The current check happening in jobsets is incorrect.
The wanted constraint is stated as follow :
- If type is 0 (legacy), then the flake field should be null, and
both nixExprInput and nixExprPath should be non-null
- If type is 1 (flake), then the flake field should be non-null, and
both nixExprInput and nixExprPath should be null
The current version will not catch (i.e. it will accept) situations
where you have for instance :
type = 1, nixExprPath null, nixExprInput non-null, flake non-null
This commit fixes that.
I split(ted) that into two constraints, to make it more readable and
easier to extend if a new type appears in the future.
The complete query could be instead :
( type = 0
AND nixExprInput IS NOT NULL AND nixExprPath IS NOT NULL AND flake IS NULL )
OR ( type = 1
AND nixExprInput IS NULL AND nixExprPath IS NULL AND flake IS NOT NULL )
(but an "OR" cannot be split, hence the other formulation)
DBIx likes to eagerly select all columns without a way to really tell
it so. Therefore, this splits this one large column in to its own
table.
I'd also like to make "jobsets" use this table too, but that is on hold
to stop the bleeding caused by the extreme amount of traffic this is
causing.
The database has these constraints:
check ((type = 0) = (nixExprInput is not null and nixExprPath is not null)),
check ((type = 1) = (flake is not null)),
which prevented switching to flakes in a declarative jobspec, since the
nixexpr{path,input} fields were not nulled in such an update
Co-Authored-By: Graham Christensen <graham@grahamc.com>
This search query is pretty heavy. Defaulting to 500 has caused
Hydra's web UI to appear to be down. Since 500 can take it down, users
probably shouldn't be allowed t ask for that many.
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
