Duplicating this data on every record of the builds table cost
approximately 4G of duplication.
Note that the database migration included took about 4h45m on an
untuned server which uses very slow rotational disks in a RAID5 setup,
with not a lot of RAM. I imagine in production it might take an hour
or two, but not 4. If this should become a chunked migration, I can do
that.
Note: Because of the question about chunked migrations, I have NOT
YET tested this migration thoroughly enough for merge.
Looking at AWS' Performance Insights for a Hydra instance, I found
the hydra-queue-runner's query:
select id, buildStatus, releaseName, closureSize, size
from Builds b
join BuildOutputs o on b.id = o.build
where
finished = ?
and (buildStatus = ? or buildStatus = ?)
and path = $1
was the slowest query by at least 10x. Running an explain on this
showed why:
hydra=> explain select id, buildStatus, releaseName, closureSize, size
from Builds b join BuildOutputs o on b.id = o.build where
finished = 1 and (buildStatus = 0 or buildStatus = 6) and
path = '/nix/store/s93khs2dncf2cy273mbyr4fb4ns3db20-MIDIVisualizer-5.1';
QUERY PLAN
------------------------------------------------------------------------
Gather (cost=1000.43..33718.98 rows=2 width=56)
Workers Planned: 2
-> Nested Loop (cost=0.43..32718.78 rows=1 width=56)
-> Parallel Seq Scan on buildoutputs o (cost=0.00..32710.32
rows=1
width=4)
Filter: (path = '/nix/store/s93kh...snip...'::text)
-> Index Scan using indexbuildsonjobsetidfinishedid on builds b
(cost=0.43..8.45 rows=1 width=56)
Index Cond: ((id = o.build) AND (finished = 1))
Filter: ((buildstatus = 0) OR (buildstatus = 6))
(8 rows)
A paralell sequential scan is definitely better than a sequential scan, but the
cost ranging from 0 to 32710 is not great. Looking at the table, I saw the `path`
column is completely unindex:
hydra=> \d buildoutputs
Table "public.buildoutputs"
Column | Type | Collation | Nullable | Default
--------+---------+-----------+----------+---------
build | integer | | not null |
name | text | | not null |
path | text | | not null |
Indexes:
"buildoutputs_pkey" PRIMARY KEY, btree (build, name)
Foreign-key constraints:
"buildoutputs_build_fkey" FOREIGN KEY (build) REFERENCES builds(id)
ON DELETE CASCADE
Since we always do exact matches on the path and don't care about ordering,
and since the path column is very high cardinality a `hash` index is a
good candidate. Note that I did test a btree index and it performed
similarly well, but slightly worse.
After creating the index (this took about 10 seconds) on a test database:
create index IndexBuildOutputsPath on BuildOutputs using hash(path);
We get a *significantly* reduced cost:
hydra=> explain select id, buildStatus, releaseName, closureSize, size
hydra-> from Builds b join BuildOutputs o on b.id = o.build where
hydra-> finished = 1 and (buildStatus = 0 or buildStatus = 6) and
hydra-> path = '/nix/store/s93khs2dncf2cy273mbyr4fb4ns3db20-MIDIVisualizer-5.1';
QUERY PLAN
-------------------------------------------------------------------------------------------------------
Nested Loop (cost=0.43..41.41 rows=2 width=56)
-> Index Scan using buildoutputs_path_hash on buildoutputs o (cost=0.00..16.05 rows=3 width=4)
Index Cond: (path = '/nix/store/s93khs2dncf2cy273mbyr4fb4ns3db20-MIDIVisualizer-5.1'::text)
-> Index Scan using indexbuildsonjobsetidfinishedid on builds b (cost=0.43..8.45 rows=1 width=56)
Index Cond: ((id = o.build) AND (finished = 1))
Filter: ((buildstatus = 0) OR (buildstatus = 6))
(6 rows)
For direct comparison, the overall query plan was changed:
From: Gather (cost=1000.43..33718.98 rows=2 width=56)
To: Nested Loop (cost= 0.43.....41.41 rows=2 width=56)
and the query plan for buildoutputs changed from a maximum cost of
32,710 down to 16.
In practical terms, the query's planning and execution time was reduced:
Before (ms) | Try 1 | Try 2 | Try 3
------------+---------+---------+--------
Planning | 0.898 | 0.416 | 0.383
Execution | 138.644 | 172.331 | 375.585
After (ms) | Try 1 | Try 2 | Try 3
------------+---------+---------+--------
Planning | 0.298 | 0.290 | 0.296
Execution | 219.625 | 0.035 | 0.034
Requires the following configuration options
enable_github_login = 1
github_client_id
github_client_secret
Or github_client_secret_file which points to a file with the secret
Fixes this error:
ERROR: failed to process declarative jobset test:inputs,
DBIx::Class::Storage::DBI::_dbh_execute(): DBI Exception: DBD::Pg::st
execute failed: ERROR: null value in column "emailoverride" violates
not-null constraint
This would start happening if the network connection between the Hydra
server and the remote build server breaks after sucessfully importing
at least one output of a derivation, but before having finished
importing all outputs.
Fixes#816.
These make the hydra-queue-runner logs very noisy even when not using the GitlabStatus plugin.
Also, they shouldn't be necessary except when developing the plugin itself and should have been removed before release.
It might happen that a job from the aggregate returned an error!
This is what the vague "[json.exception.type_error.302] type must be string, but is null"
was all about in this instance; there was no `drvPath` to stringify!
So we now actively watch for errors and copy them to the aggregate job.
The vague "[json.exception.type_error.302] type must be string, but is null"
is **absolutely** unhelpful in the way Hydra currently handles it on
evaluation.
This is handling *unexpected* errors only; the following commit will
handle the specific instance of the previously mentioned error.
Samuel Dionne-Riel