Mastering Snowflake Query Performance: A Deep Dive into Optimization Strategies

Query Optimization is the cornerstone of efficient data processing. Snowflake is renowned for its robust features by offering a range of optimization techniques to raise query speed and efficiency. We are going to explore four advanced strategies in this blog series that Snowflake engineers can use to unlock the potential of query performance.

1. Auto Clustering
2. Materialized Views
3. Search Optimization Service(SOS)
4. Query Acceleration Service (QAS)

1. Auto Clustering: The Foundation of Performance:

• Auto clustering is a feature that automatically reorganizes tables in micro partitions (MPs) based on their clustering key.
• This helps to improve the performance of queries that frequently access the table by grouping related data together in the same micro-partition.
• Auto clustering is enabled by default, and it is usually a good idea to leave it enabled.

Clustering Key Best Practices in Snowflake:

• Balancing DML Workloads and Queries: Choosing a good clustering key is important to make a balance between DML (Data Manipulation Language) workloads and queries. A complex clustering key can make DML operations more expensive, while a clustering key that is too simple may not provide much benefit for queries.
• Selecting 3 to 4 columns for optimal clustering: Snowflake recommends selecting 3 to 4 columns for a clustering key for optimal performance because clustering keys with more than 4 columns can have a negative impact on DML operations.
• Prioritizing the least to most distinct value columns: It is important to prioritize columns with the least distinct values first because columns with more distinct values will result in more micro-partitions, which can slow down queries.
• The significance of column order: The first column in the clustering key is the most important, and the last column is the least important because Snowflake will first try to find matching rows based on the first column, and then it will move on to the second column, and so on.

Now, How to Create Effective Clustering Keys in Snowflake?

• String clustering key and truncation for optimization: The length of the string column is important to optimize performance, you can truncate the string column to a shorter length.
• Favoring numeric datatypes: Numeric datatypes are more efficient than string datatypes for clustering keys.
• Using date as a leading clustering key: Date columns are often good candidates for leading clustering keys because dates are frequently used in queries, and grouping rows together by date can improve performance.

You can find syntax/examples of clustering keys in the Snowflake documentation:

Clustering Keys & Clustered Tables | Snowflake Documentation

2. Materialized Views: Enhancing Query Efficiency:

Materialized views (MVs) are a powerful feature in Snowflake that can be used to improve the efficiency of queries. MVs are pre-computed views that are stored in Snowflake’s storage layer. This means that when you query an MV, Snowflake does not need to re-execute the underlying query. Instead, it can simply return the results of the MV.

MVs can also be used to improve the performance of queries that access data from multiple tables.

How MVs help for optimizing query performance?

• MVs can be clustered: We can also define the clustering keys for MVs. This can improve the performance of queries that access the MV by allowing Snowflake to find the data that it needs quickly.
• Customizing MV Clustering Keys Distinct from the Base Table: The clustering key for an MV and the base table can be different. This can be useful to optimize the MV for a specific set of queries.
• Impact of Changing Clustering Keys on MVs: If you change the clustering key for a base table, it will also affect the clustering key for any MVs that are based on that table because the MVs are re-computed based on the new clustering key.
• Addressing Micro-partition Level Updates: MVs are updated in micro-partitions. This means that only the micro-partitions that are affected by the change are updated. This can improve the performance of MV updates.

When to use MVs?

• The frequency of the query: If the query is run frequently, then an MV can save time and resources because results are pre-computed.
• The complexity of the query: If the query is complex, then an MV can improve performance by reducing the number of steps that Snowflake needs to take to execute the query.
• The size of the table: If the table has a large amount of data, then an MV can improve performance by storing the results of the query in a smaller and more efficient format.
• The frequency of updates to the table: If the table is frequently updated, then an MV can improve performance by updating the MV in micro-partitions, rather than updating the entire MV.

Drawback of MVs:

• MVs can take up additional storage space.

You can find the syntax of MVs in the Snowflake documentation:

CREATE MATERIALIZED VIEW | Snowflake Documentation

Summary:

Clustering keys are a good choice for queries that scan the entire table, and MVs are a good choice for queries that frequently access the same data.

Clustering keys and MVs are both powerful tools that can be used to improve the performance of queries in Snowflake. There are other techniques (SOS & QAS) that can improve the performance of queries that scan a large number of micro-partitions or that are outliers.

I hope you found this part of the blog post helpful.

Stay tuned for part 2, where we will discuss Search Optimization and Query Acceleration.

References:

1. https://docs.snowflake.com/en/user-guide/tables-clustering-keys#what-is-a-clustering-key
2. https://docs.snowflake.com/en/user-guide/views-materialized