but keep the type of primitive values as strings), right? Thanks, Yin On Thu, Oct 1, 2015 at 2:27 PM, Ewan Leith <ewan.le...@realitymine.com<mailto:ewan.le...@realitymine.com>> wrote: We could, but if a client sends some unexpected records in the schema (which happens more than I'd like, our schema seems to constantly evolve), its fantastic how Spark picks up on that data and includes it. Passing in a fixed schema loses that nice additional ability, though it's what we'll probably have to adopt if we can't come up with a way to keep the inference working. Thanks, Ewan ------ Original message------ From: Reynold Xin Date: Thu, 1 Oct 2015 22:12 To: Ewan Leith; Cc: dev@spark.apache.org<mailto:dev@spark.apache.org>; Subject:Re: Dataframe nested schema inference from Json without type conflicts You can pass the schema into json directly, can't you? On Thu, Oct 1, 2015 at 10:33 AM, Ewan Leith <ewan.le...@realitymine.com<mailto:ewan.le...@realitymine.com>> wrote: Hi all, We really like the ability to infer a schema from JSON contained in an RDD, but when we’re using Spark Streaming on small batches of data, we sometimes find that Spark infers a more specific type than it should use, for example if the json in that small batch only contains integer values for a String field, it’ll class the field as an Integer type on one Streaming batch, then a String on the next one. Instead, we’d rather match every value as a String type, then handle any casting to a desired type later in the process. I don’t think there’s currently any simple way to avoid this that I can see, but we could add the functionality in the JacksonParser.scala file, probably in convertField. https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala Does anyone know an easier and cleaner way to do this? Thanks, Ewan
