Looking at the docs Create continuous view it's possible to create something like
CREATE CONTINUOUS VIEW cv AS
SELECT
ticketid,
status,
avg(status_duration) OVER w
FROM stream
WINDOW w AS (PARTITION BY ticketid);
But I get the error
ERROR: continuous queries don't support WINDOW functions
Am I misunderstanding the docs? Can someone clarify how window functions can be used in continuous views?
Thanks
Related
I'm trying to understand window functions a bit better, and I'm stumped as to why I can't run a nested aggregate function normally, but I can when using a window function.
This is the dbfiddle I'm working off of: https://dbfiddle.uk/?rdbms=postgres_11&fiddle=76d62fcf4066053db18783e70269438c
Before running the window function, basically everything else in my query is evaluated (JOIN and GROUP BY).
So I believe the data the window function is working off of is something like this (after grouping):
Or is it something like this?
So why can I do this: SUM(COUNT(votes.option_id)) OVER(), but I can't do it without OVER()?
As far as I understand, OVER() makes the SUM(COUNT(votes.option_id)) run on this related data set, but it's still a nested aggregate function.
What am I missing?
Thank you very much!
If you have something like SUM(COUNT(votes.option_id)) OVER() you can think of COUNT(votes.option_id) as a column generated in the GROUP BY clause.
According to the documentation:
The rows considered by a window function are those of the “virtual table” produced by the query's FROM clause as filtered by its WHERE, GROUP BY, and HAVING clauses if any.
This means that window functions operate at a level above the GROUP BY clause and any aggregates, and therefore aggregates are available to be used inside window functions. In your example the "virtual table" corresponds to the second picture.
The reason you cannot nest aggregate functions is that you cannot have multiple levels of GROUP BY on the same query. Similarly you cannot nest window functions. The documentation is clear on what type of expression are allowed inside aggregate and window functions. For aggregates functions we can use:
any value expression that does not itself contain an aggregate expression or a window function call
while for window functions we can use:
any value expression that does not itself contain window function calls
Here is my issue, I'm working on a project of water supply where I have to merge line features representing pipes if they have the same material of construction and if they are touching each other. The merge is done two by two what it means that some features will be duplicated in some cases like described in the figure below :
this shows exactly my issue. After the merge I will get three records but what I want is just one record which encompasses the whole pipes that fill the conditions put in the where clause :
Here is the query that helps me do the merge :
drop table if exists touches_material;
create table touches_material as
select distinct a.*,
st_Union(a.geom,b.geom) as fusion from pipe a, pipe b
where a.id < b.id and a.material = b.material and
st_touches(a.geom,b.geom)
group by a.id,a.geom,b.geom
the following picture picture shows the expected result on a test data, it's realized via QGIS GIS software :
but this is what I' getting with my query :
if you have any idea about how to achieve the aim that I invoked, I would be very thankful to get an answer from you. Best regards.
Im am using pipelinedb for testing some analysis of data streams from sensors.
I want to be able, as an example, to find events in a stream that are defined by an aggregate. E.g. find events where the difference between max(temperature) and min(temparature) in the last 5 minutes exceeds a certain range.
When trying to put aggregates in the WHERE clause I get an error message saying something like 'aggregates not allowed in continuous views where clasues'
Am I missing something here or is it just not possible?
Otherwise I like pipelinedb very very much!
Well, pipelinedb says: "continuous queries don't support HAVING clauses".
What I'm trying to do is the following:
I have a stream named geo_vital_stream, which sends some sensor data along with a geolocation. At the moment I am interested
insert into geo_vital_stream (device_id, user_id, measured_at, heartrate, energy, eda, lon, lat) VALUES( 'A005D8-E4 2.0',1,'2015-10-08 15:04:33.134000+02',96.8497201823,351.056269367,0.505791,8.07154018407,52.9531484103 );
My cv looks like this:
CREATE CONTINUOUS VIEW cv_sensor_eda AS
SELECT user_id::integer,
MAX(eda::numeric) - MIN(eda::numeric) as range_eda
FROM geo_vital_stream
WHERE (measured_at > clock_timestamp() - interval '1 minutes')
GROUP BY user_id
Now, I am interested only in those "events", where the range (range_eda execeeds a certain value in the last minute.)
Using an aggregate in a WHERE clause actually isn't legal SQL. That is accomplished using a HAVING clause, but it doesn't seem like that's what you need here. Since aggregates compute values across multiple rows, it's not clear to me how you'd retrieve individual events based on aggregates (min, max) across multiple events. Could you provide an example of what each event looks like?
My scenario is like this:
I have a couple of views in my databse (SQL Server 2005).
These views are queried from Excel across the organization.
My goal is to identify those views which have not been used by anyone for a long time.
Is there a way to count the number of times a view has been requested since a specific date?
Thanks
Avi
You can use following query to get some queries those executed. You can place "Like" operator in dest.text field to check for views.
SELECT deqs.last_execution_time AS [Time], dest.text AS [Query]
FROM sys.dm_exec_query_stats AS deqs
CROSS APPLY sys.dm_exec_sql_text(deqs.sql_handle) AS dest
ORDER BY deqs.last_execution_time DES
I think a combination of DMVs and sysobjects could tell you this. This should hopefully show you all queries run that refer to a view, the name of the view, when it was last run etc.
SELECT s2.text AS Query,
so.name AS ViewName,
creation_time,
last_execution_time,
execution_count
FROM sys.dm_exec_query_stats AS s1
CROSS APPLY sys.Dm_exec_sql_text(sql_handle) AS s2
INNER JOIN sys.objects so
ON so.object_id = s2.objectid
AND so.type = 'V'
I don't think that you'll be able to do this unless you are running a trace 24/7. You could turn on auditing in order to monitor it. But, it would be a big task for however has to read through the logs.
How do I change a PostgreSQL query into a mongodb bson call? I have the same use case listed at http://archives.postgresql.org/pgsql-general/2011-10/msg00157.php I would like to calculate the delta time between two log entries by using something like lag or lead. Is there anything similar in mongodb to Postgres' lag / lead syntax?
select
index,
starttime,
endtime,
starttime - lag(endtime) over(order by starttime asc) as delta
from test
http://www.postgresql.org/docs/8.4/static/functions-window.html
I was looking at http://www.mongovue.com/2010/11/03/yet-another-mongodb-map-reduce-tutorial/ and it seems that map / reduce / finalize should do it. Map the id, start and end time, reduce does nothing, then do a inner join on its self (the double fors) during the finalize. I can almost, kind of, sort of, see it...
This is something you'll have to do in your application. Right now, mongoDB doesn't support anything like this.
You can rewrite some of the window functions as subqueries. See if that's possible in the aggregation framework. This subquery should after the filtering and grouping are done.
Couchbase is going to have the standard window functions. https://blog.couchbase.com/on-par-with-window-functions-in-n1ql/