@kapa.ai

def write(
        self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]
    ) -> Iterable[AirbyteMessage]:
        """
        Reads the input stream of messages, config, and catalog to write data to Couchbase.
        """
        cluster = self._get_cluster(config)
        bucket_name = config["bucket"]
        scope_name = config.get("scope", "_default")
        batch_size = config.get("batch_size", 1000)

        # Log connection config structure
        <http://logger.info|logger.info>(f"Connection Config Structure: {{'bucket': '{bucket_name}', 'scope': '{scope_name}'}}")

        # Get stream configurations including sync modes and primary keys
        stream_configs = {}
        for stream in configured_catalog.streams:
            stream_configs[stream.stream.name] = {
                'collection_name': self._sanitize_collection_name(stream.stream.name),
                'sync_mode': stream.destination_sync_mode,
                'primary_key': stream.stream.source_defined_primary_key
            }
        
        <http://logger.info|logger.info>(f"Starting write to Couchbase with {len(stream_configs)} streams")

        # Set up collections and clear data for overwrite mode
        collections = {}
        for stream_name, stream_config in stream_configs.items():
            collection = self._setup_collection(cluster, bucket_name, scope_name, stream_config['collection_name'])
            if stream_config['sync_mode'] == DestinationSyncMode.overwrite:
                self._clear_collection(cluster, bucket_name, scope_name, stream_config['collection_name'])
            collections[stream_name] = collection

        buffer = {}
        
        for message in input_messages:
            # json dumps for logging purposes
            logger.debug(f"Processing message: {json.dumps(message.to_dict())}")
            if message.type == Type.STATE:
                try:
                    self._flush_buffer(collections, buffer, stream_configs)
                    yield message
                except Exception as e:
                    logger.error(f"Error processing state message: {str(e)}")
                    raise

            elif message.type == Type.RECORD:
                data = message.record.data
                stream = message.record.stream
                
                try:
                    if stream not in stream_configs:
                        logger.warning(f"Encountered new stream {stream}. Adding to streams and creating collection.")
                        collection_name = self._sanitize_collection_name(stream)
                        stream_configs[stream] = {
                            'collection_name': collection_name,
                            'sync_mode': DestinationSyncMode.append,
                            'primary_key': []
                        }
                        collections[stream] = self._setup_collection(cluster, bucket_name, scope_name, collection_name)
                    
                    if stream not in buffer:
                        buffer[stream] = []
                    
                    document = self._prepare_document(
                        stream, 
                        data, 
                        stream_configs[stream]['primary_key'],
                        stream_configs[stream]['sync_mode']
                    )
                    
                    if len(buffer[stream]) == 0:
                        <http://logger.info|logger.info>(f"Document Structure for stream '{stream}': {document}")
                    
                    buffer[stream].append(document)

                    if len(buffer[stream]) >= batch_size:
                        self._flush_buffer(collections, {stream: buffer[stream]}, stream_configs)
                        buffer[stream] = []
                except Exception as e:
                    logger.error(f"Error processing record for stream {stream}: {str(e)}")
                    raise

        # Flush any remaining messages
        if any(buffer.values()):
            try:
                self._flush_buffer(collections, buffer, stream_configs)
            except Exception as e:
                logger.error(f"Error in final buffer flush: {str(e)}")
                raise

can json dump work with message

Yes,

json.dumps()

can work with

message

in your code. The

message

object is an instance of

AirbyteMessage

, which is a data class in Airbyte. Data classes in Python are basically regular classes which are geared towards storing state rather than containing a lot of logic. In your code,

message.to_dict()

is used to convert the

AirbyteMessage

object to a dictionary before passing it to

json.dumps()

. This is because

json.dumps()

works with objects that have a native JSON serialization format such as dict, list, str, int, float, bool, None. Here is the relevant line from your code:

python logger.debug(f"Processing message: {json.dumps(message.to_dict())}")

This line is converting the

message

object to a dictionary and then converting that dictionary to a JSON formatted string. For more details about

AirbyteMessage

and its structure, you can refer to the [Airbyte Protocol Documentation](https://docs.airbyte.com/understanding-airbyte/airbyte-protocol#airbytemessage).

@kapa.ai 'AirbyteMessage' object has no attribute 'to_dict' 436 Traceback (most recent call last): 437 File "/airbyte/integration_code/main.py", line 11, in <module> 438 DestinationCouchbase().run(sys.argv[1:]) 439 File "/usr/local/lib/python3.9/site-packages/airbyte_cdk/destinations/destination.py", line 119, in run 440 for message in output_messages: 441 File "/usr/local/lib/python3.9/site-packages/airbyte_cdk/destinations/destination.py", line 113, in run_cmd 442 yield from self._run_write(config=config, configured_catalog_path=parsed_args.catalog, input_stream=wrapped_stdin) 443 File "/usr/local/lib/python3.9/site-packages/airbyte_cdk/destinations/destination.py", line 49, in _run_write 444 yield from self.write(config=config, configured_catalog=catalog, input_messages=input_messages) 445 File "/airbyte/integration_code/destination_couchbase/destination.py", line 104, in write 446 logger.debug(f"Processing message: {json.dumps(message.to_dict())}") 447

A message from kapa.ai

The error message

'AirbyteMessage' object has no attribute 'to_dict'

indicates that the

AirbyteMessage

object does not have a

to_dict

method. This is because

AirbyteMessage

is a data class and does not inherently have a

to_dict

method. To convert an

AirbyteMessage

object to a dictionary, you can use the

asdict

function from the

dataclasses

module in Python's standard library. Here's how you can do it:

python from dataclasses import asdict  # Assuming `message` is an instance of `AirbyteMessage` dict_message = asdict(message)

Then, you can pass

dict_message

to `json.dumps()`:

python logger.debug(f"Processing message: {json.dumps(dict_message)}")

Please replace the line in your code with the above and it should resolve the issue.

@kapa.ai

logger.debug(f"Processing message: {json.dumps(message)}")

will this work

A message from kapa.ai

The line of code you provided:

python logger.debug(f"Processing message: {json.dumps(message)}")

will not work if

message

is an instance of a class (like

AirbyteMessage

) that is not serializable by

json.dumps()

. The

json.dumps()

function in Python is used to convert a Python object into a JSON string. It can only process data types that have a natural conversion to JSON such as dictionary, list, string, integer, float, boolean, None. If

message

is an instance of a custom class (like

AirbyteMessage

), you would need to provide a way to convert that class to a built-in data type before you can use

json.dumps()

. As mentioned in the previous response, you can use the

asdict

function from the

dataclasses

module to convert the

AirbyteMessage

object to a dictionary, which can then be passed to `json.dumps()`:

python from dataclasses import asdict  dict_message = asdict(message) logger.debug(f"Processing message: {json.dumps(dict_message)}")

This will work assuming

message

is an instance of

AirbyteMessage

or any other data class.

@kapa.ai Warning from destination: Something went wrong in the connector. See the logs for more details. asdict() should be called on dataclass instances

A message from kapa.ai

The warning message you're seeing, "Something went wrong in the connector. See the logs for more details.", indicates that there was an error during the execution of the connector. The exact cause of the error can be determined by examining the logs. The error message "asdict() should be called on dataclass instances" suggests that the

asdict()

function from the

dataclasses

module was called on an object that is not an instance of a dataclass. In Python, a dataclass is a class which is typically used to store data and does not contain much functionality. The

asdict()

function is used to convert an instance of a dataclass into a dictionary. If you're seeing this error, it means that the object you're trying to convert into a dictionary using

asdict()

is not an instance of a dataclass. You should ensure that the object you're passing to

asdict()

is indeed a dataclass instance. Here's a basic example of how you can use `asdict()`:

python from dataclasses import dataclass, asdict  @dataclass class ExampleDataClass:     field1: str     field2: int  # Create an instance of the dataclass example_instance = ExampleDataClass("example string", 123)  # Convert the dataclass instance to a dictionary example_dict = asdict(example_instance)

In this example,

example_instance

is an instance of a dataclass, so it can be converted into a dictionary using

asdict()

. If you're still facing issues, I would recommend checking the logs for more detailed error messages or traceback information. This can help you pinpoint the exact line of code or operation that is causing the error.

@kapa.ai how can i print the message then

A message from kapa.ai

If you want to print the

message

object for debugging purposes, you can use the built-in

print()

function in Python. However, since

message

is an instance of a custom class (like

AirbyteMessage

), you might want to print its representation. In Python, every class can provide a

__repr__

method that returns a string representation of the object. If the

AirbyteMessage

class has a

__repr__

method, you can simply print the

message

object like this:

python print(message)

If the

AirbyteMessage

class does not provide a

__repr__

method, you will see a less informative output like

<AirbyteMessage object at 0x7f8e8e7b3a90>

. In this case, you might want to print the attributes of the

message

object one by one, depending on what attributes the

AirbyteMessage

class has. For example, if

AirbyteMessage

has attributes

type

and

record

, you can print them like this:

python print(f"type: {message.type}, record: {message.record}")

Please replace

type

and

record

with the actual attribute names of the

AirbyteMessage

class. Remember, the way to print the

message

object largely depends on what information you want to see and how the

AirbyteMessage

class is implemented.

#
# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
#

from dataclasses import InitVar, dataclass
from typing import Annotated, Any, Dict, List, Mapping, Optional

from airbyte_protocol_dataclasses.models import *
from serpyco_rs.metadata import Alias


@dataclass
class AirbyteStateBlob:
    """
    A dataclass that dynamically sets attributes based on provided keyword arguments and positional arguments.
    Used to "mimic" pydantic Basemodel with ConfigDict(extra='allow') option.

    The `AirbyteStateBlob` class allows for flexible instantiation by accepting any number of keyword arguments
    and positional arguments. These are used to dynamically update the instance's attributes. This class is useful
    in scenarios where the attributes of an object are not known until runtime and need to be set dynamically.

    Attributes:
        kwargs (InitVar[Mapping[str, Any]]): A dictionary of keyword arguments used to set attributes dynamically.

    Methods:
        __init__(*args: Any, **kwargs: Any) -> None:
            Initializes the `AirbyteStateBlob` by setting attributes from the provided arguments.

        __eq__(other: object) -> bool:
            Checks equality between two `AirbyteStateBlob` instances based on their internal dictionaries.
            Returns `False` if the other object is not an instance of `AirbyteStateBlob`.
    """

    kwargs: InitVar[Mapping[str, Any]]

    def __init__(self, *args: Any, **kwargs: Any) -> None:
        # Set any attribute passed in through kwargs
        for arg in args:
            self.__dict__.update(arg)
        for key, value in kwargs.items():
            setattr(self, key, value)

    def __eq__(self, other: object) -> bool:
        return False if not isinstance(other, AirbyteStateBlob) else bool(self.__dict__ == other.__dict__)


# The following dataclasses have been redeclared to include the new version of AirbyteStateBlob
@dataclass
class AirbyteStreamState:
    stream_descriptor: StreamDescriptor  # type: ignore [name-defined]
    stream_state: Optional[AirbyteStateBlob] = None


@dataclass
class AirbyteGlobalState:
    stream_states: List[AirbyteStreamState]
    shared_state: Optional[AirbyteStateBlob] = None


@dataclass
class AirbyteStateMessage:
    type: Optional[AirbyteStateType] = None  # type: ignore [name-defined]
    stream: Optional[AirbyteStreamState] = None
    global_: Annotated[
        AirbyteGlobalState | None, Alias("global")
    ] = None  # "global" is a reserved keyword in python ⇒ Alias is used for (de-)serialization
    data: Optional[Dict[str, Any]] = None
    sourceStats: Optional[AirbyteStateStats] = None  # type: ignore [name-defined]
    destinationStats: Optional[AirbyteStateStats] = None  # type: ignore [name-defined]


@dataclass
class AirbyteMessage:
    type: Type  # type: ignore [name-defined]
    log: Optional[AirbyteLogMessage] = None  # type: ignore [name-defined]
    spec: Optional[ConnectorSpecification] = None  # type: ignore [name-defined]
    connectionStatus: Optional[AirbyteConnectionStatus] = None  # type: ignore [name-defined]
    catalog: Optional[AirbyteCatalog] = None  # type: ignore [name-defined]
    record: Optional[AirbyteRecordMessage] = None  # type: ignore [name-defined]
    state: Optional[AirbyteStateMessage] = None
    trace: Optional[AirbyteTraceMessage] = None  # type: ignore [name-defined]
    control: Optional[AirbyteControlMessage] = None  # type: ignore [name-defined]

@kapa.ai this is how it looks like

A message from kapa.ai

Thank you for providing the class definitions. Based on these, you can print an instance of

AirbyteMessage

by accessing its attributes directly. Here's an example:

python print(f"type: {message.type}") print(f"log: {message.log}") print(f"spec: {message.spec}") print(f"connectionStatus: {message.connectionStatus}") print(f"catalog: {message.catalog}") print(f"record: {message.record}") print(f"state: {message.state}") print(f"trace: {message.trace}") print(f"control: {message.control}")

In this example,

message

is an instance of

AirbyteMessage

. Each

print

statement will print the value of the corresponding attribute of the

message

object. Please note that the output of this code will depend on the

__repr__

or

__str__

methods of the classes of each attribute. If these methods are not defined, you might see less informative output like

<AirbyteLogMessage object at 0x7f8e8e7b3a90>

. If you want to print the attributes of the nested objects (like

message.record

if it's not

None

), you would need to access them in a similar way. For example, if

AirbyteRecordMessage

has attributes

namespace

,

stream

,

data

, and

emitted_at

, you can print them like this:

python if message.record is not None:     print(f"record.namespace: {message.record.namespace}")     print(f"record.stream: {message.record.stream}")     print(f"record.data: {message.record.data}")     print(f"record.emitted_at: {message.record.emitted_at}")

Again, replace

namespace

,

stream

,

data

, and

emitted_at

with the actual attribute names of the

AirbyteRecordMessage

class.