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This guide is designed and created with the novice in mind. There might be some content you have been very familiar with, but it will still be covered in detail for the completeness of a model design process.
Following through with this systematic step-by-step guide will help you design any model of great performance in the first place with minimal effort.
Without further ado, let’s dive in
Here is a template you can use to collect helpful information for modeling purposes:
You can create a table in Kyligence in two ways:
Note: This step is only to create an empty table that will be shared with all projects in a workspace
Pay close attention to Ultra High Cardinality / High Cardinality columns used by aggregate queries and see if there is an alternative to replace those columns.
Note: This approach is one of the best practices for handling UHC / HC use cases and probably the most effective one. Please do make an effort to try it if possible.
For example, a timestamp typed column can be avoided by scaling back the granularity of a timestamp to year, month, week, date, or even hour.
Another good example is that a double-typed column could be rounded up to an integer column to lower the cardinality.
Note: Ultra-High Cardinality / High Cardinality columns have no impact on detailed query performance because detailed queries are scanning through non-aggregated data instead of aggregated records.
Before getting into further details of model design, let’s first have a quick look at the Top 3 design principles which would guide you through the model design journey and help you get the best performance out straightaway.
In the next blog, we will look at each of the building blocks for a perfect query performance in detail. Stay Tuned!
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