How to effectively update table with over terabyte of size with spark (perf tuning tips)

Partitioning: Ensure that your Delta Lake table is partitioned appropriately. Partitioning can significantly speed up queries by restricting the amount of data that needs to be scanned. Caching: If you have enough memory available, you can cache the DataFrame in memory before performing any transformations or updates. This can be done using the .cache() method. Bucketing: If applicable, consider bucketing your table. Bucketing can improve performance when you need to sample or aggregate data. Tuning: Tune the number of shuffle partitions to match your cluster configuration. You can set this value using spark.conf.set("spark.sql.shuffle.partitions", num_partitions). Optimize UDF: Make sure the UDF (User-Defined Function) generate_sha1_hash_key is efficient. You might also explore built-in Spark functions for hash calculations. Cluster Configuration: Ensure that your Databricks cluster is correctly sized to handle a large dataset like this. You may need a cluster with sufficient memory and CPU resources. Distribute the Work: If possible, split the work into smaller chunks and process them in parallel using a distributed approach. This can be achieved through partitioning and parallel processing. Data Skew: Be aware of data skew. If certain keys are more common than others, it can lead to performance issues. Consider techniques like bucketing or manual skew handling. Table Optimization: Delta Lake provides optimization features like Z-Ordering. Depending on your query patterns, you can use these features to optimize table storage. Compression: Use efficient compression techniques to reduce the storage size and improve query performance. Cluster Scaling: If you face performance bottlenecks, you might need to scale your cluster vertically (add more resources to each node) or horizontally (add more nodes to the cluster). Monitoring: Continuously monitor your cluster's performance and query execution. Databricks provides various monitoring and performance optimization tools. Remember that optimizing large-scale data processing is an ongoing process.