10 Snowflake Clustering Best Practices
Clustering is a great way to improve performance and scalability of your snowflake database. Here are 10 best practices to follow.
Snowflake is a cloud-based data warehouse service that offers a unique architecture and set of features that make it well-suited for certain types of data workloads. One of the key features of Snowflake is its ability to automatically cluster data based on certain columns.
In this article, we will discuss 10 best practices for using Snowflake’s clustering feature. By following these best practices, you can improve the performance and efficiency of your Snowflake data warehouse.
1. Use the right data types
If you use the wrong data type, your cluster will be less efficient and use more resources than necessary. For example, if you use a data type that can’t be compressed, your data will take up more space and slow down query performance.
It’s also important to choose data types that are compatible with the clustering algorithms you’re using. Some data types are not supported by certain algorithms, which can lead to errors.
Finally, make sure you understand the implications of each data type before using it. Some data types have special meaning in snowflake clustering, and using them incorrectly can cause unexpected results.
2. Avoid using NULLs in your tables
When you have NULL values in your tables, snowflake will store those values as separate clusters. So, if you have a table with 1 million rows and 10% of them are NULL, snowflake will store those 100,000 NULL values in a separate cluster.
This can cause two problems. First, it can impact performance because when you query the table, snowflake will need to check both the main table and the NULL cluster. Second, it can impact storage costs because you’re effectively storing the same data twice.
To avoid these problems, make sure you use the NOT NULL constraint when creating your tables. This will ensure that all values are stored in the main table and that NULL values are not created in the first place.
3. Don’t use VARCHAR for columns with a fixed length
When you use VARCHAR for a column with a fixed length, the snowflake cluster key will be created with that column as its leading edge. So, if you have a table with a clustered key on (last_name, first_name), and you use VARCHAR(20) for last_name and VARCHAR(30) for first_name, the cluster key will be created with last_name as the leading edge. This means that all of the records with the same last_name value will be stored together, which is not ideal.
It’s much better to use CHAR for columns with a fixed length. That way, the cluster key will be created with the fixed-length columns in the correct order, and the records will be stored together more efficiently.
4. Avoid duplicate values in clustered keys
When you have duplicate values in clustered keys, snowflake will store those duplicates in different blocks. That means that when you query the data, snowflake will need to read from multiple blocks, which is less efficient.
To avoid this issue, make sure that your clustered keys are unique. You can do this by using a sequence or by concatenating other columns in the table.
5. Don’t cluster on low cardinality columns
When you cluster on a low cardinality column, the number of values in that column is small relative to the number of rows in the table. This means that the chance of two rows having the same value in that column is relatively high. So, when you cluster on a low cardinality column, you’re likely to end up with a lot of duplicate values in the cluster column, which defeats the purpose of clustering in the first place.
A good rule of thumb is to only cluster on columns that have at least 10 distinct values.
6. Cluster on multiple columns
When you cluster on a single column, all of the values in that column are stored together. That means that if you have a lot of duplicate values, those duplicates will be stored next to each other. But if you cluster on multiple columns, the values in each column are spread out, so there’s less chance of duplication.
Plus, clustering on multiple columns gives you more flexibility when querying your data. For example, let’s say you have a table with two columns, A and B. If you cluster on column A, you can only query on column A. But if you cluster on both columns, you can query on either column.
So, clustering on multiple columns is more efficient and provides more flexibility. It’s definitely a best practice that you should follow.
7. Clustering is not always useful
Suppose you have a table with two columns, A and B. Column A is the primary key, and column B is an integer value. You decide to cluster the table on column B.
Now suppose you run the following query:
SELECT * FROM TABLE WHERE A = 1;
This query will only return rows where column A is equal to 1, so the clustering on column B will not be used. In fact, the query will likely be slower because the database will have to scan the entire table instead of just the clustered portion.
Therefore, it’s important to only cluster on columns that will be used in filters or conditions in your queries. Otherwise, you’re just wasting time and resources.
8. Keep an eye on clustering skew
Clustering skew is when the data in a cluster is not evenly distributed across the nodes in the cluster. This can happen for a number of reasons, but it’s usually due to the way the data is being accessed. For example, if most of the queries are only accessing a small subset of the data, then that data will be “skewed” towards the nodes that those queries are running on.
This can cause a number of problems, including decreased performance and increased costs. It can also make it difficult to add or remove nodes from the cluster, as the skewed data will need to be rebalanced.
To avoid these problems, it’s important to monitor clustering skew and take action to prevent it. There are a few ways to do this, including using partitioning and data replication.
9. Clustering is not free
When you cluster your data, you are essentially duplicating that data across multiple servers. This means that you will need more storage space to accommodate the duplicate data. Additionally, clustering can impact performance because queries will need to be run on each server in the cluster.
Therefore, it’s important to weigh the costs and benefits of clustering before deciding whether or not it’s right for your data.
10. Test and monitor performance
When you’re working with a large data set, it’s important to make sure that your clustering algorithm is able to handle the size and complexity of the data. The only way to be sure of this is to test it on a sample of the data before running it on the entire data set.
Once you’ve run the clustering algorithm, it’s also important to monitor the performance of the clusters. This will help you identify any areas where the clustering isn’t working as well as it could be, and it will also give you an idea of how scalable the solution is.
