Z-ordering in Databricks (specifically within Delta Lake) is a technique to optimize data layout for faster query performance. It co-locates related information within the same set of files, leveraging the data-skipping capabilities of Delta Lake to drastically reduce the amount of data that needs to be read during queries.

How it Works

• Co-locality: Z-ordering rearranges data so that values from frequently filtered columns are stored together. This enables Delta Lake to skip entire files that don’t contain the values relevant to a query.
• Data Skipping: Delta Lake automatically utilizes this co-locality when executing queries, dramatically reducing the amount of data scanned and leading to faster results.

When to Use Z-ordering

Z-ordering is particularly beneficial when:

• High Cardinality Columns: The column you frequently filter on has a large number of distinct values (e.g., customer IDs, product IDs).
• Predictable Filters: You know which columns are commonly used in filtering predicates.
• Large Tables: Z-ordering has the biggest impact on large tables where data skipping can lead to substantial performance gains.

How to Z-order

You can Z-order a Delta table using the following syntax:

OPTIMIZE table_name 
ZORDER BY (column1, column2, ...)

Important Considerations

• Z-ordering is not idempotent, meaning multiple runs on the same data might not produce the same result. However, it is designed to be incremental, so re-ordering on unchanged data has minimal overhead.
• The effectiveness of Z-ordering decreases with each additional column specified. Focus on the most important columns for filtering.
• Z-ordering incurs a cost as it involves rewriting data files. Evaluate the trade-off between this cost and the potential performance gains.