Approximate count distincts are typically used to find the number of unique values, or cardinality, in a large dataset. When you calculate cardinality in a dataset, the time it takes to process the query is proportional to how large the dataset is. So if you wanted to find the cardinality of a dataset that contained only 20 entries, the calculation would be very fast. Finding the cardinality of a dataset that contains 20 million entries, however, can take a significant amount of time and compute resources. Approximate count distincts do not calculate the exact cardinality of a dataset, but rather estimate the number of unique values, to reduce memory consumption and improve compute time by avoiding spilling the intermediate results to the secondary storage.

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