I have a database that stores a bunch of custom fields using hstore
. In order to merge it into another database that doesn't support hstore
, I'd like to split the keys into extra columns.
Users can add new custom fields and so I can't rely on knowledge of the keys ahead of time. Which makes the answer at "Attributes from an hstore-column as separate columns in a view?" not applicable to my problem.
Where a record doesn't have a key present in other records, it should get the same column with a null value.
How do I do this?
4 Answers 4
This can be done, very efficiently, too. Not in a single statement, though, since SQL demands to know the return type at call time. So you need two steps. The solution involves a number of advanced techniques ...
Assuming the same table as @Denver in his answer:
CREATE TABLE hstore_test (
id serial PRIMARY KEY
, hstore_col hstore
);
Solution 1: Simple SELECT
After I wrote the crosstab solution below it struck me that a simple "brute force" solution is probably faster. Basically, the query @Denver already posted, built dynamically:
Step 1a: Generate query
SELECT format(
'SELECT id, h->%s
FROM (SELECT id, hstore_col AS h FROM hstore_test) t;'
, string_agg(quote_literal(key) || ' AS ' || quote_ident(key), ', h->')
) AS sql
FROM (
SELECT DISTINCT key
FROM hstore_test, skeys(hstore_col) key
ORDER BY 1
) sub;
The subquery (SELECT id, hstore_col AS h FROM hstore_test)
is just to get in the column alias h
for your hstore
column.
Step 1b: Execute query
This generates a query of the form:
SELECT id, h->'key1' AS key1, h->'key2' AS key2, h->'key3' AS key3
FROM (SELECT id, hstore_col AS h FROM hstore_test) t;
Result:
id | key1 | key2 | key3
----+-------+-------+-------
1 | val11 | val12 | val13
2 | val21 | val22 |
3 | | | -- for a row where hstore_col IS NULL
Solution 2: crosstab()
For lots of keys this may perform better. Probably not. You'll have to test. Result is the same as for solution 1.
You need the additional extension tablefunc
which provides the crosstab()
function. Read this first if you are not familiar:
Step 2a: Generate query
SELECT format(
$s$SELECT * FROM crosstab(
$$SELECT h.id, kv.*
FROM hstore_test h, each(hstore_col) kv
ORDER BY 1, 2$$
, $$SELECT unnest(%L::text[])$$
) AS t(id int, %s text);
$s$
, array_agg(key) -- escapes strings automatically
, string_agg(quote_ident(key), ' text, ') -- needs escaping!
) AS sql
FROM (
SELECT DISTINCT key
FROM hstore_test, skeys(hstore_col) key
ORDER BY 1
) sub;
Note the nested levels of dollar-quoting.
I use this explicit form in the main query instead of the short CROSS JOIN
in the auxiliary query to preserve rows with empty or NULL hstore
values:
LEFT JOIN LATERAL each(hstore_col) kv ON TRUE
Related:
Step 2b: Execute query
This generates a query of the form:
SELECT * FROM crosstab(
$$SELECT h.id, kv.*
FROM hstore_test h
LEFT JOIN LATERAL each(hstore_col) kv ON TRUE
ORDER BY 1, 2$$
, $$SELECT unnest('{key1,key2,key3}'::text[])$$
) AS t(id int, key1 text, key2 text, key3 text);
You may want to inspect it for plausibility before running the first time. This should deliver optimized performance.
Notes
Both solutions work for any number of keys up to the physical limit of ~ 1600 columns in a Postgres table.
Both also work for keys of any shape or form up to the maximum length for identifiers, which is 63 bytes per default.
Besides the hstore function
each()
that was already mentioned by s.m., I also use the related functionskeys()
to identify keys.Be sure to quote column names correctly to avoid possible SQL injection attacks by way of maliciously formed key names. I take care of that with
quote_literal()
andquote_ident()
.
-
1Thanks again! If anyone's interested, the cost for my use case for solution 1 was
Planning time: 0.615 ms
,Execution time: 2474.708 ms
to generate the query. AndPlanning time: 0.534 ms
,Execution time: 3956.939 ms
to run it. And solution 2:Planning time: 5.079 ms
,Execution time: 2420.472 ms
to generate the query andPlanning time: 0.034 ms
,Execution time: 4009.264 ms
to run it. So not a lot of difference between the two for a table with 1255409 rows and 27 unique keys in the hstore_col.coagmano– coagmano2015年12月07日 23:46:52 +00:00Commented Dec 7, 2015 at 23:46
I realize I'm a bit late—and by now you sure have it figured out—but, seeing the comment you left on Denver Timothy's answer, I thought I would leave an answer for everybody else:
select (each(hstore_col)).key from hstore_test;
This will create a row for each key
contained in hstore_col
, so you won't need to know what the keys are beforehand.
You'll want to use the ->
operator on the column (see here).
Records without the same key in other records will show as NULL.
create table hstore_test (id serial, hstore_col hstore);
insert into hstore_test (hstore_col) values ('key1=>val11, key2=>val12, key3=>val13'), ('key1=>val21, key2=>val22');
select hstore_col->'key1' as key1, hstore_col->'key2' as key2, hstore_col->'key3' as key3 from hstore_test;
┌───────┬───────┬───────┐
│ key1 │ key2 │ key3 │
├───────┼───────┼───────┤
│ val11 │ val12 │ val13 │
│ val21 │ val22 │ NULL │
└───────┴───────┴───────┘
(2 rows)
Here is a similar answer.
Based on s.m.'s answer it seems you can combine EACH()
with both .key
and .value
to generate columns automatically.
Setup test table like in Ian Timothy's answer:
create table hstore_test (id serial, hstore_col hstore);
insert into hstore_test (hstore_col) values ('key1=>val11, key2=>val12, key3=>val13'), ('key1=>val21, key2=>val22');
Query
SELECT id, (EACH(hstore_col)).key, (EACH(hstore_col)).value FROM hstore_test;
which returns
id | key | value
----+------+-------
1 | key1 | val11
1 | key2 | val12
1 | key3 | val13
2 | key1 | val21
2 | key2 | val22
I was a bit unsure whether two EACH()
inside the same query are guaranteed to produce the same order. I found an example in the hstore documentation using the same mechanism so I guess it's explicitly supported.
-
Thanks! This is a good addition. I can't remember why I selected Erwin's answer over s.m.'s but it might have been because of this? It looks much simpler anywaycoagmano– coagmano2020年04月22日 05:08:15 +00:00Commented Apr 22, 2020 at 5:08
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