.where("xxx IS NOT NULL") will give you the rows that couldn't be parsed.
On Tue, Dec 6, 2016 at 6:31 AM, Yehuda Finkelstein <
yeh...@veracity-group.com> wrote:
> Hi all
>
>
>
> I’m trying to parse json using existing schema and got rows with NULL’s
>
> //get schema
>
> val df_schema = spark.sqlContext.sql("select c1,c2,…cn t1 limit 1")
>
> //read json file
>
> val f = sc.textFile("/tmp/x")
>
> //load json into data frame using schema
>
> var df = spark.sqlContext.read.option("columnNameOfCorruptRecord","
> xxx").option("mode","PERMISSIVE").schema(df_schema.schema).json(f)
>
>
>
> in documentation it say that you can query the corrupted rows by this
> columns à columnNameOfCorruptRecord
>
> o “columnNameOfCorruptRecord (default is the value specified in
> spark.sql.columnNameOfCorruptRecord): allows renaming the new field
> having malformed string created by PERMISSIVE mode. This overrides
> spark.sql.columnNameOfCorruptRecord.”
>
>
>
> The question is how to fetch those corrupted rows ?
>
>
>
>
>
> Thanks
>
> Yehuda
>
>
>
>
>
