Have you tried dropmalformed option ?
On Mon, Jul 3, 2023, 1:34 PM Shashank Rao <shashank93...@gmail.com> wrote:
> Update: Got it working by using the *_corrupt_record *field for the first
> case (record 4)
>
> schema = schema.add("_corrupt_record", DataTypes.StringType);
> Dataset<Row> ds = spark.read().schema(schema).option("mode",
> "PERMISSIVE").json("path").collect();
> ds = ds.filter(functions.col("_corrupt_record").isNull()).collect();
>
> However, I haven't figured out on how to ignore record 5.
>
> Any help is appreciated.
>
> On Mon, 3 Jul 2023 at 19:24, Shashank Rao <shashank93...@gmail.com> wrote:
>
>> Hi all,
>> I'm trying to read around 1,000,000 JSONL files present in S3 using
>> Spark. Once read, I need to write them to BigQuery.
>> I have a schema that may not be an exact match with all the records.
>> How can I filter records where there isn't an exact schema match:
>>
>> Eg: if my records were:
>> {"x": 1, "y": 1}
>> {"x": 2, "y": 2}
>> {"x": 3, "y": 3}
>> {"x": 4, "y": "4"}
>> {"x": 5, "y": 5, "z": 5}
>>
>> and if my schema were:
>> root
>> |-- x: long (nullable = true)
>> |-- y: long (nullable = true)
>>
>> I need the records 4 and 5 to be filtered out.
>> Record 4 should be filtered out since y is a string instead of long.
>> Record 5 should be filtered out since z is not part of the schema.
>>
>> I tried applying my schema on read, but it does not work as needed:
>>
>> StructType schema = new StructType().add("a",
>> DataTypes.LongType).add("b", DataTypes.LongType);
>> Dataset<Row> ds = spark.read().schema(schema).json("path/to/file")
>>
>> This gives me a dataset that has record 4 with y=null and record 5 with x
>> and y.
>>
>> Any help is appreciated.
>>
>> --
>> Thanks,
>> Shashank Rao
>>
>
>
> --
> Regards,
> Shashank Rao
>
