SAMPLE clause allows for approximated
SELECT query processing.
When data sampling is enabled, the query is not performed on all the data, but only on a certain fraction of data (sample). For example, if you need to calculate statistics for all the visits, it is enough to execute the query on the 1/10 fraction of all the visits and then multiply the result by 10.
Approximated query processing can be useful in the following cases:
- When you have strict latency requirements (like below 100ms) but you can’t justify the cost of additional hardware resources to meet them.
- When your raw data is not accurate, so approximation does not noticeably degrade the quality.
- Business requirements target approximate results (for cost-effectiveness, or to market exact results to premium users).
The features of data sampling are listed below:
- Data sampling is a deterministic mechanism. The result of the same
SELECT .. SAMPLEquery is always the same.
- Sampling works consistently for different tables. For tables with a single sampling key, a sample with the same coefficient always selects the same subset of possible data. For example, a sample of user IDs takes rows with the same subset of all the possible user IDs from different tables. This means that you can use the sample in subqueries in the IN clause. Also, you can join samples using the JOIN clause.
- Sampling allows reading less data from a disk. Note that you must specify the sampling key correctly. For more information, see Creating a MergeTree Table.
SAMPLE clause the following syntax is supported:
|SAMPLE Clause Syntax||Description|
k is the number from 0 to 1 (both fractional and decimal notations are supported). For example,
SAMPLE 1/2 or
SAMPLE k clause, the sample is taken from the
k fraction of data. The example is shown below:
count() * 10 AS PageViews
CounterID = 34
GROUP BY Title
ORDER BY PageViews DESC LIMIT 1000
In this example, the query is executed on a sample from 0.1 (10%) of data. Values of aggregate functions are not corrected automatically, so to get an approximate result, the value
count() is manually multiplied by 10.
n is a sufficiently large integer. For example,
In this case, the query is executed on a sample of at least
n rows (but not significantly more than this). For example,
SAMPLE 10000000 runs the query on a minimum of 10,000,000 rows.
Since the minimum unit for data reading is one granule (its size is set by the
index_granularity setting), it makes sense to set a sample that is much larger than the size of the granule.
When using the
SAMPLE n clause, you do not know which relative percent of data was processed. So you do not know the coefficient the aggregate functions should be multiplied by. Use the
_sample_factor virtual column to get the approximate result.
_sample_factor column contains relative coefficients that are calculated dynamically. This column is created automatically when you create a table with the specified sampling key. The usage examples of the
_sample_factor column are shown below.
Let’s consider the table
visits, which contains the statistics about site visits. The first example shows how to calculate the number of page views:
SELECT sum(PageViews * _sample_factor)
The next example shows how to calculate the total number of visits:
The example below shows how to calculate the average session duration. Note that you do not need to use the relative coefficient to calculate the average values.
SAMPLE K OFFSET M
m are numbers from 0 to 1. Examples are shown below.
In this example, the sample is 1/10th of all data:
SAMPLE 1/10 OFFSET 1/2
Here, a sample of 10% is taken from the second half of the data.