I have table with 3 columns (postgres 9.6) : serial , timestamp , clock_name
Usually there is 1 second different between each row but sometimes the interval is bigger.
I'm trying to get the number of occasions that the timestamp interval between 2 rows was bigger than 10 seconds (lets say I limit this to 1000 rows)
I would like to do this in one query (probably select from select) but I have no idea how to write such a query , my sql knowladge is very basic.
Any help will be appreciated
You can use window functions to retrieve the next record record given the current record.
Using the ORDER BY on the function to ensure things are in time stamp order and using PARTITION to keep the clocks separate you can find for each row the row that follows it.
WITH links AS
(
SELECT
id, ts, clock, LEAD(ts) OVER (PARTITION BY clock ORDER BY ts) AS next_ts
FROM myTable
)
SELECT * FROM links
WHERE
EXTRACT(EPOCH FROM (next_ts - ts)) > 10
You can then just compare the time stamps.
Window functions https://www.postgresql.org/docs/current/static/functions-window.html
Or if you prefer to use derived tables instead of WITH clause.
SELECT * FROM (
SELECT
id, ts, clock, LEAD(ts) OVER (PARTITION BY clock ORDER BY ts) AS next_ts
FROM myTable
) links
WHERE
EXTRACT(EPOCH FROM (next_ts - ts)) > 10
Related
I have a table in Postgres 14.2
Table name is test
There are 3 columns: date, high, and five_day_mavg (date is PK if it matters)
I have a select statement which properly calculates a 5 day moving average based on the data in high.
select date,
avg(high) over (order by date rows between 4 preceding and current row) as mavg_calc
from test
It products output as such:
I have 2 goals:
First to store the output of the query in five_day_mavg.
Second to store this in such a way that when I a new row with data
in high, it automatically calculates that value
The closest I got was:
update test set five_day_mavg = a.mav_calc
from (
select date,
avg(high) over (order by date rows between 4 preceding and current row) as mav_calc
from test
) a;
but all that does is sets the value of every row in five_day_mavg to entire average of high
Thanks to #a_horse_with_no_name
I played around with the WHERE clause
update test l set five_day_mavg = b.five_day_mavg from (select date, avg(high) over (order by date rows between 4 preceding and current row) as five_day_mavg from test )b where l.date = b.date;
a couple of things. I defined each table. The original table I aliased as l, the temporary table created by doing a windows function (the select statement in parenthesis) I aliased as b and I joined with the WHERE clause on date which is the index/primary key.
Also, I was using 'a' as the letter for alias, and I think that may have contributed to the issue.
Either way, solved now.
Suppose I have data as shown in image
I want to create a third column that will give me names of different types of Alarm names occurred in 20 minutes' time from the Alarm name column, So I can understand which Alarms are related.
I am not sure I full understand the output you want, but you can collect the error messages using array_agg() and a window function that uses a window that is -10 minutes and +10 minutes around the "current" timestamp.
Something along the lines:
select created_at,
error_message,
array_agg(error_message) over (order by created_at range between interval '10 minute' preceding and '10 minute' following) as nearby_errors
from error_log
order by created_at;
My table consists of two fields, CalDay a timestamp field with time set on 00:00:00 and UserID.
Together they form a compound key but it is important to have in mind that we have many rows for each given calendar day and there is no fixed number of rows for a given day.
Based on this dataset I would need to calculate how many distinct users there are over a set window of time, say 30d.
Using postgres 9.3 I cannot use COUNT(Distinct UserID) OVER ... nor I can work around the issue using DENSE_RANK() OVER (... RANGE BETWEEN) because RANGE only accepts UNBOUNDED.
So I went the old fashioned way and tried with a scalar subquery:
SELECT
xx.*
,(
SELECT COUNT(DISTINCT UserID)
FROM data_table AS yy
WHERE yy.CalDay BETWEEN xx.CalDay - interval '30 days' AND xx.u_ts
) as rolling_count
FROM data_table AS xx
ORDER BY yy.CalDay
In theory, this should work, right? I am not sure yet because I started the query about 20 mins ago and it is still running. Here lies the problem, the dataset is still relatively small (25000 rows) but will grow over time. I would need something that scales and performs better.
I was thinking that maybe - just maybe - using the unix epoch instead of the timestamp could help but it is only a wild guess. Any suggestion would be welcome.
This should work. Can't comment on speed, but should be a lot less than your current one. Hopefully you have indexes on both these fields.
SELECT t1.calday, COUNT(DISTINCT t1.userid) AS daily, COUNT(DISTINCT t2.userid) AS last_30_days
FROM data_table t1
JOIN data_table t2
ON t2.calday BETWEEN t1.calday - '30 days'::INTERVAL AND t1.calday
GROUP BY t1.calday
UPDATE
Tested it with a lot of data. The above works but is slow. Much faster to do it like this:
SELECT t1.*, COUNT(DISTINCT t2.userid) AS last_30_days
FROM (
SELECT calday, COUNT(DISTINCT userid) AS daily
FROM data_table
GROUP BY calday
) t1
JOIN data_table t2
ON t2.calday BETWEEN t1.calday - '30 days'::INTERVAL AND t1.calday
GROUP BY 1, 2
So instead of building up a massive table for all the JOIN combinations and then grouping/aggregating, it first gets the "daily" data, then joins the 30 day on that. Keeps the join much smaller and returns quickly (just under 1 second for 45000 rows in the source table on my system).
I have a table with an ID column called mmsi and another column of timestamp, with multiple timestamps per mmsi.
For each mmsi I want to calculate the standard deviation of the difference between consecutive timestamps.
I'm not very experienced with SQL but have tried to construct a function as follows:
SELECT
mmsi, stddev(time_diff)
FROM
(SELECT mmsi,
EXTRACT(EPOCH FROM (timestamp - lag(timestamp) OVER (ORDER BY mmsi ASC, timestamp ASC)))
FROM ais_messages.ais_static
ORDER BY mmsi ASC, timestamp ASC) AS time_diff
WHERE time_diff IS NOT NULL
GROUP BY mmsi;
Your query looks on the right track, but it has several problems. You labelled your subquery, which looks almost right, with an alias which you then select. But this subquery returns multiple rows and columns so this doesn't make any sense. Here is a corrected version:
SELECT
t.mmsi,
STDDEV(t.time_diff) AS std
FROM
(
SELECT
mmsi,
EXTRACT(EPOCH FROM (timestamp - LAG(timestamp) OVER
(PARTITION BY mmsi ORDER BY timestamp))) AS time_diff
FROM ais_messages.ais_static
ORDER BY mmsi, timestamp
) t
WHERE t.time_diff IS NOT NULL
GROUP BY t.mmsi
This approach should be fine but there is one edge case where it might not behave as expected. If a given mmsi group have only one record, then it would not even appear in the result set of standard deviations. This is because the LAG calculation would return NULL for that single record and it would be filtered off.
I need to sum values that are + 2 months or within a quarter period (related date table)
is there a way to use dense rank to partition those periods (custom periods)?
select
FiscalMonth
,Value
from table
The sql will have to do the following:
Join the value table and the period table
Include the period in the select list and sum the value, grouping by the period
i.e
select b.period, sum(a.value)
from table a
inner join period b on a.FiscalMonth between b.StartMonth and b.EndMonth
group by b.period
Note: The join condition will have to be modified based on what data you actually have in the period table.
Hope this helps
Well, If you need value from an X interval, by month you could use something like:
SELECT *
FROM yourTable
MONTH(some_date) = MONTH(CURRENT_DATE - INTERVAL 1 MONTH) //Could be X interval!
This is an example (which show the results of the previous month, from the actual one). Just trying to write that it is possible to massage the query in functions on intervals.
Of course, you could use the SUMcommand for the adding.