Yes, drop malformed does filter out record4. However, record 5 is not.
On Tue, 4 Jul 2023 at 07:41, Vikas Kumar <vku...@etsy.com> wrote:
> 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
>>
>
--
Regards,
Shashank Rao
