Hello,
I'm seeking guidance in how to improve the performance of a slow query and
to have some other sets of eyes confirm that what I wrote does what I
intend.
According to the PostgreSQL wiki there is a set of metadata that I should
provide to help you help me. So let's begin there.
PostgreSQL version: PostgreSQL 12.5 on x86_64-pc-linux-gnu, compiled by gcc
(GCC) 4.8.5 20150623 (Red Hat 4.8.5-11), 64-bit
Full table and index schema:
CREATE TABLE attempt_scores (
id bigint GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
attempt_report_id bigint NOT NULL,
score_value double precision NOT NULL,
created_at timestamptz NOT NULL DEFAULT now(),
attempt_report_updated_at timestamptz NOT NULL,
student_id int NOT NULL,
course_id int NOT NULL,
assignment_id int NOT NULL,
score_name citext NOT NULL CHECK (length(trim(score_name)) > 0),
attempted_by citext NOT NULL CHECK (length(trim(attempted_by)) > 0),
CONSTRAINT for_upsert UNIQUE (attempt_report_id, score_name)
);
CREATE INDEX ON attempt_scores (attempt_report_updated_at);
COMMENT ON TABLE attempt_scores IS
$$The collection of assignment scores extracted from the LMS database.$$;
COMMENT ON COLUMN attempt_scores.attempt_report_id IS
$$Each assignment attempt has an associated attempt report (attempt_reports)
where the scores of the attempt is recorded. This column is the pk value from
that table.$$;
COMMENT ON COLUMN attempt_scores.score_value IS $$The score's value.$$;
COMMENT ON COLUMN attempt_scores.created_at IS $$The timestamp the
record was created.$$;
COMMENT ON COLUMN attempt_scores.student_id IS $$The student's ID.$$;
COMMENT ON COLUMN attempt_scores.course_id IS $$The course's primary
key in the LMS database.$$;
COMMENT ON COLUMN attempt_scores.assignment_id IS $$The assignment's
primary key in the LMS database.$$;
COMMENT ON COLUMN attempt_scores.score_name IS $$The source/name of
the score.$$;
COMMENT ON COLUMN attempt_scores.attempted_by IS 'The users.role column in LMS';
COMMENT ON COLUMN attempt_scores.attempt_report_updated_at IS
$$The timestamp value of attempt_reports.updated_at on the LMS side. We use it
to find new rows added since the last time we exported to Salesforce.$$;
Table metadata:
relname | relpages | reltuples | relallvisible | relkind | relnatts |
relhassubclass | reloptions | pg_table_size
----------------+----------+-----------+---------------+---------+----------+----------------+------------+---------------
attempt_scores | 130235 | 9352640 | 0 | r | 10
| f | [NULL] | 1067180032
Other context: The PostgreSQL database is an Amazon RDS instance.
Next up is the query and a description of what it's supposed to do.
-- What this query is supposed to do is to compute averages for a set of
scoring/learning metrics but it's not so
-- straight forward. There is an umbrella metric that summarises the others
called the student performance index (SPI)
-- and the folks who want this info want the averages to be driven by the
SPI. So the basic algorithm is that for each
-- student/assignment pair, find the assignment that has the highest SPI
then use that to collect and average the
-- component metrics.
EXPLAIN (ANALYZE, BUFFERS)
WITH max_spi AS (
SELECT student_id, assignment_id, max(score_value) spi
FROM attempt_scores
WHERE score_name = 'student_performance_index'
GROUP BY student_id, assignment_id
HAVING max(score_value) > 0
), reports AS (
SELECT max(attempt_report_id) attempt_report_id, max(score_value) spi
FROM max_spi m NATURAL JOIN attempt_scores
WHERE score_value = m.spi
GROUP BY student_id, assignment_id
)
SELECT
avg(spi) spi,
avg(CASE score_name WHEN 'digital_clinical_experience' THEN score_value
END) dce,
avg(CASE score_name WHEN 'tier1_subjective_data_collection' THEN
score_value END) sdc
FROM reports NATURAL JOIN attempt_scores;
Finally, the EXPLAIN output and some links.
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=672426.02..672426.03 rows=1 width=24) (actual
time=903359.923..903368.957 rows=1 loops=1)
Buffers: shared hit=6167172 read=4199539, temp read=99551 written=99678
I/O Timings: read=839121.853
-> Nested Loop (cost=672389.80..672425.91 rows=8 width=37) (actual
time=36633.920..885232.956 rows=7034196 loops=1)
Buffers: shared hit=6167172 read=4199539, temp read=99551
written=99678
I/O Timings: read=839121.853
-> GroupAggregate (cost=672389.37..672389.39 rows=1 width=24)
(actual time=36628.945..41432.502 rows=938244 loops=1)
Group Key: attempt_scores_2.student_id,
attempt_scores_2.assignment_id
Buffers: shared hit=191072 read=329960, temp read=99551
written=99678
I/O Timings: read=18210.866
-> Sort (cost=672389.37..672389.37 rows=1 width=24)
(actual time=36628.111..39562.421 rows=2500309 loops=1)
Sort Key: attempt_scores_2.student_id,
attempt_scores_2.assignment_id
Sort Method: external merge Disk: 83232kB
Buffers: shared hit=191072 read=329960, temp
read=99551 written=99678
I/O Timings: read=18210.866
-> Gather (cost=425676.58..672389.36 rows=1
width=24) (actual time=25405.650..34716.694 rows=2500309 loops=1)
Workers Planned: 2
Workers Launched: 2
Buffers: shared hit=191072 read=329960, temp
read=78197 written=78260
I/O Timings: read=18210.866
-> Hash Join (cost=424676.58..671389.26 rows=1
width=24) (actual time=25169.930..34121.825 rows=833436 loops=3)
Hash Cond: ((attempt_scores_1.student_id =
attempt_scores_2.student_id) AND (attempt_scores_1.assignment_id =
attempt_scores_2.assignment_id) AND (attempt_scores_1.score_value =
(max(attempt_scores_2.score_value))))
Buffers: shared hit=191072 read=329960,
temp read=78197 written=78260
I/O Timings: read=18210.866
-> Parallel Seq Scan on attempt_scores
attempt_scores_1 (cost=0.00..169204.33 rows=3896933 width=24) (actual
time=0.013..5775.887 rows=3118127 loops=3)
Buffers: shared hit=41594 read=88641
I/O Timings: read=14598.128
-> Hash (cost=419397.94..419397.94
rows=235808 width=16) (actual time=25160.038..25160.555 rows=938244 loops=3)
Buckets: 131072 (originally 131072)
Batches: 16 (originally 4) Memory Usage: 3786kB
Buffers: shared hit=149408
read=241311, temp read=15801 written=27438
I/O Timings: read=3610.261
-> GroupAggregate
(cost=392800.28..417039.86 rows=235808 width=16) (actual
time=23175.268..24589.121 rows=938244 loops=3)
Group Key:
attempt_scores_2.student_id, attempt_scores_2.assignment_id
Filter:
(max(attempt_scores_2.score_value) > '0'::double precision)
Rows Removed by Filter: 2908
Buffers: shared hit=149408
read=241311, temp read=15801 written=15864
I/O Timings: read=3610.261
-> Sort
(cost=392800.28..395879.64 rows=1231743 width=16) (actual
time=23174.917..23654.979 rows=1206355 loops=3)
Sort Key:
attempt_scores_2.student_id, attempt_scores_2.assignment_id
Sort Method: external
merge Disk: 30760kB
Worker 0: Sort Method:
external merge Disk: 30760kB
Worker 1: Sort Method:
external merge Disk: 30760kB
Buffers: shared
hit=149408 read=241311, temp read=15801 written=15864
I/O Timings:
read=3610.261
-> Seq Scan on
attempt_scores attempt_scores_2 (cost=0.00..247143.00 rows=1231743
width=16) (actual time=16980.832..21585.313 rows=1206355 loops=3)
Filter:
(score_name = 'student_performance_index'::citext)
Rows Removed by
Filter: 8148027
Buffers: shared
hit=149394 read=241311
I/O Timings:
read=3610.261
-> Index Scan using for_upsert on attempt_scores
(cost=0.43..36.42 rows=8 width=37) (actual time=0.394..0.896 rows=7
loops=938244)
Index Cond: (attempt_report_id =
(max(attempt_scores_1.attempt_report_id)))
Buffers: shared hit=5976100 read=3869579
I/O Timings: read=820910.987
Planning Time: 14.357 ms
Execution Time: 903426.284 ms
(55 rows)
Time: 903571.689 ms (15:03.572)
The explain output visualized on explain.depesz.com:
https://explain.depesz.com/s/onPZ
--
Thank you for your time and consideration,
Dane
