Hi Sumeet,
Python Row-based operation will be supported in the releases-1.13. I guess
you are looking at the code of the master branch. Since you are using the
Python Table API, you can use python udf to parse your data. For the
details of python UDF, you can refer to the doc[1].
[1]
Additional observation: From the Flink repo, the file
"flink-python/pyflink/table/table.py" seems to support map(), flat_map()
and other row based operations although the 1.12 documentation doesn't
reflect that. Is that correct? From the code, it appears that these
operations are supported in
Thanks Piotrek! I forgot to mention that I'm using PyFlink and mostly Table
APIs. The documentation (
https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/table/tableApi.html#row-based-operations)
suggests that Map() function is not currently supported in Python. So, what
do you think
Hi,
One thing that you can do is to read this record using Avro keeping
`Result` as `bytes` and in a subsequent mapping function, you could change
the record type and deserialize the result. In Data Stream API:
source.map(new MapFunction { ...} )
Best,
Piotrek
śr., 14 kwi 2021 o 03:17 Sumeet
Hi,
I'm reading data from Kafka, which is Avro encoded and has the following
general schema:
{
"name": "SomeName",
"doc": "Avro schema with variable embedded encodings",
"type": "record",
"fields": [
{
"name": "Name",
"doc": "My name",
"type": "string"
},
{
