The partition_depth_histogram in Snowflake's SYSTEM$CLUSTERING_INFORMATION function can be a bit confusing. I tried to break down your questions with examples:

1. Why is it always 17 buckets?

The number of buckets in the partition_depth_histogram is not always 17. It's actually a combination of two factors:

Fixed Buckets (0-16): The first 17 buckets (numbered from "00000" to "00016") represent a fixed range from 0 to 16 overlap depth with increments of 1. This provides detailed information about micro-partitions with very low overlap.

Dynamic Buckets (Larger than 16): For overlap depths exceeding 16, the buckets increase in size based on a doubling scheme. This means the next bucket would be "00032" (twice the size of the previous bucket) and so on. This approach efficiently represents the distribution of overlap depth for a wider range of values.

2. What is the relation between micro-partitions and buckets in the histogram?

The partition_depth_histogram doesn't directly map micro-partitions to specific buckets. Instead, it shows the number of micro-partitions that fall within a certain overlap depth range.

Bucket Value: Each bucket represents a specific overlap depth range. Value in the Bucket: The value associated with a bucket (e.g., 98 in "00032") indicates the number of micro-partitions in that specific overlap depth range.

3.What do the numbers in the histogram mean?

The numbers in the histogram represent the count of micro-partitions within a specific overlap depth range.

In your example:

There are 0 micro-partitions with an overlap depth of 0 ("00000"). There are 3 micro-partitions with an overlap depth between 2 and 3 ("00002"). There are 98 micro-partitions with an overlap depth between 32 and 64 ("00032"). There are 698 micro-partitions with an overlap depth exceeding 128 ("00128").

Please Note:

The provided example also shows a high average_depth (64.07) compared to the total number of micro-partitions (1156). This suggests that a significant portion of the data is accessed by multiple queries, potentially leading to good query performance due to data sharing.