The document describes improvements to building histograms for database tables. It outlines collecting a histogram using samples of rows rather than a full table scan to avoid sorting all values and improve performance. The new implementation allows the user to specify a sampling percentage and constructs an equal-width histogram using multiple samples to estimate the min and max values and then bucket the values between those ranges.

1. Sampling based histogram

2. 2
Current implementation
ANALYZE TABLE PERSISTENT FOR ALL
●
Collect histogram by doing full table scan
●
Histogram stored as equal-height 256 bytes
●
Stored in mysql database in column_stats
●
Stores all values in memory (or on disk if needed)
●
Slow

3. 3
Improvements
●
Collect Histogram using samples
●
Avoid sorting
●
O(#rows log(#rows))

4. 4
New Implementation
● User tells % of sampling
● We need min & max in order to build the histogram
● Equal-width Histogram

5. 5
Sampling Steps
● First histogram
●
Sample values to get a good estimate for min & max
●
Sample again to construct histogram.
●The buckets are between min and max + 2 extra for >
max and < min
●If we know min & max ( or we already have a
histogram ) then start sampling!
●If there are too many values < min & > max, change
min & max and restart sampling