-dev +user StructType(StructField(data,ArrayType(StructType(StructField( > *stuff,ArrayType(*StructType(StructField(onetype,ArrayType(StructType(StructField(id,LongType,true), > StructField(name,StringType,true)),true),true), StructField(othertype, > ArrayType(StructType(StructField(company,StringType,true), > StructField(id,LongType,true)),true),true)),true),true)),true),true))
Its not a great error message, but as the schema above shows, stuff is an array, not a struct. So, you need to pick a particular element (using []) before you can pull out a specific field. It would be easier to see this if you ran sqlContext.read.json(s1Rdd).printSchema(), which gives you a tree view. Try the following. sqlContext.read.schema(s1schema).json(s2Rdd).select("data.stuff[0].onetype") On Wed, Mar 2, 2016 at 1:44 AM, Ewan Leith <ewan.le...@realitymine.com> wrote: > When you create a dataframe using the *sqlContext.read.schema()* API, if > you pass in a schema that’s compatible with some of the records, but > incompatible with others, it seems you can’t do a .select on the > problematic columns, instead you get an AnalysisException error. > > > > I know loading the wrong data isn’t good behaviour, but if you’re reading > data from (for example) JSON files, there’s going to be malformed files > along the way. I think it would be nice to handle this error in a nicer > way, though I don’t know the best way to approach it. > > > > Before I raise a JIRA ticket about it, would people consider this to be a > bug or expected behaviour? > > > > I’ve attached a couple of sample JSON files and the steps below to > reproduce it, by taking the inferred schema from the simple1.json file, and > applying it to a union of simple1.json and simple2.json. You can visually > see the data has been parsed as I think you’d want if you do a .select on > the parent column and print out the output, but when you do a select on the > problem column you instead get an exception. > > > > *scala> val s1Rdd = sc.wholeTextFiles("/tmp/simple1.json").map(x => x._2)* > > s1Rdd: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[171] at map at > <console>:27 > > > > *scala> val s1schema = sqlContext.read.json(s1Rdd).schema* > > s1schema: org.apache.spark.sql.types.StructType = > StructType(StructField(data,ArrayType(StructType(StructField(stuff,ArrayType(StructType(StructField(onetype,ArrayType(StructType(StructField(id,LongType,true), > StructField(name,StringType,true)),true),true), > StructField(othertype,ArrayType(StructType(StructField(company,StringType,true), > StructField(id,LongType,true)),true),true)),true),true)),true),true)) > > > > *scala> > sqlContext.read.schema(s1schema).json(s2Rdd).select("data.stuff").take(2).foreach(println)* > > [WrappedArray(WrappedArray([WrappedArray([1,John Doe], [2,Don > Joeh]),null], [null,WrappedArray([ACME,2])]))] > > [WrappedArray(WrappedArray([null,WrappedArray([null,1], [null,2])], > [WrappedArray([2,null]),null]))] > > > > *scala> > sqlContext.read.schema(s1schema).json(s2Rdd).select("data.stuff.onetype")* > > org.apache.spark.sql.AnalysisException: cannot resolve > 'data.stuff[onetype]' due to data type mismatch: argument 2 requires > integral type, however, 'onetype' is of string type.; > > at > org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42) > > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:65) > > at > org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:57) > > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319) > > at > org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:319) > > > > (The full exception is attached too). > > > > What do people think, is this a bug? > > > > Thanks, > > Ewan > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > For additional commands, e-mail: dev-h...@spark.apache.org >