I have a result set
name stage value
---- ----- -----
jim 1 4
jim 1 8
paul 1 8
paul 1 8
want to remove the rows where 8 is the only value against a person
keep the 2 jim rows and lose the 2 paul rows
You can use not exists. For a select query:
select t.*
from t
where not exists (select 1
from t t2
where t2.name = t.name and t2.value = 8
);
Similar logic (except using exists rather than not exists) can be used for a delete -- if you really want to delete the rows from the table.
If you have a complex query that you don't want to repeat, then window functions are helpful:
select t.*
from (select t.*,
sum(case when value = 8 then 1 else 0 end) over (partition by name) as cnt_8
from t
) t
where cnt_8 = 0;
If your database support analytical function then you can use count as follows:
Select * from
(Select t.*,
Count(case when value <> 8 then 1 end) over (partition by name) as cnt
From your_table t) t
Where cnt > 0
Assuming you also have an ID column (defined as an auto increment integer) defined in your table this query would select the row with the highest id for each unique combination:
select max(id) from t group by name,stage,value
In your example this would only return the latest id for rows having values paul,1,8 in columns name,stage,value respectively.
You can then use the prior query to filter out any duplciates using it in the where clause:
select * from t
where id in (select max(id) from t group by name,stage,value)
Finally you can also delete rows that are not unique if that's your goal:
delete from t
where not id in (select max(id) from t group by name,stage,value)
I have two tables.
TABLE A:
OBJECTID ID
NULL 41230
NULL 00004
NULL 00005
TABLE B:
OBJECTID ID
241231 00001
241230 00002
I'm trying to write a query that increments values for the OBJECTID field in Table A based on the max value in Table B. For example the OBJECTID field for the first row in Table A would then be 241232.
Using ROW_NUMBER() over (Order by OBJECTID ASC) works if I wanted to start with the value 1 and increment. But I need it to join on Table B and start on Table B's max value and then increment.
I've tried this but get a query error Query error: Table-valued function not found tableB:
UPDATE `tableA`
SET OBJECTID = (SELECT MAX(OBJECTID) as seq
FROM `tableB`
((SELECT ROW_NUMBER() over (Order by seq ASC))
)) WHERE OBJECTID IS NULL;
In BigQuery is easier create a new table that overwrite the actual. You can do it with this select:
with max_id as (
select max(objectid) as objectid from tableB
),
table_a_new_id as (
select
* except (objectid),
(select objectid from max_id) + dense_rank() over (order by id) as objectid
from tableA
where objectid is null
)
select * from table_a_new_id
union all
select * from tableA where objectid is not null
If you can't replace the table directly, you can save the result in a temporary table and then run the update:
update tableA
set tableA.objectid = new_table_a.objectid
from temp_new_tableA
where tableA.objectid is null and tableA.id = temp_new_tableA.id
I want to select without duplicate ids and keep row '5d' and not '5e' in select statement.
table
id | name
1 | a
2 | b
3 | c
5 | d
5 | e
I tried:
SELECT id, name
FROM table t
INNER JOIN (SELECT DISTINCT id FROM table) t2 ON t.id = t2.id
For the given example an aggregation using min() would work.
SELECT id,
min(name) name
FROM table
GROUP BY id;
You can also use ROW_NUMBER():
SELECT id, name
FROM (
SELECT id, name, ROW_NUMBER() OVER(PARTITION BY id ORDER BY name) rn
FROM mytable
) x
WHERE rn = 1
This will retain the record that has the smallest name (so '5d' will come before '5e'). With this technique, you can also use a sort criteria on another column that the one where duplicates exists (which an aggregate query with MIN() cannot do). Also, queries using window functions usually perform better than the equivalent aggregate query.
If you want to keep the row with the smallest name then you can use not exists:
select t.* from tablename t
where not exists (
select 1 from tablename
where id = t.id and name < t.name
)
I have a Postgres table created with the following statement. This table is filled by as dump of data from another service.
CREATE TABLE data_table (
date date DEFAULT NULL,
dimension1 varchar(64) DEFAULT NULL,
dimension2 varchar(128) DEFAULT NULL
) TABLESPACE pg_default;
One of the steps in a ETL I'm building is extracting the unique values of dimension1 and inserting them in another intermediary table.
However, during some tests I found out that the 2 commands below do not return the same results. I would expect for both to return the same sum.
The first command returns more results compared with the second (1466 rows vs. 1504.
-- command 1
SELECT DISTINCT count(dimension1)
FROM data_table;
-- command 2
SELECT count(*)
FROM (SELECT DISTINCT ON (dimension1) dimension1
FROM data_table
GROUP BY dimension1) AS tmp_table;
Any obvious explanations for this? Alternatively to an explanation, is there any suggestion of any check on the data I should do?
EDIT: The following queries both return 1504 (same as the "simple" DISTINCT)
SELECT count(*)
FROM data_table WHERE dimension1 IS NOT NULL;
SELECT count(dimension1)
FROM data_table;
Thank you!
DISTINCT and DISTINCT ON have completely different semantics.
First the theory
DISTINCT applies to an entire tuple. Once the result of the query is computed, DISTINCT removes any duplicate tuples from the result.
For example, assume a table R with the following contents:
#table r;
a | b
---+---
1 | a
2 | b
3 | c
3 | d
2 | e
1 | a
(6 rows)
SELECT distinct * from R will result:
# select distinct * from r;
a | b
---+---
1 | a
3 | d
2 | e
2 | b
3 | c
(5 rows)
Note that distinct applies to the entire list of projected attributes: thus
select distinct * from R
is semantically equivalent to
select distinct a,b from R
You cannot issue
select a, distinct b From R
DISTINCT must follow SELECT. It applies to the entire tuple, not to an attribute of the result.
DISTINCT ON is a postgresql addition to the language. It is similar, but not identical, to group by.
Its syntax is:
SELECT DISTINCT ON (attributeList) <rest as any query>
For example:
SELECT DISTINCT ON (a) * from R
It semantics can be described as follows. Compute the as usual--without the DISTINCT ON (a)---but before the projection of the result, sort the current result and group it according to the attribute list in DISTINCT ON (similar to group by). Now, do the projection using the first tuple in each group and ignore the other tuples.
Example:
select * from r order by a;
a | b
---+---
1 | a
2 | e
2 | b
3 | c
3 | d
(5 rows)
Then for every different value of a (in this case, 1, 2 and 3), take the first tuple. Which is the same as:
SELECT DISTINCT on (a) * from r;
a | b
---+---
1 | a
2 | b
3 | c
(3 rows)
Some DBMS (most notably sqlite) will allow you to run this query:
SELECT a,b from R group by a;
And this give you a similar result.
Postgresql will allow this query, if and only if there is a functional dependency from a to b. In other words, this query will be valid if for any instance of the relation R, there is only one unique tuple for every value or a (thus selecting the first tuple is deterministic: there is only one tuple).
For instance, if the primary key of R is a, then a->b and:
SELECT a,b FROM R group by a
is identical to:
SELECT DISTINCT on (a) a, b from r;
Now, back to your problem:
First query:
SELECT DISTINCT count(dimension1)
FROM data_table;
computes the count of dimension1 (number of tuples in data_table that where dimension1 is not null). This query
returns one tuple, which is always unique (hence DISTINCT
is redundant).
Query 2:
SELECT count(*)
FROM (SELECT DISTINCT ON (dimension1) dimension1
FROM data_table
GROUP BY dimension1) AS tmp_table;
This is query in a query. Let me rewrite it for clarity:
WITH tmp_table AS (
SELECT DISTINCT ON (dimension1)
dimension1 FROM data_table
GROUP by dimension1)
SELECT count(*) from tmp_table
Let us compute first tmp_table. As I mentioned above,
let us first ignore the DISTINCT ON and do the rest of the
query. This is a group by by dimension1. Hence this part of the query
will result in one tuple per different value of dimension1.
Now, the DISTINCT ON. It uses dimension1 again. But dimension1 is unique already (due to the group by). Hence
this makes the DISTINCT ON superflouos (it does nothing).
The final count is simply a count of all the tuples in the group by.
As you can see, there is an equivalence in the following query (it applies to any relation with an attribute a):
SELECT (DISTINCT ON a) a
FROM R
and
SELECT a FROM R group by a
and
SELECT DISTINCT a FROM R
Warning
Using DISTINCT ON results in a query might be non-deterministic for a given instance of the database.
In other words, the query might return different results for the same tables.
One interesting aspect
Distinct ON emulates a bad behaviour of sqlite in a much cleaner way. Assume that R has two attributes a and b:
SELECT a, b FROM R group by a
is an illegal statement in SQL. Yet, it runs on sqlite. It simply takes a random value of b from any of the tuples in the group of same values of a.
In Postgresql this statement is illegal. Instead, you must use DISTINCT ON and write:
SELECT DISTINCT ON (a) a,b from R
Corollary
DISTINCT ON is useful in a group by when you want to access a value that is functionally dependent on the group by attributes. In other words, if you know that for every group of attributes they always have the same value of the third attribute, then use DISTINCT ON that group of attributes. Otherwise you would have to make a JOIN to retrieve that third attribute.
The first query gives the number of not null values of dimension1, while the second one returns the number of distinct values of the column. These numbers obviously are not equal if the column contains duplicates or nulls.
The word DISTINCT in
SELECT DISTINCT count(dimension1)
FROM data_table;
makes no sense, as the query returns a single row. Maybe you wanted
SELECT count(DISTINCT dimension1)
FROM data_table;
which returns the number of distinct not null values of dimension1. Note, that it is not the same as
SELECT count(*)
FROM (
SELECT DISTINCT ON (dimension1) dimension1
FROM data_table
-- GROUP BY dimension1 -- redundant
) AS tmp_table;
The last query yields the number of all (null or not) distinct values of the column.
To learn and understand what happens by visual example.
Here's a bit of SQL to execute on a PostgreSQL:
DROP TABLE IF EXISTS test_table;
CREATE TABLE test_table (
id int NOT NULL primary key,
col1 varchar(64) DEFAULT NULL
);
INSERT INTO test_table (id, col1) VALUES
(1,'foo'), (2,'foo'), (3,'bar'), (4,null);
select count(*) as total1 from test_table;
-- returns: 4
-- Because the table has 4 records.
select distinct count(*) as total2 from test_table;
-- returns: 4
-- The count(*) is just one value. Making 1 total unique can only result in 1 total.
-- So the distinct is useless here.
select col1, count(*) as total3 from test_table group by col1 order by col1;
-- returns 3 rows: ('bar',1),('foo',2),(NULL,1)
-- Since there are 3 unique col1 values. NULL's are included.
select distinct col1, count(*) as total4 from test_table group by col1 order by col1;
-- returns 3 rows: ('bar',1),('foo',2),(NULL,1)
-- The result is already grouped, and therefor already unique.
-- So again, the distinct does nothing extra here.
select count(distinct col1) as total5 from test_table;
-- returns 2
-- NULL's aren't counted in a count by value. So only 'foo' & 'bar' are counted
select distinct on (col1) id, col1 from test_table order by col1 asc, id desc;
-- returns 3 rows: (2,'a'),(3,'b'),(4,NULL)
-- So it gets the records with the maximum id per unique col1
-- Note that the "order by" matters here. Changing that DESC to ASC would get the minumum id.
select count(*) as total6 from (select distinct on (col1) id, col1 from test_table order by col1 asc, id desc) as q;
-- returns 3.
-- After seeing the previous query, what else would one expect?
select distinct col1 from test_table order by col1;
-- returns 3 unique values : ('bar'),('foo'),(null)
select distinct id, col1 from test_table order by col1;
-- returns all records.
-- Because id is the primary key and therefore makes each returned row unique
Here's a more direct summary that might useful for Googlers, answering the title but not the intricacies of the full post:
SELECT DISTINCT
availability: ISO
behaviour:
SELECT DISTINCT col1, col2, col3 FROM mytable
returns col1, col2 and col3 and omits any rows in which all of the tuple (col1, col2, col3) are the same. E.g. you could get a result like:
1 2 3
1 2 4
because those two rows are not identical due to the 4. But you could never get:
1 2 3
1 2 4
1 2 3
because 1 2 3 appears twice, and both rows are exactly the same. That is what DISTINCT prevents.
vs GROUP BY: SELECT DISTINCT is basically a subset of GROUP BY where you can't use aggregate functions: Is there any difference between GROUP BY and DISTINCT
SELECT DISTINCT ON
availability: PostgreSQL extension, WONTFIXED by SQLite
behavior: unlike DISTINCT, DISTINCT ON allows you to separate
what you want to be unique
from what you want to return
E.g.:
SELECT DISTINCT ON(col1) col2, col3 FROM mytable
returns col2 and col3, and does not return any two rows with the same col1. E.g.:
1 2 3
1 4 5
could not happen, because we have 1 twice on col1.
And e.g.:
SELECT DISTINCT ON(col1, col2) col2, col3 FROM mytable
would prevent any duplicated (col1, col2) tuples, e.g. you could get:
1 2 3
1 4 3
as it has different (1, 2) and (1, 4) tuples, but not:
1 2 3
1 2 4
where (1, 2) happens twice, only one of those two could appear.
We can uniquely determine which one of the possible rows will be selected with ORDER BY which guarantees that the first match is taken, e.g.:
SELECT DISTINCT ON(col1, col2) col2, col3 FROM mytable
ORDER BY col1 DESC, col2 DESC, col3 DESC
would ensure that among:
1 2 3
1 2 4
only 1 2 4 would be picked as it happens first on our DESC sorting.
vs GROUP BY: DISTINCT ON is not a subset of GROUP BY because it allows you to access extra rows not present in the GROUP BY, which is generally not allowed in GROUP BY, unless:
you group by primary key in Postgres (unique not null is a TODO for them)
or if that is allows as an ISO extension as in SQLite/MySQL
This makes DISTINCT ON extremely useful to fulfill the common use case of "find the full row that reaches the maximum/minimum of some column": Is there any difference between GROUP BY and DISTINCT
E.g. to find the city of each country that has the most sales:
SELECT DISTINCT ON ("country") "country", "city", "amount"
FROM "Sales"
ORDER BY "country" ASC, "amount" DESC, "city" ASC
or equivalently with * if we want all columns:
SELECT DISTINCT ON ("country") *
FROM "Sales"
ORDER BY "country" ASC, "amount" DESC, "city" ASC
Here each country appears only once, within each country we then sort by amount DESC and take the first, and therefore highest, amount.
RANK and ROW_NUMBER window functions
These can be used basically as supersets of DISTINCT ON, and implemented tested as of both SQLite 3.34 and PostgreSQL 14.3. I highly recommend also looking into them, see e.g.: How to SELECT DISTINCT of one column and get the others?
This is how the above "city with the highest amount of each country" query would look like with ROW_NUMBER:
SELECT *
FROM (
SELECT
ROW_NUMBER() OVER (
PARTITION BY "country"
ORDER BY "amount" DESC, "city" ASC
) AS "rnk",
*
FROM "Sales"
) sub
WHERE
"sub"."rnk" = 1
ORDER BY
"sub"."country" ASC
Try
SELECT count(dimension1a)
FROM (SELECT DISTINCT ON (dimension1) dimension1a
FROM data_table
ORDER BY dimension1) AS tmp_table;
DISTINCT ON appears to be synonymous with GROUP BY.
There are plenty of posts on SO where a solution is given to take out rows that are in one way or form duplicate to other rows, leaving only 1.
What I am looking for is how I can delete all rows from my temp-table that do not have a unique ID:
ID other_values
-----------------------------
1 foo bar
2 bar baz
2 null
2 something
3 else
I don't care about the other values; once the ID is not unique, I want all rows out, the result being:
ID other_values
-----------------------------
1 foo bar
3 else
How can I do this?
Try this:
--delete all rows from my temp-table that do not have a unique ID
DELETE from MYTABLE
WHERE ID IN (SELECT ID FROM MYTABLE GROUP BY ID HAVING COUNT(*) > 1)
I would use a DELETE command in conjunction with a subquery to detect duplicates
DELETE
FROM mytable
WHERE ID IN (SELECT ID FROM mytable GROUP BY ID HAVING COUNT(*) > 1)
Use Cte to delete rows.
WITH cte
AS (
SELECT id
,Other_values
,ROW_NUMBER() OVER (
PARTITION BY id ORDER BY id
) rownum
FROM mytable
)
DELETE
FROM cte
WHERE rownum > 1