Commit Graph

6 Commits

Author SHA1 Message Date
Alex Chi Z
9b98823d61 bottom-most-compaction: use in test_gc_feedback + fix bugs (#8103)
Adds manual compaction trigger; add gc compaction to test_gc_feedback

Part of https://github.com/neondatabase/neon/issues/8002

```
test_gc_feedback[debug-pg15].logical_size: 50 Mb
test_gc_feedback[debug-pg15].physical_size: 2269 Mb
test_gc_feedback[debug-pg15].physical/logical ratio: 44.5302 
test_gc_feedback[debug-pg15].max_total_num_of_deltas: 7 
test_gc_feedback[debug-pg15].max_num_of_deltas_above_image: 2 
test_gc_feedback[debug-pg15].logical_size_after_bottom_most_compaction: 50 Mb
test_gc_feedback[debug-pg15].physical_size_after_bottom_most_compaction: 287 Mb
test_gc_feedback[debug-pg15].physical/logical ratio after bottom_most_compaction: 5.6312 
test_gc_feedback[debug-pg15].max_total_num_of_deltas_after_bottom_most_compaction: 4 
test_gc_feedback[debug-pg15].max_num_of_deltas_above_image_after_bottom_most_compaction: 1
```

## Summary of changes

* Add the manual compaction trigger
* Use in test_gc_feedback
* Add a guard to avoid running it with retain_lsns
* Fix: Do `schedule_compaction_update` after compaction
* Fix: Supply deltas in the correct order to reconstruct value

---------

Signed-off-by: Alex Chi Z <chi@neon.tech>
2024-06-25 23:00:14 +00:00
Alex Chi Z
3e63d0f9e0 test(pageserver): quantify compaction outcome (#7867)
A simple API to collect some statistics after compaction to easily
understand the result.

The tool reads the layer map, and analyze range by range instead of
doing single-key operations, which is more efficient than doing a
benchmark to collect the result. It currently computes two key metrics:

* Latest data access efficiency, which finds how many delta layers /
image layers the system needs to iterate before returning any key in a
key range.
* (Approximate) PiTR efficiency, as in
https://github.com/neondatabase/neon/issues/7770, which is simply the
number of delta files in the range. The reason behind that is, assume no
image layer is created, PiTR efficiency is simply the cost of collect
records from the delta layers, and the replay time. Number of delta
files (or in the future, estimated size of reads) is a simple yet
efficient way of estimating how much effort the page server needs to
reconstruct a page.

Signed-off-by: Alex Chi Z <chi@neon.tech>
2024-06-10 10:42:13 +02:00
Arpad Müller
34fa34d15c Dump layer map json in test_gc_feedback.py (#7179)
The layer map json is an interesting file for that test, so dump it to
make debugging easier.
2024-03-20 18:39:46 +00:00
Christian Schwarz
dedf66ba5b remove gc_feedback mechanism (#6863)
It's been dead-code-at-runtime for 9 months, let's remove it.
We can always re-introduce it at a later point.

Came across this while working on #6861, which will touch
`time_for_new_image_layer`. This is an opporunity to make that function
simpler.
2024-02-26 10:05:24 +01:00
Joonas Koivunen
762a8a7bb5 python: more linting (#4734)
Ruff has "B" class of lints, including B018 which will nag on useless
expressions, related to #4719. Enable such lints and fix the existing
issues.

Most notably:
- https://beta.ruff.rs/docs/rules/mutable-argument-default/
- https://beta.ruff.rs/docs/rules/assert-false/

---------

Co-authored-by: Alexander Bayandin <alexander@neon.tech>
2023-07-18 12:56:40 +03:00
Konstantin Knizhnik
417f37b2e8 Pass set of wanted image layers from GC to compaction (#3673)
## Describe your changes

Right now the only criteria for image layer generation is number of
delta layer since last image layer.
If we have "stairs" layout of delta layers (see link below) then it can
happen that there a lot of old delta layers which can not be reclaimed
by GC because are not fully covered with image layers.

This PR constructs list of "wanted" image layers in GC (which image
layers are needed to be able to remove old layers)
and pass this list to compaction task which performs generation of image
layers.
So right now except deltas count criteria we also take in account
"wishes" of GC.

## Issue ticket number and link

See 
https://neondb.slack.com/archives/C033RQ5SPDH/p1676914249982519


## Checklist before requesting a review
- [ ] I have performed a self-review of my code.
- [ ] If it is a core feature, I have added thorough tests.
- [ ] Do we need to implement analytics? if so did you add the relevant
metrics to the dashboard?
- [ ] If this PR requires public announcement, mark it with
/release-notes label and add several sentences in this section.

---------

Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
2023-05-24 08:01:41 +03:00