This might be a novice question – I'm still learning. I'm on PostgreSQL 9.6 with the following query:
SELECT locales, count(locales) FROM (
SELECT lower((regexp_matches(locale, '([a-z]{2,3}(-[a-z]{2,3})?)', 'i'))[1])
AS locales FROM users)
AS _ GROUP BY locales
My query returns the following dynamic rows:
locales
count
en
10
fr
7
de
3
n additional locales (~300)...
n-count
I'm trying to rotate it so that locale values end up as columns with a single row, like this:
en
fr
de
n additional locales (~300)...
10
7
3
n-count
I'm having to do this to play nice with a time-series db/app
I've tried using crosstab(), but all the examples show better defined tables with 3 or more columns.
I've looked at examples using join, but I can't figure out how to do it dynamically.
Base query
In Postgres 10 or later you could use the simpler and faster regexp_match() instead of regexp_matches(). (Since you only take the first match per row anyway.) But don't bother and use the even simpler substring() instead:
SELECT lower(substring(locale, '(?i)[a-z]{2,3}(?:-[a-z]{2,3})?')) AS locale
, count(*)::int AS ct
FROM users
WHERE locale ~* '[a-z]{2,3}' -- eliminate NULL, allow index support
GROUP BY 1
ORDER BY 2 DESC, 1
Simpler and faster than your original base query.
About those ordinal numbers in GROUP BY and ORDER BY:
Select first row in each GROUP BY group?
Subtle difference: regexp_matches() returns no row for no match, while substring() returns null. I added a WHERE clause to eliminate non-matches a-priori - and allow index support if applicable, but I don't expect indexes to help here.
Note the prefixed (?i), that's a so-called "embedded option" to use case-insensitive matching.
Added a deterministic ORDER BY clause. You'd need that for a simple crosstab().
Aside: you might need _ in the pattern instead of - for locales like "en_US".
Pivot
Try as you might, SQL does not allow dynamic result columns in a single query. You need two round trips to the server. See;
How do I generate a pivoted CROSS JOIN where the resulting table definition is unknown?
You can use a dynamically generated crosstab() query. Basics:
PostgreSQL Crosstab Query
Dynamic query:
PostgreSQL convert columns to rows? Transpose?
But since you generate a single row of plain integer values, I suggest a simple approach:
SELECT 'SELECT ' || string_agg(ct || ' AS ' || quote_ident(locale), ', ')
FROM (
SELECT lower(substring(locale, '(?i)[a-z]{2,3}(?:-[a-z]{2,3})?')) AS locale
, count(*)::int AS ct
FROM users
WHERE locale ~* '[a-z]{2,3}'
GROUP BY 1
ORDER BY 2 DESC, 1
) t
Generates a query of the form:
SELECT 10 AS en, 7 AS fr, 3 AS de, 3 AS "de-at"
Execute it to produce your desired result.
In psql you can append \gexec to the generating query to feed the generated SQL string back to the server immediately. See:
My function returned a string. How to execute it?
Related
I have a table with a field A where each entry is a fixed length array A of integers (say length=1000). I want to know how to convert it into 1000 columns, with column name given by index_i, for i=0,1,2,...,999, and each element is the corresponding integer. I can have it done by something like
A[OFFSET(0)] as index_0,
A[OFFSET(1)] as index_1
A[OFFSET(2)] as index_2,
A[OFFSET(3)] as index_3,
A[OFFSET(4)] as index_4,
...
A[OFFSET(999)] as index_999,
I want to know what would be an elegant way of doing this. thanks!
The first thing to say is that, sadly, this is going to be much more complicated than most people expect. It can be conceptually easier to pass the values into a scripting language (e.g. Python) and work there, but clearly keeping things inside BigQuery is going to be much more performant. So here is an approach.
Cross-joining to turn array fields into long-format tables
I think the first thing you're going to want to do is get the values out of the arrays and into rows.
Typically in BigQuery this is accomplished using CROSS JOIN. The syntax is a tad unintuitive:
WITH raw AS (
SELECT "A" AS name, [1,2,3,4,5] AS a
UNION ALL
SELECT "B" AS name, [5,4,3,2,1] AS a
),
long_format AS (
SELECT name, vals
FROM raw
CROSS JOIN UNNEST(raw.a) AS vals
)
SELECT * FROM long_format
UNNEST(raw.a) is taking those arrays of values and turning each array into a set of (five) rows, every single one of which is then joined to the corresponding value of name (the definition of a CROSS JOIN). In this way we can 'unwrap' a table with an array field.
This will yields results like
name | vals
-------------
A | 1
A | 2
A | 3
A | 4
A | 5
B | 5
B | 4
B | 3
B | 2
B | 1
Confusingly, there is a shorthand for this syntax in which CROSS JOIN is replaced with a simple comma:
WITH raw AS (
SELECT "A" AS name, [1,2,3,4,5] AS a
UNION ALL
SELECT "B" AS name, [5,4,3,2,1] AS a
),
long_format AS (
SELECT name, vals
FROM raw, UNNEST(raw.a) AS vals
)
SELECT * FROM long_format
This is more compact but may be confusing if you haven't seen it before.
Typically this is where we stop. We have a long-format table, created without any requirement that the original arrays all had the same length. What you're asking for is harder to produce - you want a wide-format table containing the same information (relying on the fact that each array was the same length.
Pivot tables in BigQuery
The good news is that BigQuery now has a PIVOT function! That makes this kind of operation possible, albeit non-trivial:
WITH raw AS (
SELECT "A" AS name, [1,2,3,4,5] AS a
UNION ALL
SELECT "B" AS name, [5,4,3,2,1] AS a
),
long_format AS (
SELECT name, vals, offset
FROM raw, UNNEST(raw.a) AS vals WITH OFFSET
)
SELECT *
FROM long_format PIVOT(
ANY_VALUE(vals) AS vals
FOR offset IN (0,1,2,3,4)
)
This makes use of WITH OFFSET to generate an extra offset column (so that we know which order the values in the array originally had).
Also, in general pivoting requires us to aggregate the values returned in each cell. But here we expect exactly one value for each combination of name and offset, so we simply use the aggregation function ANY_VALUE, which non-deterministically selects a value from the group you're aggregating over. Since, in this case, each group has exactly one value, that's the value retrieved.
The query yields results like:
name vals_0 vals_1 vals_2 vals_3 vals_4
----------------------------------------------
A 1 2 3 4 5
B 5 4 3 2 1
This is starting to look pretty good, but we have a fundamental issue, in that the column names are still hard-coded. You wanted them generated dynamically.
Unfortunately expressions for the pivot column values aren't something PIVOT can accept out-of-the-box. Note that BigQuery has no way to know that your long-format table will resolve neatly to a fixed number of columns (it relies on offset having the values 0-4 for each and every set of records).
Dynamically building/executing the pivot
And yet, there is a way. We will have to leave behind the comfort of standard SQL and move into the realm of BigQuery Procedural Language.
What we must do is use the expression EXECUTE IMMEDIATE, which allows us to dynamically construct and execute a standard SQL query!
(as an aside, I bet you - OP or future searchers - weren't expecting this rabbit hole...)
This is, of course, inelegant to say the least. But here is the above toy example, implemented using EXECUTE IMMEDIATE. The trick is that the executed query is defined as a string, so we just have to use an expression to inject the full range of values you want into this string.
Recall that || can be used as a string concatenation operator.
EXECUTE IMMEDIATE """
WITH raw AS (
SELECT "A" AS name, [1,2,3,4,5] AS a
UNION ALL
SELECT "B" AS name, [5,4,3,2,1] AS a
),
long_format AS (
SELECT name, vals, offset
FROM raw, UNNEST(raw.a) AS vals WITH OFFSET
)
SELECT *
FROM long_format PIVOT(
ANY_VALUE(vals) AS vals
FOR offset IN ("""
|| (SELECT STRING_AGG(CAST(x AS STRING)) FROM UNNEST(GENERATE_ARRAY(0,4)) AS x)
|| """
)
)
"""
Ouch. I've tried to make that as readable as possible. Near the bottom there is an expression that generates the list of column suffices (pivoted values of offset):
(SELECT STRING_AGG(CAST(x AS STRING)) FROM UNNEST(GENERATE_ARRAY(0,4)) AS x)
This generates the string "0,1,2,3,4" which is then concatenated to give us ...FOR offset IN (0,1,2,3,4)... in our final query (as in the hard-coded example before).
REALLY dynamically executing the pivot
It hasn't escaped my notice that this is still technically insisting on your knowing up-front how long those arrays are! It's a big improvement (in the narrow sense of avoiding painful repetitive code) to use GENERATE_ARRAY(0,4), but it's not quite what was requested.
Unfortunately, I can't provide a working toy example, but I can tell you how to do it. You would simply replace the pivot values expression with
(SELECT STRING_AGG(DISTINCT CAST(offset AS STRING)) FROM long_format)
But doing this in the example above won't work, because long_format is a Common Table Expression that is only defined inside the EXECUTE IMMEDIATE block. The statement in that block won't be executed until after building it, so at build-time long_format has yet to be defined.
Yet all is not lost. This will work just fine:
SELECT *
FROM d.long_format PIVOT(
ANY_VALUE(vals) AS vals
FOR offset IN ("""
|| (SELECT STRING_AGG(DISTINCT CAST(offset AS STRING)) FROM d.long_format)
|| """
)
)
... provided you first define a BigQuery VIEW (for example) called long_format (or, better, some more expressive name) in a dataset d. That way, both the job that builds the query and the job that runs it will have access to the values.
If successful, you should see both jobs execute and succeed. You should then click 'VIEW RESULTS' on the job that ran the query.
As a final aside, this assumes you are working from the BigQuery console. If you're instead working from a scripting language, that gives you plenty of options to either load and manipulate the data, or build the query in your scripting language rather than massaging BigQuery into doing it for you.
Consider below approach
execute immediate ( select '''
select * except(id) from (
select to_json_string(A) id, * except(A)
from your_table, unnest(A) value with offset
)
pivot (any_value(value) index for offset in ('''
|| (select string_agg('' || val order by offset) from unnest(generate_array(0,999)) val with offset) || '))'
)
If to apply to dummy data like below (with 10 instead of 1000 elements)
select [10,11,12,13,14,15,16,17,18,19] as A union all
select [20,21,22,23,24,25,26,27,28,29] as A union all
select [30,31,32,33,34,35,36,37,38,39] as A
the output is
This is my column in Redshift
SHIPMENT_ID
-----------------------------------------
FBA15KS66741, FBA15KS6673D
FBA15NHV7PXX (Oct 20th)
FBA15XNW0SWY 27 balance 2 of 2
FBA15M575MDL & FBA15M59W1Y5
FBA15NHV7PXX (Oct 20th)
FBA15D7WPZVR /FBA15D7WWTPK/FBA15D7WW1GL
I would like to make it
SHIPMENT_ID
-----------------------------------------
FBA15KS66741, FBA15KS6673D
FBA15NHV7PXX
FBA15XNW0SWY
FBA15M575MDL, FBA15M59W1Y5
FBA15NHV7PXX
FBA15D7WPZVR, FBA15D7WWTPK, FBA15D7WW1GL
In SQL only, what is the best way to handle this?
This works in PostgreSQL, so may work in Redshift depending on feature availability in PG8.
WITH items AS
(
SELECT shipment_id,
ARRAY_TO_STRING(REGEXP_MATCHES(shipment_id,'FBA15[0-9a-zA-z]{7}','g'),'') AS unique_shipment_ids
FROM dat
)
SELECT shipment_id,
STRING_AGG(unique_shipment_ids,',') AS shipment_id_csv
FROM items
GROUP BY shipment_id;
I've assumed:
Each item begins with the characters 'FBA15'
There are exactly 7 characters after the first 5
You can edit the regexp pattern if my assumptions are incorrect.
The approach is:
Use REGEXP_MATCHES to capture each item within each row. This creates multiple rows per unique value in shipment_id
Use ARRAY_TO_STRING to convert those values to text, rather than text[]
Use STRING_AGG to join them back together with a comma separator
I found that I could not use STRING_AGG directly around REGEXP_MATCHES as I get the error aggregate function calls cannot contain set-returning function calls, so opted for a CTE. I assume a subquery would work as well.
I'm trying to figure out from a column type, which aggregates the data type supports. There's a lot of variety amongst types, just a sample below (some of these support more aggregates, of course):
uuid count()
text count(), min(), max()
integer count(), min, max(),avg(),sum()
I've been thrashing around in the system catalogs and views, but haven't found what I'm after. (See "thrashing around.") I've poked at pg_type, pg_aggregate, pg_operator, and a few more.
Is there a straightforward way to start from a column type and gather all supported aggregates?
For background, I'm writing a client-side cross-tab code generator, and the UX is better when the tool automatically prevents you from selecting an aggregation that's not supported. I've hacked in some hard-coded rules for now, but would like to improve the system.
We're on Postgres 11.4.
A plain list of available aggregate functions can be based on pg_proc like this:
SELECT oid::regprocedure::text AS agg_func_plus_args
FROM pg_proc
WHERE prokind = 'a'
ORDER BY 1;
Or with separate function name and arguments:
SELECT proname AS agg_func, pg_get_function_identity_arguments(oid) AS args
FROM pg_proc
WHERE prokind = 'a'
ORDER BY 1, 2;
pg_proc.prokind replaces proisagg in Postgres 11. In Postgres 10 or older use:
...
WHERE proisagg
...
Related:
How to drop all of my functions in PostgreSQL?
How to get function parameter lists (so I can drop a function)
To get a list of available functions for every data type (your question), start with:
SELECT type_id::regtype::text, array_agg(proname) AS agg_functions
FROM (
SELECT proname, unnest(proargtypes::regtype[])::text AS type_id
FROM pg_proc
WHERE proisagg
ORDER BY 2, 1
) sub
GROUP BY type_id;
db<>fiddle here
Just a start. Some of the arguments are just "direct" (non-aggregated) (That's also why some functions are listed multiple times - due to those additional non-aggregate columns, example string_agg). And there are special cases for "ordered-set" and "hypothetical-set" aggregates. See the columns aggkind and aggnumdirectargs of the additional system catalog pg_aggregate. (You may want to exclude the exotic special cases for starters ...)
And many types have an implicit cast to one of the types listed by the query. Prominent example string_agg() works with varchar, too, but it's only listed for text above. You can extend the query with information from pg_cast to get the full picture.
Plus, some aggregates work for pseudo types "any", anyarray etc. You'll want to factor those in for every applicable data type.
The complication of multiple aliases for the same data type names can be eliminated easily, though: cast to regtype to get canonical names. Or use pg_typeof() which returns standard names. Related:
Type conversion. What do I do with a PostgreSQL OID value in libpq in C?
PostgreSQL syntax error in parameterized query on "date $1"
How do I translate PostgreSQL OID using python
Man, that is just stunning Thank you. The heat death of the universe will arrive before I could have figured that out. I had to tweak one line for PG 11 compatibility...says the guy who did not say what version he was on. I've reworked the query to get close to what I'm after and included a bit of output for the archives.
with aggregates as (
SELECT pro.proname aggregate_name,
CASE
WHEN array_agg(typ.typname ORDER BY proarg.position) = '{NULL}'::name[] THEN
'{}'::name[]
ELSE
array_agg(typ.typname ORDER BY proarg.position)
END aggregate_types
FROM pg_proc pro
CROSS JOIN LATERAL unnest(pro.proargtypes) WITH ORDINALITY proarg (oid,
position)
LEFT JOIN pg_type typ
ON typ.oid = proarg.oid
WHERE pro. prokind = 'a' -- I needed this for PG 11, I didn't say what version I was using.
GROUP BY pro.oid,
pro.proname
ORDER BY pro.proname),
-- The *super helpful* code above is _way_ past my skill level with Postgres. So, thrashing around a bit to get close to what I'm after.
-- First up, a CTE to sort everything by aggregation and then combine the types.
aggregate_summary as (
select aggregate_name,
array_agg(aggregate_types) as types_array
from aggregates
group by 1
order by 1)
-- Finally, the previous CTE is used to get the details and a count of the types.
select aggregate_name,
cardinality(types_array) as types_count, -- Couldn't get array_length to work here. ¯\_(ツ)_/¯
types_array
from aggregate_summary
limit 5;
And a bit of output:
aggregate_name types_count types_array
array_agg 2 {{anynonarray},{anyarray}}
avg 7 {{int8},{int4},{int2},{numeric},{float4},{float8},{interval}}
bit_and 4 {{int2},{int4},{int8},{bit}}
bit_or 4 {{int2},{int4},{int8},{bit}}
bool_and 1 {{bool}}
Still on my wish list are
Figuring out how to execute arrays (we aren't using array fields now, and only have a few places that we ever might. At that point, I don't expect we'll try and support pivots on arrays. tab tool
Getting all of the aliases for the various types. it seems like (?) int8, etc. can come through from pg_attribute in multiple ways. For example, timestamptz can come back from "timestamp with time zone".
These results are going to be consumed by client-side code and processed, so I don't need to get Postgres to figure everything out in one query, just enough for me to get the job done.
In any case, thanks very, very much.
There's the pg_proc catalog table, that lists all functions. The column proisagg marks aggregation functions and the column proargtypes holds an array of the OIDs of the argument types.
So for example to get a list of all aggregation functions with the names of their arguments' type you could use:
SELECT pro.proname aggregationfunctionname,
CASE
WHEN array_agg(typ.typname ORDER BY proarg.position) = '{NULL}'::name[] THEN
'{}'::name[]
ELSE
array_agg(typ.typname ORDER BY proarg.position)
END aggregationfunctionargumenttypes
FROM pg_proc pro
CROSS JOIN LATERAL unnest(pro.proargtypes) WITH ORDINALITY proarg (oid,
position)
LEFT JOIN pg_type typ
ON typ.oid = proarg.oid
WHERE pro.proisagg
GROUP BY pro.oid,
pro.proname
ORDER BY pro.proname;
Of course you may need to extend that, e.g. joining and respecting the schemas (pg_namespace) and check for compatible types in pg_type (have a look at the typcategory column for that), etc..
Edit:
I overlooked, that proisagg was removed in version 11 (I'm still mostly on a 9.6) as the other answers mentioned. So for the sake of completeness: As of version 11 replace WHERE pro.proisagg with WHERE pro.prokind = 'a'.
I've been playing around with the suggestions a bit, and want to post one adaptation based on one of Erwin's scripts:
select type_id::regtype::text as type_name,
array_agg(proname) as aggregate_names
from (
select proname,
unnest(proargtypes::regtype[])::text AS type_id
from pg_proc
where prokind = 'a'
order by 2, 1
) subquery
where type_id in ('"any"', 'bigint', 'boolean','citext','date','double precision','integer','interval','numeric','smallint',
'text','time with time zone','time without time zone','timestamp with time zone','timestamp without time zone')
group by type_id;
That brings back details on the types specified in the where clause. Not only is this useful for my current work, it's useful to my understanding generally. I've run into cases where I've had to recast something, like an integer to a double, to get it to work with an aggregate. So far, this has been pretty much trial and error. If you run the query above (or one like it), it's easier to see from the output where you need recasting between similar seeming types.
I use below query to get content rows which has my_regex_pattern. But I don't know how many times the pattern hit for every row. What is the best way to get match count for every row in Postgres?
For example if a row's content is 'abcdefabcgh' and my regular expression is 'abc', I want 2 since 'abcdefabcgh' has two 'abc'.
SELECT content
FROM table1
WHERE content ~ 'my_regex_pattern'
Or how can I get rows which has matches more than a specific number. For example just give me records which has abc more than 4 times.
Of course you can make it work with regexp_matches(). Or better yet, regexp_split_to_table(). To apply to a whole table, use a LATERAL join (requires Postgres 9.3+):
SELECT content, ct
FROM table1 t, LATERAL (
SELECT count(*) - 1 AS ct
FROM regexp_split_to_table(t.content, 'abc')
) c
WHERE t.content ~ 'abc'; -- eliminate rows without match
For simple patterns like in the example in your question, you could also:
SELECT content, (length(content) - length(replace(content, 'abc', ''))) / length('abc')
FROM table1
WHERE content LIKE '%abc%';
Typically faster, since regular expression functions are costly. Also works for older versions.
I'm working with a database, where one of the fields I extract is something like:
1-117 3-134 3-133
Each of these number sets represents a different set of data in another table. Taking 1-117 as an example, 1 = equipment ID, and 117 = equipment settings.
I have another table from which I need to extract data based on the previous field. It has two columns that split equipment ID and settings. Essentially, I need a way to go from the queried column 1-117 and run a query to extract data from another table where 1 and 117 are two separate corresponding columns.
So, is there anyway to split this number to run this query?
Also, how would I split those three numbers (1-117 3-134 3-133) into three different query sets?
The tricky part here is that this column can have any number of sets here (such as 1-117 3-133 or 1-117 3-134 3-133 2-131).
I'm creating these queries in a stored procedure as part of a larger document to display the extracted data.
Thanks for any help.
Since you didn't provide the DB vendor, here's two posts that answer this question for SQL Server and Oracle respectively...
T-SQL: Opposite to string concatenation - how to split string into multiple records
Splitting comma separated string in a PL/SQL stored proc
And if you're using some other DBMS, go search for "splitting text ". I can almost guarantee you're not the first one to ask, and there's answers for every DBMS flavor out there.
As you said the format is constant though, you could also do something simpler using a SUBSTRING function.
EDIT in response to OP comment...
Since you're using SQL Server, and you said that these values are always in a consistent format, you can do something as simple as using SUBSTRING to get each part of the value and assign them to T-SQL variables, where you can then use them to do whatever you want, like using them in the predicate of a query.
Assuming that what you said is true about the format always being #-### (exactly 1 digit, a dash, and 3 digits) this is fairly easy.
WITH EquipmentSettings AS (
SELECT
S.*,
Convert(int, Substring(S.AwfulMultivalue, V.Value * 6 - 5, 1) EquipmentID,
Convert(int, Substring(S.AwfulMultivalue, V.Value * 6 - 3, 3) Settings
FROM
SourceTable S
INNER JOIN master.dbo.spt_values V
ON V.Value BETWEEN 1 AND Len(S.AwfulMultivalue) / 6
WHERE
V.type = 'P'
)
SELECT
E.Whatever,
D.Whatever
FROM
EquipmentSettings E
INNER JOIN DestinationTable D
ON E.EquipmentID = D.EquipmentID
AND E.Settings = D.Settings
In SQL Server 2005+ this query will support 1365 values in the string.
If the length of the digits can vary, then it's a little harder. Let me know.
Incase if the sets does not increase by more than 4 then you can use Parsename to retrieve the result
Declare #Num varchar(20)
Set #Num='1-117 3-134 3-133'
select parsename(replace (#Num,' ','.'),3)
Result :- 1-117
Now again use parsename on the same resultset
Select parsename(replace(parsename(replace (#Num,' ','.'),3),'-','.'),1)
Result :- 117
If the there are more than 4 values then use split functions