Are you looking for "relaxed" mode that simply return nulls for fields that
doesn't exist or have incompatible schema?
On Wed, Mar 2, 2016 at 11:12 AM, Ewan Leith <ewan.le...@realitymine.com>
wrote:
> Thanks Michael, it's not a great example really, as the data I'm working with
> has some source files that do fit the schema, and some that don't (out of
> millions that do work, perhaps 10 might not).
>
> In an ideal world for us the select would probably return the valid records
> only.
>
> We're trying out the new dataset APIs to see if we can do some pre-filtering
> that way.
>
> Thanks,
> Ewan
>
> -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
>>
>>
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>
>
