Which partition scheme makes sense for my time based IoT-datagrams?

Hi all,

I have a table storing datagrams from some IoT-devices, with one
datagram per device per day most likely for around 75'000 devices
currently. I want to test query performance with a partitioned table
and am interested in the following queries mostly:

* querying arbitrary datagrams by their ID
* querying datagrams being X old based on some timestamp
* 15 minutes
* 15 days
* 15 months

My app isn't prepared to e.g. migrate things in the background,
instead I would like to have Postgres deal with all apsects as
transparent as possible. At least for the next few years, things
worked without partitions in the past as well.

Therefore I thought of simply partitioning by RANGE of the timestamp
when the datagram has been received and create individual partitions
per half a year. That means that in most cases only the most current
1 partition needs to be queried, with the last 3 in many other cases.

Other approaches I've read on this list were e.g. partitioning the
most current months individually and afterwards moving rows to some
other "archive"-like table. Besides of the lack of the necessary
infrastructure, in my use case in theory users need to be able to ask
for the last 15 months at some arbitrary point in history at any time.
I wouldn't like to deal with different tables or alike in my app.

My approach would result in 24 tables already, with 2 more per year.
Does that for itself sound bad already? Is that a limit the planner
can deal with most likely or do I don't even need to care for hundreds
or partitions?

If partitioned by timestamp, how are lookups by ID performed? Is that
a sequential scan on all partitions, e.g. with using available indexes
per partition?

Is there some way to RANGE by timestamp and ID, by keeping the
half-year tables only? So that the planner knows easier which
partition to look at in case of IDs only? Or is that simply like
querying an ID-index of each partition?

The following is what I have currently, extra verbose to support
Postgres 10:

> 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
> ) PARTITION BY RANGE (captured_at);

> CREATE TABLE datagram_y1970_h1 PARTITION OF datagram FOR VALUES FROM ('1970-01-01') TO ('1970-07-01');
> CREATE TABLE datagram_y1970_h2 PARTITION OF datagram FOR VALUES FROM ('1970-07-01') TO ('1971-01-01');
> [...]

> ALTER TABLE datagram_y1970_h1 ADD CONSTRAINT pk_datagram_y1970_h1 PRIMARY KEY (id);
> ALTER TABLE datagram_y1970_h1 ADD CONSTRAINT ck_datagram_y1970_h1_oms_data_avail CHECK (oms_enc IS NOT NULL OR oms_dec IS NOT NULL);
> ALTER TABLE datagram_y1970_h1 ADD CONSTRAINT fk_datagram_y1970_h1_src_re FOREIGN KEY (src_re) REFERENCES real_estate (id);
> ALTER TABLE datagram_y1970_h1 ADD CONSTRAINT fk_datagram_y1970_h1_src_clt FOREIGN KEY (src_clt) REFERENCES collector (id);
> ALTER TABLE datagram_y1970_h1 ADD CONSTRAINT fk_datagram_y1970_h1_src_meter FOREIGN KEY (src_meter) REFERENCES meter (id);
> ALTER TABLE datagram_y1970_h2 ADD CONSTRAINT pk_datagram_y1970_h2 PRIMARY KEY (id);
> ALTER TABLE datagram_y1970_h2 ADD CONSTRAINT ck_datagram_y1970_h2_oms_data_avail CHECK (oms_enc IS NOT NULL OR oms_dec IS NOT NULL);
> ALTER TABLE datagram_y1970_h2 ADD CONSTRAINT fk_datagram_y1970_h2_src_re FOREIGN KEY (src_re) REFERENCES real_estate (id);
> ALTER TABLE datagram_y1970_h2 ADD CONSTRAINT fk_datagram_y1970_h2_src_clt FOREIGN KEY (src_clt) REFERENCES collector (id);
> ALTER TABLE datagram_y1970_h2 ADD CONSTRAINT fk_datagram_y1970_h2_src_meter FOREIGN KEY (src_meter) REFERENCES meter (id);
> [...]

> CREATE INDEX idx_datagram_y1970_h1_for_time_window ON datagram_y1970_h1 USING btree (src_meter, captured_at DESC);
> CREATE INDEX idx_datagram_y1970_h2_for_time_window ON datagram_y1970_h2 USING btree (src_meter, captured_at DESC);
> [...]

An example query condition is the following, while "P5D" can simply be
"P15M" or alike instead.

> WHERE (
> "real_estate"."id" IN ([...])
> AND "meter"."id" IN ([...])
> AND "datagram"."captured_at" BETWEEN (CAST('2020-08-28T10:34:32.855+02:00' AS TIMESTAMP WITH TIME ZONE) - CAST('P5D' AS INTERVAL)) AND (CAST('2020-08-28T10:34:32.855+02:00' AS TIMESTAMP WITH TIME ZONE) + CAST('P0D' AS INTERVAL))
> )

Thanks for sharing your opinions!

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

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Für Rückfragen stehe ich Ihnen sehr gerne zur Verfügung.

Mit freundlichen Grüßen

Thorsten Schöning

Tel: 05151 9468 0
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