Distribution analysis overview
Functions for analyzing data distribution with histograms and approximate row counts
Distribution analysis functions help you understand how data is distributed across your datasets and perform fast approximate row counting on large tables.
Samples
Section titled “Samples”Histogram distribution
Section titled “Histogram distribution”Create a histogram showing the distribution of battery levels across devices:
SELECT device_id, histogram(battery_level, 20, 60, 5)FROM readingsGROUP BY device_idLIMIT 10;The histogram partitions values into buckets between 20 and 60, with 5 equal-width buckets. The result includes an underflow bucket (values < 20) and an overflow bucket (values >= 60).
Approximate row count
Section titled “Approximate row count”Get a fast approximate count of rows in a hypertable without a full table scan:
ANALYZE conditions;
SELECT * FROM approximate_row_count('conditions');This uses database statistics to provide a quick estimate, which is particularly useful for very large tables where exact counts would be expensive.
Available functions
Section titled “Available functions”Distribution analysis
Section titled “Distribution analysis”histogram(): partition a dataset into buckets and get the number of counts in each bucket
Approximate aggregation
Section titled “Approximate aggregation”approximate_row_count(): estimate the number of rows in a table using catalog statistics