This question already has answers here:
Split comma separated column data into additional columns
(3 answers)
Closed 4 years ago.
I'm using the function string_agg to show together some values of the same ID, and it works well because it shows the info concatenated:
SELECT string_agg(table1.value , '#BR#') as "values"
FROM table1 WHERE id=1
id | values
--------------
1 | v1#BR#v2#BR#v3#BR#v4#BR#v5#BR#v6
Is there a way I can show the info on separated columns? Something like this:
id | values | values | values | values | values | values |
----------------------------------------------------------
1 | v1 | v2 | v3 | v4 | v5 | v6
If you can define a sensible upper limit for the number of columns you could use something like this:
select id,
v[1] as value_1,
v[2] as value_2,
v[3] as value_3,
v[4] as value_4,
v[5] as value_5
from (
select id, array_agg(value) as v
from table1
where id = 1
) t;
If you expect more columns, just add more expressions to the outer SELECT list
Note that accessing array elements that don't exists will not result in an error, it will only yield NULL. So you can create any number of columns you like even if you know that there won't be values for all of them all the time.
Related
This question already has answers here:
ORDER BY the IN value list
(17 answers)
Closed 3 months ago.
How to let the query result be ordered by the exact order of passed items in the WHERE clause?
For example, using this query:
SELECT id, name FROM my_table
WHERE id in (1,3,5,2,4,6)
ORDER BY id
Result:
id | name
---------
1 | a
2 | b
3 | c
4 | d
5 | e
6 | f
What I expected:
id | name
---------
1 | a
3 | c
5 | e
2 | b
4 | d
6 | f
I noticed that there is a FIELD() function in MySQL. Is there an equivalent function in PostgreSQL?
Pass an array and use WITH ORDINALITY. That's cleanest and fastest:
SELECT id, t.name
FROM unnest ('{1,3,5,2,4,6}'::int[]) WITH ORDINALITY u(id, ord)
JOIN my_table t USING (id)
ORDER BY u.ord;
Assuming values in the passed array are distinct. Else, this solution preserves duplicates, while IN removes them. You'd have to define which behavior you want. But then the desired sort order is also ambiguous, which would make the question moot.
See:
ORDER BY the IN value list
PostgreSQL unnest() with element number
#chris Kao, use Position in postgresql.
Approach : 1
SELECT id, name FROM my_table
WHERE id in (1,3,5,2,4,6)
order by position(id::text in '1,3,5,2,4,6')
output:
id|name|
--+----+
1|a |
3|c |
5|e |
2|b |
4|d |
6|f |
Aprroach : 2
select id, name
from my_table mt
where id in (1,3,5,2,4,6)
order by array_position(array[1,3,5,2,4,6], mt.id);
I am trying to alphabetically sort each value in a column with Snowflake. For example I have:
| NAME |
| ---- |
| abc |
| bca |
| acb |
and want
| NAME |
| ---- |
| abc |
| abc |
| abc |
how would I go about doing that? I've tried using SPLIT and the ordering the rows, but that doesn't seem to work without a specific delimiter.
Using REGEXP_REPLACE to introduce separator between each character, STRTOK_SPLIT_TO_TABLE to get individual letters as rows and LISTAGG to combine again as sorted string:
SELECT tab.col, LISTAGG(s.value) WITHIN GROUP (ORDER BY s.value) AS result
FROM tab
, TABLE(STRTOK_SPLIT_TO_TABLE(REGEXP_REPLACE(tab.col, '(.)', '\\1~'), '~')) AS s
GROUP BY tab.col;
For sample data:
CREATE OR REPLACE TABLE tab
AS
SELECT 'abc' AS col UNION
SELECT 'bca' UNION
SELECT 'acb';
Output:
Similar implementation as Lukasz's, but using regexp_extract_all to extract individual characters in the form of an array that we later split to rows using flatten . The listagg then stitches it back in the order we specify in within group clause.
with cte (col) as
(select 'abc' union
select 'bca' union
select 'acb')
select col, listagg(b.value) within group (order by b.value) as col2
from cte, lateral flatten(regexp_extract_all(col,'.')) b
group by col;
I want to compare an array of values against the the rows of a table and return only the rows in which the data are different.
Suppose I have myTable:
| ItemCode | ItemName | FrgnName |
|----------|----------|----------|
| CD1 | Apple | Mela |
| CD2 | Mirror | Specchio |
| CD3 | Bag | Borsa |
Now using the SQL instruction IN I would like to compare the rows above against an array of values pasted from an excel file and so in theory I would have to write something like:
WHERE NOT IN (
ARRAY[CD1, Apple, Mella],
ARRAY[CD2, Miror, Specchio],
ARRAY[CD3, Bag, Borsa]
)
The QUERY should return rows 1 and 2 "MELLA" and "MIROR" are in fact typos.
You could use a VALUES expression to emulate a table of those arrays, like so:
... myTable AS t
LEFT JOIN (
VALUES (1, 'CD1','Apple','Mella')
, (1, 'CD2', 'Miror', 'Specchio')
, (1, 'CD3', 'Bag', 'Borsa')
) AS v(rowPresence, a, b, c)
ON t.ItemCode = v.a AND t.ItemName = v.b AND t.FrgnName = v.c
WHERE v.rowPresence IS NULL
Technically, in your scenario, you can do without the "rowPresence" field I added, since none of the values in your arrays are NULL any would do; I basically added it to point to a more general case.
I have the following table and want to merge multiple rows based on neighboring rows.
INPUT
EXPECTED OUTPUT
The logic is that since "abc" is connected to "abcd" in the first row and "abcd" is connected to "abcde" in the second row and so on, thus "abc", "abcd", "abcde", "abcdef" are connected and put in one array. The same applied to the rest rows. The number of connected neighboring rows are arbitrary.
The question is how to do that using Hive script without any UDF. Do I have to use Spark for this type of operation? Thanks very much.
One idea I had is to tag rows first as
How to do that using Hive script only?
This is an example of a CONNECT BY query which is not supported in HIVE or SPARK, unlike DB2 or ORACLE, et al.
You can simulate such a query with Spark Scala, but it is far from handy. Putting a tag in means the question is less relevant then, imo.
Here is a work-around using Hive script to get the intermediate table.
drop table if exists step1;
create table step1 STORED as orc as
with src as
(
select split(u.tmp,",")[0] as node_1, split(u.tmp,",")[1] as node_2
from
(select stack (7,
"abc,abcd",
"abcd,abcde",
"abcde,abcdef",
"bcd,bcde",
"bcde,bcdef",
"cdef,cdefg",
"def,defg"
) as tmp
) u
)
select node_1, node_2, if(node_2 = lead(node_1, 1) over (order by node_1), 1, 0) as tag, row_number() OVER (order by node_1) as row_num
from src;
drop table if exists step2;
create table step2 STORED as orc as
SELECT tag, row_number() over (ORDER BY tag) as row_num
FROM (
SELECT cast(v.tag as int) as tag
FROM (
SELECT
split(regexp_replace(repeat(concat(cast(key as string), ","), end_idx-start_idx), ",$",""), ",") as tags --repeat the row number by the number of rows
FROM (
SELECT COALESCE(lag(row_num, 1) over(ORDER BY row_num), 0) as start_idx, row_num as end_idx, row_number() over (ORDER BY row_num) as key
FROM step1 where tag=0
) a
) b
LATERAL VIEW explode(tags) v as tag
) c ;
drop table if exists step3;
create table step3 STORED as orc as
SELECT
a.node_1, a.node_2, b.tag
FROM step1 a
JOIN step2 b
ON a.row_num=b.row_num;
The final table looks like
select * from step3;
+---------------+---------------+------------+
| step3.node_1 | step3.node_2 | step3.tag |
+---------------+---------------+------------+
| abc | abcd | 1 |
| abcd | abcde | 1 |
| abcde | abcdef | 1 |
| bcd | bcde | 2 |
| bcde | bcdef | 2 |
| cdef | cdefg | 3 |
| def | defg | 4 |
+---------------+---------------+------------+
The third column can be used to collect node pairs.
I have the following query:
select column_name, count(column_name)
from table
group by column_name
having count(column_name) > 1;
What would be the difference if I replaced all calls to count(column_name) to count(*)?
This question was inspired by How do I find duplicate values in a table in Oracle?.
To clarify the accepted answer (and maybe my question), replacing count(column_name) with count(*) would return an extra row in the result that contains a null and the count of null values in the column.
count(*) counts NULLs and count(column) does not
[edit] added this code so that people can run it
create table #bla(id int,id2 int)
insert #bla values(null,null)
insert #bla values(1,null)
insert #bla values(null,1)
insert #bla values(1,null)
insert #bla values(null,1)
insert #bla values(1,null)
insert #bla values(null,null)
select count(*),count(id),count(id2)
from #bla
results
7 3 2
Another minor difference, between using * and a specific column, is that in the column case you can add the keyword DISTINCT, and restrict the count to distinct values:
select column_a, count(distinct column_b)
from table
group by column_a
having count(distinct column_b) > 1;
A further and perhaps subtle difference is that in some database implementations the count(*) is computed by looking at the indexes on the table in question rather than the actual data rows. Since no specific column is specified, there is no need to bother with the actual rows and their values (as there would be if you counted a specific column). Allowing the database to use the index data can be significantly faster than making it count "real" rows.
The explanation in the docs, helps to explain this:
COUNT(*) returns the number of items in a group, including NULL values and duplicates.
COUNT(expression) evaluates expression for each row in a group and returns the number of nonnull values.
So count(*) includes nulls, the other method doesn't.
We can use the Stack Exchange Data Explorer to illustrate the difference with a simple query. The Users table in Stack Overflow's database has columns that are often left blank, like the user's Website URL.
-- count(column_name) vs. count(*)
-- Illustrates the difference between counting a column
-- that can hold null values, a 'not null' column, and count(*)
select count(WebsiteUrl), count(Id), count(*) from Users
If you run the query above in the Data Explorer, you'll see that the count is the same for count(Id) and count(*)because the Id column doesn't allow null values. The WebsiteUrl count is much lower, though, because that column allows null.
The COUNT(*) sentence indicates SQL Server to return all the rows from a table, including NULLs.
COUNT(column_name) just retrieves the rows having a non-null value on the rows.
Please see following code for test executions SQL Server 2008:
-- Variable table
DECLARE #Table TABLE
(
CustomerId int NULL
, Name nvarchar(50) NULL
)
-- Insert some records for tests
INSERT INTO #Table VALUES( NULL, 'Pedro')
INSERT INTO #Table VALUES( 1, 'Juan')
INSERT INTO #Table VALUES( 2, 'Pablo')
INSERT INTO #Table VALUES( 3, 'Marcelo')
INSERT INTO #Table VALUES( NULL, 'Leonardo')
INSERT INTO #Table VALUES( 4, 'Ignacio')
-- Get all the collumns by indicating *
SELECT COUNT(*) AS 'AllRowsCount'
FROM #Table
-- Get only content columns ( exluce NULLs )
SELECT COUNT(CustomerId) AS 'OnlyNotNullCounts'
FROM #Table
COUNT(*) – Returns the total number of records in a table (Including NULL valued records).
COUNT(Column Name) – Returns the total number of Non-NULL records. It means that, it ignores counting NULL valued records in that particular column.
Basically the COUNT(*) function return all the rows from a table whereas COUNT(COLUMN_NAME) does not; that is it excludes null values which everyone here have also answered here.
But the most interesting part is to make queries and database optimized it is better to use COUNT(*) unless doing multiple counts or a complex query rather than COUNT(COLUMN_NAME). Otherwise, it will really lower your DB performance while dealing with a huge number of data.
Further elaborating upon the answer given by #SQLMeance and #Brannon making use of GROUP BY clause which has been mentioned by OP but not present in answer by #SQLMenace
CREATE TABLE table1 (
id INT
);
INSERT INTO table1 VALUES
(1),
(2),
(NULL),
(2),
(NULL),
(3),
(1),
(4),
(NULL),
(2);
SELECT * FROM table1;
+------+
| id |
+------+
| 1 |
| 2 |
| NULL |
| 2 |
| NULL |
| 3 |
| 1 |
| 4 |
| NULL |
| 2 |
+------+
10 rows in set (0.00 sec)
SELECT id, COUNT(*) FROM table1 GROUP BY id;
+------+----------+
| id | COUNT(*) |
+------+----------+
| 1 | 2 |
| 2 | 3 |
| NULL | 3 |
| 3 | 1 |
| 4 | 1 |
+------+----------+
5 rows in set (0.00 sec)
Here, COUNT(*) counts the number of occurrences of each type of id including NULL
SELECT id, COUNT(id) FROM table1 GROUP BY id;
+------+-----------+
| id | COUNT(id) |
+------+-----------+
| 1 | 2 |
| 2 | 3 |
| NULL | 0 |
| 3 | 1 |
| 4 | 1 |
+------+-----------+
5 rows in set (0.00 sec)
Here, COUNT(id) counts the number of occurrences of each type of id but does not count the number of occurrences of NULL
SELECT id, COUNT(DISTINCT id) FROM table1 GROUP BY id;
+------+--------------------+
| id | COUNT(DISTINCT id) |
+------+--------------------+
| NULL | 0 |
| 1 | 1 |
| 2 | 1 |
| 3 | 1 |
| 4 | 1 |
+------+--------------------+
5 rows in set (0.00 sec)
Here, COUNT(DISTINCT id) counts the number of occurrences of each type of id only once (does not count duplicates) and also does not count the number of occurrences of NULL
It is best to use
Count(1) in place of column name or *
to count the number of rows in a table, it is faster than any format because it never go to check the column name into table exists or not
There is no difference if one column is fix in your table, if you want to use more than one column than you have to specify that how much columns you required to count......
Thanks,
As mentioned in the previous answers, Count(*) counts even the NULL columns, whereas count(Columnname) counts only if the column has values.
It's always best practice to avoid * (Select *, count *, …)