Snowflake Data Clustering: Best Practices for Optimizing Table Performance

In the world of big data and cloud computing, optimizing database performance is crucial for both cost efficiency and query speed. Data clustering can significantly enhance query performance when used correctly. In this article, we’ll dive deep into Snowflake’s data clustering, exploring best practices and strategies to supercharge your table performance.

What is Snowflake Data Clustering?

Data clustering in Snowflake refers to the organization of table data based on one or more columns, known as the clustering key. This organization affects how data is stored in micro-partitions, which are Snowflake’s units of data storage and management.

When data is well-clustered, Snowflake can quickly locate and retrieve relevant data, significantly reducing the amount of data scanned during query execution. This leads to faster query performance and lower compute costs.

Understanding Micro-partitions

Before we delve into clustering best practices, it’s essential to understand micro-partitions:

• Micro-partitions are contiguous units of storage, each containing between 50 MB and 500 MB of uncompressed data.
• Snowflake automatically splits tables into micro-partitions.
• Each micro-partition contains metadata about the data it stores, including min and max values for each column.

Why are Micro-partitions Important for Clustering?

1. Query Performance: When a query is executed, Snowflake uses the metadata in micro-partitions to quickly determine which partitions contain relevant data. This process, known as pruning, allows Snowflake to skip scanning irrelevant partitions, significantly improving query performance. Pruning is the process of eliminating unnecessary data from consideration during query execution. Effective clustering enables Snowflake to quickly identify and skip over irrelevant micro-partitions, significantly reducing the amount of data scanned and improving query performance.
2. Data Organization: Clustering organizes data within these micro-partitions based on the specified clustering key(s). This organization enables more efficient pruning during query execution.
3. Adaptive Data Management: As data changes over time, Snowflake can reorganize micro-partitions to maintain optimal clustering, either manually or through automatic clustering.

Understanding micro-partitions is key to grasping how Snowflake manages data and why proper clustering can lead to substantial performance improvements.

More info on micro partitions can be found in the Snowflake docs.

Visualization of how data is organized in micro-partitions

Clustering Information: Snowflake maintains clustering metadata for the micro-partitions in a table, including:

• The total number of micro-partitions that comprise the table.
• The number of micro-partitions containing values that overlap with each other.
• The depth of the overlapping micro-partitions.

Clustering Depth: The clustering depth measures the average depth (1 or greater) of the overlapping micro-partitions for specified columns in a table. A smaller average depth indicates better clustering. This metric can be used to monitor the clustering “health” of a table over time and determine if it would benefit from explicitly defining a clustering key.

Best Practices for Snowflake Data Clustering

1. Choose the Right Clustering Keys

Selecting appropriate clustering keys is crucial for optimal performance. Consider these factors:

• Cardinality considerations: Cardinality refers to the number of unique values in a column. High cardinality means a column has many unique values (e.g., a user ID column), while low cardinality means few unique values (e.g., a boolean column). Aim for columns with medium to high cardinality, but avoid extremely high cardinality. For example, a timestamp column with nanosecond precision might have too high cardinality, while a boolean column would have too low cardinality.
• Data distribution: Ideal clustering keys have an even distribution of values. Highly skewed data can lead to uneven clustering.
• Query patterns: Analyze your workload to identify columns frequently used in WHERE clauses, JOIN conditions, and GROUP BY statements.
• Compound keys: When using multiple columns, order them from lowest to highest cardinality for optimal pruning.

Best practice: Choose columns that are frequently used in WHERE clauses and JOIN conditions, with medium to high cardinality.

Example:

ALTER TABLE sales CLUSTER BY (date, region);

Snowflake goes in-depth on how to select a cluster key here.

2. Limit the Number of Clustering Keys

While you can specify up to 16 columns as clustering keys, it’s generally best to limit this to 3–4 columns.

Best practice: Start with fewer clustering keys and add more only if necessary, based on query patterns and performance metrics.

3. Order Matters in Multi-column Clustering Keys

The order of columns in a clustering key impacts data organization.

Best practice: Place columns with higher cardinality first in the clustering key definition.

Example:

-- Good: date has higher cardinality than region
ALTER TABLE sales CLUSTER BY (date, region);

-- Less optimal: region likely has lower cardinality than date
ALTER TABLE sales CLUSTER BY (region, date);

4. Monitor Clustering Efficiency

Regularly check the clustering efficiency of your tables using the system function SYSTEM$CLUSTERING_INFORMATION.

Example:

SELECT SYSTEM$CLUSTERING_INFORMATION('sales', '(date)');

Best practice: Set up automated monitoring to alert you when clustering efficiency drops below a certain threshold.

5. Leverage Automatic Clustering

Snowflake offers automatic clustering, which continuously reorganizes data in the background to maintain clustering efficiency.

• How it works: Snowflake continuously monitors clustered tables and automatically reclusters data when it determines the table would benefit from reorganization.
• When it triggers: Reclustering isn’t immediate. It occurs based on table size, query patterns, and the current clustering state.
• Cost implications: Automatic clustering consumes credits. The cost depends on the amount of data being reclustered and the frequency of changes to the table.

Use the SYSTEM$ESTIMATE_AUTOMATIC_CLUSTERING_COSTS function to estimate the cost of enabling or changing clustering keys.

6. Estimating and Managing Costs

Use the SYSTEM$ESTIMATE_AUTOMATIC_CLUSTERING_COSTS function to forecast costs:

SELECT SYSTEM$ESTIMATE_AUTOMATIC_CLUSTERING_COSTS('orders', '(order_date, customer_id)');

Understand that estimates can vary significantly from actual costs, especially if future DML patterns differ from historical patterns. Consider the trade-off between query performance improvements and clustering costs.

For cost control, you can suspend automatic clustering temporarily:

ALTER TABLE orders SUSPEND RECLUSTER;

Understanding Trade-offs

While clustering can significantly improve query performance, it’s important to balance this against potential costs:

1. Compute costs for automatic clustering
2. Storage costs for maintaining optimally clustered data
3. Potential impact on data ingestion performance

Carefully monitor query performance improvements and associated costs to ensure clustering provides a net benefit for your specific use case.

7. Align Clustering Keys with Common Query Patterns

Analyze your most common and resource-intensive queries to inform your clustering strategy.

Best practice: Use Snowflake’s query history to identify frequently filtered columns and align your clustering keys accordingly.

8. Consider Time-based Clustering for Large Tables

For tables with a time or date dimension that grows continuously, consider using a time-based clustering key.

Best practice: Use a date or timestamp column as the first clustering key for tables with time-series data.

Example:

ALTER TABLE events CLUSTER BY (event_timestamp, user_id);

9. Be Aware of Clustering Limitations:

Clustering keys cannot be defined for hybrid tables. and are not supported when creating a table using CREATE TABLE AS SELECT.

Conclusion

Effective use of data clustering in Snowflake can dramatically improve query performance and reduce costs. By following these best practices, you can optimize your table performance and make the most of Snowflake’s powerful features.

Remember, clustering strategies should evolve with your data and query patterns. Regularly review and adjust your clustering keys to ensure optimal performance as your data grows and changes.

Struggling with Snowflake costs?

All usage-based cloud platforms can get expensive when not used carefully. There are a ton of controls teams can fiddle with to get a handle on their Snowflake costs. At Greybeam, we’ve built a query performance and observability platform that automagically optimizes SQL queries sent to Snowflake, saving you thousands in compute costs. Reach out to kyle@corktownanalytics.com to learn more about how we can optimize your Snowflake environment.