Google Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It provides a way to author, schedule, and monitor workflows in a more manageable and efficient way. Cloud Composer simplifies the setup process compared to setting up Apache Airflow manually, making it significantly easier for users.

Apache Airflow itself is a platform for programmatically authoring, scheduling, and monitoring workflows. It’s scalable, dynamic, extensible, and elegant, allowing users to define workflows in Python. This means that pipelines are not only dynamic but also easily adjustable and extensible. Airflow also features a useful UI for monitoring, scheduling, and managing workflows, as well as robust integrations with various cloud services, including Google Cloud Platform, Amazon Web Services, and Microsoft Azure. This makes it easy to apply Airflow to current infrastructure and extend it to new technologies.

When using Cloud Composer, there are several factors to consider for tuning and optimizing its performance, especially when dealing with memory utilization and task execution. Each task in Airflow, like a REST call to the Dataproc cluster API, is executed as a Celery worker, which consumes a certain amount of RAM. The memory utilization depends on several factors, including the type of Airflow task, the allocatable memory on the Kubernetes cluster, Cloud Composer’s built-in processes overhead, and the type of virtual machine being used.

For instance, a standard n1-standard-1 virtual machine in a Cloud Composer setup has an allocatable memory of around 2.75 GB (2.56 GiB). The costs associated with running a Cloud Composer cluster can vary depending on the size and type of the workers, with the monthly costs increasing for larger workers. However, these larger workers provide more compute power for a relatively small increase in cost.

In terms of configuration, settings like core.parallelism and celery.worker_concurrency need to be manually applied in the Apache Airflow configuration to match the memory and compute capabilities of the Cloud Composer environment. Also, modifying properties like scheduler.min_file_process_interval and scheduler.parsing_processes can help in optimizing the CPU utilization.

Monitoring performance metrics is crucial in a dynamic batch job scheduling environment like Cloud Composer. Metrics like worker pod eviction count and the number of running task instances provide insights into when to scale the cluster up or down.

For more detailed information and insights into Cloud Composer and Apache Airflow, you can visit the Passionate Developer blog on GCP Cloud Composer and Apache Airflow tuning and the Apache Airflow official site.