There are several features in Snowflake that contribute to your environment’s performance and scalability. Two of the most important in my experience have been micro-partitions and clustering. In short, micro-partitions are the fundamental storage units of Snowflake, and clustering is a feature that organizes data within micro-partitions based on one or more columns. The two go hand-in-hand and to better understand how they compliment each other we need to understand how each works individually.
In the text below, I am going to explain in-depth what micro-partitions and clustering each do, their benefits and considerations that must be taken into account when using them. So, without further ado, let’s get started!
What Are Snowflake Micro-Partitions?
In Snowflake, a micro-partition is a small, self-contained unit of data storage within a table. Imagine a table as a collection of individual pages, and each page represents a micro-partition. To understand micro-partitions, let’s use an analogy:
Think of a large puzzle that you’re trying to solve. Instead of dumping all the puzzle pieces onto the table at once, you divide them into smaller groups based on their colors or patterns. Each group contains a handful of puzzle pieces, and you work with one group at a time. These smaller groups are similar to micro-partitions in Snowflake.
In Snowflake, when you create a table and load data into it, Snowflake automatically divides the data into these smaller groups called micro-partitions. Each micro-partition contains a specific range of rows from the table. The data within a micro-partition is immutable, meaning it cannot be changed once it’s created.
Benefits of Micro-Partitions
Micro-partitions are a beneficial tool for improving query performance, automatic data optimization, and improving concurrency and scalability. You can find an in-depth explanation of each benefit below.
Micro-partitions play a crucial role in improving query performance in Snowflake. When you execute a query in Snowflake, it can analyze the query and determine which micro-partitions contain relevant data based on the query filters. Snowflake’s query optimizer is designed to skip reading unnecessary micro-partitions, which significantly reduces the amount of data that needs to be scanned during query execution. This optimization technique, known as micro-partition pruning, leads to faster query performance by minimizing the amount of data processed.
Think of it like searching for a specific item in a well-organized room. If the room is divided into multiple sections, and you know the item you’re looking for is only in one specific section, you can directly go to that section without wasting time searching the entire room. Similarly, by dividing data into micro-partitions, Snowflake can quickly identify and retrieve only the relevant data for a given query, resulting in faster responses.
Automatic Data Optimization
Snowflake automatically applies data compression and optimization techniques to micro-partitions. When data is loaded into Snowflake, it is stored in a hybrid-columnar format, which enables efficient compression at the column level. Hybrid-columnar storage allows Snowflake to partition the data into micro-partitions horizontally, where each micro-partition stores, in effect, mini-pages of data according to PAX scheme (along with offsets and other metadata).
To visualize this, imagine organizing different colored blocks. Instead of storing each block separately, you can group blocks of the same color together, stack them, and compress them to save space. When you need to access a particular color, you can quickly identify the compressed stack and retrieve the required blocks. This compression technique used by Snowflake helps reduce storage costs while maintaining query performance.
Concurrency and Scalability
Micro-partitions enable Snowflake to handle concurrent queries efficiently and scale horizontally as needed. Snowflake can process multiple queries simultaneously by working on separate micro-partitions in parallel. This parallelism allows Snowflake to distribute the query workload across different compute resources, resulting in faster query execution and efficient resource utilization.
Think of it as having multiple people simultaneously searching for different items within different sections of a well-organized room. By dividing the work among multiple individuals, you can accomplish the task more quickly and effectively. Similarly, Snowflake’s ability to work on different micro-partitions concurrently allows it to handle large workloads and scale horizontally to meet increasing demands.
Now that you understand some of the benefits of micro-partitions, it is imperative that I also address some of the considerations that Snowflake makes when creating micro-partitions.
Choosing the Correct Key
Snowflake distributes data across micro-partitions based on a clustering key when it is manually defined. When designing tables, selecting an appropriate clustering key is crucial. The clustering key determines how data is physically stored and affects query performance. It should be chosen based on the expected patterns of data access to ensure optimal data pruning during queries. I will explain this in more detail in the clustering section below.
NOTE: Snowflake will choose the date of load as the partitioning strategy in lieu of a user-set clustering key. This means the data is ingested and stored in Snowflake as it comes into the database. Best practice here is to use a SORT BY operator to ensure the data is partitioned in a desired manner.
Maintaining Optimal Size
Micro-partitions should not be too small or too large.
If micro-partitions are too small, it can result in increased metadata overhead. Think of it as having many small boxes to store books in. Each box requires its own label and management, which can become inefficient and resource-intensive.
On the other hand, if micro-partitions are too large, it can lead to unnecessary data scanning during queries. Imagine putting all your books into a single giant box. Whenever you need to find a specific book, you would need to search through the entire box, which is time-consuming.
To maintain an optimal size for micro-partitions, Snowflake automatically manages the size based on the volume and characteristics of the data. However, monitoring and managing the size of micro-partitions through the use of clustering keys is important to ensure efficient performance. Snowflake’s automatic data optimization capabilities help in this regard.
It is possible for users to control how Snowflake creates micro-partitions by manually defining the clustering key. This is the highlight of our next section.
Clustering in Snowflake
In Snowflake, clustering is a technique used to organize and store data within tables in a way that improves query performance. It involves grouping similar values of one or more columns together physically. Clustering is typically worth it on table greater than 1 TB as your performance gain won’t be worth the additional automated re-clustering cost on smaller tables. To understand clustering, let’s consider an analogy:
Imagine you have a bookshelf with books arranged randomly. Whenever you need to find a specific book, you have to search through the entire bookshelf, which takes time and effort. Now, if you organize the books based on their genres, such as grouping all the fantasy books together, all the science-fiction books together, and so on, it becomes much easier and quicker to find the desired book. This organization is similar to clustering in Snowflake.
In Snowflake, when you create a table and specify clustering on one or more columns, Snowflake physically rearranges the data within the table based on those columns. The goal is to co-locate similar values together, making it more efficient for Snowflake to access the data when executing queries.
Here’s an example to illustrate the concept further. Let’s say you have a table called “Sales” with columns like “Product,” “Date” and “Quantity.” You can choose to cluster the data based on the “Product” column. When you cluster the table on the “Product” column, Snowflake will physically group together rows with the same product values. So, all the rows for a particular product will be stored together within a micro-partition.
Benefits of Clustering
Improved Query Performance
When you cluster a table in Snowflake, it means that the data within the table is physically organized in a way that groups similar values together. This organization has a direct impact on query performance, providing the following benefits:
- Reduced Data Scanning: When you execute a query that involves filtering or searching based on the clustered column(s), Snowflake can skip reading unnecessary micro-partitions that don’t contain relevant data. It knows where to find the desired data more efficiently because similar values are co-located. This reduces the amount of data that needs to be scanned, resulting in faster query execution.
- Enhanced Concurrency: Clustering also improves concurrency, which refers to the ability to handle multiple queries simultaneously. When queries are executed in parallel, Snowflake can assign different micro-partitions to each query, allowing them to be processed independently. This parallelism enables Snowflake to handle large workloads and scale efficiently.
Reduced Storage Space
Clustering not only benefits query performance but also helps optimize storage utilization by providing the following benefits:
- Improved Data Compression: Snowflake uses advanced compression techniques, particularly columnar compression, to reduce the amount of storage space required for data. When data is clustered, similar values are grouped together physically, which enhances the effectiveness of compression algorithms. Snowflake can achieve higher compression ratios within each micro-partition, resulting in reduced storage requirements and cost savings.
- Efficient Storage Utilization: By organizing similar values together, clustering avoids storing redundant or duplicate data. Instead of storing the same values multiple times, Snowflake stores them once within a micro-partition. This efficient storage utilization further contributes to lowering overall storage costs.
Now that we understand some of the benefits of clustering, what do we have to decide and do to reap these benefits?
Choosing the Clustering Columns
When deciding which columns to use for clustering, it’s essential to select ones that are commonly used in your queries for filtering or searching. The purpose is to group together similar values that are frequently accessed together. For example, if you often search for sales data based on the “Product” column, clustering the table on that column would be beneficial.
Some factors to consider when creating a clustering key are as follows:
- Common Filters: Identify the columns that are frequently used in the WHERE clause of your queries to filter data.
- Cardinality: Cardinality refers to the number of distinct values in a column. It’s generally better to choose columns with higher cardinality for clustering, as they provide more opportunities for effective grouping. Just be sure not to take this to the extreme. Selecting a column with extremely high cardinality can have adverse effects.
- Distribution: Ensure that the chosen clustering columns have a good distribution of values across the dataset. If the values are heavily skewed or concentrated in a few categories, the benefits of clustering may be reduced.
Clustering and Loading
The order in which data is loaded into a table can impact clustering effectiveness. Snowflake achieves optimal clustering when data is loaded in sorted order based on the clustering columns. It’s recommended to use the ORDER BY clause during data loading to maintain the desired order.
Some factors to consider when loading data are as follows:
- Sorted Loading: If possible, sort the data files based on the clustering columns before loading them into Snowflake. This ensures that the data is physically arranged in the desired order, improving the clustering efficiency.
- Sequential Loading: If you’re continuously loading data into a table, try to load the data in a sequential manner based on the clustering columns. Sequential loading can help maintain the desired clustering order and maximize the benefits. For example, if your clustering column is date then you should load data from the oldest date to the newest date.
Clustering and Data Modification
Frequent data modifications like updates, inserts or deletes can impact clustering effectiveness. When data changes within a table, it can result in data fragmentation and reduced clustering benefits. In such cases, periodic re-clustering may be required to restore optimal performance.
Some factors to consider when dealing with data modification are as follows:
- Scheduled Re-clustering: Depending on the rate of data modifications, you may need to schedule re-clustering operations to reorganize the data and restore efficient clustering. Snowflake provides options to perform re-clustering manually or automate it through maintenance windows or integration with ETL processes.
- Analyze Data Modification Impact: If your table undergoes significant data modifications, monitor the impact on clustering and query performance. Consider the trade-offs between frequent re-clustering and the overhead it introduces versus the benefits gained.
NOTE: Initial micro-partitions will have a longer lifecycle on tables that are updated infrequently. Tables that are updated frequently may benefit from re-clustering.
Clustering depth for a table measures the average depth of overlapping micro-partitions for a specified column in a table. While it is an important topic to understand my colleague Chris Hastie did an incredible job explaining clustering depth in an older article. If you’d like more information on this topic, then check out the Clustering Information section of his previous post on clustering. In this post, he discusses the SYSTEM$CLUSTERING_INFORMATION function in detail and dives into the exact meaning, with examples, behind partition counts, overlaps and depth.
Micro-partitions and clustering are essential features in Snowflake that significantly contribute to performance and scalability. Micro-partitions serve as the building blocks of data storage, offering benefits such as improved query performance, automatic data optimization, and enhanced concurrency and scalability. Clustering, on the other hand, organizes data within micro-partitions based on one or more columns, leading to reduced data scanning, faster data retrieval, storage efficiency and cost savings.
By successfully leveraging the power of micro-partitions and clustering in Snowflake, you can optimize query performance, storage efficiency and overall data management, ultimately driving better and more cost-effective insights and decision-making from your data.
By leveraging the power of micro-partitions and clustering in Snowflake and considering the relevant factors, you can optimize query performance, storage efficiency, and overall data management, ultimately driving better insights and decision-making from your data.
If you’d like to learn more about Snowflake and the possibility of implementing or improving your cloud data warehousing setup, then please feel free to reach out to us at InterWorks here.