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