pnikic-db opened a new pull request, #55828:
URL: https://github.com/apache/spark/pull/55828
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### What changes were proposed in this pull request?
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This pull request proposes the addition of a new window function,
`counter_diff`, which would be used to convert cumulative counters to delta
format by computing the differences between consecutive values. The function
would include special handling for counter resets, when the cumulative value
gets reset to zero.
#### Syntax
```sql
counter_diff(value [, start_time]) OVER window_definition
```
#### Arguments
* `value`: A cumulative counter. Must be numeric and non-negative.
* `start_time`: An optional timestamp parameter which indicates when the
counter was last set to zero. It is used to better detect counter resets.
* `window_definition`: The clause describing the windowing:
* **PARTITION BY**: used to separate independent counters. Good
partitioning columns would be the metric name, as well as any attributes tied
to the metric.
* **ORDER BY**: used to order the observations by the associated timestamp
in ascending order.
#### Example
```sql
SELECT m, t, c, counter_diff(c) OVER (PARTITION BY m ORDER BY t) AS diff
FROM VALUES
('http_requests', TIMESTAMP '2026-01-01T00:00:00', 100),
('http_requests', TIMESTAMP '2026-01-01T00:01:00', 200),
('http_requests', TIMESTAMP '2026-01-01T00:02:00', 400)
AS tab (m, t, c)
```
| m | t | c | diff |
|---|---|---|---|
| http_requests | 2026-01-01 00:00:00 | 100 | NULL |
| http_requests | 2026-01-01 00:01:00 | 200 | 100 |
| http_requests | 2026-01-01 00:02:00 | 400 | 200 |
### Why are the changes needed?
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Counters are metrics with monotonically increasing values. One example is
the number of HTTP requests processed on a server. With each request, the
counter increases. Counters can be represented in two temporalities:
_cumulative_ or _delta_:
* With _delta_ temporality, each observation represents the increase of the
counter since the last observation.
* With _cumulative_ temporality, each observation represents the total
accumulated value of the counter.
* With cumulative counters, it is possible for the counter to reset to
zero, for example when a restart occurs.
The cumulative representation is typically better for storage and
transmission, as it is handles missed observations better.
* For delta counters, if a single observation is lost, the increase is lost
from the total counter value.
* For cumulative counters, the observation is lost, but the total counter
value does not decrease.
However, the delta representation is required for performing analytics on
the metric, as they can be aggregated and bucketized.
`counter_diff` acts as the bridge between these two representations. Given a
cumulative counter, it computes the differences between consecutive values,
resulting in the equivalent delta representation for the counter.
### Does this PR introduce _any_ user-facing change?
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The `counter_diff` window function is a new function.
### How was this patch tested?
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A new test, `counter-diff.sql`, has been added with various SQL queries
involving `counter_diff` and their expected outputs.
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