@kapa.ai I dont get server side pagination, but I need to send a new api requests every 10.000 rows. I don't get any information like the offset or something else. it really just is. take the last state from the api call and run again api call. hjow to do this with the httpstream class

To handle pagination in your scenario where you need to make a new API request every 10,000 rows without receiving explicit pagination information from the server, you can implement a custom pagination strategy using the

HttpStream

class in Airbyte. Here's how you can approach this: 1. Use the

next_page_token

method to keep track of the number of records processed and determine when to make the next request. 2. In your

parse_response

method, count the number of records processed. 3. When you reach 10,000 records, return a token from

next_page_token

to trigger the next request. 4. Use the last state from the previous API call as the starting point for the next request. Here's a basic implementation example:

python class YourCustomStream(HttpStream):     def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.records_processed = 0         self.last_state = None      def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:         if self.records_processed >= 10000:             self.records_processed = 0             return {"last_state": self.last_state}         return None      def request_params(         self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None     ) -> MutableMapping[str, Any]:         params = super().request_params(stream_state, stream_slice, next_page_token)         if next_page_token:             params["last_state"] = next_page_token["last_state"]         return params      def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:         records = response.json()  # Adjust this based on your API response structure         for record in records:             self.records_processed += 1             self.last_state = record["id"]  # Adjust this based on your record structure             yield record

In this example: 1. We keep track of

records_processed

and

last_state

. 2. In

next_page_token

, we return a token with the

last_state

when we've processed 10,000 records. 3. In

request_params

, we include the

last_state

in the request parameters when making the next API call. 4. In

parse_response

, we update

records_processed

and

last_state

as we process each record. This approach allows you to paginate through your API results by making a new request every 10,000 records, using the last state from the previous call as the starting point for the next request. Remember to adjust the code according to your specific API structure and requirements. This example assumes that each record has an

id

field that can be used as the state, but you should modify this based on your actual data structure. [Source: HTTP-API-based Connectors](https://docs.airbyte.com/connector-development/cdk-python/http-streams#pagination)

@kapa.ai I did it as you wished but when I am doing the next request he does not know the old state and sends the same request again

A message from kapa.ai

I apologize for the confusion in my previous answer. It seems the state management wasn't properly addressed. Let's improve the implementation to ensure the state is maintained correctly between requests. The issue you're experiencing is likely because the state isn't being properly stored and retrieved between sync runs. In Airbyte, state management for incremental syncs is typically handled using the

IncrementalMixin

. Here's an updated approach that should work better:

python
from airbyte_cdk.sources.streams import IncrementalMixin

class YourCustomStream(HttpStream, IncrementalMixin):
    cursor_field = "id"  # Adjust this to your actual cursor field

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.records_processed = 0
        self._cursor_value = None

    @property
    def state(self) -> Mapping[str, Any]:
        if self._cursor_value:
            return {self.cursor_field: self._cursor_value}
        return {}

    @state.setter
    def state(self, value: Mapping[str, Any]):
        self._cursor_value = value.get(self.cursor_field)

    def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:
        if self.records_processed >= 10000:
            self.records_processed = 0
            return {"continue": True}
        return None

    def request_params(
        self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None
    ) -> MutableMapping[str, Any]:
        params = super().request_params(stream_state, stream_slice, next_page_token)
        if self._cursor_value:
            params[self.cursor_field] = self._cursor_value
        return params

    def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:
        records = response.json()  # Adjust this based on your API response structure
        for record in records:
            self.records_processed += 1
            yield record
            self._cursor_value = max(self._cursor_value, record[self.cursor_field]) if self._cursor_value else record[self.cursor_field]

    def read_records(self, *args, **kwargs) -> Iterable[Mapping[str, Any]]:
        for record in super().read_records(*args, **kwargs):
            yield record
        self._cursor_value = None  # Reset cursor value after each page

This implementation uses the

IncrementalMixin

and properly manages the state: 1. We define a

cursor_field

which is used to track the progress of the sync. 2. The

state

property and setter handle storing and retrieving the current state. 3. In

request_params

, we use the

_cursor_value

to set the starting point for each request. 4. In

parse_response

, we update the

_cursor_value

with each record processed. 5. The

read_records

method resets the

_cursor_value

after each page to ensure we don't miss any records.

I apologize for the confusion in my previous answer. It seems the state management wasn't properly addressed. Let's improve the implementation to ensure the state is maintained correctly between requests. The issue you're experiencing is likely because the state isn't being properly stored and retrieved between sync runs. In Airbyte, state management for incremental syncs is typically handled using the

IncrementalMixin

. Here's an updated approach that should work better:

python from airbyte_cdk.sources.streams import IncrementalMixin  class YourCustomStream(HttpStream, IncrementalMixin):     cursor_field = "id"  # Adjust this to your actual cursor field      def __init__(self, *args, **kwargs):         super().__init__(*args, **kwargs)         self.records_processed = 0         self._cursor_value = None      @property     def state(self) -> Mapping[str, Any]:         if self._cursor_value:             return {self.cursor_field: self._cursor_value}         return {}      @state.setter     def state(self, value: Mapping[str, Any]):         self._cursor_value = value.get(self.cursor_field)      def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:         if self.records_processed >= 10000:             self.records_processed = 0             return {"continue": True}         return None      def request_params(         self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None     ) -> MutableMapping[str, Any]:         params = super().request_params(stream_state, stream_slice, next_page_token)         if self._cursor_value:             params[self.cursor_field] = self._cursor_value         return params      def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:         records = response.json()  # Adjust this based on your API response structure         for record in records:             self.records_processed += 1             yield record             self._cursor_value = max(self._cursor_value, record[self.cursor_field]) if self._cursor_value else record[self.cursor_field]      def read_records(self, *args, **kwargs) -> Iterable[Mapping[str, Any]]:         for record in super().read_records(*args, **kwargs):             yield record         self._cursor_value = None  # Reset cursor value after each page

This implementation uses the

IncrementalMixin

and properly manages the state: 1. We define a

cursor_field

which is used to track the progress of the sync. 2. The

state

property and setter handle storing and retrieving the current state. 3. In

request_params

, we use the

_cursor_value

to set the starting point for each request. 4. In

parse_response

, we update the

_cursor_value

with each record processed. 5. The

read_records

method resets the

_cursor_value

after each page to ensure we don't miss any records. This approach should maintain the state correctly between sync runs, ensuring that each new request starts from where the previous one left off. Remember to adjust the

cursor_field

and the logic in

parse_response

to match your specific API and data structure. [Source: Incremental Streams](https://docs.airbyte.com/connector-development/cdk-python/incremental-stream#incrementalmixin) If you're still experiencing issues, it might be helpful to add some logging to track the state values at different points in the sync process to help debug the problem.

@kapa.ai does it make a difference in which order my code is especially when it comes to the setting and getting of the cursor_value

A message from kapa.ai

Yes, the order of operations when setting and getting the

cursor_value

can make a significant difference in how your incremental sync behaves. Here are some key points to consider: 1. State Initialization: The

state

property getter should be called before any data processing begins. This ensures that you start with the correct state from the previous sync.

python    @property    def state(self) -> Mapping[str, Any]:        if self._cursor_value:            return {self.cursor_field: self._cursor_value}        return {}

2. State Update: The state should be updated after processing each record, typically in the

parse_response

method:

python    def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:        records = response.json()        for record in records:            yield record            self._cursor_value = max(self._cursor_value, record[self.cursor_field]) if self._cursor_value else record[self.cursor_field]

3. State Usage in Requests: The current state should be used when forming the next request, usually in the

request_params

method:

python    def request_params(self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None) -> MutableMapping[str, Any]:        params = super().request_params(stream_state, stream_slice, next_page_token)        if self._cursor_value:            params[self.cursor_field] = self._cursor_value        return params

4. State Reset: If you're implementing pagination, you might want to reset the cursor value after each page to ensure you don't miss any records:

python    def read_records(self, *args, **kwargs) -> Iterable[Mapping[str, Any]]:        for record in super().read_records(*args, **kwargs):            yield record        self._cursor_value = None  # Reset cursor value after each page

The order of these operations is crucial for maintaining the correct state throughout the sync process. If you set the cursor value too early or read it too late, you might end up with incorrect sync behavior, such as missing records or unnecessary data retrieval. [Source: Incremental Streams](https://docs.airbyte.com/connector-development/cdk-python/incremental-stream#incrementalmixin) Remember, the exact implementation might vary depending on your specific API and data structure, but these principles should guide you in maintaining the correct order of operations for setting and getting the cursor value.

@kapa.ai how can I set and get the cursor_value when I send the next_page_token?

A message from kapa.ai

To set and get the

cursor_value

when sending the

next_page_token

, you can modify your implementation of the

HttpStream

class. Here's how you can approach this: 1. Set the

cursor_value

in the

parse_response

method:

python def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:     records = response.json()  # Adjust based on your API response structure     for record in records:         self._cursor_value = max(self._cursor_value, record[self.cursor_field]) if self._cursor_value else record[self.cursor_field]         yield record

2. Use the

cursor_value

in the

next_page_token

method:

python def next_page_token(self, response: requests.Response) -> Optional[Mapping[str, Any]]:     if self.records_processed >= 10000:         self.records_processed = 0         return {"cursor": self._cursor_value}     return None

3. Retrieve and use the

cursor_value

in the

request_params

method:

python def request_params(     self, stream_state: Mapping[str, Any], stream_slice: Mapping[str, Any] = None, next_page_token: Mapping[str, Any] = None ) -> MutableMapping[str, Any]:     params = super().request_params(stream_state, stream_slice, next_page_token)     if next_page_token:         params[self.cursor_field] = next_page_token["cursor"]     elif self._cursor_value:         params[self.cursor_field] = self._cursor_value     return params

This approach ensures that: 1. The

cursor_value

is updated with each record processed. 2. When a new page is needed, the current

cursor_value

is included in the

next_page_token

. 3. The

cursor_value

from the

next_page_token

is used in the parameters for the next request. Remember to adjust the field names and logic according to your specific API requirements. [Source: HTTP-API-based Connectors](https://docs.airbyte.com/connector-development/cdk-python/http-streams#pagination) By following this pattern, you can effectively manage the

cursor_value

across pagination requests, ensuring that each new request starts from the correct point in your data set.

@kapa.ai can I also include the logic here:

def next_page_token(
        self,
        response: requests.Response
    ) -> Optional[Mapping[str, Any]]:
        """
        Overrides the abstract property from the parent class `HttpStream`.

        :returns: The token for the next page from the input response object. Returning None means there are no more pages to read in this response.
        """
        if self._record_counter >= 10000:
            <http://logging.info|logging.info>(f"Return last state: {self._record_counter}")
            print(f"return last state in pagination: {self._record_counter}")
            self._record_counter = 0
            print(f"return new state in pagination: {self._record_counter}")
            return {"last_state": self._cursor_value}  # Return last state for the next request
        return None

    def request_body_data(
        self,
        stream_state: Optional[Mapping[str, Any]],
        stream_slice: Optional[Mapping[str, Any]] = None,
        next_page_token: Optional[Mapping[str, Any]] = None
    ) -> Optional[Mapping[str, Any]]:

        # If no stream_state, return the query as is
        if not stream_state:
            return self.query

        # Add filter term from stream_state
        self.query = xmltodict.parse(self.query)
        self.query["QueryRequest"]["FilterTerms"] = {
            "FilterTerm": {
                "ColumnName": self.cursor_field,
                "Operator": "GREATER",
                "Values": {"string": str(stream_state[self.cursor_field])},
            }
        }
        self.query = xmltodict.unparse(self.query)

        return self.query
def read_records(
        self, 
        sync_mode: SyncMode,
        cursor_field: Optional[List[str]] = None,
        stream_slice: Optional[Mapping[str, Any]] = None,
        stream_state: Optional[Mapping[str, Any]] = None
    ) -> Iterable[Mapping[str, Any]]:
        if stream_state and sync_mode == SyncMode.incremental:
            self._cursor_value = stream_state[self.cursor_field]

        if not stream_state and sync_mode == SyncMode.incremental:
            self._cursor_value = 0

        print(f"record_counter start: {self._record_counter}")
        for record in super().read_records(
            sync_mode, cursor_field, stream_slice, stream_state
            ):
            if sync_mode == SyncMode.incremental:
                if str(record[self.cursor_field]) > str(self._cursor_value):
                    self._cursor_value = str(record[self.cursor_field])
            self._record_counter += 1  # Count the number of records processed
            yield record

so that if it returns the next_page_token: {"last_state": self._cursor_value} that we then use the cursor_value for the

A message from kapa.ai

Yes, you can include the logic you've shown in your implementation. Your approach combines pagination with state management, which is a good practice for incremental syncs. Let's break down how this works and how you can use the

cursor_value

in the next request: 1. In your

next_page_token

method: You're correctly returning the

_cursor_value

when you've processed 10,000 records. This is good for pagination control. 2. In your

request_body_data

method: You're using the

stream_state

to update the query with a filter based on the

cursor_field

. This is correct for incremental syncs. 3. In your

read_records

method: You're updating the

_cursor_value

as you process each record, which is the right approach. To use the

cursor_value

from the

next_page_token

in the next request, you should modify your

request_body_data

method to also check for the

next_page_token

. Here's how you can do that:

python def request_body_data(     self,     stream_state: Optional[Mapping[str, Any]],     stream_slice: Optional[Mapping[str, Any]] = None,     next_page_token: Optional[Mapping[str, Any]] = None ) -> Optional[Mapping[str, Any]]:     self.query = xmltodict.parse(self.query)      if next_page_token:         # Use the cursor_value from next_page_token         cursor_value = next_page_token["last_state"]     elif stream_state:         # Use the cursor_value from stream_state         cursor_value = stream_state[self.cursor_field]     else:         # No state or token, return the query as is         return xmltodict.unparse(self.query)      # Add filter term using the cursor_value     self.query["QueryRequest"]["FilterTerms"] = {         "FilterTerm": {             "ColumnName": self.cursor_field,             "Operator": "GREATER",             "Values": {"string": str(cursor_value)},         }     }      return xmltodict.unparse(self.query)

This modification ensures that: 1. If there's a

next_page_token

, it uses the

last_state

(which is your

_cursor_value

) from it. 2. If there's no

next_page_token

but there is a

stream_state

, it uses that. 3. If neither exists, it returns the query without modification. This approach allows you to seamlessly use the

cursor_value

across pagination requests and between sync runs, ensuring that you're always fetching the most up-to-date data. Remember to adjust the field names and XML structure according to your specific API requirements. [Source: Incremental Streams](https://docs.airbyte.com/connector-development/cdk-python/incremental-stream#incrementalmixin) By implementing this logic, you're ensuring that your connector correctly handles both pagination and incremental syncs, using the

cursor_value

effectively in both scenarios.

@kapa.ai in which method will the __cursor_value_ get updated before the next request will be sind?

A message from kapa.ai

Based on the code you've shared and the context of our discussion, the

_cursor_value

is primarily updated in two methods: 1. In the

parse_response

method: This is where the

_cursor_value

is updated for each record processed. Here's an example of how it might look:

python    def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:        records = response.json()  # Adjust based on your API response structure        for record in records:            self._cursor_value = max(self._cursor_value, record[self.cursor_field]) if self._cursor_value else record[self.cursor_field]            yield record

2. In the

read_records

method: You've shown that you're updating the

_cursor_value

in this method as well:

python    def read_records(self, sync_mode: SyncMode, cursor_field: Optional[List[str]] = None, stream_slice: Optional[Mapping[str, Any]] = None, stream_state: Optional[Mapping[str, Any]] = None) -> Iterable[Mapping[str, Any]]:        # ... other code ...        for record in super().read_records(sync_mode, cursor_field, stream_slice, stream_state):            if sync_mode == SyncMode.incremental:                if str(record[self.cursor_field]) > str(self._cursor_value):                    self._cursor_value = str(record[self.cursor_field])            self._record_counter += 1            yield record

The

_cursor_value

is updated in these methods before the next request is sent. The updated value is then used in the

next_page_token

method to determine if a new page should be requested and what the last state was. It's important to note that the

read_records

method is typically called for each page of results, so the

_cursor_value

is continuously updated as records are processed, ensuring that it always represents the most recent state before the next request is made. [Source: Incremental Streams](https://docs.airbyte.com/connector-development/cdk-python/incremental-stream#incrementalmixin) Remember, the exact implementation may vary depending on your specific connector's needs, but these are the key methods where the

_cursor_value

is typically updated before the next request is sent.

@kapa.ai where is the difference between parse_response and read_records?

A message from kapa.ai

The

parse_response

and

read_records

methods serve different purposes in the Airbyte CDK, although they are closely related. Let me explain the key differences: 1.

parse_response

method: - This method is responsible for parsing the raw API response and extracting individual records. - It's typically called for each API response received. - It yields individual records from the response. - It's where you would implement the logic to extract data from the specific format of your API response (e.g., JSON, XML). Example:

python    def parse_response(self, response: requests.Response, **kwargs) -> Iterable[Mapping]:        data = response.json()        for record in data['results']:            yield record

2.

read_records

method: - This is a higher-level method that orchestrates the entire reading process for a stream. - It handles pagination, calling

parse_response

for each page of results. - It's where you implement stream-wide logic, such as updating the stream state or handling incremental syncs. - It yields records from all pages of the API response. Example:

python    def read_records(self, sync_mode: SyncMode, cursor_field: Optional[List[str]] = None, stream_slice: Optional[Mapping[str, Any]] = None, stream_state: Optional[Mapping[str, Any]] = None) -> Iterable[Mapping[str, Any]]:        for page in self.paginate():            for record in self.parse_response(page, stream_state=stream_state):                if sync_mode == SyncMode.incremental:                    self._cursor_value = max(self._cursor_value, record[self.cursor_field])                yield record

The main difference is that

parse_response

deals with individual API responses, while

read_records

manages the overall process of reading data from the stream, including pagination and state management. In most cases, you'll need to implement

parse_response

to handle the specific format of your API responses. The

read_records

method is often handled by the base

HttpStream

class, but you might override it if you need custom logic for pagination or state management. [Source: HTTP-API-based Connectors](https://docs.airbyte.com/connector-development/cdk-python/http-streams#pagination) Remember, the exact implementation may vary depending on your specific connector's needs and the structure of your API responses.