## Problem
While configuring or reconfiguring PG due to PageServer movements, it's
possible PG may get stuck if PageServer is moved around after fetching
the spec from StorageController.
## Summary of changes
To fix this issue, this PR introduces two changes:
1. Fail the PG query directly if the query cannot request configuration
for certain number of times.
2. Introduce a new state `RefreshConfiguration` in compute tools to
differentiate it from `RefreshConfigurationPending`. If compute tool is
already in `RefreshConfiguration` state, then it will not accept new
request configuration requests.
## How is this tested?
Chaos testing.
Co-authored-by: Chen Luo <chen.luo@databricks.com>
## Problem
In our experience running the system so far, almost all of the "hang
compute" situations are due to the compute (postgres) pointing at the
wrong pageservers. We currently mainly rely on the promethesus exporter
(PGExporter) running on PG to detect and report any down time, but these
can be unreliable because the read and write probes the PGExporter runs
do not always generate pageserver requests due to caching, even though
the real user might be experiencing down time when touching uncached
pages.
We are also about to start disk-wiping node pool rotation operations in
prod clusters for our pageservers, and it is critical to have a
convenient way to monitor the impact of these node pool rotations so
that we can quickly respond to any issues. These metrics should provide
very clear signals to address this operational need.
## Summary of changes
Added a pair of metrics to detect issues between postgres' PageStream
protocol (e.g. get_page_at_lsn, get_base_backup, etc.) communications
with pageservers:
* On the compute node (compute_ctl), exports a counter metric that is
incremented every time postgres requests a configuration refresh.
Postgres today only requests these configuration refreshes when it
cannot connect to a pageserver or if the pageserver rejects its request
by disconnecting.
* On the pageserver, exports a counter metric that is incremented every
time it receives a PageStream request that cannot be handled because the
tenant is not known or if the request was routed to the wrong shard
(e.g. secondary).
### How I plan to use metrics
I plan to use the metrics added here to create alerts. The alerts can
fire, for example, if these counters have been continuously increasing
for over a certain period of time. During rollouts, misrouted requests
may occasionally happen, but they should soon die down as
reconfigurations make progress. We can start with something like raising
the alert if the counters have been increasing continuously for over 5
minutes.
## How is this tested?
New integration tests in
`test_runner/regress/test_hadron_ps_connectivity_metrics.py`
Co-authored-by: William Huang <william.huang@databricks.com>
## Problem
Currently PG backpressure parameters are enforced globally. With tenant
splitting, this makes it hard to balance small tenants and large
tenants. For large tenants with more shards, we need to increase the
lagging because each shard receives total/shard_count amount of data,
while doing so could be suboptimal to small tenants with fewer shards.
## Summary of changes
This PR makes these parameters to be enforced at the shard level, i.e.,
PG will compute the actual lag limit by multiply the shard count.
## How is this tested?
Added regression test.
Co-authored-by: Chen Luo <chen.luo@databricks.com>
## Problem
This is a follow-up to TODO, as part
of the effort to rewire the compute reconfiguration/notification
mechanism to make it more robust. Please refer to that commit or ticket
BRC-1778 for full context of the problem.
## Summary of changes
The previous change added mechanism in `compute_ctl` that makes it
possible to refresh the configuration of PG on-demand by having
`compute_ctl` go out to download a new config from the control
plane/HCC. This change wired this mechanism up with PG so that PG will
signal `compute_ctl` to refresh its configuration when it suspects that
it could be talking to incorrect pageservers due to a stale
configuration.
PG will become suspicious that it is talking to the wrong pageservers in
the following situations:
1. It cannot connect to a pageserver (e.g., getting a network-level
connection refused error)
2. It can connect to a pageserver, but the pageserver does not return
any data for the GetPage request
3. It can connect to a pageserver, but the pageserver returns a
malformed response
4. It can connect to a pageserver, but there is an error receiving the
GetPage request response for any other reason
This change also includes a minor tweak to `compute_ctl`'s config
refresh behavior. Upon receiving a request to refresh PG configuration,
`compute_ctl` will reach out to download a config, but it will not
attempt to apply the configuration if the config is the same as the old
config is it replacing. This optimization is added because the act of
reconfiguring itself requires working pageserver connections. In many
failure situations it is likely that PG detects an issue with a
pageserver before the control plane can detect the issue, migrate
tenants, and update the compute config. In this case even the latest
compute config won't point PG to working pageservers, causing the
configuration attempt to hang and negatively impact PG's
time-to-recovery. With this change, `compute_ctl` only attempts
reconfiguration if the refreshed config points PG to different
pageservers.
## How is this tested?
The new code paths are exercised in all existing tests because this
mechanism is on by default.
Explicitly tested in `test_runner/regress/test_change_pageserver.py`.
Co-authored-by: William Huang <william.huang@databricks.com>
## Problem
We don't have a well-documented, periodic benchmark for TPC-C like OLTP
workload.
## Summary of changes
# Benchbase TPC-C-like Performance Results
Runs TPC-C-like benchmarks on Neon databases using
[Benchbase](https://github.com/cmu-db/benchbase).
Docker images are built
[here](https://github.com/neondatabase-labs/benchbase-docker-images)
We run the benchmarks at different scale factors aligned with different
compute sizes we offer to customers.
For each scale factor, we determine a max rate (see Throughput in warmup
phase) and then run the benchmark at a target rate of approx. 70 % of
the max rate.
We use different warehouse sizes which determine the working set size -
it is optimized for LFC size of the respected pricing tier.
Usually we should get LFC hit rates above 70 % for this setup and quite
good, consistent (non-flaky) latencies.
## Expected performance as of first testing this
| Tier | CU | Warehouses | Terminals | Max TPS | LFC size | Working set
size | LFC hit rate | Median latency | p95 latency |
|------------|------------|---------------|-----------|---------|----------|------------------|--------------|----------------|-------------|
| free | 0.25-2 | 50 - 5 GB | 150 | 800 | 5 GB | 6.3 GB | 95 % | 170 ms
| 600 ms |
| serverless | 2-8 | 500 - 50 GB | 230 | 2000 | 26 GB | ?? GB | 91 % |
50 ms | 200 ms |
| business | 2-16 | 1000 - 100 GB | 330 | 2900 | 51 GB | 50 GB | 72 % |
40 ms | 180 ms |
Each run
- first loads the database (not shown in the dashboard).
- Then we run a warmup phase for 20 minutes to warm up the database and
the LFC at unlimited target rate (max rate) (highest throughput but
flaky latencies).
The warmup phase can be used to determine the max rate and adjust it in
the github workflow in case Neon is faster in the future.
- Then we run the benchmark at a target rate of approx. 70 % of the max
rate for 1 hour (expecting consistent latencies and throughput).
## Important notes on implementation:
- we want to eventually publish the process how to reproduce these
benchmarks
- thus we want to reduce all dependencies necessary to run the
benchmark, the only thing needed are
- docker
- the docker images referenced above for benchbase
- python >= 3.9 to run some config generation steps and create diagrams
- to reduce dependencies we deliberatly do NOT use some of our python
fixture test infrastructure to make the dependency chain really small -
so pls don't add a review comment "should reuse fixture xy"
- we also upload all generator python scripts, generated bash shell
scripts and configs as well as raw results to S3 bucket that we later
want to publish once this benchmark is reviewed and approved.
## Problem
* Fixes LKB-743
We get regular assertion failures on staging caused by a race with chaos
injector. If chaos injector decides to migrate a tenant shard between
the background optimisation planning and applying optimisations then we
attempt to migrate and already migrated shard and hit an assertion
failure.
## Summary of changes
@VladLazar fixed a variant of this issue by
adding`validate_optimization` recently, however it didn't validate the
specific property this other assertion requires. Fix is just to update
it to cover all the expected properties.
## Problem
Compiling `neon-pg-ext-v17` results in these linker warnings for
`libcommunicator.a`:
```
$ make -j`nproc` -s neon-pg-ext-v17
Installing PostgreSQL v17 headers
Compiling PostgreSQL v17
Compiling neon-specific Postgres extensions for v17
ld: warning: object file (/Users/erik.grinaker/Projects/neon/target/debug/libcommunicator.a[1159](25ac62e5b3c53843-curve25519.o)) was built for newer 'macOS' version (15.5) than being linked (15.0)
ld: warning: object file (/Users/erik.grinaker/Projects/neon/target/debug/libcommunicator.a[1160](0bbbd18bda93c05b-aes_nohw.o)) was built for newer 'macOS' version (15.5) than being linked (15.0)
ld: warning: object file (/Users/erik.grinaker/Projects/neon/target/debug/libcommunicator.a[1161](00c879ee3285a50d-montgomery.o)) was built for newer 'macOS' version (15.5) than being linked (15.0)
[...]
```
## Summary of changes
Set `MACOSX_DEPLOYMENT_TARGET` to the current local SDK version (15.5 in
this case), which links against object files for that version.
## Problem
Compute retries are finite (e.g. 5x in a basebackup) -- with a 50%
failure rate we have pretty good chance of exceeding that and the test
failing.
Fixes: https://databricks.atlassian.net/browse/LKB-2278
## Summary of changes
- Turn connection error rate down to 20%
Co-authored-by: John Spray <john.spray@databricks.com>
## Problem
We have been dealing with a number of issues with the SC compute
notification mechanism. Various race conditions exist in the
PG/HCC/cplane/PS distributed system, and relying on the SC to send
notifications to the compute node to notify it of PS changes is not
robust. We decided to pursue a more robust option where the compute node
itself discovers whether it may be pointing to the incorrect PSs and
proactively reconfigure itself if issues are suspected.
## Summary of changes
To support this self-healing reconfiguration mechanism several pieces
are needed. This PR adds a mechanism to `compute_ctl` called "refresh
configuration", where the compute node reaches out to the control plane
to pull a new config and reconfigure PG using the new config, instead of
listening for a notification message containing a config to arrive from
the control plane. Main changes to compute_ctl:
1. The `compute_ctl` state machine now has a new State,
`RefreshConfigurationPending`. The compute node may enter this state
upon receiving a signal that it may be using the incorrect page servers.
2. Upon entering the `RefreshConfigurationPending` state, the background
configurator thread in `compute_ctl` wakes up, pulls a new config from
the control plane, and reconfigures PG (with `pg_ctl reload`) according
to the new config.
3. The compute node may enter the new `RefreshConfigurationPending`
state from `Running` or `Failed` states. If the configurator managed to
configure the compute node successfully, it will enter the `Running`
state, otherwise, it stays in `RefreshConfigurationPending` and the
configurator thread will wait for the next notification if an incorrect
config is still suspected.
4. Added various plumbing in `compute_ctl` data structures to allow the
configurator thread to perform the config fetch.
The "incorrect config suspected" notification is delivered using a HTTP
endpoint, `/refresh_configuration`, on `compute_ctl`. This endpoint is
currently not called by anyone other than the tests. In a follow up PR I
will set up some code in the PG extension/libpagestore to call this HTTP
endpoint whenever PG suspects that it is pointing to the wrong page
servers.
## How is this tested?
Modified `test_runner/regress/test_change_pageserver.py` to add a
scenario where we use the new `/refresh_configuration` mechanism instead
of the existing `/configure` mechanism (which requires us sending a full
config to compute_ctl) to have the compute node reload and reconfigure
its pageservers.
I took one shortcut to reduce the scope of this change when it comes to
testing: the compute node uses a local config file instead of pulling a
config over the network from the HCC. This simplifies the test setup in
the following ways:
* The existing test framework is set up to use local config files for
compute nodes only, so it's convenient if I just stick with it.
* The HCC today generates a compute config with production settings
(e.g., assuming 4 CPUs, 16GB RAM, with local file caches), which is
probably not suitable in tests. We may need to add another test-only
endpoint config to the control plane to make this work.
The config-fetch part of the code is relatively straightforward (and
well-covered in both production and the KIND test) so it is probably
fine to replace it with loading from the local config file for these
integration tests.
In addition to making sure that the tests pass, I also manually
inspected the logs to make sure that the compute node is indeed
reloading the config using the new mechanism instead of going down the
old `/configure` path (it turns out the test has bugs which causes
compute `/configure` messages to be sent despite the test intending to
disable/blackhole them).
```test
2024-09-24T18:53:29.573650Z INFO http request{otel.name=/refresh_configuration http.method=POST}: serving /refresh_configuration POST request
2024-09-24T18:53:29.573689Z INFO configurator_main_loop: compute node suspects its configuration is out of date, now refreshing configuration
2024-09-24T18:53:29.573706Z INFO configurator_main_loop: reloading config.json from path: /workspaces/hadron/test_output/test_change_pageserver_using_refresh[release-pg16]/repo/endpoints/ep-1/spec.json
PG:2024-09-24 18:53:29.574 GMT [52799] LOG: received SIGHUP, reloading configuration files
PG:2024-09-24 18:53:29.575 GMT [52799] LOG: parameter "neon.extension_server_port" cannot be changed without restarting the server
PG:2024-09-24 18:53:29.575 GMT [52799] LOG: parameter "neon.pageserver_connstring" changed to "postgresql://no_user@localhost:15008"
...
```
Co-authored-by: William Huang <william.huang@databricks.com>
PR #12431 set initial lease deadline = 0s for tests.
This turned test_hot_standby_gc flaky because it now runs GC: it started
failing with `tried to request a page version that was garbage
collected`
because the replica reads below applied gc cutoff.
The leading theory is that, we run the timeline_gc() before the first
standby_horizon push arrives at PS. That is definitively a thing that
can happen with the current standby_horizon mechanism, and it's now
tracked as such in https://databricks.atlassian.net/browse/LKB-2499.
We don't have logs to confirm this theory though, but regardless,
try the fix in this PR and see if it stabilizes things.
Refs
- flaky test issue: https://databricks.atlassian.net/browse/LKB-2465
## Problem
## Summary of changes
Another go at #12341. LKB-2497
We now only need 1 connect mechanism (and 1 more for testing) which
saves us some code and complexity. We should be able to remove the final
connect mechanism when we create a separate worker task for
pglb->compute connections - either via QUIC streams or via in-memory
channels.
This also now ensures that connect_once always returns a ConnectionError
type - something simple enough we can probably define a serialisation
for in pglb.
* I've abstracted connect_to_compute to always use TcpMechanism and the
ProxyConfig.
* I've abstracted connect_to_compute_and_auth to perform authentication,
managing any retries for stale computes
* I had to introduce a separate `managed` function for taking ownership
of the compute connection into the Client/Connection pair
## Problem
`postgres_exporter` has database collector enabled by default and it
doesn't filter out invalid databases, see
06a553c816/collector/pg_database.go (L67)
so if it hits one, it starts spamming logs
```
ERROR: [NEON_SMGR] [reqid d9700000018] could not read db size of db 705302 from page server at lsn 5/A2457EB0
```
## Summary of changes
We don't use `pg_database_size_bytes` metric anyway, see
5e19b3fd89/apps/base/compute-metrics/scrape-compute-pg-exporter-neon.yaml (L29)
so just turn it off by passing `--no-collector.database`.
## Problem
A large insert or a large row will cause the codec to allocate a large
buffer. The codec never shrinks the buffer however. LKB-2496
## Summary of changes
1. Introduce a naive GC system for codec buffers
2. Try and reduce copies as much as possible
## Problem
Tenant split test revealed another bug with PG backpressure throttling
that under some cases PS may never report its progress back to SK (e.g.,
observed when aborting tenant shard where the old shard needs to
re-establish SK connection and re-ingest WALs from a much older LSN). In
this case, PG may get stuck forever.
## Summary of changes
As a general precaution that PS feedback mechanism may not always be
reliable, this PR uses the previously introduced WAL write rate limit
mechanism to slow down write rates instead of completely pausing it. The
idea is to introduce a new
`databricks_effective_max_wal_bytes_per_second`, which is set to
`databricks_max_wal_mb_per_second` when no PS back pressure and is set
to `10KB` when there is back pressure. This way, PG can still write to
SK, though at a very low speed.
The PR also fixes the problem that the current WAL rate limiting
mechanism is too coarse grained and cannot enforce limits < 1MB. This is
because it always resets the rate limiter after 1 second, even if PG
could have written more data in the past second. The fix is to introduce
a `batch_end_time_us` which records the expected end time of the current
batch. For example, if PG writes 10MB of data in a single batch, and max
WAL write rate is set as `1MB/s`, then `batch_end_time_us` will be set
as 10 seconds later.
## How is this tested?
Tweaked the existing test, and also did manual testing on dev. I set
`max_replication_flush_lag` as 1GB, and loaded 500GB pgbench tables.
It's expected to see PG gets throttled periodically because PS will
accumulate 4GB of data before flushing.
Results:
when PG is throttled:
```
9500000 of 3300000000 tuples (0%) done (elapsed 10.36 s, remaining 3587.62 s)
9600000 of 3300000000 tuples (0%) done (elapsed 124.07 s, remaining 42523.59 s)
9700000 of 3300000000 tuples (0%) done (elapsed 255.79 s, remaining 86763.97 s)
9800000 of 3300000000 tuples (0%) done (elapsed 315.89 s, remaining 106056.52 s)
9900000 of 3300000000 tuples (0%) done (elapsed 412.75 s, remaining 137170.58 s)
```
when PS just flushed:
```
18100000 of 3300000000 tuples (0%) done (elapsed 433.80 s, remaining 78655.96 s)
18200000 of 3300000000 tuples (0%) done (elapsed 433.85 s, remaining 78231.71 s)
18300000 of 3300000000 tuples (0%) done (elapsed 433.90 s, remaining 77810.62 s)
18400000 of 3300000000 tuples (0%) done (elapsed 433.96 s, remaining 77395.86 s)
18500000 of 3300000000 tuples (0%) done (elapsed 434.03 s, remaining 76987.27 s)
18600000 of 3300000000 tuples (0%) done (elapsed 434.08 s, remaining 76579.59 s)
18700000 of 3300000000 tuples (0%) done (elapsed 434.13 s, remaining 76177.12 s)
18800000 of 3300000000 tuples (0%) done (elapsed 434.19 s, remaining 75779.45 s)
18900000 of 3300000000 tuples (0%) done (elapsed 434.84 s, remaining 75489.40 s)
19000000 of 3300000000 tuples (0%) done (elapsed 434.89 s, remaining 75097.90 s)
19100000 of 3300000000 tuples (0%) done (elapsed 434.94 s, remaining 74712.56 s)
19200000 of 3300000000 tuples (0%) done (elapsed 498.93 s, remaining 85254.20 s)
19300000 of 3300000000 tuples (0%) done (elapsed 498.97 s, remaining 84817.95 s)
19400000 of 3300000000 tuples (0%) done (elapsed 623.80 s, remaining 105486.76 s)
19500000 of 3300000000 tuples (0%) done (elapsed 745.86 s, remaining 125476.51 s)
```
Co-authored-by: Chen Luo <chen.luo@databricks.com>
This GUC will become useful for temporarily disabling Lakebase-specific
features during the code merge.
Signed-off-by: Tristan Partin <tristan.partin@databricks.com>
https://github.com/neondatabase/cloud/issues/19011
- Accept `ComputeSpec` in `/promote` instead of just passing safekeepers
and LSN. Update API spec
- Add corner case tests for promotion when promotion or perwarm fails
(using failpoints)
- Print root error for prewarm and promotion in status handlers
## Problem
There has been some inconsistencies of providing tenant config via
`tenant_create` and via other tenant config APIs due to how the
properties are processed: in `tenant_create`, the test framework calls
neon-cli and therefore puts those properties in the cmdline. In other
cases, it's done via the HTTP API by directly serializing to a JSON.
When using the cmdline, the program only accepts serde bool that is
true/false.
## Summary of changes
Convert Python bool into `true`/`false` when using neon-cli.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
OTel 0.28+ by default uses blocking operations in a dedicated thread and
doesn't start a tokio runtime. Reqwest as currently configured wants to
spawn tokio tasks.
## Summary of changes
Use blocking reqwest.
This PR just mitigates the current issue.
Session variables can be set during one sql-over-http query and observed
on another when that pooled connection is re-used. To address this we
can use `RESET ALL;` before re-using the connection. LKB-2495
To be on the safe side, we can opt for a full `DISCARD ALL;`, but that
might have performance regressions since it also clears any query plans.
See pgbouncer docs
https://www.pgbouncer.org/config.html#server_reset_query.
`DISCARD ALL` is currently defined as:
```
CLOSE ALL;
SET SESSION AUTHORIZATION DEFAULT;
RESET ALL;
DEALLOCATE ALL;
UNLISTEN *;
SELECT pg_advisory_unlock_all();
DISCARD PLANS;
DISCARD TEMP;
DISCARD SEQUENCES;
```
I've opted to keep everything here except the `DISCARD PLANS`. I've
modified the code so that this query is executed in the background when
a connection is returned to the pool, rather than when taken from the
pool.
This should marginally improve performance for Neon RLS by removing 1
(localhost) round trip. I don't believe that keeping query plans could
be a security concern. It's a potential side channel, but I can't
imagine what you could extract from it.
---
Thanks to
https://github.com/neondatabase/neon/pull/12659#discussion_r2219016205
for probing the idea in my head.
## Problem
LKB-197, #9516
To make sure the migration path is smooth.
The previous plan is to store new relations in new keyspace and old ones
in old keyspace until it gets dropped. This makes the migration path
hard as we can't validate v2 writes and can't rollback. This patch gives
us a more smooth migration path:
- The first time we enable reldirv2 for a tenant, we copy over
everything in the old keyspace to the new one. This might create a short
spike of latency for the create relation operation, but it's oneoff.
- After that, we have identical v1/v2 keyspace and read/write both of
them. We validate reads every time we list the reldirs.
- If we are in `migrating` mode, use v1 as source of truth and log a
warning for failed v2 operations. If we are in `migrated` mode, use v2
as source of truth and error when writes fail.
- One compatibility test uses dataset from the time where we enabled
reldirv2 (of the original rollout plan), which only has relations
written to the v2 keyspace instead of the v1 keyspace. We had to adjust
it accordingly.
- Add `migrated_at` in index_part to indicate the LSN where we did the
initialize.
TODOs:
- Test if relv1 can be read below the migrated_at LSN.
- Move the initialization process to L0 compaction instead of doing it
on the write path.
- Disable relcache in the relv2 test case so that all code path gets
fully tested.
## Summary of changes
- New behavior of reldirv2 migration flags as described above.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
> bullseye-backports has reached end-of-life and is no longer supported
or updated
From: https://backports.debian.org/Instructions/
This causes the compute-node image build to fail with the following
error:
```
0.099 Err:5 http://deb.debian.org/debian bullseye-backports Release
0.099 404 Not Found [IP: 146.75.122.132 80]
...
1.293 E: The repository 'http://deb.debian.org/debian bullseye-backports Release' does not have a Release file.
```
## Summary of changes
- Use archive version of `bullseye-backports`
Second attempt at #12130, now with a smaller diff.
This allows us to skip allocating for things like parameter status and
notices that we will either just forward untouched, or discard.
LKB-2494
## Problem
With safekeeper migration in mind, we can now pull/exclude the timeline
multiple times within the same safekeeper. To avoid races between out of
order requests, we need to ignore the pull/exclude requests if we have
already seen a higher generation.
- Closes: https://github.com/neondatabase/neon/issues/12186
- Closes: [LKB-949](https://databricks.atlassian.net/browse/LKB-949)
## Summary of changes
- Annotate timeline tombstones in safekeeper with request generation.
- Replace `ignore_tombstone` option with `mconf` in
`PullTimelineRequest`
- Switch membership in `pull_timeline` if the existing/pulled timeline
has an older generation.
- Refuse to switch membership if the timeline is being deleted
(`is_canceled`).
- Refuse to switch membership in compute greeting request if the
safekeeper is not a member of `mconf`.
- Pass `mconf` in `PullTimelineRequest` in safekeeper_service
---------
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
## Problem
When testing tenant splits, I found that PG can get backpressure
throttled indefinitely if the split is aborted afterwards. It turns out
that each PageServer activates new shard separately even before the
split is committed and they may start sending PageserverFeedback to PG
directly. As a result, if the split is aborted, no one resets the
pageserver feedback in PG, and thus PG will be backpressure throttled
forever unless it's restarted manually.
## Summary of changes
This PR fixes this problem by having
`walprop_pg_process_safekeeper_feedback` simply ignore all pageserver
feedback from unknown shards. The source of truth here is defined by the
shard map, which is guaranteed to be reloaded only after the split is
committed.
Co-authored-by: Chen Luo <chen.luo@databricks.com>
## Problem
As part of the reldirv2 rollout: LKB-197.
We will use number of db/rels as a criteria whether to rollout reldirv2
directly on the write path (simplest and easiest way of rollout). If the
number of rel/db is small then it shouldn't take too long time on the
write path.
## Summary of changes
* Compute db/rel count during basebackup.
* Also compute it during logical size computation.
* Collect maximum number of db/rel across all timelines in the feature
flag propeties.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We did not test some Public API calls, such as using a timestamp to
create a branch, reset_to_parent.
## Summary of changes
Tests now include some other operations: reset_to_parent, a branch
creation from any time in the past, etc.
Currently, the API calls are only exposed; the semantics are not
verified.
---------
Co-authored-by: Alexey Masterov <alexey.masterov@databricks.com>
## Problem
We drive the get page requests that have started processing to
completion. So in the case when the compute received a reconfiguration
request and the old connection has a request procesing on the
pageserver, we are going to issue the warning.
I spot checked a few instances of the warning and in all cases the
compute was already connected to the correct pageserver.
## Summary of Changes
Downgrade to INFO. It would be nice to somehow figure out if the
connection has been terminated in the meantime, but the terminate libpq
message is still in the pipe while we're doing the shard resolution.
Closes LKB-2381
The argument to BufTagInit was called 'spcOid', and it was also setting
a field called 'spcOid'. The field name would erroneously also be
expanded with the macro arg. It happened to work so far, because all the
users of the macro pass a variable called 'spcOid' for the 'spcOid'
argument, but as soon as you try to pass anything else, it fails. And
same story for 'dbOid' and 'relNumber'. Rename the arguments to avoid
the name collision.
Also while we're at it, add parens around the arguments in a few macros,
to make them safer if you pass something non-trivial as the argument.
## Problem
We don't detect if safekeeper migration fails after the the commiting
the membership configuration to the database. As a result, we might
leave stale timelines on excluded safekeepers and do not notify
cplane/safekepeers about new configuration.
- Implements solution proposed in
https://github.com/neondatabase/neon/pull/12432
- Closes: https://github.com/neondatabase/neon/issues/12192
- Closes: [LKB-944](https://databricks.atlassian.net/browse/LKB-944)
## Summary of changes
- Add `sk_set_notified_generation` column to `timelines` database
- Update `*_notified_generation` in database during the finish state.
- Commit reconciliation requests to database atomically with membership
configuration.
- Reload pending ops and retry "finish" step if we detect
`*_notified_generation` mismatch.
- Add failpoints and test that we handle failures well
## Problem
Post LKB-198 rollout. We added a new strategy to generate image layers
at the L0-L1 boundary instead of the latest LSN to ensure too many L0
layers do not trigger image layer creation.
## Summary of changes
We already rolled it out to all users so we can remove the feature flag
now.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Currently, the exporter exposes the same LFC metrics that are exposed by
the "autoscaling" sql_exporter in the docker image. With this, we can
remove the dedicated sql_exporter instance. (Actually doing the removal
is left as a TODO until this is rolled out to production and we have
changed autoscaling-agent to fetch the metrics from this new endpoint.)
The exporter runs as a Postgres background worker process. This is
extracted from the Rust communicator rewrite project, which will use the
same worker process for much more, to handle the communications with the
pageservers. For now, though, it merely handles the metrics requests.
In the future, we will add more metrics, and perhaps even APIs to
control the running Postgres instance.
The exporter listens on a Unix Domain socket within the Postgres data
directory. A Unix Domain socket is a bit unconventional, but it has some
advantages:
- Permissions are taken care of. Only processes that can access the data
directory, and therefore already have full access to the running
Postgres instance, can connect to it.
- No need to allocate and manage a new port number for the listener
It has some downsides too: it's not immediately accessible from the
outside world, and the functions to work with Unix Domain sockets are
more low-level than TCP sockets (see the symlink hack in
`postgres_metrics_client.rs`, for example).
To expose the metrics from the local Unix Domain Socket to the
autoscaling agent, introduce a new '/autoscaling_metrics' endpoint in
the compute_ctl's HTTP server. Currently it merely forwards the request
to the Postgres instance, but we could add rate limiting and access
control there in the future.
---------
Co-authored-by: Conrad Ludgate <conrad@neon.tech>
It's helpful to correlate requests and responses in local investigations
where the issue is reproducible. Hence, log the rel, fork and block of
the get page response.
NB: effectively a no-op in the neon env since the handling is config
gated
in storcon
## Problem
When a pageserver suffers from a local disk/node failure and restarts,
the storage controller will receive a re-attach call and return all the
tenants the pageserver is suppose to attach, but the pageserver will not
act on any tenants that it doesn't know about locally. As a result, the
pageserver will not rehydrate any tenants from remote storage if it
restarted following a local disk loss, while the storage controller
still thinks that the pageserver have all the tenants attached. This
leaves the system in a bad state, and the symptom is that PG's
pageserver connections will fail with "tenant not found" errors.
## Summary of changes
Made a slight change to the storage controller's `re_attach` API:
* The pageserver will set an additional bit `empty_local_disk` in the
reattach request, indicating whether it has started with an empty disk
or does not know about any tenants.
* Upon receiving the reattach request, if this `empty_local_disk` bit is
set, the storage controller will go ahead and clear all observed
locations referencing the pageserver. The reconciler will then discover
the discrepancy between the intended state and observed state of the
tenant and take care of the situation.
To facilitate rollouts this extra behavior in the `re_attach` API is
guarded by the `handle_ps_local_disk_loss` command line flag of the
storage controller.
---------
Co-authored-by: William Huang <william.huang@databricks.com>
## Problem
See https://databricks.slack.com/archives/C092W8NBXC0/p1752924508578339
In case of larger number of databases and large `max_connections` we can
open too many connection for parallel apply config which may cause `Too
many open files` error.
## Summary of changes
Limit maximal number of parallel config apply connections by 100.
---------
Co-authored-by: Kosntantin Knizhnik <konstantin.knizhnik@databricks.com>
## Problem
While running tenant split tests I ran into a situation where PG got
stuck completely. This seems to be a general problem that was not found
in the previous chaos testing fixes.
What happened is that if PG gets throttled by PS, and SC decided to move
some tenant away, then PG reconfiguration could be blocked forever
because it cannot talk to the old PS anymore to refresh the throttling
stats, and reconfiguration cannot proceed because it's being throttled.
Neon has considered the case that configuration could be blocked if the
PG storage is full, but forgot the backpressure case.
## Summary of changes
The PR fixes this problem by simply skipping throttling while PS is
being configured, i.e., `max_cluster_size < 0`. An alternative fix is to
set those throttle knobs to -1 (e.g., max_replication_apply_lag),
however these knobs were labeled with PGC_POSTMASTER so their values
cannot be changed unless we restart PG.
## How is this tested?
Tested manually.
Co-authored-by: Chen Luo <chen.luo@databricks.com>
## Problem
We want to have the data-api served by the proxy directly instead of
relying on a 3rd party to run a deployment for each project/endpoint.
## Summary of changes
With the changes below, the proxy (auth-broker) becomes also a
"rest-broker", that can be thought of as a "Multi-tenant" data-api which
provides an automated REST api for all the databases in the region.
The core of the implementation (that leverages the subzero library) is
in proxy/src/serverless/rest.rs and this is the only place that has "new
logic".
---------
Co-authored-by: Ruslan Talpa <ruslan.talpa@databricks.com>
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
Co-authored-by: Conrad Ludgate <conrad@neon.tech>
