Related
I have a somewhat complicated SUPER array that I brought in to Redshift using a REST API. The 'API_table' currently looks like this:table example
One of the sample columns "values" reads as follows:
values
[{"value":[{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"},,{"value":"6.8","qualifiers":["P"],"dateTime":"2023-01-30T20:00:00.000-05:00"},...
I've queried the "value" data using:
SELECT c.values[0].value[0].value as v
FROM API_table c;
However, this only returns the first value "6.9" in each row and not all the "value" items in the row. The same approach doesn't work for extracting the "dateTime" items as it produced NULL values:
SELECT c.values[0].value[0].dateTime as dt
FROM API_table c;
The above example only resembles one row of the table. My question is-- are there ways to query the data in every row of the table so that all the values ("value" & "dateTime") of every row can be extracted onto a new table?
The desired result is:
v
dt
6.9
2023-01-30T17:45:00.000-05:00
6.9
2023-01-30T18:00:00.000-05:00
6.9
2023-01-30T18:15:00.000-05:00
Many thanks.
I tried the following query but it only returned singular "value' results for each row.
SELECT c.values[0].value[0].value as v
FROM API_table c;
When applied to the "dateTime" items, it yielded NULL values:
SELECT c.values[0].value[0].dateTime as dt
FROM API_table c;
===================================================================
#BillWeiner thanks, I worked through both the CTE and test case examples and got the desired results (especially with CTE). The only issue that remains is knowing how to select the original table/column that contains the entire super array so that it can be inserted into test1 (or col1 in the CTE case).
There are super arrays in every row of column 'values' so the issue remains in selecting the column 'values' and extracting each of the multiple value ("6.9") and dateTime objects from each row.
================================================================
I've managed to get the query going when the json strings are explicitly stated in the insert into test1 values query.
Now I'm running this query:
SET enable_case_sensitive_identifier TO true;
create table test1 (jvalues varchar(2048));
insert into test1 select c.values from ph_api c;
create table test2 as select json_parse(jvalues) as svalues from test1;
with recursive numbers(n) as
( select 0 as n
union all
select n + 1
from numbers n
where n.n < 20
),
exp_top as
( select c.svalues[n].value
from test2 c
cross join numbers n
)
,
exp_bot as
( select c.value[n]
from exp_top c
cross join numbers n
where c.value is not null
)
select *, value.value as v, value."dateTime" as dt
from exp_bot
where value is not null;
However, I'm getting an error--ERROR: column "jvalues" is of type character varying but expression is of type super Hint: You will need to rewrite or cast the expression. when I try to insert the source table with insert into test1 SELECT c.values from table c;
I would like to be able to SELECT this source data:
sourceinfo
variable
values
{"siteName":"YAN","siteCode":[{"value":"01"}]
{“variableCode":[{"value":"00600","network":"ID"}
[{“value":[{"value":"3.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"4.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"}]
{"siteName":"YAN","siteCode":[{"value":"01"}]
{“variableCode":[{"value":"00600","network":"ID"}
[{“value":[{"value":"5.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"}]
as the jvalues so that it could be unrolled into a desired result of:
v
dt
3.9
2023-01-30T17:30:00.000-05:00
4.9
2023-01-30T17:45:00.000-05:00
5.9
2023-01-30T18:00:00.000-05:00
6.9
2023-01-30T18:15:00.000-05:00
================================================================
The following query worked to select the desired json strings:
with exp_top as
( select s.value
from <source_table> c, c.values s
)
select s.value, s."dateTime" from exp_top c, c.value s;
Yes. You need to expand each array element into its own row. A recursive CTE (or something similar) will be needed to expand the arrays into rows. This can be done based on the max array length in the super or with some fixed set of numbers. This set of numbers will need to be crossed joined with your table to extract each array element.
I wrote up a similar answer previously - Extract value based on specific key from array of jsons in Amazon Redshift - take a look and see if this gets you unstuck. Let me know if you need help adapting this to your situation.
==============================================================
Based on the comments it looks like a more specific example is needed. This little test case should help you understand what is needed to make this work.
I've repeated your data a few times to create multiple rows and to populate the outer array with 2 inner arrays. This hopefully show how to unroll multiple nested arrays manually (the compact Redshift unrolling method is below but hard to understand if you don't get the concepts down first).
First set up the test data:
SET enable_case_sensitive_identifier TO true;
create table test1 (jvalues varchar(2048));
insert into test1 values
('[{"value":[{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:45:00.000-05:00"},{"value":"6.8","qualifiers":["P"],"dateTime":"2023-01-30T20:00:00.000-05:00"}]}, {"value":[{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:45:00.000-05:00"},{"value":"6.8","qualifiers":["P"],"dateTime":"2023-01-30T20:00:00.000-05:00"}]}]'),
('[{"value":[{"value":"5.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"5.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"},{"value":"8.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:45:00.000-05:00"},{"value":"6.8","qualifiers":["P"],"dateTime":"2023-01-30T20:00:00.000-05:00"}]}, {"value":[{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T17:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T18:45:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:00:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:15:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:30:00.000-05:00"},{"value":"6.9","qualifiers":["P"],"dateTime":"2023-01-30T19:45:00.000-05:00"},{"value":"6.8","qualifiers":["P"],"dateTime":"2023-01-30T20:00:00.000-05:00"}]}]');
create table test2 as select json_parse(jvalues) as svalues from test1;
Note that we have to turn on case sensitivity for the session to be able to select "dateTime" correctly.
Then unroll the arrays manually:
with recursive numbers(n) as
( select 0 as n
union all
select n + 1
from numbers n
where n.n < 20
),
exp_top as
( select row_number() over () as r, n as x, c.svalues[n].value
from test2 c
cross join numbers n
)
,
exp_bot as
( select r, x, n as y, c.value[n]
from exp_top c
cross join numbers n
where c.value is not null
)
select *, value.value as v, value."dateTime" as dt
from exp_bot
where value is not null;
This version
creates the numbers 0 - 19,
expands the outer array (2 elements in each row) by cross joining
with these numbers,
expands the inner array by the same method,
produces the desired results
Redshift has a built in method for doing this unrolling of super arrays and it is defined in the FROM clause. You can produce the same results from:
with exp_top as (select inx1, s.value from test2 c, c.svalues s at inx1)
select inx1, inx2, c.value[inx2] as value, s.value, s."dateTime" from exp_top c, c.value s at inx2;
Much more compact. This code has been tested and runs as is in Redshift. If you see the "dateTime" value as NULL it is likely that you don't have case sensitivity enabled.
==========================================================
To also have the original super column in the final result:
with exp_top as (select c.svalues, inx1, s.value from test2 c, c.svalues s at inx1)
select svalues, inx1, inx2, c.value[inx2] as value, s.value, s."dateTime" from exp_top c, c.value s at inx2;
==========================================================
I think that unrolling your actual data will be simpler than the code I provided for the general question.
First you don't need to use the test1 and test2 tables, you can query your table directly. If you still want to use test2 then use your table as the source of the "create table test2 ..." statement. But let's see if we can just use your source table.
with exp_top as (
select s.value from <your table> c, c.values s
)
select s.value, s."dateTime" from exp_top c, c.value s;
This code is untested but should work.
First post, hope I don't do anything too crazy
I want to go from JSON/object to long in terms of formatting.
I have a table set up as follows (note: there will be a large but finite number of 50+ activity columns, 2 is a minimal working example). I'm not concerned about the formatting of the date column - different problem.
customer_id(varcahr), activity_count(object, int), activity_duration(object, numeric)
sample starting point
In this case I'd like to explode this into this:
customer_id(varcahr), time_period, activity_count(int), activity_duration(numeric)
sample end point - long
minimum data set
WITH smpl AS (
SELECT
'12a' AS id,
OBJECT_CONSTRUCT(
'd1910', 0,
'd1911', 26,
'd1912', 6,
'd2001', 73) as activity_count,
OBJECT_CONSTRUCT(
'd1910', 0,
'd1911', 260.1,
'd1912', 30,
'd2001', 712.3) AS activity_duration
UNION ALL
SELECT
'13b' AS id,
OBJECT_CONSTRUCT(
'd1910', 1,
'd1911', 2,
'd1912', 3,
'd2001', 4) as activity_count,
OBJECT_CONSTRUCT(
'd1910', 1,
'd1911', 2.2,
'd1912', 3.3,
'd2001', 4.3) AS activity_duration
)
select * from smpl
Extra credit for also taking this from JSON/object to wide (in Google Big Query it's SELECT id, activity_count.* FROM tbl
Thanks in advance.
I've tried tons of random FLATTEN() based joins. In this instance I probably just need one working example.
This needs to scale to a moderate but finite number of objects (e.g. 50)
I'll also see if I can combine with THIS - I'll see if I can combine it - Lateral flatten two columns without repetition in snowflake
Using FLATTEN:
WITH (...)
SELECT s1.ID, s1.KEY, s1.value AS activity_count, s2.value AS activity_duration
FROM (select ID, Key, VALUE from smpl,table(flatten(input=>activity_count))) AS s1
JOIN (select ID, Key, VALUE from smpl,table(flatten(input=>activity_duration))) AS s2
ON S1.ID = S2.ID AND S1.KEY = S2.KEY;
Output:
#Lukasz Szozda gets close but the answer doesn't scale as well with multiple variables (it's essentially a bunch of cartesian products and I'd need to do a lot of ON conditions). I have a known constraint (each field is in a strict format) so it's easy to recycle the key.
After WAY WAY WAY too much messing with this (off and on searches for weeks) it finally snapped and it's pretty easy.
SELECT
id, key, activity_count[key], activity_duration[key], activity_duration2[key]
FROM smpl, LATERAL flatten(input => activity_count);
You can also use things OTHER than key such as index
It's inspired by THIS link but I just didn't quite follow it.
https://stackoverflow.com/a/36804637/20994650
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
I'm looking to take a String-Array field in Google SQL and transpose it to show all in one column. From there I can take all unique/distinct items from it.
the image above is a sample of what I am trying
I can't get the string array to split out into resulting rows.
Any help or suggestions would be greatly appreciated
I think you can do it using unnest, assuming columnB is holding the array:
select numbers
from yourtable t
cross join unnest(t.ColumnB) numbers
and for distinct :
select distinct numbers
from yourtable t
cross join unnest(t.ColumnB) numbers
Adding this as answer (as it is too long for comments) - just to point that usually users using too verbose syntax with unnest function. For example - instead of using unnest(t.ColumnB) one can use either unnest(ColumnB) or just t.ColumnB as in examples below
select number
from your_table t, t.ColumnB number
and
select distinct number
from your_table t, t.ColumnB number
I personally prefer this shortcut version of using unnest - so wanted to share - while obviously this is a personal preferences type of things
Is the UNNEST an example of a table-function? It seems to produce a single named column if I'm understanding it correctly. Something like:
`vals`
[1,2,3]
unnest(vals) as v
`v`
1
2
3
with Table as (
select [1,2,3] vals
) select v from Table, UNNEST(vals) as v
Is this an example of a table-function? If not, what kind of function is it? Are there any other predefined table functions in BQ?
The UNNEST operator takes an ARRAY and returns a table, with one row for each element in the ARRAY. You can also use UNNEST outside of the FROM clause with the IN operator.
So, you might may call it table function if you wish :o)
You can read more about UNNEST here
It seems to produce a single named column if I'm understanding it correctly
Not exactly correct. See example below
with Table as (
select [struct(1 as a,2 as b),struct(3, 4), struct(5, 6)] vals
)
select v.* from Table, UNNEST(vals) as v
with output