Cost Optimization for Databricks Clusters: A Data Engineer’s Approach
As a data engineer, finding ways to save costs for your employer while using Databricks is essential. Here’s my approach, focusing on cluster configuration and why each step is important.
Why Optimize Costs?
With the rising cost of cloud resources, companies are eager to save money. Effective cost management in Databricks not only reduces expenses but also ensures efficient use of resources, ultimately improving the overall performance and scalability of your data pipelines.
Best Practices Overview
While many articles cover general cost-saving practices such as using job clusters, spot instances, Delta file formats, and dynamic resource allocation, this post will dive deeper into cluster configuration based on real-world experience. For a comprehensive guide, refer to Microsoft’s Best Practices.
Cluster Configuration
1. Databricks Runtime Version
Importance: Upgrading to the latest long-term support version (e.g., 14.3 LTS) ensures you benefit from the latest optimization features and resolutions for inefficiencies found in older runtimes.
Action: Regularly update your Databricks runtime to the latest LTS version.
2. Autoscaling and Auto-termination
Importance: Autoscaling allows clusters to scale based on workload demands, while auto-termination shuts down clusters when not in use. However, setting the maximum number of worker nodes too high can result in overpaying for idle resources due to delays in deallocation.
Action: Be conservative when configuring autoscaling by setting the smallest maximum number of worker nodes necessary, with an absolute maximum of eight worker nodes, leverage Metrics UI. This approach balances resource usage and cost. Additionally, enable auto-termination to avoid unnecessary charges.
3. Cluster Sizing: Less is More
Importance: Proper cluster sizing can significantly impact performance and cost. For example, choosing one worker node with 8 cores and 28 GB RAM over two nodes with 4 cores and 14 GB RAM each maintains parallelism and reduces network costs and management complexity.
Action: Opt for fewer, more powerful nodes to optimize resource usage and reduce overhead.
4. Ensure Optimal Cluster Utilization: Metrics UI/Ganglia UI
Importance: Monitoring cluster and CPU memory utilization ensures that resources are used efficiently. Metrics UI (for Databricks Runtime 13.3 and above) or Ganglia UI (for older runtimes) provide insights into resource usage, helping identify and address inefficiencies.
Action: Build isolated clusters for major data pipelines and use Metrics UI to monitor CPU utilization. Focus on the most expensive clusters/pipelines first, ensuring a minimum of 80% utilization. Metrics UI helps determine the appropriate cluster size and the number of worker nodes needed during autoscaling. Refer to this article for more details on Metrics/Ganglia UI.
5. Write Efficient Code and follow Databricks Optimization Best Practices
Importance: Writing efficient code ensures that your data pipelines run smoothly and cost-effectively. Inefficient code can lead to unnecessary resource consumption and higher costs.
Action: Follow best practices such as:
- Ensuring optimal Delta file sizes.
- Using Delta file properties like auto optimize write.
- Leveraging deletion vectors.
- Avoiding Python user-defined scalar functions.
- For more Databricks Optimization review this link
By implementing these strategies, you can significantly reduce costs and improve efficiency in your Databricks environment. Effective cost management not only saves money but also ensures that your data engineering processes are robust and scalable.
Hello, I am Nnaemezue Obi-eyisi, a Senior Azure Databricks Data Engineer at Capgemini and the founder of AfroInfoTech, an online coaching platform for Azure data engineers specializing in Databricks. My goal is to help more people break into data engineering career. If interested join my waitlist
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