Error in Prod but not in STG, Dev for Hilbert indexing can only be used on 9 or fewer columns

Question

We have a very Big Fact Table with around 215 Million Rows

We have optimize on this Big Fact Table with 11 columns

This is working fine in Dev, STG, but fails in Prod giving:

Hilbert indexing can only be used on 9 or fewer columns

Our Cluster Configuration is same for Dev, STG and Prod.

12.2 LTS (includes Apache Spark 3.3.2, Scala 2.12)

Worker Type and Driver Type: Standard_D16ds_V5 (64 GB Memory, 16 Cores)

Spot Instances: Yes

Spark Config: spark.databricks.delta.preview.enabled true

Answer 1

@Immaneni, Nanda

The error message you're encountering, "Hilbert indexing can only be used on 9 or fewer columns," suggests that there is a limitation in the indexing method you're trying to use on your fact table. Hilbert indexing is a space-filling curve approach used to optimize multi-dimensional queries, but it has constraints on the number of dimensions (columns) it can handle. In your case, it seems that this limit is set to 9 columns.

Here’s an explanation of why you might see different behavior in different environments (Dev, STG, Prod), and some steps you can take to resolve the issue:

Environment Differences - Even though you mentioned that the cluster configurations are the same across environments, there might be subtle differences in settings, versions, or even the underlying data that cause the error to manifest only in Production. Check for any environment-specific overrides or additional configuration settings that might affect indexing behavior.

Indexing Configuration - Review the indexing strategy applied in your environments. It is possible that, in Dev and STG, the indexing configuration is not applied or is somehow bypassed, resulting in no errors. Ensure that the indexing logic or scripts are identical across all environments.

Data Characteristics - The data in Production might have different characteristics (e.g., data distribution, volume) that impact indexing. Ensure that the data is similar across environments to rule out data-related issues.

Cluster and Spark Configuration - Verify the Spark and Databricks configurations. Although you mentioned that spark.databricks.delta.preview.enabled true is set, there might be other configurations impacting the indexing.

Column Limitations - Since Hilbert indexing is limited to 9 columns, you need to reduce the number of columns used in this indexing strategy. Consider the following:

• Identify the most critical columns for indexing and limit the indexing to those columns.
• If possible, redesign the indexing strategy to fit within the constraints.

Alternative Indexing Strategies - If Hilbert indexing's limitations cannot be circumvented, explore alternative indexing methods or configurations that support more columns. Investigate whether using composite indexes or a combination of indexing strategies can meet your performance requirements without exceeding column limits.

By following these steps, you should be able to identify the root cause of the issue and implement a suitable solution for your Production environment.

I hope this information helps. Please do let us know if you have any further queries.

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Answer 2

Hello Immaneni !

Thank you for posting on Microsoft Learn.

Based on this old thread :

You must reduce the number of columns to nine or less. The best practice is to use ZORDER on a maximum of three columns. When you use ZORDER on four or more columns, the effectiveness is reduced with each additional column used.

Use ZORDER on the most commonly used query predicate columns (the columns in the query "where" clause). Databricks recommends that you use ZORDER on high cardinality columns.