Hmm, I don't know enough to give you a confident answer yet. The issue you mentioned is not available in the information extracts I have. However, I can provide some general guidance on scaling Airbyte workers and managing memory usage. 1. You can customize the memory settings for Java connectors by setting the

JOB_MAIN_CONTAINER_MEMORY_REQUEST

and

JOB_MAIN_CONTAINER_MEMORY_LIMIT

environment variables to custom values. This can help you manage memory usage more effectively [source](https://docs.airbyte.com/operator-guides/scaling-airbyte#memory). 2. You can increase the number of worker replicas to allow more jobs to be processed in parallel. This can be done in the Kubernetes deployment by increasing the number of replicas for the

airbyte-worker

deployment [source](https://docs.airbyte.com/deploying-airbyte/on-kubernetes#increase-job-parallelism). 3. You can also configure the maximum number of each type of worker allowed to run in parallel by setting the environment variables

MAX_SPEC_WORKERS

,

MAX_CHECK_WORKERS

,

MAX_DISCOVERY_WORKERS

, and

MAX_SYNC_WORKERS

[source](https://docs.airbyte.com/understanding-airbyte/jobs#worker-parallization). For more information on scaling Airbyte, you can refer to the [Scaling Airbyte documentation](https://docs.airbyte.com/operator-guides/scaling-airbyte).