Google Dataproc : Top 5 Cost Optimization Strategies

Do you want to analyze your open-source data and analytics more quickly? Google offers a solution in the form of Cloud Dataproc. It is a cloud-based service that allows users to execute Apache Hadoop and Apache Spark workloads. It is intended to make managing and processing huge datasets simple for users.

Dataproc is extremely scalable, allowing customers to set up clusters of compute machines to analyze their data rapidly and simply. It also connects with other Google Cloud services, such as Google BigQuery and Google Cloud Storage, making data storage, processing, and analysis a breeze. Dataproc is an appealing alternative for enterprises that need to handle massive volumes of data fast and cost-effectively.

In this article, we will learn how to use Dataproc and optimize its cost for high volume workloads.

Benefits of Dataproc:

Let’s see some potential benefits of using Dataproc include the following:

Getting started with Dataproc:

Let’s create a Dataproc Cluster, first find Dataproc in the navigation menu and click on a cluster, then it will ask to Enable Dataproc, after that you will be able to create the cluster.

Once you click “Create Cluster”, it gives you the option to select Cluster Type, Name of Cluster, Location, Auto-Scaling Options, and more. There are three types of clusters: Standard, Single Node, and High Availability.

The Standard cluster consists of 1 master and N worker node. This Single Node has only 1 master and 0 worker nodes. For production purposes, you can always opt for the High Availability cluster.

For this learning purpose, a single node cluster is sufficient which has only 1 master Node.

As we’ve selected the Single Node Cluster option, this means the auto-scaling is disabled as the cluster consists of only 1 master node.

The Configure Nodes option allows us to select the type of machine families like Compute Optimized, GPU, and General-Purpose.

Here, we’ll be using the General-Purpose machine option. From here, you can choose Machine Type, Primary Disk Size, and Disk-Type options. We are trying to use the most basic configuration for this blog you may choose as per your requirement.

Next move to Customize Cluster option and select the default network configuration:

Here we will use the default security option by Google. Click “Create”, it’ll start creating the cluster. After a few minutes, the cluster will be ready for use.

Working on Spark and Hadoop becomes much easier if you’re using Dataproc.

Understanding Dataproc Pricing & Costs

Dataproc pricing depends upon the size of clusters and the duration of time that they run. The size of a cluster is based on the number of virtual CPUs (vCPUs) across the entire cluster, including the master and worker nodes. The duration of a cluster is the time duration between cluster creation and cluster stopping or deletion.

The Dataproc pricing formula is:

$0.010 * n of vCPUs * duration

Although the pricing formula is expressed as an hourly rate, but Dataproc is billed by the second, and all clusters are billed in one-second clock-time increments, subject to a 1-minute minimum billing.

Pricing example

Consider a cluster that has the following configuration:

This cluster has 24 vCPUs, 4 for the master and 20 for the workers. If the cluster runs for 2 hours, then Dataproc pricing would be this by using the following formula:

Dataproc charge = # of vCPUs * hours * Dataproc price = 24 * 2 * $0.01 = $0.48

Dataproc Cost Optimization Strategies

There are several strategies that organizations can use to optimize their costs when using Dataproc. Some potential strategies include:

Conclusion

Considering these for managing Dataproc in your application will definitely help you to save money. The best long-term strategy is to establish a Finops practice within your company. Economize is committed to the idea of making your cloud spending simpler and noise-free to help engineering teams like yours understand and optimize it. Get started today with a personalized demo for your organization.