hello, I have airbyte deployed on AWS EKS, we currently have airbyte deployed on t3.medium with 3 nodes. I have following questions regarding airbyte capacity planning:

1. how much cpu and memory is taken by each synch in Airbyte. It seems it creates new pod for each synch
2. Are there any config parameters to have better scalability in airbyte specifically for worker pods.?

In Airbyte, workers are responsible for executing operations such as Discover, Read, Sync, etc. Each job has a corresponding worker executing its work. The resource consumption of each sync job depends on the nature of the job and the data it's handling. For instance, the Source worker reads up to 10,000 records in memory, which can present problems for database sources with tables that have large row sizes. For example, a table with an average row size of 0.5MBs will require 0.5 * 10000 / 1000 = 5GBs of RAM [source](https://docs.airbyte.com/operator-guides/scaling-airbyte). In terms of scalability, you can tune the instance size to your workload. It's recommended to start with a mid-sized cloud instance (e.g. 4 or 8 cores) and gradually tune the instance size to your workload. There are two resources to be aware of when thinking of scale: Memory and Disk space [source](https://docs.airbyte.com/operator-guides/scaling-airbyte). You can customize the memory usage by setting

JOB_MAIN_CONTAINER_MEMORY_REQUEST

and

JOB_MAIN_CONTAINER_MEMORY_LIMIT

environment variables to custom values. Note that all Source database connectors are Java connectors, which means that users currently need to over-specify memory resource for Java connectors [source](https://docs.airbyte.com/operator-guides/scaling-airbyte). Unfortunately, the extracts do not provide specific information on how much CPU is consumed by each sync job or any specific configuration parameters for better scalability of worker pods. For more detailed information, you may want to refer to the [Airbyte documentation](https://docs.airbyte.com/).