Extract/Interpret embedded byte data from a record

[Sumeet Malhotra]

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"

    },

    {

      "name": "ID",

      "doc": "My ID",

      "type": "string"

    },

    {

      "name": "Result",

      "doc": "Result data, could be encoded differently",

      "type": "bytes"

    },

    {

      "name": "ResultEncoding",

      "doc": "Result encoding media type (e.g. application/avro, application/json)",

      "type": "string"

    },

  ]

}

Basically, the "Result" field is bytes whose interpretation depends upon the "ResultEncoding" field i.e. either avro or json. The "Result" byte stream has its own well defined schema also.

My use case involves extracting/aggregating data from within the embedded "Result" field. What would be the best approach to perform this runtime decoding and extraction of fields from the embedded byte data? Would user defined functions help in this case?

Thanks in advance!

Sumeet

[Piotr Nowojski-4]

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<record_with_bytes, record_with_deserialized_result> { ...} )

Best,

Piotrek

śr., 14 kwi 2021 o 03:17 Sumeet Malhotra <[hidden email]> napisał(a):

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"

    },

    {

      "name": "ID",

      "doc": "My ID",

      "type": "string"

    },

    {

      "name": "Result",

      "doc": "Result data, could be encoded differently",

      "type": "bytes"

    },

    {

      "name": "ResultEncoding",

      "doc": "Result encoding media type (e.g. application/avro, application/json)",

      "type": "string"

    },

  ]

}

Basically, the "Result" field is bytes whose interpretation depends upon the "ResultEncoding" field i.e. either avro or json. The "Result" byte stream has its own well defined schema also.

My use case involves extracting/aggregating data from within the embedded "Result" field. What would be the best approach to perform this runtime decoding and extraction of fields from the embedded byte data? Would user defined functions help in this case?

Thanks in advance!

Sumeet

[Sumeet Malhotra]

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 would be my options here. Should I convert to a data stream to perform this in Python?

Thanks again,

Sumeet

On Wed, Apr 14, 2021 at 7:09 PM Piotr Nowojski <[hidden email]> wrote:

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<record_with_bytes, record_with_deserialized_result> { ...} )

Best,

Piotrek

śr., 14 kwi 2021 o 03:17 Sumeet Malhotra <[hidden email]> napisał(a):

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"

    },

    {

      "name": "ID",

      "doc": "My ID",

      "type": "string"

    },

    {

      "name": "Result",

      "doc": "Result data, could be encoded differently",

      "type": "bytes"

    },

    {

      "name": "ResultEncoding",

      "doc": "Result encoding media type (e.g. application/avro, application/json)",

      "type": "string"

    },

  ]

}

Basically, the "Result" field is bytes whose interpretation depends upon the "ResultEncoding" field i.e. either avro or json. The "Result" byte stream has its own well defined schema also.

My use case involves extracting/aggregating data from within the embedded "Result" field. What would be the best approach to perform this runtime decoding and extraction of fields from the embedded byte data? Would user defined functions help in this case?

Thanks in advance!

Sumeet

[Sumeet Malhotra]

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 Python.

Thanks,

Sumeet

On Thu, Apr 15, 2021 at 6:31 AM Sumeet Malhotra <[hidden email]> wrote:

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 would be my options here. Should I convert to a data stream to perform this in Python?

Thanks again,

Sumeet

On Wed, Apr 14, 2021 at 7:09 PM Piotr Nowojski <[hidden email]> wrote:

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<record_with_bytes, record_with_deserialized_result> { ...} )

Best,

Piotrek

śr., 14 kwi 2021 o 03:17 Sumeet Malhotra <[hidden email]> napisał(a):

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"

    },

    {

      "name": "ID",

      "doc": "My ID",

      "type": "string"

    },

    {

      "name": "Result",

      "doc": "Result data, could be encoded differently",

      "type": "bytes"

    },

    {

      "name": "ResultEncoding",

      "doc": "Result encoding media type (e.g. application/avro, application/json)",

      "type": "string"

    },

  ]

}

Basically, the "Result" field is bytes whose interpretation depends upon the "ResultEncoding" field i.e. either avro or json. The "Result" byte stream has its own well defined schema also.

My use case involves extracting/aggregating data from within the embedded "Result" field. What would be the best approach to perform this runtime decoding and extraction of fields from the embedded byte data? Would user defined functions help in this case?

Thanks in advance!

Sumeet

[Xingbo Huang]

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] https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/python/table-api-users-guide/udfs/python_udfs.html#scalar-functions

Best,
Xingbo

Sumeet Malhotra <[hidden email]> 于2021年4月15日周四 上午9:08写道：

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 Python.

Thanks,

Sumeet

On Thu, Apr 15, 2021 at 6:31 AM Sumeet Malhotra <[hidden email]> wrote:

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 would be my options here. Should I convert to a data stream to perform this in Python?

Thanks again,

Sumeet

On Wed, Apr 14, 2021 at 7:09 PM Piotr Nowojski <[hidden email]> wrote:

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<record_with_bytes, record_with_deserialized_result> { ...} )

Best,

Piotrek

śr., 14 kwi 2021 o 03:17 Sumeet Malhotra <[hidden email]> napisał(a):

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"

    },

    {

      "name": "ID",

      "doc": "My ID",

      "type": "string"

    },

    {

      "name": "Result",

      "doc": "Result data, could be encoded differently",

      "type": "bytes"

    },

    {

      "name": "ResultEncoding",

      "doc": "Result encoding media type (e.g. application/avro, application/json)",

      "type": "string"

    },

  ]

}

Basically, the "Result" field is bytes whose interpretation depends upon the "ResultEncoding" field i.e. either avro or json. The "Result" byte stream has its own well defined schema also.

My use case involves extracting/aggregating data from within the embedded "Result" field. What would be the best approach to perform this runtime decoding and extraction of fields from the embedded byte data? Would user defined functions help in this case?

Thanks in advance!

Sumeet