Airbyte can run streams within a sync concurrently, depending on the configuration and the sync mode used. Here are some key points to understand: 1. Concurrent Syncs: - Airbyte supports running streams concurrently in all sync modes. This means that multiple streams can be processed at the same time, which can significantly speed up the data synchronization process. - For example, the Salesforce source has been updated to support concurrent syncs in all sync modes, as noted in the [Salesforce documentation](https://docs.airbyte.com/integrations/sources/salesforce#reference). 2. Sync Modes: - Airbyte provides different sync modes that define how data is read from a source and written to a destination. The two primary sync modes are

incremental

and

full_refresh

. -

Incremental

mode sends only the new data since the last sync, while

full_refresh

mode resends all data for the stream on every sync. More details can be found in the [Airbyte Protocol documentation](https://docs.airbyte.com/understanding-airbyte/airbyte-protocol#source-sync-modes). 3. Configuring Streams: - You can choose which streams to sync and how they are loaded to the destination in the "Schema" tab of the connection settings. This includes toggling sync on or off for individual streams and selecting the sync mode for each stream. Detailed instructions are available in the [Configuring Connections documentation](https://docs.airbyte.com/cloud/managing-airbyte-cloud/configuring-connections#modify-streams). In summary, Airbyte can run streams concurrently within a sync, which can optimize the synchronization process. The specific behavior can be configured based on the sync mode and the settings for each stream. For more detailed information, you can refer to the following documentation: - [Airbyte Protocol - Source Sync Modes](https://docs.airbyte.com/understanding-airbyte/airbyte-protocol#source-sync-modes) - [Salesforce Source - Reference](https://docs.airbyte.com/integrations/sources/salesforce#reference) - [Configuring Connections - Modify Streams](https://docs.airbyte.com/cloud/managing-airbyte-cloud/configuring-connections#modify-streams)

In terms of scaling, Airbyte can be deployed as a multi-instance deployment, and it does not require a single instance for stable operation. Here are some key points to consider: 1. Scaling with Kubernetes: - Airbyte allows scaling sync workloads horizontally using Kubernetes. The core components (API server, worker, etc.) run as deployments, while the scheduler launches connector-related pods on different nodes. This setup facilitates scaling by distributing the workload across multiple instances. - For more details, refer to the [Deploy Airbyte on Kubernetes using Helm](https://docs.airbyte.com/deploying-airbyte/on-kubernetes-via-helm#overview) documentation. 2. Scaling with Restack: - Restack makes it easier to deploy multiple instances of Airbyte on the same or multiple clusters. This allows you to test the latest version before upgrading or have dedicated instances for development and production. - You can find more information in the [Deploy multiple instances of Airbyte on Restack](https://docs.airbyte.com/deploying-airbyte/on-restack#deploy-multiple-instances-of-airbyte) documentation. 3. Scaling Considerations: - Workers do all the heavy lifting within Airbyte, and scaling Airbyte involves ensuring that the Docker container or Kubernetes Pod running the jobs has sufficient resources to execute its work. Sync jobs, which are the majority of jobs run, use two workers: one to read from the source and another to write to the destination. - It is recommended to start with a mid-sized cloud instance (e.g., 4 or 8 cores) and gradually tune the instance size to your workload. More details can be found in the [Scaling Airbyte](https://docs.airbyte.com/operator-guides/scaling-airbyte#what-to-scale) documentation. In summary, Airbyte supports multi-instance deployments, and you can scale it horizontally using Kubernetes or Restack. This flexibility allows you to manage and optimize your data synchronization processes effectively. For more detailed information, you can refer to the following documentation: - [Deploy Airbyte on Kubernetes using Helm](https://docs.airbyte.com/deploying-airbyte/on-kubernetes-via-helm#overview) - [Deploy multiple instances of Airbyte on Restack](https://docs.airbyte.com/deploying-airbyte/on-restack#deploy-multiple-instances-of-airbyte) - [Scaling Airbyte](https://docs.airbyte.com/operator-guides/scaling-airbyte#what-to-scale)

To configure the resource requirements for Airbyte job containers, you can use the following environment variables: -

JOB_MAIN_CONTAINER_CPU_REQUEST

-

JOB_MAIN_CONTAINER_CPU_LIMIT

-

JOB_MAIN_CONTAINER_MEMORY_REQUEST

-

JOB_MAIN_CONTAINER_MEMORY_LIMIT

These environment variables help manage the minimum and maximum CPU and memory usage for the job containers, ensuring that resources are allocated efficiently and within the desired limits. Here’s how you can use them: 1. Define the Environment Variables: - You can set these environment variables in your

.env

file or directly in your deployment configuration. 2. Units: - The units for these variables follow either Docker or Kubernetes conventions, depending on your deployment. For Docker, you can use suffixes like

b

,

k

,

m

,

g

to indicate bytes, kilobytes, megabytes, or gigabytes. For Kubernetes, you can use similar units. 3. Example Configuration:

env    JOB_MAIN_CONTAINER_CPU_REQUEST=1    JOB_MAIN_CONTAINER_CPU_LIMIT=2    JOB_MAIN_CONTAINER_MEMORY_REQUEST=1G    JOB_MAIN_CONTAINER_MEMORY_LIMIT=5G

4. Usage in Docker: - When using Docker, these settings will be applied to the job containers to ensure they have the specified minimum and maximum CPU and memory resources. 5. Usage in Kubernetes: - In a Kubernetes deployment, these settings will be applied to the pods running the job containers, ensuring they adhere to the specified resource requests and limits. ### Detailed Explanation -

JOB_MAIN_CONTAINER_CPU_REQUEST

: Defines the minimum CPU usage for the job container. This ensures that the container gets at least this amount of CPU resources. -

JOB_MAIN_CONTAINER_CPU_LIMIT

: Defines the maximum CPU usage for the job container. This prevents the container from using more than this amount of CPU resources. -

JOB_MAIN_CONTAINER_MEMORY_REQUEST

: Defines the minimum RAM usage for the job container. This ensures that the container gets at least this amount of memory. -

JOB_MAIN_CONTAINER_MEMORY_LIMIT

: Defines the maximum RAM usage for the job container. This prevents the container from using more than this amount of memory. ### References For more detailed information, you can refer to the following documentation: - [Configuring Connector Resources - Instance-Wide Requirements](https://docs.airbyte.com/operator-guides/configuring-connector-resources#configuring-instance-wide-requirements) - [Docker Resource Constraints](https://docs.docker.com/config/containers/resource_constraints/) - [Kubernetes Resource Management](https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/) By setting these environment variables, you can ensure that your Airbyte job containers have the appropriate resources allocated to them, preventing issues such as out-of-memory exceptions or inefficient resource usage.