On 15.01.2025 12:47, Ilia Evdokimov wrote:
On 06.01.2025 18:57, Andrey M. Borodin wrote:
1. This code seems a little different from your patch. It is trying to avoid engaging PRNG. I'm not sure
it's a good idea, but still. Also, it uses "<=", not "<".
xact_is_sampled = log_xact_sample_rate != 0 &&
(log_xact_sample_rate == 1 ||
pg_prng_double(&pg_global_prng_state) <= log_xact_sample_rate);
Sorry for the delayed reply. Andrey was right about this
suggestion—first, it makes the code more readable for others, and
second, it avoids engaging the PRNG on edge values of 0.0 and 1.0.
I’ve attached patch v11 with these changes.
Patch looks fine. Thank you!
On 14.01.2025 15:00, Ilia Evdokimov wrote:
Alena, Sami – I apologize for not including you in the previous email.
If you're interested in this approach, I'm open to any suggestions.
[0]:
https://www.postgresql.org/message-id/1b13d748-5e98-479c-9222-3253a734a038%40tantorlabs.com
This is a difficult question. I tend to agree with Alexander Korotkov's
proposal to add a filter that saves information about queries whose
statistical information satisfies the conditions of the configured
filter. However, I don’t believe query execution time is a sufficient
metric for this purpose. It is too unstable and influenced by many
external factors, such as system load. For instance, various background
processes like vacuum, checkpointer, or background writer mechanisms
could be running simultaneously.
Additionally, a query may take a long time to execute because another
large query is consuming most of the system's resources at the same
time. In such cases, the long execution time of the triggered query may
not indicate anything remarkable. Furthermore, statistics for
resource-intensive queries may appear normal if the query simply
processed a large volume of data, such as when it involves a Cartesian
product or a full join.
Therefore, I think the idea of using filters is more promising,
especially if multiple filters are implemented. For example, we could
add filters for buffer usage (pages read or modified), differences in
cardinality (predicted vs. actual), username, application name, and
other criteria. These filters would help reduce the volume of queries
tracked by the pg_stat_statements extension. However, there might be
challenges in keeping this state up to date, given the volatile and
unstable nature of the system.
Your proposed parameter could also reduce the volume of queries for
which statistics need to be saved, but it is too unpredictable for
analysis. This unpredictability makes it unclear how the data would be
interpreted in the future. For instance, a query that genuinely impacted
performance might not be processed by pg_stat_statements simply due to
randomness, while a small, insignificant query could be selected
instead. Analyzing such statistical information might lead to misleading
conclusions.
--
Regards,
Alena Rybakina
Postgres Professional
