I am accessing table that takes in every encounter between two vehicles (i do not have permissions to change this table). When an encounter occurs, it'll take in one row for each perspective of the encounter- Vehicle X encountered Vehicle Y and another row for Vehicle Y encountered Vehicle X. Here's some sample data:
Location Vehicle1 Vehicle2
103923 5594800 54114
105938 40547 1855442
103923 2588603 5659158
103923 54114 5594800
103923 5659158 2588603
105938 1855442 40547
There are no duplicates in any row, values are all unique. But every value in Vehicle1 exists in vehicle2. How would i get it so only one of each pair exists?
GREATEST and LEAST functions might help.
DELETE ... USING syntax
DELETE
FROM t a USING
( SELECT location,
greatest(Vehicle1 , Vehicle2) as vehicle1,
least(Vehicle1 , Vehicle2) as vehicle2
FROM t
GROUP BY 1,2,3 HAVING COUNT(*) > 1 ) b
WHERE a.location = b.location
AND a.Vehicle1 = b.Vehicle1
AND a.Vehicle2 = b.Vehicle2;
Related
I have a composite type:
CREATE TYPE mydata_t AS
(
user_id integer,
value character(4)
);
Also, I have a table, uses this composite type as an array of mydata_t.
CREATE TABLE tbl
(
id serial NOT NULL,
data_list mydata_t[],
PRIMARY KEY (id)
);
Here I want to update the mydata_t in data_list, where mydata_t.user_id is 100000
But I don't know which array element's user_id is equal to 100000
So I have to make a search first to find the element where its user_id is equal to 100000 ... that's my problem ... I don't know how to make the query .... in fact, I want to update the value of the array element, where it's user_id is equal to 100000 (Also where the id of tbl is for example 1) ... What will be my query?
Something like this (I know it's wrong !!!)
UPDATE "tbl" SET "data_list"[i]."value"='YYYY'
WHERE "id"=1 AND EXISTS (SELECT ROW_NUMBER() OVER() AS i
FROM unnest("data_list") "d" WHERE "d"."user_id"=10000 LIMIT 1)
For example, this is my tbl data:
Row1 => id = 1, data = ARRAY[ROW(5,'YYYY'),ROW(6,'YYYY')]
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'YYYY')]
Now i want to update tbl where id is 2 and set the value of one of the tbl.data elements to 'XXXX' where the user_id of element is equal to 11
In fact, the final result of Row2 will be this:
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'XXXX')]
If you know the value value, you can use the array_replace() function to make the change:
UPDATE tbl
SET data_list = array_replace(data_list, (11, 'YYYY')::mydata_t, (11, 'XXXX')::mydata_t)
WHERE id = 2
If you do not know the value value then the situation becomes more complex:
UPDATE tbl SET data_list = data_arr
FROM (
-- UPDATE doesn't allow aggregate functions so aggregate here
SELECT array_agg(new_data) AS data_arr
FROM (
-- For the id value, get the data_list values that are NOT modified
SELECT (user_id, value)::mydata_t AS new_data
FROM tbl, unnest(data_list)
WHERE id = 2 AND user_id != 11
UNION
-- Add the values to update
VALUES ((11, 'XXXX')::mydata_t)
) x
) y
WHERE id = 2
You should keep in mind, though, that there is an awful lot of work going on in the background that cannot be optimised. The array of mydata_t values has to be examined from start to finish and you cannot use an index on this. Furthermore, updates actually insert a new row in the underlying file on disk and if your array has more than a few entries this will involve substantial work. This gets even more problematic when your arrays are larger than the pagesize of your PostgreSQL server, typically 8kB. All behind the scene so it will work, but at a performance penalty. Even though array_replace sounds like changes are made in-place (and they indeed are in memory), the UPDATE command will write a completely new tuple to disk. So if you have 4,000 array elements that means that at least 40kB of data will have to be read (8 bytes for the mydata_t type on a typical system x 4,000 = 32kB in a TOAST file, plus the main page of the table, 8kB) and then written to disk after the update. A real performance killer.
As #klin pointed out, this design may be more trouble than it is worth. Should you make data_list as table (as I would do), the update query becomes:
UPDATE data_list SET value = 'XXXX'
WHERE id = 2 AND user_id = 11
This will have MUCH better performance, especially if you add the appropriate indexes. You could then still create a view to publish the data in an aggregated form with a custom type if your business logic so requires.
I have a table cusers with a primary key:
primary key(uid, lid, cnt)
And I try to insert some values into the table:
insert into cusers (uid, lid, cnt, dyn, ts)
values
(A, B, C, (
select C - cnt
from cusers
where uid = A and lid = B
order by ts desc
limit 1
), now())
on conflict do nothing
Quite often (with the possibility of 98%) a row cannot be inserted to cusers because it violates the primary key constraint, so hard select queries do not need to be executed at all. But as I can see PostgreSQL first counts the select query as a result of dyn column and only then rejects row because of uid, lid, cnt violation.
What is the best way to insert rows quickly in such situation?
Another explanation
I have a system where one row depends on another. Here is an example:
(x, x, 2, 2, <timestamp>)
(x, x, 5, 3, <timestamp>)
Two columns contain an absolute value (2 and 5) and relative value (2, 5 - 2). Each time I insert new row it should:
avoid same rows (see primary key constraint)
if new row differs, it should count a difference and put it into the dyn column (so I take the last inserted row for the user according to the timestamp and subtract values).
Another solution I've found is to use returning uid, lid, ts for inserts and get user ids which were really inserted - this is how I know they have differences from existing rows. Then I update inserted values:
update cusers
set dyn = (
select max(cnt) - min(cnt)
from (
select cnt
from cusers
where uid = A and lid = B
order by ts desc
limit 2) Table
)
where uid = A and lid = B and ts = TS
But it is not a fast approach either, as it seeks all over the ts column to find the two last inserted rows for each user. I need a fast insert query as I insert millions of rows at a time (but I do not write duplicates).
What the solution can be? May be I need a new index for this? Thanks in advance.
I have two columns of dates and I want to run a query that returns TRUE if there is a date in existence in the first column and in existence in the second column.
I know how to do it when I'm looking for a match (if the data entry in column A is the SAME as the entry in column B), but I don't know know how to find if data entry in column A and B are in existence.
Does anyone know how to do this? Thanks!
If data in a column is present, it IS NOT NULL. You can query for that on both columns, with and AND clause to get your result:
SELECT (date1 IS NOT NULL AND date2 IS NOT NULL) AS both_dates
FROM mytable;
So, rephrasing:
For any two entries in table x with date columns a and b, is there some pair of rows x1 and x2 where x1.a = x2.b?
If that's what you're trying to do, you want a self-join, e.g, presuming the presence of a single key column named id:
SELECT x1.id, x2.id, x1.a AS x1_a_x2_b
FROM mytable x1
INNER JOIN mytable x2 ON (x1.a = x2.b);
I am working in Tableau and trying to figure out how to create a filter exclusion. For example I have the following fields.
Hospital CallType CallDate
I want to filter out all hospitals where one of the Calls has a call type of ColdCall and a Call DateBetween X and Y.
I can do this easily in SQL but don't have access to this data in the SQL Database. It would be the following:
Select
Hospital
,CallType
,CallDate
Into
#TempTable
From
Database
Select
Hospital
,CallType
,CallDate
Into
#ExclusionTable
From
Database
Where
CallType = 'Cold'
and
CallDate Between X and Y
Select
Hospital
,CallType
,CallDate
From
#TempTable
Where
Hospital not in
(Select
Hospital
From
#ExclusionTable)
Any suggestions would be greatly appreciated.
Thanks,
Simple. Create a calculated field Filter:
IF CallType = "Cold" AND CallDate < X AND CallDate > Y
THEN 1
ELSE 0
END
Then drag Hospital to filter, go to Condition tab, select by field, get your Filter field, use sum > 0. It will filter out any hospital that have at least one call with your conditions (because all the calls that don't meet will be zero, and if at least one is not zero, the sum will be over 0)
For X and Y, I'd create parameters. It's easier (and safer) than trying to write the dates directly on the field. And you can manipulate then more easily too
I have a large database, that I want to do some logic to update new fields.
The primary key is id for the table harvard_assignees
The LOGIC GOES LIKE THIS
Select all of the records based on id
For each record (WHILE), if (state is NOT NULL && country is NULL), update country_out = "US" ELSE update country_out=country
I see step 1 as a PostgreSQL query and step 2 as a function. Just trying to figure out the easiest way to implement natively with the exact syntax.
====
The second function is a little more interesting, requiring (I believe) DISTINCT:
Find all DISTINCT foreign_keys (a bivariate key of pat_type,patent)
Count Records that contain that value (e.g., n=3 records have fkey "D","388585")
Update those 3 records to identify percent as 1/n (e.g., UPDATE 3 records, set percent = 1/3)
For the first one:
UPDATE
harvard_assignees
SET
country_out = (CASE
WHEN (state is NOT NULL AND country is NULL) THEN 'US'
ELSE country
END);
At first it had condition "id = ..." but I removed that because I believe you actually want to update all records.
And for the second one:
UPDATE
example_table
SET
percent = (SELECT 1/cnt FROM (SELECT count(*) AS cnt FROM example_table AS x WHERE x.fn_key_1 = example_table.fn_key_1 AND x.fn_key_2 = example_table.fn_key_2) AS tmp WHERE cnt > 0)
That one will be kind of slow though.
I'm thinking on a solution based on window functions, you may want to explore those too.