Liquid Clustering on Databricks (Databricks Runtime 13.3 and above)
I've been working on a project where I encountered difficulty handling a large amount of data that streams into a delta table on an hourly basis. As a Data Engineer, our first approach to optimizing performance is usually to optimize the SQL queries used in the code. However, given the substantial data size, we often opt for partitioning or clustering our tables.
"Manually defining partitioning columns or clustering columns poses several challenges and difficulties. Although Databricks provides the 'optimize with z-order' option to enhance performance, a major drawback is its impact on concurrent writes to delta tables. This has a downside for execution."
Read my previous article on PARTITIONING, OPTIMIZE, and Z-ORDER
https://medium.com/@nidhig631/unlocking-efficiency-optimize-z-order-and-partition-a498c174caea
Challenges with PARTITIOINIG and OPTIMIZE WITH ZORDER
- These techniques require significant user effort to attain optimal read and write query performance.
- These techniques incur extra processing costs by rewriting the data.
- Implementing these techniques manually requires detail and practical understanding.
- OPTIMIZE would cause clustering to be…
