Based on the logs you've provided, it's clear that the check operation for your Airbyte source is indeed taking around 3 minutes to complete. This duration is likely causing the timeout issue you're experiencing. To address this, we can adjust the timeout settings in Airbyte to accommodate longer-running checks. Here are a couple of approaches you can try: 1. Increase the HTTP idle timeout: As mentioned in the [Airbyte documentation on scaling](https://docs.airbyte.com/enterprise-setup/scaling-airbyte#schema-discovery-timeouts), you can increase the timeout limit by adding the following to your Airbyte configuration:

yaml    server:        extraEnvs:          - name: HTTP_IDLE_TIMEOUT            value: 20m          - name: READ_TIMEOUT            value: 30m

This sets the idle timeout to 20 minutes and the read timeout to 30 minutes, which should be more than enough for your 3-minute check. 2. Adjust the ingress proxy timeout: If you're using Kubernetes with an ingress, you can modify the ingress configuration to increase the timeout. As described in [this GitHub issue](https://github.com/airbytehq/airbyte/issues/45156), you can add the following annotation to your ingress definition:

yaml    apiVersion: <http://networking.k8s.io/v1|networking.k8s.io/v1>    kind: Ingress    metadata:      annotations:        <http://nginx.ingress.kubernetes.io/proxy-read-timeout|nginx.ingress.kubernetes.io/proxy-read-timeout>: "300"

This sets the proxy read timeout to 300 seconds (5 minutes), which should cover your 3-minute check duration. Remember to restart the relevant Airbyte services after making these changes for them to take effect. It's also worth noting that while these changes will allow the check to complete without timing out, a 3-minute check duration is quite long. You might want to investigate why the check is taking so long and see if there are any optimizations you can make on the source side to reduce this time.

I understand your concern about the long startup time for the workload pods. Let's look at some recent examples and potential causes for this issue based on the information in our knowledge sources. There are indeed some recent reports of workload pods taking a long time to start or encountering issues during startup: 1. In [this GitHub issue](https://github.com/airbytehq/airbyte/issues/44928), there were reports of pods failing to start due to timeouts. The logs showed that pulling images was taking a significant amount of time, which could contribute to long startup times:

6m54s Normal Pulled Pod/airbyte-abctl-workload-launcher-6cf787c454-8bzzc Successfully pulled image "airbyte/workload-launcher:0.64.3" in 53.619s (4m33.783s including waiting)

This suggests that image pulling could be one factor in your 3-minute startup time. 2. Another [GitHub issue](https://github.com/airbytehq/airbyte/issues/44443) reported pod creation failing with timeouts. The logs showed that the Kubernetes client was experiencing timeouts when trying to create pods:

Caused by: java.io.IOException: timeout      at io.fabric8.kubernetes.client.dsl.internal.OperationSupport.waitForResult(OperationSupport.java:504)

This indicates that there might be underlying Kubernetes issues causing delays in pod creation. 3. In a [different GitHub issue](https://github.com/airbytehq/airbyte/issues/35501), there were reports of workloads taking a long time to start, with logs showing significant time gaps between different stages of the startup process:

2024-02-21 17:37:18 platform > Creating workload d40d3c71-53bc-4abe-bfb6-5b9c1344c345_8629198_4_sync    2024-02-21 17:37:58 INFO i.a.w.l.c.WorkloadApiClient(updateStatusToLaunched):54 - Attempting to update workload: d40d3c71-53bc-4abe-bfb6-5b9c1344c345_8629198_4_sync to LAUNCHED.

This 40-second gap between creation and launch could contribute to longer overall startup times. To investigate why your pods are taking 3 minutes to start, you might want to: 1. Check if image pulling is taking a long time. Consider using a local registry or pre-pulling images to reduce this time. 2. Investigate if there are resource constraints (CPU, memory) in your Kubernetes cluster that might be delaying pod scheduling and startup. 3. Look for any network issues that might be slowing down communication between Kubernetes components. 4. Examine the logs in more detail to see if there are specific stages of the startup process that are taking longer than expected. If you need more specific guidance, it would be helpful to see more detailed logs of the entire startup process for your workload pods.