If you're experiencing memory issues with Airbyte deployed on an AWS EC2 instance, there are a few things you can try: 1. Check your EC2 instance type: For testing Airbyte, a

t2.medium

instance is recommended, while for deploying Airbyte in a production environment, a

t2.large

instance is recommended. If you're using a smaller instance type, you might run into memory issues. [source](https://docs.airbyte.com/deploying-airbyte/on-aws-ec2) 2. Configure Connector Resources: You can set environment variables to define the job container's minimum and maximum CPU and RAM usage. This can help manage the memory usage of your Airbyte deployment. Here are the environment variables you can set: -

JOB_MAIN_CONTAINER_CPU_REQUEST

- Define the job container's minimum CPU usage. -

JOB_MAIN_CONTAINER_CPU_LIMIT

- Define the job container's maximum CPU usage. -

JOB_MAIN_CONTAINER_MEMORY_REQUEST

- Define the job container's minimum RAM usage. -

JOB_MAIN_CONTAINER_MEMORY_LIMIT

- Define the job container's maximum RAM usage. [source](https://docs.airbyte.com/operator-guides/configuring-connector-resources) 3. Parallelize the syncs: If you're syncing multiple tables, you can try creating multiple connections for different tables that all sync to the same destination. This can help manage memory usage by parallelizing the syncs at a connector level. [source](https://discuss.airbyte.io/t/1977) If you're still having issues, you can reach out to the Airbyte community on [Slack](https://slack.airbyte.com/) for further assistance.