To migrate your Airbyte deployment from a kind-based setup to Google Kubernetes Engine (GKE) while preserving your connections and statuses, you'll need to follow a careful process. Based on the available knowledge sources, I can provide some guidance, but please note that there isn't a specific, documented procedure for this exact migration scenario. Here's a general approach you can consider: 1. Database Migration: If you're using an external PostgreSQL database for your kind deployment, you can reuse this database for your GKE deployment. This is the easiest way to preserve your connections and statuses. As mentioned in the [Forum Post: EC2 to EKS migration](https://discuss.airbyte.io/t/1503), taking a database dump and restoring it can work, but you might need to ensure compatibility. 2. Helm Chart Deployment: For deploying on GKE, you should use the Helm chart method. The [Documentation > Deploying Airbyte > Installation Guide](https://docs.airbyte.com/deploying-airbyte/#installation-guide) provides instructions on how to deploy Airbyte using Helm charts. Make sure to configure your

values.yaml

file to point to your existing database. 3. Configuration: You'll need to update your configuration to match the GKE environment. This includes setting up the correct ingress, as mentioned in the EC2 to EKS migration post. You might need to create an

ingress.yaml

file similar to:

yaml
   apiVersion: <http://networking.k8s.io/v1|networking.k8s.io/v1>
   kind: Ingress
   metadata:
     name: airbyte-ingress
     annotations:
       <http://kubernetes.io/ingress.class|kubernetes.io/ingress.class>: "gce"
   spec:
     rules:
     - host: airbyte.<your-domain>.com
       http:
         paths:
         - backend:
             service:
               name: airbyte-webapp-svc
               port:
                 number: 80
           path: /*
           pathType: ImplementationSpecific

4. Data Migration: If you can't reuse your existing database, you might need to manually export and import your configuration. As mentioned in the [Forum Post: Migrate Airbyte config from one deployment to another (OSS)](https://discuss.airbyte.io/t/370), you can use the export/import feature in the Airbyte UI to transfer your configuration between instances of the same version. 5. Version Compatibility: Ensure that the version of Airbyte you're deploying on GKE matches the version you were using on kind. This is crucial for successful data migration and configuration compatibility. 6. Testing: Before fully switching over, it's recommended to set up your GKE deployment alongside your kind deployment. This allows you to test and verify that all connections and statuses have been correctly migrated. Remember, this migration process can be complex and may require careful planning and execution. It's recommended to thoroughly test each step and have a rollback plan in case of issues.

To migrate your Airbyte deployment from a kind-based setup to Google Kubernetes Engine (GKE) while preserving your connections and statuses, you'll need to follow a careful process. Based on the available knowledge sources, I can provide some guidance, but please note that there isn't a specific, documented procedure for this exact migration scenario. Here's a general approach you can consider: 1. Database Migration: If you're using an external PostgreSQL database for your kind deployment, you can reuse this database for your GKE deployment. This is the easiest way to preserve your connections and statuses. As mentioned in the [Forum Post: EC2 to EKS migration](https://discuss.airbyte.io/t/1503), taking a database dump and restoring it can work, but you might need to ensure compatibility. 2. Helm Chart Deployment: For deploying on GKE, you should use the Helm chart method. The [Documentation > Deploying Airbyte > Installation Guide](https://docs.airbyte.com/deploying-airbyte/#installation-guide) provides instructions on how to deploy Airbyte using Helm charts. Make sure to configure your

values.yaml

file to point to your existing database. 3. Configuration: You'll need to update your configuration to match the GKE environment. This includes setting up the correct ingress, as mentioned in the EC2 to EKS migration post. You might need to create an

ingress.yaml

file similar to:

yaml    apiVersion: <http://networking.k8s.io/v1|networking.k8s.io/v1>    kind: Ingress    metadata:      name: airbyte-ingress      annotations:        <http://kubernetes.io/ingress.class|kubernetes.io/ingress.class>: "gce"    spec:      rules:      - host: airbyte.<your-domain>.com        http:          paths:          - backend:              service:                name: airbyte-webapp-svc                port:                  number: 80            path: /*            pathType: ImplementationSpecific

4. Data Migration: If you can't reuse your existing database, you might need to manually export and import your configuration. As mentioned in the [Forum Post: Migrate Airbyte config from one deployment to another (OSS)](https://discuss.airbyte.io/t/370), you can use the export/import feature in the Airbyte UI to transfer your configuration between instances of the same version. 5. Version Compatibility: Ensure that the version of Airbyte you're deploying on GKE matches the version you were using on kind. This is crucial for successful data migration and configuration compatibility. 6. Testing: Before fully switching over, it's recommended to set up your GKE deployment alongside your kind deployment. This allows you to test and verify that all connections and statuses have been correctly migrated. Remember, this migration process can be complex and may require careful planning and execution. It's recommended to thoroughly test each step and have a rollback plan in case of issues. For more detailed guidance on GKE deployment, you can refer to the [Airbyte documentation on Kubernetes deployment](https://docs.airbyte.com/deploying-airbyte/on-kubernetes-via-helm). If you encounter specific issues during the migration, you may need to reach out to Airbyte's community support channels or consider professional services for a smoother transition.

New message text here

Based on the error message you've provided, it appears that you're encountering an issue with the Airbyte Temporal pod configuration. This error is related to a YAML file parsing problem, specifically in the

config/docker.yaml

file. Let's address this issue based on the information from our knowledge sources. This error is similar to the one reported in the [Github Issue: Temporal Fails to Start with Helm](https://github.com/airbytehq/airbyte/issues/40511). In that case, the issue was resolved by addressing a problem with the database password in the configuration file. Here are some steps you can take to resolve this issue: 1. Check the

config/docker.yaml

file: Focus on line 17, as the error message indicates that this line contains an unknown escape character. 2. Look for special characters in the configuration: The most likely cause is that there's a special character in a string (probably a password or other credential) that YAML is interpreting as an escape character. Common problematic characters include backslashes (\), quotes ("), or other special symbols. 3. Properly escape or quote the problematic string: If you find a string with special characters, make sure it's properly quoted. For example, if you have a password with special characters, you might need to enclose it in single quotes:

yaml    password: 'my@complex!password\with$special&characters'

4. Double-check all credentials: As mentioned in the Github issue, the database password was the culprit in that case. Make sure all your credentials (database passwords, API keys, etc.) are properly formatted in the YAML file. 5. Validate your YAML: Use a YAML validator tool to check the entire

config/docker.yaml

file for any syntax errors. 6. Redeploy after fixing: Once you've corrected the YAML file, redeploy your Airbyte instance to apply the changes. If you continue to face issues after these steps, you might want to consider: - Temporarily removing any complex configurations from line 17 and nearby lines to isolate the problem. - Checking for any recent changes to your configuration that might have introduced this error. - Ensuring that you're using the correct version of the configuration file for your Airbyte version. Remember, when dealing with configuration files, it's crucial to be careful with special characters and proper YAML syntax. If you're unsure about the correct format, consult the Airbyte documentation or seek help from the Airbyte community forums. [Github Issue: Temporal Fails to Start with Helm](https://github.com/airbytehq/airbyte/issues/40511)

To configure Airbyte installed on Google Kubernetes Engine (GKE) to use Google Cloud Storage (GCS) as the log storage location, you need to modify your Helm chart values and set up the necessary credentials. Here's how you can do it: 1. Create a GCS bucket: First, create a GCS bucket to store your logs if you haven't already done so. 2. Set up credentials: Create a service account with the necessary permissions to access the GCS bucket. Download the JSON key file for this service account. 3. Create a Kubernetes secret: Create a Kubernetes secret containing the GCP credentials. You can do this using the following command:

kubectl create secret generic airbyte-config-secrets --from-file=gcp.json=/path/to/your/gcp-credentials.json

4. Update your

values.yaml

file: Modify your Helm chart's

values.yaml

file to configure GCS storage. Add the following configuration:

yaml    global:      storage:        type: "GCS"        storageSecretName: airbyte-config-secrets        bucket:          log: your-gcs-bucket-name          state: your-gcs-bucket-name          workloadOutput: your-gcs-bucket-name        gcs:          projectId: your-gcp-project-id          credentialsPath: /secrets/gcs-log-creds/gcp.json

Replace

your-gcs-bucket-name

with the name of the GCS bucket you created, and

your-gcp-project-id

with your GCP project ID. 5. Apply the changes: After updating your

values.yaml

file, apply the changes to your Airbyte deployment using Helm:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

6. Verify the configuration: Check the logs of your Airbyte pods to ensure they are now being stored in GCS. It's important to note that you should ensure the service account has the necessary permissions to read and write to the GCS bucket. The required permissions typically include:

storage.multipartUploads.abort storage.multipartUploads.create storage.objects.create storage.objects.delete storage.objects.get storage.objects.list

If you encounter any issues, make sure to check the Airbyte worker and server logs for any error messages related to GCS access. For more detailed information, you can refer to the [Airbyte documentation on configuring external logging](https://docs.airbyte.com/enterprise-setup/implementation-guide#configuring-external-logging) and [State and Logging Storage configuration](https://docs.airbyte.com/deploying-airbyte/integrations/storage#values).

@kapa.ai MountVolume.SetUp failed for volume "gcs-log-creds-volume" : secret "airbyte-gcs-log-creds" not found

This error message indicates that there's an issue with mounting a secret volume in your Airbyte deployment on GKE. The specific problem is that the Kubernetes secret "airbyte-gcs-log-creds" is not found, which is needed for GCS log storage configuration. Let's address this issue based on the information from our knowledge sources: 1. Secret Name Mismatch: The error suggests that the deployment is looking for a secret named "airbyte-gcs-log-creds", but this secret doesn't exist in your Kubernetes cluster. This could be due to a mismatch between the secret name specified in your Helm chart configuration and the actual secret name in your cluster. 2. Configuration in values.yaml: According to the [Github Issue about GCS storage without existing credentials secret](https://github.com/airbytehq/airbyte/issues/48502), there might be an inconsistency in how the secret name is specified in the Helm chart. Make sure your

values.yaml

file has the correct configuration:

yaml
   global:
     storage:
       type: "GCS"
       storageSecretName: airbyte-config-secrets  # This should match the name of your created secret
       bucket:
         log: your-gcs-bucket-name
         state: your-gcs-bucket-name
         workloadOutput: your-gcs-bucket-name
       gcs:
         projectId: your-gcp-project-id
         credentialsPath: /secrets/gcs-log-creds/gcp.json

3. Secret Creation: Ensure that you have created the Kubernetes secret with the correct name. If you haven't created it yet, you can do so with this command:

kubectl create secret generic airbyte-config-secrets --from-file=gcp.json=/path/to/your/gcp-credentials.json

Make sure the secret name matches the

storageSecretName

in your

values.yaml

. 4. Helm Chart Version: As mentioned in the Github issue, there might be inconsistencies in secret handling across different components in some Helm chart versions. Ensure you're using the latest stable version of the Airbyte Helm chart. 5. Workaround: If the issue persists, you can try the workaround suggested in the Github issue by explicitly adding

extraEnv

values in the worker and launcher configuration:

yaml
   worker:
     extraEnv:
       - name: CONTAINER_ORCHESTRATOR_SECRET_NAME
         value: airbyte-config-secrets  # This should match your actual secret name

   workload-launcher:
     extraEnv:
       - name: CONTAINER_ORCHESTRATOR_SECRET_NAME
         value: airbyte-config-secrets  # This should match your actual secret name

6. Verify Secret Existence: Double-check that the secret actually exists in your Kubernetes cluster:

kubectl get secret airbyte-config-secrets

If it doesn't exist, you'll need to create it as mentioned in step 3. After making these changes, redeploy your Airbyte instance using Helm:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

This error message indicates that there's an issue with mounting a secret volume in your Airbyte deployment on GKE. The specific problem is that the Kubernetes secret "airbyte-gcs-log-creds" is not found, which is needed for GCS log storage configuration. Let's address this issue based on the information from our knowledge sources: 1. Secret Name Mismatch: The error suggests that the deployment is looking for a secret named "airbyte-gcs-log-creds", but this secret doesn't exist in your Kubernetes cluster. This could be due to a mismatch between the secret name specified in your Helm chart configuration and the actual secret name in your cluster. 2. Configuration in values.yaml: According to the [Github Issue about GCS storage without existing credentials secret](https://github.com/airbytehq/airbyte/issues/48502), there might be an inconsistency in how the secret name is specified in the Helm chart. Make sure your

values.yaml

file has the correct configuration:

yaml    global:      storage:        type: "GCS"        storageSecretName: airbyte-config-secrets  # This should match the name of your created secret        bucket:          log: your-gcs-bucket-name          state: your-gcs-bucket-name          workloadOutput: your-gcs-bucket-name        gcs:          projectId: your-gcp-project-id          credentialsPath: /secrets/gcs-log-creds/gcp.json

3. Secret Creation: Ensure that you have created the Kubernetes secret with the correct name. If you haven't created it yet, you can do so with this command:

kubectl create secret generic airbyte-config-secrets --from-file=gcp.json=/path/to/your/gcp-credentials.json

Make sure the secret name matches the

storageSecretName

in your

values.yaml

. 4. Helm Chart Version: As mentioned in the Github issue, there might be inconsistencies in secret handling across different components in some Helm chart versions. Ensure you're using the latest stable version of the Airbyte Helm chart. 5. Workaround: If the issue persists, you can try the workaround suggested in the Github issue by explicitly adding

extraEnv

values in the worker and launcher configuration:

yaml    worker:      extraEnv:        - name: CONTAINER_ORCHESTRATOR_SECRET_NAME          value: airbyte-config-secrets  # This should match your actual secret name     workload-launcher:      extraEnv:        - name: CONTAINER_ORCHESTRATOR_SECRET_NAME          value: airbyte-config-secrets  # This should match your actual secret name

6. Verify Secret Existence: Double-check that the secret actually exists in your Kubernetes cluster:

kubectl get secret airbyte-config-secrets

If it doesn't exist, you'll need to create it as mentioned in step 3. After making these changes, redeploy your Airbyte instance using Helm:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

If you continue to face issues, you may need to check the logs of other Airbyte components for additional error messages or consider reaching out to the Airbyte community for further assistance. [Github Issue: Using GCS storage without existing credentials secret results in broken configuration](https://github.com/airbytehq/airbyte/issues/48502)

This error suggests that there's an issue with the GCS (Google Cloud Storage) credentials being used by the Airbyte server pod. Specifically, the error indicates that the private key in the service account JSON file is not in the correct PKCS#8 format. Let's address this issue based on the information from our knowledge sources: 1. Credential Format: The error "Invalid PKCS#8 data" typically occurs when the private key in the service account JSON file is not properly formatted or has been corrupted. This could happen if the JSON file was modified or if there was an issue during the secret creation process. 2. Secret Creation: Revisit the process of creating the Kubernetes secret containing your GCP credentials. Make sure you're using the original, unmodified service account JSON file downloaded from Google Cloud Console. Here's how you should create the secret:

kubectl create secret generic airbyte-config-secrets --from-file=gcp.json=/path/to/your/gcp-credentials.json

Ensure that the

gcp.json

file is the original file and hasn't been modified. 3. Credential Verification: Double-check that the service account JSON file you're using is valid and contains all the necessary fields, including the private key in the correct format. 4. Secret Mounting: Ensure that the secret is being correctly mounted in the Airbyte server pod. Check your

values.yaml

file to make sure the path to the credentials file is correct:

yaml
   global:
     storage:
       type: "GCS"
       storageSecretName: airbyte-config-secrets
       gcs:
         credentialsPath: /secrets/gcs-log-creds/gcp.json

5. Permissions: Verify that the service account has the necessary permissions to access the GCS bucket. The required permissions typically include:

storage.multipartUploads.abort
   storage.multipartUploads.create
   storage.objects.create
   storage.objects.delete
   storage.objects.get
   storage.objects.list

6. Regenerate Service Account Key: If the issue persists, try regenerating a new service account key in the Google Cloud Console and create a new Kubernetes secret with the fresh credentials. 7. Check for Line Breaks: Sometimes, line breaks in the JSON file can cause issues. Ensure that the JSON file doesn't contain any unexpected line breaks, especially within the private key section. 8. Verify Secret Content: You can check the content of the secret to ensure it's correctly stored:

kubectl get secret airbyte-config-secrets -o jsonpath="{.data.gcp\.json}" | base64 --decode

This command will display the content of the secret. Verify that it matches your original service account JSON file. If you've verified all these points and the issue persists, you may need to:

This error suggests that there's an issue with the GCS (Google Cloud Storage) credentials being used by the Airbyte server pod. Specifically, the error indicates that the private key in the service account JSON file is not in the correct PKCS#8 format. Let's address this issue based on the information from our knowledge sources: 1. Credential Format: The error "Invalid PKCS#8 data" typically occurs when the private key in the service account JSON file is not properly formatted or has been corrupted. This could happen if the JSON file was modified or if there was an issue during the secret creation process. 2. Secret Creation: Revisit the process of creating the Kubernetes secret containing your GCP credentials. Make sure you're using the original, unmodified service account JSON file downloaded from Google Cloud Console. Here's how you should create the secret:

kubectl create secret generic airbyte-config-secrets --from-file=gcp.json=/path/to/your/gcp-credentials.json

Ensure that the

gcp.json

file is the original file and hasn't been modified. 3. Credential Verification: Double-check that the service account JSON file you're using is valid and contains all the necessary fields, including the private key in the correct format. 4. Secret Mounting: Ensure that the secret is being correctly mounted in the Airbyte server pod. Check your

values.yaml

file to make sure the path to the credentials file is correct:

yaml    global:      storage:        type: "GCS"        storageSecretName: airbyte-config-secrets        gcs:          credentialsPath: /secrets/gcs-log-creds/gcp.json

5. Permissions: Verify that the service account has the necessary permissions to access the GCS bucket. The required permissions typically include:

storage.multipartUploads.abort    storage.multipartUploads.create    storage.objects.create    storage.objects.delete    storage.objects.get    storage.objects.list

6. Regenerate Service Account Key: If the issue persists, try regenerating a new service account key in the Google Cloud Console and create a new Kubernetes secret with the fresh credentials. 7. Check for Line Breaks: Sometimes, line breaks in the JSON file can cause issues. Ensure that the JSON file doesn't contain any unexpected line breaks, especially within the private key section. 8. Verify Secret Content: You can check the content of the secret to ensure it's correctly stored:

kubectl get secret airbyte-config-secrets -o jsonpath="{.data.gcp\.json}" | base64 --decode

This command will display the content of the secret. Verify that it matches your original service account JSON file. If you've verified all these points and the issue persists, you may need to: - Check the Airbyte server logs for any additional error messages or stack traces that might provide more context. - Consider temporarily using a different service account or creating a new one to isolate whether the issue is with the specific credentials or a more general configuration problem. - Ensure that you're using a compatible version of Airbyte with your GKE and GCS setup. For more detailed troubleshooting, you might want to refer to the [Airbyte documentation on configuring external logging](https://docs.airbyte.com/enterprise-setup/implementation-guide#configuring-external-logging) and [State and Logging Storage configuration](https://docs.airbyte.com/deploying-airbyte/integrations/storage#values). If the problem continues after trying these steps, you may need to reach out to Airbyte support or the community forums for more specific assistance, as this could indicate a deeper issue with the interaction between Airbyte and GCS in your particular setup.

Based on the error message you're seeing in the Airbyte UI, it appears you're encountering a database access issue. This error is similar to ones mentioned in our knowledge sources. Let's analyze the problem and suggest some solutions: 1. Database Connection Issue: The error suggests that Airbyte is unable to access its database. This could be due to various reasons such as incorrect database credentials, network issues, or database server problems. 2. Similar Reported Issues: This error is very similar to the one reported in the [Forum Post: Airbyte Server Error (Unknown Error Occurred)](https://discuss.airbyte.io/t/2408). In that case, the user was also getting a 409 Conflict error when trying to access certain parts of the UI. 3. Possible Causes: - Database credentials mismatch - Database server is down or unreachable - Insufficient permissions for the Airbyte user in the database - Database migration issues 4. Troubleshooting Steps: a. Check Server Logs: The error message suggests checking the server logs for more information. Look for any database-related error messages in the airbyte-server pod logs. b. Verify Database Configuration: Ensure that the database connection details in your Airbyte configuration (usually in the

values.yaml

file if you're using Helm) are correct. c. Database Accessibility: Verify that the database is running and accessible from the Airbyte server pod. You can try to connect to the database manually from within the pod to isolate network or credential issues. d. Check Database Migrations: Ensure that all necessary database migrations have been applied successfully. You can check this in the Airbyte server logs during startup. e. Permissions: Verify that the Airbyte database user has the necessary permissions to read and write to all required tables. 5. Potential Solution: If you've recently migrated or changed your database configuration, you might need to ensure that all components are using the correct and up-to-date database information. As mentioned in the [Forum Post: Kubernetes check connection issues](https://discuss.airbyte.io/t/594), sometimes connection issues can arise due to misconfigurations or version mismatches. 6. Version Compatibility: Ensure that your Airbyte version is compatible with your current database version. As noted in one of the server logs from our knowledge sources:

2025-02-19 15:14:26,415 [main]	WARN	o.j.t.JooqLogger(lambda$warn$7):461 - Version mismatch         : Database version is older than what dialect POSTGRES supports: 13.18.

This suggests that version mismatches can cause issues. If these steps don't resolve the issue, you may need to:

Based on the error message you're seeing in the Airbyte UI, it appears you're encountering a database access issue. This error is similar to ones mentioned in our knowledge sources. Let's analyze the problem and suggest some solutions: 1. Database Connection Issue: The error suggests that Airbyte is unable to access its database. This could be due to various reasons such as incorrect database credentials, network issues, or database server problems. 2. Similar Reported Issues: This error is very similar to the one reported in the [Forum Post: Airbyte Server Error (Unknown Error Occurred)](https://discuss.airbyte.io/t/2408). In that case, the user was also getting a 409 Conflict error when trying to access certain parts of the UI. 3. Possible Causes: - Database credentials mismatch - Database server is down or unreachable - Insufficient permissions for the Airbyte user in the database - Database migration issues 4. Troubleshooting Steps: a. Check Server Logs: The error message suggests checking the server logs for more information. Look for any database-related error messages in the airbyte-server pod logs. b. Verify Database Configuration: Ensure that the database connection details in your Airbyte configuration (usually in the

values.yaml

file if you're using Helm) are correct. c. Database Accessibility: Verify that the database is running and accessible from the Airbyte server pod. You can try to connect to the database manually from within the pod to isolate network or credential issues. d. Check Database Migrations: Ensure that all necessary database migrations have been applied successfully. You can check this in the Airbyte server logs during startup. e. Permissions: Verify that the Airbyte database user has the necessary permissions to read and write to all required tables. 5. Potential Solution: If you've recently migrated or changed your database configuration, you might need to ensure that all components are using the correct and up-to-date database information. As mentioned in the [Forum Post: Kubernetes check connection issues](https://discuss.airbyte.io/t/594), sometimes connection issues can arise due to misconfigurations or version mismatches. 6. Version Compatibility: Ensure that your Airbyte version is compatible with your current database version. As noted in one of the server logs from our knowledge sources:

2025-02-19 15:14:26,415 [main] WARN o.j.t.JooqLogger(lambda$warn$7):461 - Version mismatch         : Database version is older than what dialect POSTGRES supports: 13.18.

This suggests that version mismatches can cause issues. If these steps don't resolve the issue, you may need to: 1. Restart the Airbyte server pod to see if it resolves the connection issue. 2. Consider rolling back to a previous known-good configuration if this issue started after a recent change. 3. Check for any recent changes in your networking setup, especially if Airbyte is running in a Kubernetes environment. For more detailed assistance, you would need to provide the specific server logs related to this error. If the problem persists after trying these steps, you might want to reach out to the Airbyte community forums or support channels with your detailed logs and configuration (with sensitive information redacted). [Forum Post: Airbyte Server Error (Unknown Error Occurred)](https://discuss.airbyte.io/t/2408) [Forum Post: Kubernetes check connection issues](https://discuss.airbyte.io/t/594)

Based on the information you've provided and the knowledge sources available, it appears that your Postgres to BigQuery connection is experiencing repeated failures. Let's analyze the possible issues: 1. Resource Constraints: The log shows that the destination BigQuery container is allocated with the following resources:

resources: ResourceRequirements(claims=[], limits={memory=2Gi, cpu=2}, requests={memory=1Gi, cpu=1}, additionalProperties={})

This allocation might not be sufficient for the volume of data you're trying to sync. As mentioned in a [Github issue about excessive CPU and memory limits](https://github.com/airbytehq/airbyte/issues/35749), resource constraints can cause sync failures. 2. Retry Mechanism: The log indicates that the sync has failed completely 3 times:

successiveCompleteFailures=3, totalCompleteFailures=3

Airbyte is implementing a retry mechanism with increasing backoff times. As described in the [Airbyte documentation on sync jobs](https://docs.airbyte.com/understanding-airbyte/jobs#sync-jobs), there's a retry policy in place for failed syncs. 3. Potential Causes: - Database connection issues - BigQuery API rate limits or quotas - Data incompatibility or transformation errors - Network instability 4. Troubleshooting Steps: a. Check Logs: Look for specific error messages in the logs of both source (Postgres) and destination (BigQuery) connectors. b. Resource Allocation: Consider increasing the resources allocated to the BigQuery destination connector, especially if you're syncing large volumes of data. c. Connection Settings: Verify the connection settings for both Postgres and BigQuery. Ensure that credentials are still valid and that network connectivity is stable. d. Data Validation: Check if there are any data incompatibility issues between Postgres and BigQuery, such as unsupported data types or excessively large fields. e. Incremental Sync: If possible, try setting up an incremental sync instead of a full refresh to reduce the amount of data transferred in each sync. 5. Configuration Adjustments: As mentioned in the [documentation on configuring Airbyte](https://docs.airbyte.com/operator-guides/configuring-airbyte#jobs), you can adjust retry parameters such as

SYNC_JOB_MAX_ATTEMPTS

or

SYNC_JOB_MAX_TIMEOUT_DAYS

to allow for more retry attempts or longer sync durations. 6. Version Check: Ensure you're using compatible versions of the Postgres source (not mentioned in your log) and BigQuery destination (1.4.5) connectors. Sometimes, updating to the latest stable versions can resolve compatibility issues.

Based on the information you've provided and the knowledge sources available, it appears that your Postgres to BigQuery connection is experiencing repeated failures. Let's analyze the possible issues: 1. Resource Constraints: The log shows that the destination BigQuery container is allocated with the following resources:

resources: ResourceRequirements(claims=[], limits={memory=2Gi, cpu=2}, requests={memory=1Gi, cpu=1}, additionalProperties={})

This allocation might not be sufficient for the volume of data you're trying to sync. As mentioned in a [Github issue about excessive CPU and memory limits](https://github.com/airbytehq/airbyte/issues/35749), resource constraints can cause sync failures. 2. Retry Mechanism: The log indicates that the sync has failed completely 3 times:

successiveCompleteFailures=3, totalCompleteFailures=3

Airbyte is implementing a retry mechanism with increasing backoff times. As described in the [Airbyte documentation on sync jobs](https://docs.airbyte.com/understanding-airbyte/jobs#sync-jobs), there's a retry policy in place for failed syncs. 3. Potential Causes: - Database connection issues - BigQuery API rate limits or quotas - Data incompatibility or transformation errors - Network instability 4. Troubleshooting Steps: a. Check Logs: Look for specific error messages in the logs of both source (Postgres) and destination (BigQuery) connectors. b. Resource Allocation: Consider increasing the resources allocated to the BigQuery destination connector, especially if you're syncing large volumes of data. c. Connection Settings: Verify the connection settings for both Postgres and BigQuery. Ensure that credentials are still valid and that network connectivity is stable. d. Data Validation: Check if there are any data incompatibility issues between Postgres and BigQuery, such as unsupported data types or excessively large fields. e. Incremental Sync: If possible, try setting up an incremental sync instead of a full refresh to reduce the amount of data transferred in each sync. 5. Configuration Adjustments: As mentioned in the [documentation on configuring Airbyte](https://docs.airbyte.com/operator-guides/configuring-airbyte#jobs), you can adjust retry parameters such as

SYNC_JOB_MAX_ATTEMPTS

or

SYNC_JOB_MAX_TIMEOUT_DAYS

to allow for more retry attempts or longer sync durations. 6. Version Check: Ensure you're using compatible versions of the Postgres source (not mentioned in your log) and BigQuery destination (1.4.5) connectors. Sometimes, updating to the latest stable versions can resolve compatibility issues. To get more specific insights into the problem, you would need to provide more detailed error logs, particularly from the failed attempts. If the issue persists after trying these steps, you may want to consider reaching out to Airbyte support or community forums with more detailed logs and information about your specific setup and data volumes. [Github Issue: [helm] Excessive cpu and memory limits in job containers](https://github.com/airbytehq/airbyte/issues/35749) [Documentation > Understanding-airbyte > Workloads & Jobs > Sync Jobs](https://docs.airbyte.com/understanding-airbyte/jobs#sync-jobs) [Documentation > Operator-guides > Configuring Airbyte > Jobs](https://docs.airbyte.com/operator-guides/configuring-airbyte#jobs)

Based on the error message you're seeing in your GKE cluster events, it appears you're encountering a resource allocation issue. This error indicates that there are not enough CPU resources available in your cluster to schedule new pods. Let's analyze this problem and suggest some solutions: 1. Resource Constraints: The error "0/2 nodes are available: 2 Insufficient cpu" means that both nodes in your cluster don't have enough CPU resources to accommodate the new pod(s) being scheduled. 2. Pod Resource Requests: This issue is likely related to the CPU requests set for your Airbyte pods. As mentioned in the [Github Issue about excessive CPU and memory limits](https://github.com/airbytehq/airbyte/issues/35749), Airbyte pods can have high default resource requests, which may not fit within your current node capacities. 3. Possible Solutions: a. Adjust Pod Resource Requests: You can modify the resource requests for Airbyte pods in your

values.yaml

file. For example:

yaml
      global:
        jobs:
          resources:
            requests:
              cpu: 100m
            limits:
              cpu: 200m

This sets lower CPU requests and limits for job pods. Adjust these values based on your actual needs and cluster capacity. b. Increase Cluster Resources: If possible, consider scaling up your GKE cluster by either: - Adding more nodes to your cluster - Using larger node types with more CPU resources c. Review and Adjust Other Deployments: Check if other applications in your cluster are consuming a large amount of CPU. You might need to adjust their resource allocations as well. 4. Node Autoscaling: If you haven't enabled node autoscaling on your GKE cluster, consider turning it on. This allows GKE to automatically add nodes when resources are insufficient. 5. Pod Priority and Preemption: The error also mentions "No preemption victims found for incoming pod." This suggests that pod priority and preemption are not effectively configured. You might want to review your pod priority classes and ensure that critical pods have higher priority. 6. Monitoring and Alerts: Set up monitoring and alerts for cluster resource usage to proactively address resource constraints before they cause scheduling issues. 7. Optimize Sync Jobs: If this is happening during sync jobs, consider optimizing your sync schedules to distribute the load over time, rather than having many resource-intensive jobs running simultaneously. To implement these changes, you'll need to modify your Helm chart values and potentially your GKE cluster configuration. After making changes, redeploy your Airbyte instance:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

Based on the error message you're seeing in your GKE cluster events, it appears you're encountering a resource allocation issue. This error indicates that there are not enough CPU resources available in your cluster to schedule new pods. Let's analyze this problem and suggest some solutions: 1. Resource Constraints: The error "0/2 nodes are available: 2 Insufficient cpu" means that both nodes in your cluster don't have enough CPU resources to accommodate the new pod(s) being scheduled. 2. Pod Resource Requests: This issue is likely related to the CPU requests set for your Airbyte pods. As mentioned in the [Github Issue about excessive CPU and memory limits](https://github.com/airbytehq/airbyte/issues/35749), Airbyte pods can have high default resource requests, which may not fit within your current node capacities. 3. Possible Solutions: a. Adjust Pod Resource Requests: You can modify the resource requests for Airbyte pods in your

values.yaml

file. For example:

yaml       global:         jobs:           resources:             requests:               cpu: 100m             limits:               cpu: 200m

This sets lower CPU requests and limits for job pods. Adjust these values based on your actual needs and cluster capacity. b. Increase Cluster Resources: If possible, consider scaling up your GKE cluster by either: - Adding more nodes to your cluster - Using larger node types with more CPU resources c. Review and Adjust Other Deployments: Check if other applications in your cluster are consuming a large amount of CPU. You might need to adjust their resource allocations as well. 4. Node Autoscaling: If you haven't enabled node autoscaling on your GKE cluster, consider turning it on. This allows GKE to automatically add nodes when resources are insufficient. 5. Pod Priority and Preemption: The error also mentions "No preemption victims found for incoming pod." This suggests that pod priority and preemption are not effectively configured. You might want to review your pod priority classes and ensure that critical pods have higher priority. 6. Monitoring and Alerts: Set up monitoring and alerts for cluster resource usage to proactively address resource constraints before they cause scheduling issues. 7. Optimize Sync Jobs: If this is happening during sync jobs, consider optimizing your sync schedules to distribute the load over time, rather than having many resource-intensive jobs running simultaneously. To implement these changes, you'll need to modify your Helm chart values and potentially your GKE cluster configuration. After making changes, redeploy your Airbyte instance:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

Remember to monitor the cluster after making these changes to ensure they resolve the issue without causing new problems. If you continue to face issues after trying these steps, you may need to perform a more detailed analysis of your cluster's resource usage and Airbyte's specific resource needs in your environment. [Github Issue: [helm] Excessive cpu and memory limits in job containers](https://github.com/airbytehq/airbyte/issues/35749)

Based on the error message you're encountering, it seems you're having an issue with cookie settings when trying to log in to the Airbyte UI. This problem is typically related to deploying Airbyte over an unsecured HTTP connection. Let's address this issue using the information from our knowledge sources. The error suggests that your credentials are correct, but the server is unable to set a cookie due to security settings. This is a common issue when Airbyte is deployed without HTTPS. To resolve this, you need to disable secure cookies. Here's how you can do that: 1. Update your

values.yaml

file: Add the following configuration to your

values.yaml

file:

yaml    global:      auth:        cookieSecureSetting: "false"

This setting disables secure cookies, allowing them to be set over non-HTTPS connections. 2. Apply the changes: After updating your

values.yaml

file, apply the changes to your Airbyte deployment using Helm:

helm upgrade --install airbyte airbyte/airbyte -f values.yaml

3. Restart Airbyte: You may need to restart the Airbyte server pod for the changes to take effect. It's important to note that disabling secure cookies reduces security by allowing cookies to be transmitted over non-secure connections. As mentioned in the [Airbyte documentation on authentication](https://docs.airbyte.com/deploying-airbyte/integrations/authentication#cookie-security-settings), this setting should only be used if HTTPS is not available. For a more secure setup, consider the following recommendations: 1. Use HTTPS: If possible, set up HTTPS for your Airbyte deployment. This is the most secure approach and allows you to keep secure cookies enabled. 2. Use a reverse proxy: If you're using a reverse proxy (like Nginx) for SSL termination, make sure it's correctly configured to pass the necessary headers to Airbyte. 3. SameSite cookie setting: If you need to allow cookies to be sent in a cross-site context, you can also modify the

cookieSameSiteSetting

in your `values.yaml`:

yaml    global:      auth:        cookieSameSiteSetting: "None"

However, use this setting cautiously as it can make your application more susceptible to CSRF attacks. Remember, these changes are meant for development or testing environments. For production deployments, it's strongly recommended to use HTTPS to ensure secure communication and data protection. [Documentation > Deploying-airbyte > Integrations > Authentication](https://docs.airbyte.com/deploying-airbyte/integrations/authentication#cookie-security-settings) If you continue to face issues after applying these changes, you may need to check your network configuration, ensure there are no conflicting security policies, and verify that your Airbyte instance is accessible on the correct port and protocol.

To create a connection in Airflow for triggering jobs on Airbyte, you need to follow these steps: 1. Access the Airflow UI: Navigate to your Airflow web interface, which is typically accessible at

<http://localhost:8080>

if you're running it locally. 2. Go to the Connections page: In the Airflow UI, click on "Admin" in the top menu, then select "Connections" from the dropdown. 3. Add a new connection: Click on the "+" button to create a new connection. 4. Configure the connection: Fill in the following details: - Connection Id: Choose a unique identifier for this connection (e.g., "airbyte_connection") - Connection Type: Select "Airbyte" from the dropdown menu - Host: Enter the hostname where your Airbyte instance is running (e.g., "localhost" or "airbyte-server") - Port: Enter the port number for the Airbyte API (default is 8000) - Login: Enter the Airbyte username (default is "airbyte") - Password: Enter the Airbyte password (default is "password") 5. Test and save the connection: Click on the "Test" button to verify the connection, and if successful, click "Save" to create the connection. Here's an example of how this configuration might look, based on the [Airflow and Airbyte OSS - Better Together](https://airbyte.com/tutorials/how-to-use-airflow-and-airbyte-together) tutorial:

Connection Id: airflow-call-to-airbyte-example Connection Type: Airbyte Host: host.docker.internal Login: airbyte Password: password Port: 8000

Note: The use of

host.docker.internal

is specific to Docker setups and resolves to the internal IP address used by the host. After creating this connection, you can use it in your Airflow DAGs to trigger Airbyte jobs. Here's a simple example of how to use this connection in a DAG:

python from airflow import DAG from airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator from airflow.utils.dates import days_ago  with DAG(dag_id='trigger_airbyte_job_example',          default_args={'owner': 'airflow'},          schedule_interval='@daily',          start_date=days_ago(1)     ) as dag:      trigger_airbyte_sync = AirbyteTriggerSyncOperator(         task_id='airbyte_trigger_sync',         airbyte_conn_id='airflow-call-to-airbyte-example',         connection_id='YOUR_AIRBYTE_CONNECTION_ID',         asynchronous=False,         timeout=3600,         wait_seconds=3     )

Remember to replace 'YOUR_AIRBYTE_CONNECTION_ID' with the actual ID of the Airbyte connection you want to trigger. [Airflow and Airbyte OSS - Better Together](https://airbyte.com/tutorials/how-to-use-airflow-and-airbyte-together)

@kapa.ai What if I'm running airbyte on k8s, and I have a client id and secret, and using an nginx ingress controller

If you're running Airbyte on Kubernetes (k8s) with a client ID and secret, and using an NGINX ingress controller, you'll need to adjust your Airflow connection configuration accordingly. Here's how you can set up the connection in this scenario: 1. Access the Airflow UI: Navigate to your Airflow web interface as usual. 2. Go to the Connections page: Click on "Admin" in the top menu, then select "Connections". 3. Add a new connection: Click on the "+" button to create a new connection. 4. Configure the connection: Fill in the following details: - Connection Id: Choose a unique identifier (e.g., "airbyte_k8s_connection") - Connection Type: Select "Airbyte" - Host: Enter the hostname of your NGINX ingress (e.g., "airbyte.your-domain.com") - Port: Leave this blank if you're using standard HTTPS port 443 - Login: Enter your client ID - Password: Enter your client secret - Extra: Add the following JSON to ensure HTTPS is used:

json      {"auth_type": "oauth2", "use_ssl": true}

5. Save the connection: Click "Save" to create the connection. When using this connection in your Airflow DAG, you'll need to adjust your code slightly. Here's an example:

python from airflow import DAG from airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator from airflow.utils.dates import days_ago  with DAG(dag_id='trigger_airbyte_k8s_job_example',          default_args={'owner': 'airflow'},          schedule_interval='@daily',          start_date=days_ago(1)     ) as dag:      trigger_airbyte_sync = AirbyteTriggerSyncOperator(         task_id='airbyte_trigger_sync',         airbyte_conn_id='airbyte_k8s_connection',         connection_id='YOUR_AIRBYTE_CONNECTION_ID',         asynchronous=False,         timeout=3600,         wait_seconds=3     )

Remember to replace 'YOUR_AIRBYTE_CONNECTION_ID' with the actual ID of the Airbyte connection you want to trigger. A few important points to note: 1. Ensure that your NGINX ingress is properly configured to route requests to Airbyte. You may need to set up appropriate annotations in your ingress configuration. 2. If you're using a self-signed certificate, you might need to disable SSL verification in your Airflow connection. However, this is not recommended for production environments. 3. Make sure that the Airbyte API is accessible through your ingress. You may need to configure specific paths in your ingress rules to expose the API endpoints. 4. If you're using Airbyte Enterprise, you might need to adjust the authentication method according to your specific setup. For more detailed information on configuring ingress for Airbyte, you can refer to the [Airbyte documentation on Ingress](https://docs.airbyte.com/deploying-airbyte/integrations/ingress). [Documentation > Deploying-airbyte > Integrations > Ingress](https://docs.airbyte.com/deploying-airbyte/integrations/ingress)

Based on the error message you're seeing and the additional information you've provided, it appears that there's an authentication issue when Airflow is trying to connect to your Airbyte instance. Let's troubleshoot this problem step by step: 1. Authentication Method: The 401 Unauthorized error suggests that the credentials (client ID and secret) you're using are not being accepted or are not being sent correctly. Since you mentioned that you can access the Airbyte server through the configured ingress without authentication, it's possible that the authentication method you're using in the Airflow connection is not matching what Airbyte expects. 2. Airbyte Authentication Configuration: First, verify that authentication is actually enabled on your Airbyte instance. If you're able to access it without authentication, it's possible that auth is not enabled on the Airbyte side. Check your Airbyte configuration (typically in the

values.yaml

file if you're using Helm) to ensure authentication is enabled:

yaml
   global:
     auth:
       enabled: true

3. Airflow Connection Configuration: Double-check your Airflow connection settings. Make sure you're using the correct client ID and secret. Also, ensure that the "Extra" field in your Airflow connection is correctly configured:

json
   {"auth_type": "oauth2", "use_ssl": true}

4. NGINX Ingress Configuration: Ensure that your NGINX ingress is correctly configured to pass authentication headers to Airbyte. You might need to add annotations to your ingress configuration to preserve authentication headers:

yaml
   <http://nginx.ingress.kubernetes.io/auth-response-headers|nginx.ingress.kubernetes.io/auth-response-headers>: Authorization

5. Airbyte API Version: Make sure that the Airflow provider you're using is compatible with your Airbyte version. Incompatibilities can sometimes lead to authentication issues. 6. SSL/TLS Configuration: If you're using HTTPS (which you should be), ensure that SSL/TLS is correctly configured on both the Airbyte and Airflow sides. 7. Debugging Steps: - Try to make a manual API call to your Airbyte instance using curl or Postman with the same credentials to see if you can authenticate successfully. - Check the Airbyte server logs for any authentication-related errors when Airflow tries to connect. - Temporarily enable debug logging in Airflow to get more detailed information about the connection attempt. 8. Alternative Authentication Method: If you continue to face issues, you might want to consider using API key authentication instead of OAuth2, if supported by your Airbyte version. This can sometimes be easier to configure:

json
   {"auth_type": "api_key", "api_key": "your_api_key_here"}

9. Airbyte Enterprise Features: If you're using Airbyte Enterprise, ensure that you're following the specific authentication requirements for your edition, which might differ from the open-source version.

Based on the error message you're seeing and the additional information you've provided, it appears that there's an authentication issue when Airflow is trying to connect to your Airbyte instance. Let's troubleshoot this problem step by step: 1. Authentication Method: The 401 Unauthorized error suggests that the credentials (client ID and secret) you're using are not being accepted or are not being sent correctly. Since you mentioned that you can access the Airbyte server through the configured ingress without authentication, it's possible that the authentication method you're using in the Airflow connection is not matching what Airbyte expects. 2. Airbyte Authentication Configuration: First, verify that authentication is actually enabled on your Airbyte instance. If you're able to access it without authentication, it's possible that auth is not enabled on the Airbyte side. Check your Airbyte configuration (typically in the

values.yaml

file if you're using Helm) to ensure authentication is enabled:

yaml    global:      auth:        enabled: true

3. Airflow Connection Configuration: Double-check your Airflow connection settings. Make sure you're using the correct client ID and secret. Also, ensure that the "Extra" field in your Airflow connection is correctly configured:

json    {"auth_type": "oauth2", "use_ssl": true}

4. NGINX Ingress Configuration: Ensure that your NGINX ingress is correctly configured to pass authentication headers to Airbyte. You might need to add annotations to your ingress configuration to preserve authentication headers:

yaml    <http://nginx.ingress.kubernetes.io/auth-response-headers|nginx.ingress.kubernetes.io/auth-response-headers>: Authorization

5. Airbyte API Version: Make sure that the Airflow provider you're using is compatible with your Airbyte version. Incompatibilities can sometimes lead to authentication issues. 6. SSL/TLS Configuration: If you're using HTTPS (which you should be), ensure that SSL/TLS is correctly configured on both the Airbyte and Airflow sides. 7. Debugging Steps: - Try to make a manual API call to your Airbyte instance using curl or Postman with the same credentials to see if you can authenticate successfully. - Check the Airbyte server logs for any authentication-related errors when Airflow tries to connect. - Temporarily enable debug logging in Airflow to get more detailed information about the connection attempt. 8. Alternative Authentication Method: If you continue to face issues, you might want to consider using API key authentication instead of OAuth2, if supported by your Airbyte version. This can sometimes be easier to configure:

json    {"auth_type": "api_key", "api_key": "your_api_key_here"}

9. Airbyte Enterprise Features: If you're using Airbyte Enterprise, ensure that you're following the specific authentication requirements for your edition, which might differ from the open-source version. If after trying these steps you're still encountering issues, you may need to: - Review the [Airbyte documentation on authentication](https://docs.airbyte.com/deploying-airbyte/integrations/authentication) for any recent changes or specific requirements for your setup. - Check the Airbyte community forums or GitHub issues for similar problems and solutions. - Consider reaching out to Airbyte support or the community with detailed logs and configuration information (with sensitive data redacted) for more specific assistance. Remember to always use secure methods when handling and transmitting authentication credentials, especially in production environments. [Documentation > Deploying-airbyte > Integrations > Authentication](https://docs.airbyte.com/deploying-airbyte/integrations/authentication)

Based on the knowledge sources provided,

_airbyte_meta

(not

_airbyte_meta_data

) is a metadata column that Airbyte adds to your data in destination tables. This column contains additional information about each record in a structured JSON format. The

_airbyte_meta

field contains: 1. A

sync_id

field - This is a monotonically increasing integer that identifies which sync job produced this record. As mentioned in the documentation, "This ID has no inherent meaning, but is guaranteed to increase monotonically across syncs" [Airbyte Metadata fields](https://docs.airbyte.com/understanding-airbyte/airbyte-metadata-fields). 2. A

changes

field - This is a list that records any modifications Airbyte performed on the record. For example, if a record contained a value that didn't match the stream's schema, the destination connector might write

null

to the destination and add an entry to this

changes

list. Here's an example of what the

_airbyte_meta

field might look like:

json {   "sync_id": 1234,   "changes": [     {       "field": "foo",       "change": "NULLED",       "reason": "DESTINATION_SERIALIZATION_ERROR"     }   ] }

This field is particularly useful for: - Tracking which sync job produced which records - Identifying and troubleshooting data quality issues - Understanding how Airbyte has modified your data during processing The

_airbyte_meta

column is part of a set of metadata fields that Airbyte adds to your data, which also includes

_airbyte_raw_id

,

_airbyte_generation_id

,

_airbyte_extracted_at

, and sometimes

_airbyte_loaded_at

. [Airbyte Metadata fields](https://docs.airbyte.com/understanding-airbyte/airbyte-metadata-fields) [Typing and Deduping](https://docs.airbyte.com/using-airbyte/core-concepts/typing-deduping)

@kapa.ai I can't find the airbyte_meta field in my destination after upgrading to destinations V2 in bigquery

I understand you're having trouble finding the

_airbyte_meta

field in your BigQuery destination after upgrading to Destinations V2. Let me help you with this issue. When upgrading to Destinations V2, the schema of your tables changes, and the

_airbyte_meta

field should be included in both the raw and final tables. Let's clarify where you should be looking for this field: 1. In the raw tables: The raw tables are now located in the

airbyte_internal

dataset by default (unless you've overridden this). According to the [BigQuery documentation](https://docs.airbyte.com/integrations/destinations/bigquery#output-schema), the raw table should contain these fields: -

_airbyte_raw_id

-

_airbyte_generation_id

-

_airbyte_extracted_at

-

_airbyte_loaded_at

-

_airbyte_meta

-

_airbyte_data

2. In the final tables: The final tables should also include the

_airbyte_meta

field. As mentioned in the [BigQuery documentation](https://docs.airbyte.com/integrations/destinations/bigquery#output-schema), the final table contains: -

airbyte_raw_id

-

_airbyte_generation_id

-

airbyte_extracted_at

-

_airbyte_meta

- Plus all the columns from your stream schema If you're not seeing the

_airbyte_meta

field, there could be a few reasons: 1. The upgrade might not have completed successfully. As mentioned in the [Destinations V2 upgrade guide](https://docs.airbyte.com/release_notes/upgrading_to_destinations_v2#quick-start-to-upgrading), you need to reset your connection after upgrading to ensure the new schema is applied. 2. You might be looking at tables that were created before the upgrade. Destinations V2 doesn't automatically migrate existing data, so you'll need to run a new sync to see the new schema. 3. There could be a configuration issue with your BigQuery destination. Make sure you're using version 2.0.0 or later of the BigQuery destination connector. To verify the upgrade was successful, check that: 1. Your raw tables are in the

airbyte_internal

dataset 2. Your final tables have the expected schema with the

_airbyte_meta

field 3. You've run a sync after upgrading to Destinations V2 If you've confirmed all these points and still don't see the

_airbyte_meta

field, you might want to try resetting your connection and running a new sync to ensure the new schema is applied correctly.

Based on your experience with the BigQuery destination after upgrading to Destinations V2, it appears that your existing raw tables from V1 weren't migrated to the new

airbyte_internal

schema as expected. This behavior is actually documented in the Airbyte upgrade documentation. According to the [Upgrading to Destinations V2 guide](https://docs.airbyte.com/release_notes/upgrading_to_destinations_v2#upgrading-as-a-user-of-raw-tables): > When you upgrade to a Destinations V2 compatible version of your destination, we will leave a copy of your existing raw tables as they are, and new syncs will work from a new copy we make in the new

airbyte_internal

schema. Although existing downstream dashboards will go stale, they will not be broken. This means that by design, Airbyte doesn't move or delete your existing V1 raw tables during the upgrade process. Instead, it creates new tables in the

airbyte_internal

schema for future syncs while leaving the old tables untouched. If you want to migrate your existing data to the new schema, you would need to manually copy it over. The [Upgrading to Destinations V2 guide](https://docs.airbyte.com/release_notes/upgrading_to_destinations_v2#upgrading-connections-one-by-one-with-dual-writing) provides some guidance on how to do this, including SQL templates you can use to copy data from the old schema to the new one. For BigQuery specifically, you would need to create SQL statements to copy data from your existing raw tables to the new format in the

airbyte_internal

schema. If you need to maintain historical data in the new format, you might want to consider the dual-writing approach described in the documentation, which allows you to set up parallel connections during the transition period. [Upgrading to Destinations V2](https://docs.airbyte.com/release_notes/upgrading_to_destinations_v2#upgrading-as-a-user-of-raw-tables)

I understand your point now. Based on the Destinations V2 documentation, the raw tables should indeed be placed in the

airbyte_internal

schema after upgrading to Destinations V2. This behavior you're experiencing where the raw tables were replaced with the new V2 format but remained in the original schema instead of moving to

airbyte_internal

appears to be inconsistent with the expected behavior. According to the [Upgrading to Destinations V2 documentation](https://docs.airbyte.com/release_notes/upgrading_to_destinations_v2): > Internal Airbyte tables in the

airbyte_internal

schema: Airbyte will now generate all raw tables in the

airbyte_internal

schema. We no longer clutter your destination schema with raw data tables. This issue could be related to a few possibilities: 1. There might be a configuration issue in your BigQuery destination connector settings. Check if there's a "Raw Table Schema Name" setting that might be overriding the default

airbyte_internal

schema. 2. There could be a bug in the BigQuery destination connector implementation of Destinations V2. This has happened with other connectors - for example, in a [GitHub issue for the BigQuery destination](https://github.com/airbytehq/airbyte/issues/38473), there was a problem where

airbyte_internal

was still being created even when a different raw dataset was specified. 3. The version of the BigQuery destination connector you're using might have a specific implementation detail that differs from the general Destinations V2 documentation. To address this issue, you could: 1. Check your BigQuery destination configuration to see if there's a setting for the raw table schema name. 2. Consider upgrading to the latest version of the BigQuery destination connector if you're not already using it. 3. If the issue persists, it might be worth reporting this behavior to the Airbyte team as a potential bug or documentation inconsistency. For now, if your tables are functioning correctly with the new V2 format (even though they're not in the expected schema), your syncs should continue to work properly.