Hi all,
BACKGROUND
I have an app storing sensor data from some IoT-devices, currently
~108 million individual datagrams spanning the last 6+ years. Various
different time based queries became slower over time, so I'm testing
some changes to the schema to improve things.
For historical reasons, the datagrams have been stored in 4 different
tables: 2 tables because the datagrams contained two "domains", one
table to map where the datagram came from and another one to maintain
some history of status changes per device. That resulted in 3 JOINs in
most cases, additional indexes per table increasing storage and
duplication of data of columns for foreign keys increasing storage as
well.
One important thing to note might be that data is NOT monotonic
increasing regarding time only. Instead it might be that newer rows
for some device are older than other, some old rows might be deleted
to add newer one for the same day and stuff like that. Though, that is
an exception from the rule, in most cases new rows per device means
more current data.
TABLE CHANGES
People on this list suggested to merge at least some of the tables
again to reduce the number of JOINs, possibly use partitioning, check
indexes and e.g. use GIN. So I decided to test with ALL former tables
being merged into one, as that would make partitioning easier, some
indexes, foreign keys etc. unnecessary and should at least reduce
the amount of used storage.
> CREATE TABLE clt_rec
> (
> id bigserial NOT NULL,
> oms_rec bigint NOT NULL,
> captured_at timestamp with time zone NOT NULL,
> rssi smallint NOT NULL
> );
> CREATE TABLE oms_rec
> (
> id bigserial NOT NULL,
> meter integer NOT NULL,
> encrypted bytea,
> decrypted bytea
> );
> CREATE TABLE clt_rec_src
> (
> id bigserial NOT NULL,
> real_estate integer NOT NULL,
> collector integer NOT NULL,
> clt_rec bigserial
> );
> CREATE TABLE meter_status_history
> (
> id serial NOT NULL,
> oms_rec bigint NOT NULL,
> status smallint
> );
vs.
> CREATE TABLE datagram
> (
> id bigserial NOT NULL,
> src_re integer NOT NULL,
> src_clt integer NOT NULL,
> src_meter integer NOT NULL,
> captured_at timestamp with time zone NOT NULL,
> captured_rssi smallint NOT NULL,
> oms_status smallint NOT NULL,
> oms_enc bytea,
> oms_dec bytea
> );
STORAGE IMPROVEMENTS
The old schema consumed ~42 GiB, while the new is ~16 GiB without the
formerly available indexes and ~25 GiB with the same logical indexes.
Which means BTREE with ~2,5 GiB per index on the logically same
columns used in queries like before. Alternatively I've tested BRIN
index on the same columns as well, but at least their consumption is
negligible. From that point of view the new table is an improvement.
> relname pg_size_pretty
> ------------------------------------------------ --------------
> datagram 25 GB
> idx_datagram_src_meter_btree 2323 MB
> idx_datagram_src_re_btree 2323 MB
> pk_datagram 2323 MB
> idx_datagram_captured_at_btree 2323 MB
> idx_datagram_src_clt_btree 2323 MB
> idx_datagram_captured_at_brin 512 kB
> idx_datagram_src_meter_brin 400 kB
> idx_datagram_src_re_brin 400 kB
> idx_datagram_src_clt_brin 400 kB
OLD QUERY PLAN
* 2 workers used
* BTREE-index idx_clt_rec_captured_at used with 3 iterations
> Unique (cost=1669026.62..1669182.57 rows=10397 width=51) (actual time=1295.902..1406.325 rows=22681 loops=1)
> Planning Time: 25.434 ms
> Execution Time: 1409.988 ms
> -> Sort (cost=1669026.62..1669052.61 rows=10397 width=51) (actual time=1295.901..1388.117 rows=104607 loops=1)
> Sort Method: external merge Disk: 5952kB
> Sort Key: real_estate.number, flat.number, meter.mfct_code, meter.reading_serial, meter.type, clt_rec.captured_at DESC
> -> Gather (cost=265793.42..1668332.93 rows=10397 width=51) (actual time=119.077..681.224 rows=104607 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Hash Join (cost=264793.42..1666293.23 rows=4332 width=51) (actual time=96.080..638.802 rows=34869 loops=3)
> Hash Cond: (oms_rec.meter = meter.id)
> -> Nested Loop (cost=1.14..1400747.39 rows=189399 width=20) (actual time=0.145..496.366 rows=171456 loops=3)
> -> Hash (cost=264709.53..264709.53 rows=6620 width=39) (actual time=95.521..95.528 rows=40044 loops=3)
> Buckets: 65536 (originally 8192) Batches: 1 (originally 1) Memory Usage: 3016kB
> -> Parallel Index Scan using idx_clt_rec_captured_at on clt_rec (cost=0.57..14853.95 rows=189399 width=24) (actual time=0.098..81.556 rows=171456 loops=3)
> -> Index Scan using pk_oms_rec on oms_rec (cost=0.57..7.32 rows=1 width=12) (actual time=0.002..0.002 rows=1 loops=514369)
> -> Hash Join (cost=145.59..264709.53 rows=6620 width=39) (actual time=9.883..86.390 rows=40044 loops=3)
> Index Cond: (id = clt_rec.oms_rec)
> Index Cond: ((captured_at >= ('2020-08-28 10:34:32.855+02'::timestamp with time zone - '5 days'::interval)) AND (captured_at <= ('2020-08-28 10:34:32.855+02'::timestamp with time zone + '00:00:00'::interval)))
> Hash Cond: (meter.meter_bcd = meter_bcd.id)
> -> Index Scan using pk_meter on meter (cost=0.42..264348.00 rows=40044 width=25) (actual time=9.034..74.556 rows=40044 loops=3)
> -> Hash (cost=143.38..143.38 rows=143 width=22) (actual time=0.827..0.833 rows=113 loops=3)
> Index Cond: (id = ANY ('{[...]}'::integer[]))
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=125.05..143.38 rows=143 width=22) (actual time=0.710..0.814 rows=113 loops=3)
> Hash Cond: (meter_bcd.flat = flat.id)
> -> Seq Scan on meter_bcd (cost=0.00..13.65 rows=865 width=8) (actual time=0.040..0.097 rows=865 loops=3)
> -> Hash (cost=123.60..123.60 rows=116 width=22) (actual time=0.626..0.631 rows=113 loops=3)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=110.72..123.60 rows=116 width=22) (actual time=0.538..0.611 rows=113 loops=3)
> Hash Cond: (flat.real_estate = real_estate.id)
> -> Seq Scan on flat (cost=0.00..11.03 rows=703 width=9) (actual time=0.030..0.073 rows=703 loops=3)
> -> Hash (cost=109.30..109.30 rows=113 width=21) (actual time=0.466..0.467 rows=113 loops=3)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Seq Scan on real_estate (cost=0.00..109.30 rows=113 width=21) (actual time=0.198..0.447 rows=113 loops=3)
> Rows Removed by Filter: 570
> Filter: (id = ANY ('{[...]}'::integer[]))
NEW QUERY PLAN BTREE
* NO worker planned/used
* idx_datagram_captured_at_btree used with one iteration
> Unique (cost=2005167.50..2005337.35 rows=11323 width=51) (actual time=6410.799..6522.333 rows=22681 loops=1)
> -> Sort (cost=2005167.50..2005195.81 rows=11323 width=51) (actual time=6410.798..6504.023 rows=104607 loops=1)
> Sort Key: real_estate.number, flat.number, meter.mfct_code, meter.reading_serial, meter.type, datagram.captured_at DESC
> Sort Method: external merge Disk: 5952kB
> -> Hash Join (cost=246164.35..2004405.07 rows=11323 width=51) (actual time=93.802..5776.755 rows=104607 loops=1)
> Hash Cond: (meter.meter_bcd = meter_bcd.id)
> -> Hash Join (cost=246019.19..2003889.83 rows=68494 width=37) (actual time=93.067..5744.787 rows=104607 loops=1)
> Hash Cond: (datagram.src_meter = meter.id)
> -> Index Scan using idx_datagram_captured_at_btree on datagram (cost=0.57..1756571.73 rows=495033 width=20) (actual time=0.054..5451.417 rows=514369 loops=1)
> Index Cond: ((captured_at >= ('2020-08-28 10:34:32.855+02'::timestamp with time zone - '5 days'::interval)) AND (captured_at <= ('2020-08-28 10:34:32.855+02'::timestamp with time zone + '00:00:00'::interval)))
> -> Hash (cost=245518.07..245518.07 rows=40044 width=25) (actual time=92.791..92.792 rows=40044 loops=1)
> Buckets: 65536 Batches: 1 Memory Usage: 2859kB
> -> Index Scan using pk_meter on meter (cost=0.42..245518.07 rows=40044 width=25) (actual time=8.506..83.849 rows=40044 loops=1)
> Index Cond: (id = ANY ('{[...]}'::integer[]))
> -> Hash (cost=143.38..143.38 rows=143 width=22) (actual time=0.730..0.733 rows=113 loops=1)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=125.05..143.38 rows=143 width=22) (actual time=0.627..0.716 rows=113 loops=1)
> Hash Cond: (meter_bcd.flat = flat.id)
> -> Seq Scan on meter_bcd (cost=0.00..13.65 rows=865 width=8) (actual time=0.013..0.064 rows=865 loops=1)
> -> Hash (cost=123.60..123.60 rows=116 width=22) (actual time=0.573..0.575 rows=113 loops=1)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=110.72..123.60 rows=116 width=22) (actual time=0.492..0.558 rows=113 loops=1)
> Hash Cond: (flat.real_estate = real_estate.id)
> -> Seq Scan on flat (cost=0.00..11.03 rows=703 width=9) (actual time=0.006..0.047 rows=703 loops=1)
> -> Hash (cost=109.30..109.30 rows=113 width=21) (actual time=0.445..0.446 rows=113 loops=1)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Seq Scan on real_estate (cost=0.00..109.30 rows=113 width=21) (actual time=0.180..0.429 rows=113 loops=1)
> Filter: (id = ANY ('{[...]}'::integer[]))
> Rows Removed by Filter: 570
> Planning Time: 25.226 ms
> Execution Time: 6525.808 ms
NEW QUERY PLAN BRIN
* 2 workers used
* parallel seq scan instead of BRIN-index
> Unique (cost=2957786.28..2957956.13 rows=11323 width=51) (actual time=17276.310..17388.140 rows=22681 loops=1)
> -> Sort (cost=2957786.28..2957814.59 rows=11323 width=51) (actual time=17276.309..17369.897 rows=104607 loops=1)
> Sort Key: real_estate.number, flat.number, meter.mfct_code, meter.reading_serial, meter.type, datagram.captured_at DESC
> Sort Method: external merge Disk: 5944kB
> -> Gather (cost=246962.35..2957023.85 rows=11323 width=51) (actual time=510.149..16634.476 rows=104607 loops=1)
> Workers Planned: 2
> Workers Launched: 2
> -> Hash Join (cost=245962.35..2954891.55 rows=4718 width=51) (actual time=414.249..16845.834 rows=34869 loops=3)
> Hash Cond: (datagram.src_meter = meter.id)
> -> Parallel Seq Scan on datagram (cost=0.00..2708108.53 rows=206264 width=20) (actual time=0.653..16348.221 rows=171456 loops=3)
> Filter: ((captured_at >= ('2020-08-28 10:34:32.855+02'::timestamp with time zone - '5 days'::interval)) AND (captured_at <= ('2020-08-28 10:34:32.855+02'::timestamp with time zone + '00:00:00'::interval)))
> Rows Removed by Filter: 35983845
> -> Hash (cost=245879.60..245879.60 rows=6620 width=39) (actual time=413.062..413.067 rows=40044 loops=3)
> Buckets: 65536 (originally 8192) Batches: 1 (originally 1) Memory Usage: 3016kB
> -> Hash Join (cost=145.59..245879.60 rows=6620 width=39) (actual time=9.850..400.265 rows=40044 loops=3)
> Hash Cond: (meter.meter_bcd = meter_bcd.id)
> -> Index Scan using pk_meter on meter (cost=0.42..245518.07 rows=40044 width=25) (actual time=8.971..384.016 rows=40044 loops=3)
> Index Cond: (id = ANY ('{[...]}'::integer[]))
> -> Hash (cost=143.38..143.38 rows=143 width=22) (actual time=0.869..0.873 rows=113 loops=3)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=125.05..143.38 rows=143 width=22) (actual time=0.743..0.855 rows=113 loops=3)
> Hash Cond: (meter_bcd.flat = flat.id)
> -> Seq Scan on meter_bcd (cost=0.00..13.65 rows=865 width=8) (actual time=0.039..0.105 rows=865 loops=3)
> -> Hash (cost=123.60..123.60 rows=116 width=22) (actual time=0.659..0.661 rows=113 loops=3)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Hash Join (cost=110.72..123.60 rows=116 width=22) (actual time=0.567..0.643 rows=113 loops=3)
> Hash Cond: (flat.real_estate = real_estate.id)
> -> Seq Scan on flat (cost=0.00..11.03 rows=703 width=9) (actual time=0.026..0.077 rows=703 loops=3)
> -> Hash (cost=109.30..109.30 rows=113 width=21) (actual time=0.490..0.491 rows=113 loops=3)
> Buckets: 1024 Batches: 1 Memory Usage: 13kB
> -> Seq Scan on real_estate (cost=0.00..109.30 rows=113 width=21) (actual time=0.209..0.469 rows=113 loops=3)
> Filter: (id = ANY ('{[...]}'::integer[]))
> Rows Removed by Filter: 570
> Planning Time: 30.375 ms
> Execution Time: 17391.867 ms
WHAT'S GOING ON?
I'm using the same query for both schemas with the same data and had
expected that the new table using BTREE performs at least as good/bad
as the query for the old schema. Though, things are slower and I can't
see why e.g. no parallel workers are used at all. Additionally, the
BRIN-index seems to be ignored entirely, even though other people use
it for time based data, with rows having ordered IDs and stuff like
that.
I would like to have BTREE-performance like witht he old schema, but
at best somewhat similar performance using BRIN to consume less space
of course.
Any idea what I'm doing wrong where? Thanks!
Mit freundlichen Grüßen
Thorsten Schöning
--
Thorsten Schöning
AM-SoFT IT-Service - Bitstore Hameln GmbH i.G. ist ein Mitglied der Bitstore Gruppe - Ihr Full-Service-Dienstleister für IT und TK
E-Mail: Thorsten.Schoening@xxxxxxxxxx
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AM-SoFT IT-Service - Bitstore Hameln GmbH i.G., Brandenburger Str. 7c, 31789 Hameln
AG Hannover HRB neu - Geschäftsführer: Janine Galonska
Für Rückfragen stehe ich Ihnen sehr gerne zur Verfügung.
Mit freundlichen Grüßen
Thorsten Schöning
Tel: 05151 9468 0
Fax: 05151 9468 88
Mobil:
Webseite: https://www.am-soft.de
AM-Soft IT-Service - Bitstore Hameln GmbH i.G. ist ein Mitglied der Bitstore Gruppe - Ihr Full-Service-Dienstleister für IT und TK
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<<attachment: bug_2628_old_schema_idx_btree.zip>>
<<attachment: bug_2628_new_schema_idx_btree.zip>>
<<attachment: bug_2628_new_schema_idx_brin.zip>>
