The error message you're seeing indicates that a job has failed after too many retry attempts for a specific connection. This error is often logged when a job, such as data synchronization, fails repeatedly due to issues like network errors, API rate limits, or other transient problems. In Airbyte, the RetryManager handles the retry logic. It uses a backoff policy that defines the minimum and maximum intervals between retries, and the number of successive failures allowed before the job is considered failed. The RetryManager also keeps track of the total number of complete and partial failures. Here are a few examples from the knowledge sources where similar errors were encountered: 1. A user encountered a consistent normalization failure. The RetryManager logged that it had reached the limit of successive partial failures, and the job was failed [[source]](https://github.com/airbytehq/airbyte/issues/32288). 2. Another user reported that their sync job was not working. The RetryManager had reached the limit of successive complete failures, and the job was failed [[source]](https://github.com/airbytehq/airbyte/issues/36786). 3. A user experienced intermittent WorkerExceptions with their container orchestrator. The job failed after too many retries for a specific connection [[source]](https://github.com/airbytehq/airbyte/issues/34590). To resolve this issue, you might need to investigate the cause of the repeated failures. Check the logs for any error messages preceding this one, as they might provide more context about what's causing the job to fail. If the failures are due to network issues or API rate limits, you might need to adjust your setup or usage to avoid these problems. If you're unable to resolve the issue, consider opening a new issue on the [Airbyte GitHub](https://github.com/airbytehq/airbyte/issues) for further assistance.

The ideal CPU increase for your Airbyte connection depends on the specific requirements of your connectors and the overall load on your system. The

cpuRequest

and

cpuLimit

values you're seeing in the log are the minimum and maximum CPU resources that are allocated for the connection. In the knowledge sources, there are a few instances where CPU allocation is discussed: 1. In a GitHub issue, the Airbyte team discussed provisioning a runner with 32 CPUs to assess if giving more CPU to Java connector tests would make them faster [[source]](https://github.com/airbytehq/airbyte/issues/31443). 2. In a forum post, a user suggested increasing the resources (memory and CPU) to resolve errors with a custom destination connector [[source]](https://discuss.airbyte.io/t/678). 3. In the Airbyte documentation, it's recommended to scale up your Airbyte deployment to a larger number of nodes or reduce the maximum resource usage by any given connector pod if your nodes are under high CPU or Memory usage [[source]](https://docs.airbyte.com/enterprise-setup/scaling-airbyte#concurrent-syncs). If you're experiencing performance issues or errors, you might consider increasing the

cpuLimit

for your connection. However, it's important to monitor the impact of this change on your system's overall performance and adjust as necessary. If you're unsure about the ideal CPU allocation for your specific use case, consider reaching out to the Airbyte community or support for more personalized advice.

A message from kapa.ai

The difference in memory values you're seeing could be due to different configurations for the connection in different contexts. When you make a request to the

/api/v1/connections/get

endpoint, the response includes the current configuration for the connection, which includes the

memory_request

and

memory_limit

values. These values represent the minimum and maximum memory that is allocated for the connection when it's running. On the other hand, the values you're seeing in the connection log (

memoryRequest=256Mi,memoryLimit=1024Mi

) are the default resource requirements that are set for the connection. These values are used when the connection is created, and they can be overridden by the specific configuration for the connection. If you've updated the memory allocation for the connection after it was created, this could explain why the values are different. The

/api/v1/connections/get

endpoint would return the updated values, while the connection log would still show the original default values. If you want to update the memory allocation for the connection, you can do so by making a request to the

/api/v1/connections/update

endpoint with the updated

memory_request

and

memory_limit

values in the request body. After updating the configuration, the

/api/v1/connections/get

endpoint should return the updated values. Please note that increasing the memory allocation can improve the performance of the connection, but it can also increase the load on your system. Therefore, it's important to monitor the impact of any changes on your system's overall performance.

The difference in memory values you're seeing could be due to different configurations for the connection in different contexts. The

ResourceRequirements

you see in the connection log from the UI are the default resource requirements set for the connection. These values are set in the code and apply to all connections unless specifically overridden. In your case, the default memory request is

256Mi

and the memory limit is

1024Mi

. On the other hand, the

memory_request

and

memory_limit

values you're seeing from the API response are likely specific to the connection you're querying. These values are set when you create or update the connection and can be used to override the default resource requirements. In your case, the memory request is

4096Mi

and the memory limit is

8192Mi

. If you want the values to match, you would need to update the connection's specific resource requirements to match the default values, or vice versa. You can do this through the Airbyte API or UI. Please note that adjusting these values should be done carefully, considering the specific needs of your connection and the resources available on your system. If you're unsure, it's recommended to start with the default values and adjust as necessary based on the performance and resource usage you observe.

A message from kapa.ai

The difference in memory values you're seeing could be due to different configurations for the connection in different contexts. The

ResourceRequirements

you see in the connection log from the UI are the default resource requirements set for the connection. These values are set in the code and apply to all connections unless specifically overridden. In your case, the default memory request is

256Mi

and the memory limit is

1024Mi

. On the other hand, the

memory_request

and

memory_limit

values you're seeing from the API response are likely specific to the connection you're querying. These values are set when you create or update the connection and can be used to override the default resource requirements. In your case, the memory request is

4096Mi

and the memory limit is

8192Mi

. If you want the values to match, you would need to update the connection's specific resource requirements to match the default values, or vice versa. You can do this through the Airbyte API or UI. Please note that adjusting these values should be done carefully, considering the specific needs of your connection and the resources available on your system. If you're unsure, it's recommended to start with the default values and adjust as necessary based on the performance and resource usage you observe.