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How does cardinality affect query performance?
The definition of cardinality that matters a lot for query performance is data cardinality. This is all about how many distinct values are in a column. When applied to databases, the meaning is a bit different: it’s the number of distinct values in a table column, relative to the number of rows in the table.
What is considered high cardinality data?
High-cardinality refers to columns with values that are very uncommon or unique. High-cardinality column values are typically identification numbers, email addresses, or user names. An example of a data table column with high-cardinality would be a USERS table with a column named USER_ID.
How do you deal with high cardinality?
Reducing Cardinality by using a simple Aggregating function Leave instances belonging to a value with high frequency as they are and replace the other instances with a new category which we will call other. Keep adding the frequency of these sorted (descending) unique values until a threshold is reached.
Why is high cardinality a problem in databases?
To clear up one common point of confusion: high cardinality has only become such a big issue in the time series world because of the limitations of some popular time series databases. In reality, high cardinality data is actually a solved problem, if one chooses the right database. Let’s back up for a minute and first define high cardinality.
Is the cardinality of a query a non-issue?
As long as the indexes and data for the dataset you want to query fit inside memory, which is something that can be tuned, cardinality becomes a non-issue. You have control over which columns to index, including the ability to create compound indexes over multiple columns.
When do you need the cardinality of two fields?
If you need the cardinality of the combination of two fields, create a runtime field combining them and aggregate it. The missing parameter defines how documents that are missing a value should be treated.
Why is the max cardinality of lat So High?
Because (lat, long) is a continuous field (as opposed to a discrete field like equipment_id), by indexing on location, the max cardinality of this dataset is now infinitely large (unbounded). Different databases take various approaches to handling high cardinality.