1.What did you do using databricks?
- Ingest, process, clean, and transform large volumes of structured and unstructured data using Apache Spark. Databricks supports complex ETL (extract, transform, load) workflows and optimizes data pipelines for performance and scalability.
- Build and manage data lakehouse, which combines the benefits of data lakes and data warehouses. This approach provides a single source of truth for all the data, making it easier to access, analyze, and maintain.
- Perform advanced analytics, run SQL queries, and create interactive dashboards. Databricks integrates with BI tools like Tableau and Power BI, and supports real-time stream processing and data visualization.
- Develop, train, and deploy machine learning models. Databricks offers built-in algorithms, supports popular ML libraries (such as TensorFlow and PyTorch), and provides tools for feature engineering, hyperparameter tuning, and model evaluation.
2.How do you do cluster performance (performance tuning) in databricks?
Key Strategies for Cluster Performance Tuning in Databricks
2.1.Optimize Cluster Configuration
- Select the right cluster type (Interactive/All-Purpose for development, Job/Automated for production workloads) and mode (Single Node for small/non-distributed jobs, Multi Node for large/distributed jobs)
- Choose appropriate driver and worker instance types based on workload requirements; avoid generic configurations for better efficiency
- Use larger clusters for workloads that scale linearly, as they can complete jobs faster without increasing costs, since you pay for cluster uptime, not per worker
2.2. Leverage Autoscaling and Serverless Options
- Enable autoscaling to dynamically adjust resources to workload demands, but test if it actually reduces compute costs for your specific jobs
- Consider serverless compute for ad-hoc or interactive workloads to reduce manual tuning, but be aware of the trade-offs in control and potential cost
2.3. Use the Latest Databricks Runtime and Photon Engine
- Always use the latest Long-Term Support (LTS) version of Databricks Runtime for performance enhancements
- Test workloads with and without Photon Engine, as it can significantly improve performance for compatible jobs, but may increase costs
2.4. Data Management and Optimization
- Partition data efficiently to distribute workloads evenly across nodes and minimize resource contention
- Use Delta caching instead of Spark caching for better performance outcomes
- Enable predictive optimization and regular maintenance (e.g., OPTIMIZE, VACUUM, ZORDER) to improve data layout and query speed, especially for Delta tables
2.5. Performance Testing and Monitoring
- Test performance on data that closely matches production in volume, layout, and skew
- Prewarm clusters and caches by running initial queries to reduce the latency of subsequent jobs
- Monitor resource usage, identify bottlenecks, and optimize memory management and garbage collection
2.6. Cost and Resource Efficiency
- Enable automatic termination of idle clusters to avoid unnecessary costs
- Use cluster pools to reduce startup and autoscaling times by having ready-to-use idle instances
- Optimize EBS (Elastic Block Storage) settings for better I/O performance
2.7. Query and Code Optimization
- Write efficient Spark, SQL, or Python code using best practices like predicate pushdown, join optimization, and caching
- Analyze the full execution chain, including BI tools, connectors, and SQL engines, as each component can impact overall performance