When working with databases, you understand how challenging high cardinality can be. For instance, time-series cardinality is a typical problem because it limits popular databases. This is a topic that developers commonly address since many questions surround it. However, high cardinality has an easy solution if you choose the right system. Use these tips to help navigate you through the main types of cardinality.
What is Cardinality
Cardinality itself is the number of elements in a group that contain the properties of that group. Sometimes you have a low number in the set of cardinality, but sometimes that number is high. These numbers are included in databases, columns or fields. On the other hand, time-series cardinality is more complex. They pair with metadata, which tells you about the data itself. Often the metadata is categorized to ensure a swift performance and find the needed values to precisely match tags. Some examples of high cardinality that you use daily are your email address, user name, and bank account information.
When there are multiple categorized columns, each with a large number of values, the cardinality significantly increases in size. This is what software developers refer to as high cardinality.
How To cut Down on High Cardinality
Every database takes a variety of different approaches to handle high cardinality. Fortunately, when high cardinality is involved, you are able to track it back to how it was engineered at the beginning. When you work with many time series, using the B-tree is an ideal way to index your data structure. These are the benefits of using the B-tree format:
- You see how well your database performs by fine-tuning all categories and data in the dataset. As long as the question fits inside the memory, cardinality is not an issue.
- It enables you to create compound categories over numerous columns, giving you control over which queue you should index. If your workload changes, you can add or take off any indexes you will not use.
- B-trees work best for comparing indexes created on private or continuous fields.
Keep in mind there are several ways time-series databases handle high cardinality, but B-tree is a reliable structure.
What Requirements Does High Cardinality Need
It takes high cardinality to know which hosts, IDs and processes belong to specific issues. High cardinality data has the permission to separate and identify the leading cause of the problem. Now you know exactly where and when the issue occurred.
Can You Have Low Cardinality
Unlike high cardinality, which was previously discussed, low cardinality refers to a small number of elements in a contained group containing fewer columns and unique variables. These columns are typically major classifications like status flags or values. There may be a few individual values that are common for the majority of elements being described. Therefore, you have many aspects repeating, so you do not have to count as often when going through the column content.
Is Low Cardinality Less Efficient
You might think that because low cardinality is less complex, it is not as good as the other types of cardinality. But, there are several reasons that low cardinality might be the better option. Here is why the index is compressed more accurately when the columns have lower cardinality. Also, indexed skip scans are considered when low cardinality is leading the column.
However, ordering indexes in a column is essential and significantly affects the efficiency for other important reasons. Make sure to consider if the leading column will be a known value and clustering factors.
Conclusion
Cardinality crucially influences databases with its query to implement the plan. The planner examines the column statistics to determine how many of the query might match. Depending on what it finds, it may use a different execution plan to fix problems and boost performance. Now, if anyone talks about high cardinality and low cardinality, you have a better idea of what it means.