The compute_ctl HTTP server has the following purposes:
- Allow management via the control plane
- Provide an endpoint for scaping metrics
- Provide APIs for compute internal clients
- Neon Postgres extension for installing remote extensions
- local_proxy for installing extensions and adding grants
The first two purposes require the HTTP server to be available outside
the compute.
The Neon threat model is a bad actor within our internal network. We
need to reduce the surface area of attack. By exposing unnecessary
unauthenticated HTTP endpoints to the internal network, we increase the
surface area of attack. For endpoints described in the third bullet
point, we can just run an extra HTTP server, which is only bound to the
loopback interface since all consumers of those endpoints are within the
compute.
## Problem
Endpoint kept running while timeline was deleted, causing forbidden
warnings on the pageserver when the tenant is not found.
## Summary of changes
- Explicitly stop the endpoint before the end of the test, so that it
isn't trying to talk to the pageserver in the background while things
are torn down
## Problem
During ingest_benchmark which uses `pgcopydb`
([see](https://github.com/dimitri/pgcopydb))we sometimes had outages.
- when PostgreSQL COPY step failed we got a segfault (reported
[here](https://github.com/dimitri/pgcopydb/issues/899))
- the root cause was Neon idle_in_transaction_session_timeout is set to
5 minutes which is suboptimal for long-running tasks like project import
(reported [here](https://github.com/dimitri/pgcopydb/issues/900))
## Summary of changes
Patch pgcopydb to avoid segfault.
override idle_in_transaction_session_timeout and set it to "unlimited"
Fixes flaky test_lr_with_slow_safekeeper test #10242
Fix query to `pg_catalog.pg_stat_subscription` catalog to handle table
synchronization and parallel LR correctly.
## Problem
This test may not fully detect data corruption during splits, since we
don't force-compact the entire keyspace.
## Summary of changes
Force-compact all data in `test_sharding_autosplit`.
## Problem
There is no direct backpressure for compaction and L0 read
amplification. This allows a large buildup of compaction debt and read
amplification.
Resolves#5415.
Requires #10402.
## Summary of changes
Delay layer flushes based on the number of level 0 delta layers:
* `l0_flush_delay_threshold`: delay flushes such that they take 2x as
long (default `2 * compaction_threshold`).
* `l0_flush_stall_threshold`: stall flushes until level 0 delta layers
drop below threshold (default `4 * compaction_threshold`).
If either threshold is reached, ephemeral layer rolls also synchronously
wait for layer flushes to propagate this backpressure up into WAL
ingestion. This will bound the number of frozen layers to 1 once
backpressure kicks in, since all other frozen layers must flush before
the rolled layer.
## Analysis
This will significantly change the compute backpressure characteristics.
Recall the three compute backpressure knobs:
* `max_replication_write_lag`: 500 MB (based on Pageserver
`last_received_lsn`).
* `max_replication_flush_lag`: 10 GB (based on Pageserver
`disk_consistent_lsn`).
* `max_replication_apply_lag`: disabled (based on Pageserver
`remote_consistent_lsn`).
Previously, the Pageserver would keep ingesting WAL and build up
ephemeral layers and L0 layers until the compute hit
`max_replication_flush_lag` at 10 GB and began backpressuring. Now, once
we delay/stall WAL ingestion, the compute will begin backpressuring
after `max_replication_write_lag`, i.e. 500 MB. This is probably a good
thing (we're not building up a ton of compaction debt), but we should
consider tuning these settings.
`max_replication_flush_lag` probably doesn't serve a purpose anymore,
and we should consider removing it.
Furthermore, the removal of the upload barrier in #10402 will mean that
we no longer backpressure flushes based on S3 uploads, since
`max_replication_apply_lag` is disabled. We should consider enabling
this as well.
### When and what do we compact?
Default compaction settings:
* `compaction_threshold`: 10 L0 delta layers.
* `compaction_period`: 20 seconds (between each compaction loop check).
* `checkpoint_distance`: 256 MB (size of L0 delta layers).
* `l0_flush_delay_threshold`: 20 L0 delta layers.
* `l0_flush_stall_threshold`: 40 L0 delta layers.
Compaction characteristics:
* Minimum compaction volume: 10 layers * 256 MB = 2.5 GB.
* Additional compaction volume (assuming 128 MB/s WAL): 128 MB/s * 20
seconds = 2.5 GB (10 L0 layers).
* Required compaction bandwidth: 5.0 GB / 20 seconds = 256 MB/s.
### When do we hit `max_replication_write_lag`?
Depending on how fast compaction and flushes happens, the compute will
backpressure somewhere between `l0_flush_delay_threshold` or
`l0_flush_stall_threshold` + `max_replication_write_lag`.
* Minimum compute backpressure lag: 20 layers * 256 MB + 500 MB = 5.6 GB
* Maximum compute backpressure lag: 40 layers * 256 MB + 500 MB = 10.0
GB
This seems like a reasonable range to me.
## Problem
We want to do a more robust job of scheduling tenants into their home
AZ: https://github.com/neondatabase/neon/issues/8264.
Closes: https://github.com/neondatabase/neon/issues/8969
## Summary of changes
### Scope
This PR combines prioritizing AZ with a larger rework of how we do
optimisation. The rationale is that just bumping AZ in the order of
Score attributes is a very tiny change: the interesting part is lining
up all the optimisation logic to respect this properly, which means
rewriting it to use the same scores as the scheduler, rather than the
fragile hand-crafted logic that we had before. Separating these changes
out is possible, but would involve doing two rounds of test updates
instead of one.
### Scheduling optimisation
`TenantShard`'s `optimize_attachment` and `optimize_secondary` methods
now both use the scheduler to pick a new "favourite" location. Then
there is some refined logic for whether + how to migrate to it:
- To decide if a new location is sufficiently "better", we generate
scores using some projected ScheduleContexts that exclude the shard
under consideration, so that we avoid migrating from a node with
AffinityScore(2) to a node with AffinityScore(1), only to migrate back
later.
- Score types get a `for_optimization` method so that when we compare
scores, we will only do an optimisation if the scores differ by their
highest-ranking attributes, not just because one pageserver is lower in
utilization. Eventually we _will_ want a mode that does this, but doing
it here would make scheduling logic unstable and harder to test, and to
do this correctly one needs to know the size of the tenant that one is
migrating.
- When we find a new attached location that we would like to move to, we
will create a new secondary location there, even if we already had one
on some other node. This handles the case where we have a home AZ A, and
want to migrate the attachment between pageservers in that AZ while
retaining a secondary location in some other AZ as well.
- A unit test is added for
https://github.com/neondatabase/neon/issues/8969, which is implicitly
fixed by reworking optimisation to use the same scheduling scores as
scheduling.
## Problem
This test writes ~5GB of data. It is not suitable to run in parallel
with all the other small tests in test_runner/regress.
via #9537
## Summary of changes
- Move test_parallel_copy into the performance directory, so that it
does not run in parallel with other tests
## Problem
We want to verify how much / if pgbench throughput and latency on Neon
suffers if the database contains many other relations, too.
## Summary of changes
Modify the benchmarking.yml pgbench-compare job to
- create an addiitional project at scale factor 10 GiB
- before running pgbench add n tables (initially 10k) to the database
- then compare the pgbench throughput and latency to the existing
pgbench-compare at 10 Gib scale factor
We use a realistic template for the n relations that is a partitioned
table with some realistic data types, indexes and constraints - similar
to a table that we use internally.
Example run:
https://github.com/neondatabase/neon/actions/runs/12377565956/job/34547386959
## Problem
In https://github.com/neondatabase/neon/pull/8103 we changed the test
case to have more test coverage of gc_compaction. Now that we have
`test_gc_compaction_smoke`, we can revert this test case to serve its
original purpose and revert the parameter changes.
part of https://github.com/neondatabase/neon/issues/9114
## Summary of changes
* Revert pitr_interval from 60s to 10s.
* Assert the physical/logical size ratio in the benchmark.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
## Problem
We have a scale test for the storage controller which also acts as a
good stress test for scheduling stability. However, it created nodes
with no AZs set.
## Summary of changes
- Bump node count to 6 and set AZs on them.
This is a precursor to other AZ-related PRs, to make sure any new code
that's landed is getting scale tested in an AZ-aware environment.
## Problem
we tried different parallelism settings for ingest bench
## Summary of changes
the following settings seem optimal after merging
- SK side Wal filtering
- batched getpages
Settings:
- effective_io_concurrency 100
- concurrency limit 200 (different from Prod!)
- jobs 4, maintenance workers 7
- 10 GB chunk size
## Problem
```
2024-12-03T15:42:46.5978335Z + poetry run python /__w/neon/neon/scripts/ingest_perf_test_result.py --ingest /__w/neon/neon/test_runner/perf-report-local
2024-12-03T15:42:49.5325077Z Traceback (most recent call last):
2024-12-03T15:42:49.5325603Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 165, in <module>
2024-12-03T15:42:49.5326029Z main()
2024-12-03T15:42:49.5326316Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 155, in main
2024-12-03T15:42:49.5326739Z ingested = ingest_perf_test_result(cur, item, recorded_at_timestamp)
2024-12-03T15:42:49.5327488Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-12-03T15:42:49.5327914Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 99, in ingest_perf_test_result
2024-12-03T15:42:49.5328321Z psycopg2.extras.execute_values(
2024-12-03T15:42:49.5328940Z File "/github/home/.cache/pypoetry/virtualenvs/non-package-mode-_pxWMzVK-py3.11/lib/python3.11/site-packages/psycopg2/extras.py", line 1299, in execute_values
2024-12-03T15:42:49.5335618Z cur.execute(b''.join(parts))
2024-12-03T15:42:49.5335967Z psycopg2.errors.InvalidTextRepresentation: invalid input syntax for type numeric: "concurrent-futures"
2024-12-03T15:42:49.5336287Z LINE 57: 'concurrent-futures',
2024-12-03T15:42:49.5336462Z ^
```
## Summary of changes
- `test_page_service_batching`: save non-numeric params as `labels`
- Add a runtime check that `metric_value` is NUMERIC
Before this PR, some override callbacks used `.default()`, others
used `.setdefault()`.
As of this PR, all callbacks use `.setdefault()` which I think is least
prone to failure.
Aligning on a single way will set the right example for future tests
that need such customization.
The `test_pageserver_getpage_throttle.py` technically is a change in
behavior: before, it replaced the `tenant_config` field, now it just
configures the throttle. This is what I believe is intended anyway.
## Problem
`test_sharded_ingest` ingests a lot of data, which can cause shutdown to
be slow e.g. due to local "S3 uploads" or compactions. This can cause
test flakes during teardown.
Resolves#9740.
## Summary of changes
Perform an immediate shutdown of the cluster.
This PR
- fixes smgr metrics https://github.com/neondatabase/neon/issues/9925
- adds an additional startup log line logging the current batching
config
- adds a histogram of batch sizes global and per-tenant
- adds a metric exposing the current batching config
The issue described #9925 is that before this PR, request latency was
only observed *after* batching.
This means that smgr latency metrics (most importantly getpage latency)
don't account for
- `wait_lsn` time
- time spent waiting for batch to fill up / the executor stage to pick
up the batch.
The fix is to use a per-request batching timer, like we did before the
initial batching PR.
We funnel those timers through the entire request lifecycle.
I noticed that even before the initial batching changes, we weren't
accounting for the time spent writing & flushing the response to the
wire.
This PR drive-by fixes that deficiency by dropping the timers at the
very end of processing the batch, i.e., after the `pgb.flush()` call.
I was **unable to maintain the behavior that we deduct
time-spent-in-throttle from various latency metrics.
The reason is that we're using a *single* counter in `RequestContext` to
track micros spent in throttle.
But there are *N* metrics timers in the batch, one per request.
As a consequence, the practice of consuming the counter in the drop
handler of each timer no longer works because all but the first timer
will encounter error `close() called on closed state`.
A failed attempt to maintain the current behavior can be found in
https://github.com/neondatabase/neon/pull/9951.
So, this PR remvoes the deduction behavior from all metrics.
I started a discussion on Slack about it the implications this has for
our internal SLO calculation:
https://neondb.slack.com/archives/C033RQ5SPDH/p1732910861704029
# Refs
- fixes https://github.com/neondatabase/neon/issues/9925
- sub-issue https://github.com/neondatabase/neon/issues/9377
- epic: https://github.com/neondatabase/neon/issues/9376
Improves `wait_until` by:
* Use `timeout` instead of `iterations`. This allows changing the
timeout/interval parameters independently.
* Make `timeout` and `interval` optional (default 20s and 0.5s). Most
callers don't care.
* Only output status every 1s by default, and add optional
`status_interval` parameter.
* Remove `show_intermediate_error`, this was always emitted anyway.
Most callers have been updated to use the defaults, except where they
had good reason otherwise.
# Problem
The timeout-based batching adds latency to unbatchable workloads.
We can choose a short batching timeout (e.g. 10us) but that requires
high-resolution timers, which tokio doesn't have.
I thoroughly explored options to use OS timers (see
[this](https://github.com/neondatabase/neon/pull/9822) abandoned PR).
In short, it's not an attractive option because any timer implementation
adds non-trivial overheads.
# Solution
The insight is that, in the steady state of a batchable workload, the
time we spend in `get_vectored` will be hundreds of microseconds anyway.
If we prepare the next batch concurrently to `get_vectored`, we will
have a sizeable batch ready once `get_vectored` of the current batch is
done and do not need an explicit timeout.
This can be reasonably described as **pipelining of the protocol
handler**.
# Implementation
We model the sub-protocol handler for pagestream requests
(`handle_pagrequests`) as two futures that form a pipeline:
2. Batching: read requests from the connection and fill the current
batch
3. Execution: `take` the current batch, execute it using `get_vectored`,
and send the response.
The Reading and Batching stage are connected through a new type of
channel called `spsc_fold`.
See the long comment in the `handle_pagerequests_pipelined` for details.
# Changes
- Refactor `handle_pagerequests`
- separate functions for
- reading one protocol message; produces a `BatchedFeMessage` with just
one page request in it
- batching; tried to merge an incoming `BatchedFeMessage` into an
existing `BatchedFeMessage`; returns `None` on success and returns back
the incoming message in case merging isn't possible
- execution of a batched message
- unify the timeline handle acquisition & request span construction; it
now happen in the function that reads the protocol message
- Implement serial and pipelined model
- serial: what we had before any of the batching changes
- read one protocol message
- execute protocol messages
- pipelined: the design described above
- optionality for execution of the pipeline: either via concurrent
futures vs tokio tasks
- Pageserver config
- remove batching timeout field
- add ability to configure pipelining mode
- add ability to limit max batch size for pipelined configurations
(required for the rollout, cf
https://github.com/neondatabase/cloud/issues/20620 )
- ability to configure execution mode
- Tests
- remove `batch_timeout` parametrization
- rename `test_getpage_merge_smoke` to `test_throughput`
- add parametrization to test different max batch sizes and execution
moes
- rename `test_timer_precision` to `test_latency`
- rename the test case file to `test_page_service_batching.py`
- better descriptions of what the tests actually do
## On the holding The `TimelineHandle` in the pending batch
While batching, we hold the `TimelineHandle` in the pending batch.
Therefore, the timeline will not finish shutting down while we're
batching.
This is not a problem in practice because the concurrently ongoing
`get_vectored` call will fail quickly with an error indicating that the
timeline is shutting down.
This results in the Execution stage returning a `QueryError::Shutdown`,
which causes the pipeline / entire page service connection to shut down.
This drops all references to the
`Arc<Mutex<Option<Box<BatchedFeMessage>>>>` object, thereby dropping the
contained `TimelineHandle`s.
- => fixes https://github.com/neondatabase/neon/issues/9850
# Performance
Local run of the benchmarks, results in [this empty
commit](1cf5b1463f)
in the PR branch.
Key take-aways:
* `concurrent-futures` and `tasks` deliver identical `batching_factor`
* tail latency impact unknown, cf
https://github.com/neondatabase/neon/issues/9837
* `concurrent-futures` has higher throughput than `tasks` in all
workloads (=lower `time` metric)
* In unbatchable workloads, `concurrent-futures` has 5% higher
`CPU-per-throughput` than that of `tasks`, and 15% higher than that of
`serial`.
* In batchable-32 workload, `concurrent-futures` has 8% lower
`CPU-per-throughput` than that of `tasks` (comparison to tput of
`serial` is irrelevant)
* in unbatchable workloads, mean and tail latencies of
`concurrent-futures` is practically identical to `serial`, whereas
`tasks` adds 20-30us of overhead
Overall, `concurrent-futures` seems like a slightly more attractive
choice.
# Rollout
This change is disabled-by-default.
Rollout plan:
- https://github.com/neondatabase/cloud/issues/20620
# Refs
- epic: https://github.com/neondatabase/neon/issues/9376
- this sub-task: https://github.com/neondatabase/neon/issues/9377
- the abandoned attempt to improve batching timeout resolution:
https://github.com/neondatabase/neon/pull/9820
- closes https://github.com/neondatabase/neon/issues/9850
- fixes https://github.com/neondatabase/neon/issues/9835
Adds a benchmark for logical message WAL ingestion throughput
end-to-end. Logical messages are essentially noops, and thus ignored by
the Pageserver.
Example results from my MacBook, with fsync enabled:
```
postgres_ingest: 14.445 s
safekeeper_ingest: 29.948 s
pageserver_ingest: 30.013 s
pageserver_recover_ingest: 8.633 s
wal_written: 10,340 MB
message_count: 1310720 messages
postgres_throughput: 715 MB/s
safekeeper_throughput: 345 MB/s
pageserver_throughput: 344 MB/s
pageserver_recover_throughput: 1197 MB/s
```
See
https://github.com/neondatabase/neon/issues/9642#issuecomment-2475995205
for running analysis.
Touches #9642.
## Problem
https://github.com/neondatabase/neon/pull/9746 lifted decoding and
interpretation of WAL to the safekeeper.
This reduced the ingested amount on the pageservers by around 10x for a
tenant with 8 shards, but doubled
the ingested amount for single sharded tenants.
Also, https://github.com/neondatabase/neon/pull/9746 uses bincode which
doesn't support schema evolution.
Technically the schema can be evolved, but it's very cumbersome.
## Summary of changes
This patch set addresses both problems by adding protobuf support for
the interpreted wal records and adding compression support. Compressed
protobuf reduced the ingested amount by 100x on the 32 shards
`test_sharded_ingest` case (compared to non-interpreted proto). For the
1 shard case the reduction is 5x.
Sister change to `rust-postgres` is
[here](https://github.com/neondatabase/rust-postgres/pull/33).
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
## Problem
ingest benchmark tests project migration to Neon involving steps
- COPY relation data
- create indexes
- create constraints
Previously we used only 4 copy jobs, 4 create index jobs and 7
maintenance workers. After increasing effective_io_concurrency on
compute we see that we can sustain more parallelism in the ingest bench
## Summary of changes
Increase copy jobs to 8, create index jobs to 8 and maintenance workers
to 16
## Problem
For any given tenant shard, pageservers receive all of the tenant's WAL
from the safekeeper.
This soft-blocks us from using larger shard counts due to bandwidth
concerns and CPU overhead of filtering
out the records.
## Summary of changes
This PR lifts the decoding and interpretation of WAL from the pageserver
into the safekeeper.
A customised PG replication protocol is used where instead of sending
raw WAL, the safekeeper sends
filtered, interpreted records. The receiver drives the protocol
selection, so, on the pageserver side, usage
of the new protocol is gated by a new pageserver config:
`wal_receiver_protocol`.
More granularly the changes are:
1. Optionally inject the protocol and shard identity into the arguments
used for starting replication
2. On the safekeeper side, implement a new wal sending primitive which
decodes and interprets records
before sending them over
3. On the pageserver side, implement the ingestion of this new
replication message type. It's very similar
to what we already have for raw wal (minus decoding and interpreting).
## Notes
* This PR currently uses my [branch of
rust-postgres](https://github.com/neondatabase/rust-postgres/tree/vlad/interpreted-wal-record-replication-support)
which includes the deserialization logic for the new replication message
type. PR for that is open
[here](https://github.com/neondatabase/rust-postgres/pull/32).
* This PR contains changes for both pageservers and safekeepers. It's
safe to merge because the new protocol is disabled by default on the
pageserver side. We can gradually start enabling it in subsequent
releases.
* CI tests are running on https://github.com/neondatabase/neon/pull/9747
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
## Problem
close https://github.com/neondatabase/neon/issues/9761
The test assumed that no new L0 layers are flushed throughout the
process, which is not true.
## Summary of changes
Fix the test case `test_compaction_l0_memory` by flushing in-memory
layers before compaction.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Along with the migration to Python 3.11, I switched `C(str, Enum)` with
`C(StrEnum)`; one such example is the `PgVersion` enum.
It required more changes in `PgVersion` itself (before, it accepted both
`str` and `int`, and after it, it supports only `str`), which caused the
`test_bulk_insert` test to fail.
## Summary of changes
- `test_bulk_insert`: explicitly cast pg_version from `timeline_detail`
to str
## Problem
On Debian 12 (Bookworm), Python 3.11 is the latest available version.
## Summary of changes
- Update Python to 3.11 in build-tools
- Fix ruff check / format
- Fix mypy
- Use `StrEnum` instead of pair `str`, `Enum`
- Update docs
## Problem
The first version of the ingest benchmark had some parsing and reporting
logic in shell script inside GitHub workflow.
it is better to move that logic into a python testcase so that we can
also run it locally.
## Summary of changes
- Create new python testcase
- invoke pgcopydb inside python test case
- move the following logic into python testcase
- determine backpressure
- invoke pgcopydb and report its progress
- parse pgcopydb log and extract metrics
- insert metrics into perf test database
- add additional column to perf test database that can receive endpoint
ID used for pgcopydb run to have it available in grafana dashboard when
retrieving other metrics for an endpoint
## Example run
https://github.com/neondatabase/neon/actions/runs/11860622170/job/33056264386
## Problem
Due to #9471 , the scale test occasionally gets 404s while trying to
modify the config of a timeline that belongs to a tenant being migrated.
We rarely see this narrow race in the field, but the test is quite good
at reproducing it.
## Summary of changes
- Ignore 404 errors in this test.
Analysis of the LR benchmarking tests indicates that in the duration of
test_subscriber_lag, a leftover 'slotter' replication slot can lead to
retained WAL growing on the publisher. This replication slot is not used
by any subscriber. The only purpose of the slot is to generate snapshot
files for the puspose of test_snap_files.
Signed-off-by: Tristan Partin <tristan@neon.tech>
psycopg2 has the following warning related to autocommit:
> By default, any query execution, including a simple SELECT will start
> a transaction: for long-running programs, if no further action is
> taken, the session will remain “idle in transaction”, an undesirable
> condition for several reasons (locks are held by the session, tables
> bloat…). For long lived scripts, either ensure to terminate a
> transaction as soon as possible or use an autocommit connection.
In the 2.9 release notes, psycopg2 also made the following change:
> `with connection` starts a transaction on autocommit transactions too
Some of these connections are indeed long-lived, so we were retaining
tons of WAL on the endpoints because we had a transaction pinned in the
past.
Link: https://www.psycopg.org/docs/news.html#what-s-new-in-psycopg-2-9
Link: https://github.com/psycopg/psycopg2/issues/941
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
Running `pytest.skip(...)` in a test body instead of marking the test
with `@pytest.mark.skipif(...)` makes all fixtures to be initialised,
which is not necessary if the test is going to be skipped anyway.
Also, some tests are unnecessarily skipped (e.g. `test_layer_bloating`
on Postgres 17, or `test_idle_reconnections` at all) or run (e.g.
`test_parse_project_git_version_output_positive` more than on once
configuration) according to comments.
## Summary of changes
- Move `skip_on_postgres` / `xfail_on_postgres` /
`run_only_on_default_postgres` decorators to `fixture.utils`
- Add new `skip_in_debug_build` and `skip_on_ci` decorators
- Replace `pytest.skip(...)` calls with decorators where possible
Adds a Python benchmark for sharded ingestion. This ingests 7 GB of WAL
(100M rows) into a Safekeeper and fans out to 10 shards running on 10
different pageservers. The ingest volume and duration is recorded.
## Problem
Part of https://github.com/neondatabase/neon/issues/8623
## Summary of changes
Removed all aux-v1 config processing code. Note that we persisted it
into the index part file, so we cannot really remove the field from
index part. I also kept the config item within the tenant config, but we
will not read it any more.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We were seeing timeouts on migrations in this test.
The test unfortunately tends to saturate local storage, which is shared
between the pageservers and the control plane database, which makes the
test kind of unrealistic. We will also want to increase the scale of
this test, so it's worth fixing that.
## Summary of changes
- Instead of randomly creating timelines at the same time as the other
background operations, explicitly identify a subset of tenant which will
have timelines, and create them at the start. This avoids pageservers
putting a lot of load on the test node during the main body of the test.
- Adjust the tenants created to create some number of 8 shard tenants
and the rest 1 shard tenants, instead of just creating a lot of 2 shard
tenants.
- Use archival_config to exercise tenant-mutating operations, instead of
using timeline creation for this.
- Adjust reconcile_until_idle calls to avoid waiting 5 seconds between
calls, which causes timelines with large shard count tenants.
- Fix a pageserver bug where calls to archival_config during activation
get 404
## Problem
The path to TPC-H queries was incorrectly changed in #9306.
This path is used for `test_tpch` parameterization, so all perf tests
started to fail:
```
==================================== ERRORS ====================================
__________ ERROR collecting test_runner/performance/test_perf_olap.py __________
test_runner/performance/test_perf_olap.py:205: in <module>
@pytest.mark.parametrize("query", tpch_queuies())
test_runner/performance/test_perf_olap.py:196: in tpch_queuies
assert queries_dir.exists(), f"TPC-H queries dir not found: {queries_dir}"
E AssertionError: TPC-H queries dir not found: /__w/neon/neon/test_runner/performance/performance/tpc-h/queries
E assert False
E + where False = <bound method Path.exists of PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries')>()
E + where <bound method Path.exists of PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries')> = PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries').exists
```
## Summary of changes
- Fix the path to tpc-h queries
It didn't serve much value, and was only used twice.
Path(__file__).parent is a pretty easy invocation to use.
Signed-off-by: Tristan Partin <tristan@neon.tech>
Add wrappers for a few commands that didn't have them before. Move the
logic to generate tenant and timeline IDs from NeonCli to the callers,
so that NeonCli is more purely just a type-safe wrapper around
'neon_local'.
This should generally be faster when running tests, especially those
that run with higher scales.
Ignoring test_lfc_resize since it seems like we are hitting a query
timeout for some reason that I have yet to investigate. A little bit of
improvemnt is better than none.
Signed-off-by: Tristan Partin <tristan@neon.tech>
