10 Google Cloud Composer Best Practices

Google Cloud Composer is a managed workflow orchestration service that enables users to create and manage complex workflows on Google Cloud Platform. It simplifies the process of creating and managing workflows, allowing users to focus on the tasks at hand.

However, as with any technology, there are certain best practices to follow when using Google Cloud Composer. In this article, we’ll discuss 10 best practices for using Google Cloud Composer to ensure that your workflows are efficient and secure.

1. Utilize managed services whenever possible

Managed services are cloud-based solutions that provide a fully managed environment for running applications and workloads. By using managed services, users can offload the operational burden of managing their own infrastructure to Google Cloud Platform (GCP). This allows them to focus on developing and deploying their applications instead of worrying about maintaining servers or configuring networks.

Using managed services also helps reduce costs by eliminating the need to purchase and maintain hardware. Additionally, GCP’s managed services offer scalability, reliability, and security benefits that would otherwise be difficult to achieve with traditional infrastructure. For example, GCP’s BigQuery service provides an easy way to store and query large datasets without having to manage any underlying infrastructure.

Google Cloud Composer is designed to make it easier to use managed services in GCP. It provides a unified interface for creating and managing workflows across multiple services, including BigQuery, Dataflow, Pub/Sub, and more. With Cloud Composer, users can quickly create complex workflows that span multiple services, allowing them to take advantage of the scalability, reliability, and security benefits offered by GCP’s managed services.

2. Automate routine maintenance tasks

Automating routine maintenance tasks helps to ensure that the environment is kept up-to-date and secure. Automation also reduces manual effort, which can be time consuming and error prone. Additionally, automation allows for more efficient use of resources by ensuring that only necessary tasks are performed when needed.

To automate routine maintenance tasks in Google Cloud Composer, users can create custom workflows using Apache Airflow. These workflows can be triggered on a schedule or based on certain conditions. For example, users can set up automated backups of their data, run periodic health checks, or perform other maintenance tasks such as updating software versions. Additionally, users can leverage existing open source tools like Terraform to manage infrastructure changes. This allows users to define their desired state and have it automatically applied whenever there is a change.

3. Use separate service accounts for each environment

Using separate service accounts for each environment helps to ensure that the resources associated with one environment are not accessible from another. This is especially important when dealing with sensitive data, as it prevents accidental access or malicious activity across environments.

Creating a new service account for each environment also allows you to assign different roles and permissions to each account. For example, if an environment requires more restrictive access control than others, you can create a service account with fewer privileges. This way, you can limit the scope of potential damage in case of a security breach.

To set up separate service accounts for each environment, first create a project in Google Cloud Platform (GCP). Then, go to IAM & Admin > Service Accounts and click Create Service Account. Enter a name for the service account and select the appropriate roles. Finally, click Create to generate the service account. You can then use this service account to authenticate your Composer environment.

4. Set up Cloud Storage buckets for data and logs

Cloud Storage buckets provide a secure, reliable, and cost-effective way to store data and logs. Data stored in Cloud Storage is encrypted by default, so it’s safe from unauthorized access. Additionally, the data is replicated across multiple Google Cloud regions for redundancy, ensuring that it remains available even if there are outages or other disruptions.

Setting up Cloud Storage buckets for data and logs also makes it easier to manage them. With Cloud Storage, you can easily organize your data into folders and subfolders, making it easy to find what you need when you need it. You can also set up lifecycle rules to automatically delete old files after a certain period of time, helping to keep storage costs down.

When using Google Cloud Composer, you can configure your environment to use Cloud Storage buckets for both data and logs. For data, you can specify which bucket should be used as the input and output locations for tasks. For logs, you can configure the logging driver to write log messages directly to a Cloud Storage bucket. This allows you to easily view and analyze your logs without having to manually download them from the environment.

5. Leverage the Cloud Composer Airflow UI to manage your workflows

The Airflow UI provides a graphical representation of your workflows, making it easier to understand and debug them. It also allows you to view the status of each task in real-time, so you can quickly identify any issues that may arise. Additionally, the UI enables users to easily monitor their workflow performance by providing metrics such as execution time, number of retries, and more.

Using the Airflow UI is simple. After creating a Cloud Composer environment, you can access the Airflow UI from the Google Cloud Console. From there, you can create DAGs (Directed Acyclic Graphs) which are used to define the tasks and dependencies within your workflow. You can then use the UI to trigger, pause, or delete your workflows, as well as view logs for debugging purposes.

6. Monitor resource usage in Stackdriver

Stackdriver is a monitoring service that provides visibility into the performance, uptime, and overall health of cloud-powered applications. It allows users to monitor their Google Cloud Composer environment in real time, including resource usage such as CPU utilization, memory consumption, disk space, network traffic, and more. This helps identify potential issues before they become problems, allowing for proactive resolution. Additionally, Stackdriver can be used to set up alerts when certain thresholds are exceeded, so users can take action quickly if needed. Finally, Stackdriver also offers detailed logging capabilities, which can help with troubleshooting and debugging any issues that may arise.

7. Configure custom IAM roles when deploying resources

IAM roles are used to grant access to resources in Google Cloud Platform. When deploying resources with Composer, it is important to configure custom IAM roles that limit the scope of permissions granted to only those necessary for the task at hand. This helps ensure that users have the least amount of privileges needed to perform their tasks and reduces the risk of accidental or malicious misuse of cloud resources.

To configure custom IAM roles when deploying resources with Composer, you can use the gcloud command-line tool to create a role with the desired set of permissions. You can then assign this role to the service account associated with your Composer environment. This will allow the environment to deploy resources with the limited set of permissions specified by the custom IAM role.

8. Deploy secure connections between services

When using Google Cloud Composer, it is important to ensure that all communication between services is secure. This means encrypting data in transit and authenticating the identity of each service before allowing any communication. Encryption ensures that only authorized parties can access the data being transmitted, while authentication helps prevent malicious actors from impersonating legitimate services.

To deploy secure connections between services, you should use TLS/SSL encryption for all communications. You can also use mutual TLS (mTLS) to further strengthen security by requiring both sides of a connection to authenticate themselves with certificates. Additionally, you should configure your network settings to restrict traffic to known ports and IP addresses, as well as enable firewall rules to block unauthorized requests. Finally, you should monitor your environment for suspicious activity and respond quickly if any threats are detected.

9. Enable auditing of all workflow runs

Enabling auditing of all workflow runs is beneficial because it allows users to track and monitor the progress of their workflows. This helps ensure that any errors or issues are quickly identified and addressed, as well as providing an audit trail for compliance purposes.

To enable auditing of all workflow runs in Google Cloud Composer, users must first create a logging sink in Stackdriver Logging. This can be done by navigating to the Logs Viewer page in the GCP Console and clicking on “Create Sink”. From there, users will need to provide a name for the sink, select the destination type (e.g., BigQuery), and configure the filter expression. Once this is complete, the logging sink will be created and ready to use. Finally, users should navigate to the Airflow UI and click on the Admin tab. Under the Admin tab, they should select the “Audit Logging” option and then check the box next to “Enable Audit Logging”. After doing so, all workflow runs will be automatically logged to the configured logging sink.

10. Enable auto-scaling when running long-running jobs

Auto-scaling allows for the dynamic adjustment of resources based on workloads. This means that when running long-running jobs, Google Cloud Composer can automatically scale up or down to meet the demands of the job. This helps ensure that the job is completed in a timely manner and with minimal cost.

To enable auto-scaling, users must first set up an autoscaling policy. This involves setting thresholds for scaling up and down, as well as defining the number of nodes to add or remove at each threshold. Once this is done, users can then configure their DAGs to use the autoscaling policy. This will allow the DAGs to dynamically adjust the number of nodes used depending on the current workload.