Get ready to learn more about Google Cloud Dataflow Autoscaling feature from our experts. Our Sendmail Support team is here to lend a hand with your queries and issues.

Google Cloud Dataflow Autoscaling – An Introduction

Google Cloud Dataflow can be described as a cloud-native data processing service that allows us to build as well as execute data processing pipelines for batch and stream processing.

Furthermore, it can automatically scale worker resources when required. This is known as autoscaling. It helps us optimize the cost and performance of the data processing pipelines by automatically modifying the number of workers as per the pipeline’s workload.

The load on the pipeline can be described as the number of data elements processed and monitored over a period of time. As the load increases, Google Cloud Dataflow automatically adds workers to the pipeline. Similarly, as the load decreases, it automatically removes workers.

We can easily use Dataflow via the cloud terminal, the Google Cloud console, or API. Furthermore, we can create jobs for virtual machines with Dataflow templates, and SQL statements, and then use AI notebooks. Each dataflow relies on a virtual system. Its price depends on the memory, CPU, and storage used.

Our experts recommend using Dataflow when we have to process and analyze batch data or streaming data. As a result, we get access to a detailed analysis of logs per service which also helps predict errors.

How to Configure Autoscaling in pipelines?

Autoscaling is often enabled by default. In case it is not enabled, we can enable it as seen below:

–autoscalingAlgorithm=THROUGHPUT_BASED
–maxNumWorkers=N

However, when we enable it manually, we have to be aware of the backlog, which includes the amount of data and pipelines yet to be processed. This will give us an idea of how many workers we have to assign.

Once we enable autoscaling, we can access the total backlog type, with the following commands in the UnboundedReader class:

• getTotalBacklogBytes()
• getSplitBacklogBytes()

Let us know in the comments if you need further help with Google Cloud Dataflow Autoscaling feature.

[Need assistance with a different issue? Our team is available 24/7.]

Conclusion

In conclusion, autoscaling is a feature offered by Google Cloud Dataflow. It optimizes the cost and performance of data processing pipelines. Our Support Techs demonstrate how to configure Autoscaling in pipelines as well.