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<@U04SE163WC8> what are the steps to move EKS secr...
# ask-ai
p
@kapa.ai what are the steps to move EKS secrets to AWS Secret Manager?
u
To move EKS secrets to AWS Secret Manager, you can follow these steps: 1. Creating or Updating Kubernetes Secrets If you are using AWS access keys, create a Kubernetes secret to store these credentials. If you are relying on an IAM role from an instance profile, this step can be skipped. Apply the following Kubernetes manifest, replacing the example AWS credentials with your actual credentials:
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yaml    apiVersion: v1      kind: Secret      metadata:        name: airbyte-config-secrets      type: Opaque      stringData:        s3-access-key-id: AKIAIOSFODNN7EXAMPLE # Enter your AWS Access Key ID here        s3-secret-access-key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY # Enter your AWS Secret Access Key here
2. Update Airbyte Configuration In your 
airbyte.yml
configuration file, add the following configuration, adjusting 
<aws-region>
to match your AWS region:
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yaml    global:        storage:          type: s3          storageSecretName: airbyte-config-secrets          bucket:            log: airbyte-storage            state: airbyte-storage            workloadOutput: airbyte-storage          s3:            region: <aws-region>            authenticationType: credentials # Use "credentials" or "instanceProfile"            accessKeyIdSecretKey: aws-secret-manager-access-key-id # Omit if using instanceProfile            secretAccessKeySecretKey: aws-secret-manager-secret-access-key # Omit if using instanceProfile            secretsManager:          type: awsSecretManager          storageSecretName: airbyte-config-secrets          awsSecretManager:            region: <aws-region>            authenticationType: credentials # Use "credentials" or "instanceProfile"            accessKeyIdSecretKey: aws-secret-manager-access-key-id # Omit if using instanceProfile            secretAccessKeySecretKey: aws-secret-manager-secret-access-key # Omit if using instanceProfile            tags:              - key: team                value: deployment              - key: business-unit                value: engineering
3. Remove Deprecated Configuration from 
values.yaml
 Edit your 
values.yaml
or 
airbyte-pro-values.yaml
files to remove any deprecated storage and secrets manager environment variables related to S3 and AWS Secrets Manager. Ensure configurations like 
state.storage.type: "S3"
and AWS access keys under 
server.extraEnv
and 
worker.extraEnv
are removed. Please note that modifying the configuration of connector secret storage will cause all existing connectors to fail. You will need to recreate these connectors to ensure they are reading from the appropriate secret store. For more details, you can refer to the [Airbyte documentation](https://docs.airbyte.com/deploying-airbyte/on-kubernetes-via-helm#migration-steps-1).
p
@kapa.ai what's this error about "Pipeline Error 127 io.airbyte.workload.launcher.pipeline.stages.model.StageError: io.airbyte.workers.exception.ResourceConstraintException"
u
A message from kapa.ai
u
The error message "io.airbyte.workload.launcher.pipeline.stages.model.StageError: io.airbyte.workers.exception.ResourceConstraintException" suggests that there is an issue with resource constraints during the execution of a pipeline stage in Airbyte. This error typically occurs when the resources allocated to a specific task or process are not sufficient. This could be due to limitations in CPU, memory, or other system resources. In the context of Airbyte, this could be related to the resources allocated to the Airbyte workers or the specific connectors being used. If you're running Airbyte on a system like Kubernetes, you might need to adjust the resource requests and limits for your pods. Unfortunately, without more specific information or context from the error logs, it's hard to provide a more precise solution. You might want to check the detailed error logs for more information about which resource is constrained and adjust your settings accordingly. If you're still facing issues, consider reaching out to the Airbyte community on [Github](https://github.com/airbytehq/airbyte) or their [discussion forum](https://discuss.airbyte.io/) for more specific assistance.
p
@kapa.ai Error: UPGRADE FAILED: cannot patch "airbyte-server" with kind Deployment: The order in patch list:
u
A message from kapa.ai
u
The error message "UPGRADE FAILED: cannot patch "airbyte-server" with kind Deployment: The order in patch list" suggests that there's an issue with the order of operations in the patch list when trying to upgrade the Airbyte server. This error can occur when there's a conflict between the current state of the Kubernetes object and the state defined in the patch. The patch operation fails because it's trying to modify a field in a way that's not allowed by the Kubernetes API. In the context of Airbyte, this error was encountered when running the 
install_airbyte_pro_on_helm.sh
script and was related to the 
airbyte-pro-keycloak-setup
pod. The immediate workaround was to delete the 
airbyte-pro-keycloak-setup
pod and then re-run the install script. The underlying cause was that the setup pod was an unmanaged workload (raw pod, not a Job or Deployment) [source](https://github.com/airbytehq/airbyte/issues/35500). If you're encountering a similar issue, you might want to try a similar approach: delete the problematic Kubernetes object and re-apply your configuration. If the issue persists, consider reaching out to the Airbyte community on [Github](https://github.com/airbytehq/airbyte) or their [discussion forum](https://discuss.airbyte.io/) for more specific assistance.
p
@kapa.ai airbyte-workload-launcher pod is in pending state for long. What could be the issue?
u
A message from kapa.ai
u
The 
airbyte-workload-launcher
pod being in a pending state for a long time could be due to several reasons. Here are a few potential causes based on the knowledge sources: 1. Insufficient Resources: The pod might be pending because there are not enough resources to schedule it on any node. This could be due to insufficient CPU, memory, or other system resources. 2. Persistent Volume (PV) Issues: As mentioned in the [forum post](https://discuss.airbyte.io/t/3211), the pod could be pending due to issues with the persistent volume. The user in the post faced a similar issue where the pod was pending due to a volume node affinity conflict. Resizing the persistent volume seemed to temporarily fix the issue. 3. Pod Creation Timeout: As seen in the [Github issue](https://github.com/airbytehq/airbyte/issues/44443), the pod creation could fail due to a timeout. This could be due to network issues or other underlying problems in the Kubernetes cluster. 4. Issues with Other Pods: If other pods that the 
airbyte-workload-launcher
pod depends on are in a pending state, this could cause the 
airbyte-workload-launcher
pod to also be pending. For example, in the [forum post](https://discuss.airbyte.io/t/1148), the 
airbyte-minio
pod was in a pending state, which could potentially affect other pods. To troubleshoot this issue, you can describe the pod to see why it's in a pending state using the command 
kubectl describe pod <pod-name>
. This will give you more information about the pod's status and any events or errors that might have occurred. If the issue persists, consider reaching out to the Airbyte community on [Github](https://github.com/airbytehq/airbyte) or their [discussion forum](https://discuss.airbyte.io/) for more specific assistance.
p
@kapa.ai how can I clean airbyte stale deployments on kubernetes?
u
A message from kapa.ai
u
To clean up stale Airbyte deployments on Kubernetes, you can follow these steps: 1. Delete the Airbyte Deployment: Use the 
helm delete
command to delete the Airbyte deployment. For example, if your deployment is named 
airbyte
, you would run:
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helm delete -n airbyte airbyte
This command will delete the Airbyte deployment but may leave behind some resources like statefulsets, secrets, and configmaps. 2. Delete Remaining Resources: As mentioned in the [Github issue](https://github.com/airbytehq/airbyte/issues/18591), 
helm delete
may leave behind some resources. You can delete these remaining resources using the 
kubectl delete
command. For example:
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kubectl delete -l <http://app.kubernetes.io/instance=airbyte|app.kubernetes.io/instance=airbyte> pods,statefulset,configmap,secret,service
This command will delete all pods, statefulsets, configmaps, secrets, and services that have the label 
<http://app.kubernetes.io/instance=airbyte|app.kubernetes.io/instance=airbyte>
. Please note that this will delete all data associated with the Airbyte deployment, including all connections and sync data. Make sure to backup any important data before running these commands.
p
@kapa.ai another error I am seeing in workload launcher logs for Google sheet connector io.micronaut.data.connection.jdbc.exceptions.CannotGetJdbcConnectionException: Failed to obtain JDBC Connection
u
A message from kapa.ai
u
The error message "io.micronaut.data.connection.jdbc.exceptions.CannotGetJdbcConnectionException: Failed to obtain JDBC Connection" suggests that there's an issue with establishing a JDBC connection. This could be due to several reasons such as incorrect database credentials, network connectivity issues, or the database server being down. In the context of Airbyte and the Google Sheets connector, this error could potentially be related to issues with the Google Sheets API or the configuration of the connector. Here are a few things you could check: 1. Google Sheets API: Ensure that the Google Sheets API is enabled and properly configured for your project in the Google Cloud Console. 2. Connector Configuration: Check the configuration of your Google Sheets connector in Airbyte. Make sure that all required fields are correctly filled out, including the 
client_id
, 
client_secret
, and 
refresh_token
as mentioned in the [forum post](https://discuss.airbyte.io/t/2600). 3. Database Connection: If you're using an external database with Airbyte, ensure that Airbyte can connect to it. The error message you're seeing was also reported in a [Github issue](https://github.com/airbytehq/airbyte/issues/36238) where the user was unable to connect to an external PostgreSQL database due to incorrect credentials. If the issue persists, consider reaching out to the Airbyte community on [Github](https://github.com/airbytehq/airbyte) or their [discussion forum](https://discuss.airbyte.io/) for more specific assistance.
p
@kapa.ai Unable to start the REPLICATION pod. This may be due to insufficient system resources. Please check available resources and try again.
u
A message from kapa.ai
u
The error message "Unable to start the REPLICATION pod. This may be due to insufficient system resources. Please check available resources and try again." suggests that your Kubernetes cluster might not have enough resources (CPU, memory) to start the replication pod. Here are a few steps you can take to troubleshoot this issue: 1. Check Pod Resources: Each Airbyte connector requires certain resources to run. These resources are defined in the 
ConnectorResourceRequirements
for each connector as seen in the [Github issues](https://github.com/airbytehq/airbyte/issues/39512). Check these requirements against the available resources in your Kubernetes cluster. 2. Check Kubernetes Cluster Resources: Use 
kubectl
commands to check the available resources in your Kubernetes cluster. For example, 
kubectl describe nodes
will give you information about the CPU and memory usage of each node in your cluster. 3. Increase Cluster Resources: If your cluster does not have enough resources, you might need to add more nodes to your cluster or use nodes with more resources. 4. Adjust Connector Resources: If increasing the cluster resources is not an option, you might need to adjust the resource requirements for your connectors. However, reducing the resources might affect the performance of the connectors. If the issue persists, consider reaching out to the Airbyte community on [Github](https://github.com/airbytehq/airbyte) for more specific assistance.
p
@kapa.ai how can I adjust Google sheet connector resources?
u
A message from kapa.ai
u
To adjust the resources for the Google Sheets connector in Airbyte, you can follow these steps: 1. Connect to the Database: Connect to the Airbyte database and run the following query with the image name replaced to find the connector definition id.
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sql select * from actor_definition where actor_definition.docker_repository like '%<image-name>';
2. Update the Resource Requirements: Run the following command with the resource requirements and the connection definition id filled in.
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sql update actor_definition set resource_requirements = '{"jobSpecific": [{"jobType": "sync", "resourceRequirements": {"cpu_limit": "0.5", "cpu_request": "0.5", "memory_limit": "500Mi", "memory_request": "500Mi"}}]}' where id = '<id-from-step-1>';
In the above command, replace 
cpu_limit
, 
cpu_request
, 
memory_limit
, and 
memory_request
with the desired resource values. Please note that these changes will affect all instances of the Google Sheets connector. If you want to adjust the resources for a specific connection, you can do so by updating the 
resource_requirements
field in the 
connection
table instead of the 
actor_definition
table. For more details, refer to the [Airbyte documentation](https://docs.airbyte.com/operator-guides/configuring-connector-resources#configuring-connector-specific-requirements).
p
@kapa.ai how can I change JOB_MAIN_CONTAINER_MEMORY_LIMIT?
u
A message from kapa.ai
u
To change the 
JOB_MAIN_CONTAINER_MEMORY_LIMIT
in Airbyte, you need to set this environment variable in the 
.env
file. Here are the steps: 1. Open the 
.env
file in the root directory of your Airbyte installation. 2. Look for the line that starts with 
JOB_MAIN_CONTAINER_MEMORY_LIMIT=
. If it doesn't exist, you can add it. 3. Set the value of 
JOB_MAIN_CONTAINER_MEMORY_LIMIT
to the desired limit. For example, to set the limit to 2 gigabytes, you would write:
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JOB_MAIN_CONTAINER_MEMORY_LIMIT=2g
4. Save the 
.env
file. 5. Restart Airbyte for the changes to take effect. Please note that the units for memory follow either Docker or Kubernetes, depending on the deployment. For Docker, the units are in bytes, kilobytes (k), megabytes (m), or gigabytes (g). For Kubernetes, the units are in bytes, kilobytes (K), megabytes (M), gigabytes (G), terabytes (T), petabytes (P), exabytes (E). For more details, refer to the [Airbyte documentation](https://docs.airbyte.com/operator-guides/configuring-airbyte#jobs) and the [forum post](https://discuss.airbyte.io/t/1675).
p
@kapa.ai how can I helm restart airbyte?
u
A message from kapa.ai
u
To restart Airbyte using Helm, you can use the 
helm upgrade
command. Here are the steps: 1. First, you need to update the Helm repo to get the latest charts:
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helm repo update
2. Then, you can use the 
helm upgrade
command to restart Airbyte. If you're using a 
values.yaml
file for your configuration, the command would look like this:
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helm upgrade \    --namespace airbyte \    --values ./values.yaml \    --install airbyte-enterprise \    airbyte/airbyte
Replace 
./values.yaml
with the path to your 
values.yaml
file, and 
airbyte-enterprise
with the name of your Airbyte release. Please note that this command will upgrade Airbyte to the latest version available in the Helm repo. If you want to restart Airbyte without upgrading, you can specify the current version of Airbyte using the 
--version
flag. For more details, refer to the [Airbyte documentation](https://docs.airbyte.com/enterprise-setup/implementation-guide#updating-self-managed-enterprise).
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