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I am looking for a solution to convert the table results to a JSON path.
I have a table with two columns as below. Column 1 Will always have normal values, but column 2 will have values up to 15 separated by ';' (semicolon).
ID Column1 Column2
--------------------------------------
1 T1 Re;BoRe;Va
I want to convert the above column data in to below JSON Format
{
"services":
[
{ "service": "T1"}
],
"additional_services":
[
{ "service": "Re" },
{ "service": "BoRe" },
{ "service": "Va" }
]
}
I have tried creating something like the below, but cannot get to the exact format that I am looking for
SELECT
REPLACE((SELECT d.Column1 AS services, d.column2 AS additional_services
FROM Table1 w (nolock)
INNER JOIN Table2 d (nolock) ON w.Id = d.Id
WHERE ID = 1
FOR JSON PATH), '\/', '/')
Please let me know if this is something we can achieve using T-SQL
As I mention in the comments, I strongly recommend you fix your design and normalise your design. Don't store delimited data in your database; Re;BoRe;Va should be 3 rows, not 1 delimited one. That doesn't mean you can't achieve what you want with your denormalised data, just that your design is flawed, and thus it needs being brought up.
One way to achieve what you're after is with some nested FOR JSON calls:
SELECT (SELECT V.Column1 AS service
FOR JSON PATH) AS services,
(SELECT SS.[value] AS service
FROM STRING_SPLIT(V.Column2,';') SS
FOR JSON PATH) AS additional_services
FROM (VALUES(1,'T1','Re;BoRe;Va'))V(ID,Column1,Column2)
FOR JSON PATH, WITHOUT_ARRAY_WRAPPER;
This results in the following JSON:
{
"services": [
{
"service": "T1"
}
],
"additional_services": [
{
"service": "Re"
},
{
"service": "BoRe"
},
{
"service": "Va"
}
]
}
I want to split strings into columns.
My columns should be:
account_id, resource_type, resource_name
I have a JSON file source that I have been trying to parse via ADF data flow. That hasn't worked for me, hence I flattened the data and brought it into SQL Server (I am open to parsing values via ADF or SQL if anyone can show me how). Please check the JSON file at the bottom.
Use this code to query the data I am working with.
CREATE TABLE test.test2
(
resource_type nvarchar(max) NULL
)
INSERT INTO test.test2 ([resource_type])
VALUES
('account_id:224526257458,resource_type:buckets,resource_name:camp-stage-artifactory'),
('account_id:535533456241,resource_type:buckets,resource_name:tni-prod-diva-backups'),
('account_id:369798452057,resource_type:buckets,resource_name:369798452057-s3-manifests'),
('account_id:460085747812,resource_type:buckets,resource_name:vessel-incident-report-nonprod-accesslogs')
The output that I should be able to query in SQL Server should like this:
account_id
resource_type
resource_name
224526257458
buckets
camp-stage-artifactory
535533456241
buckets
tni-prod-diva-backups
and so forth.
Please help me out and ask for clarification if needed. Thanks in advance.
EDIT:
Source JSON Format:
{
"start_date": "2021-12-01 00:00:00+00:00",
"end_date": "2021-12-31 23:59:59+00:00",
"resource_type": "all",
"records": [
{
"directconnect_connections": [
"account_id:227148359287,resource_type:directconnect_connections,resource_name:'dxcon-fh40evn5'",
"account_id:401311080156,resource_type:directconnect_connections,resource_name:'dxcon-ffxgf6kh'",
"account_id:401311080156,resource_type:directconnect_connections,resource_name:'dxcon-fg5j5v6o'",
"account_id:227148359287,resource_type:directconnect_connections,resource_name:'dxcon-fgvfo1ej'"
]
},
{
"virtual_interfaces": [
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:'dxvif-fgvj25vt'",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:'dxvif-fgbw5gs0'",
"account_id:401311080156,resource_type:virtual_interfaces,resource_name:'dxvif-ffnosohr'",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:'dxvif-fg18bdhl'",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:'dxvif-ffmf6h64'",
"account_id:390251991779,resource_type:virtual_interfaces,resource_name:'dxvif-fgkxjhcj'",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:'dxvif-ffp6kl3f'"
]
}
]
}
Since you don't have a valid JSON string and not wanting to get in the business of string manipulation... perhaps this will help.
Select B.*
From test2 A
Cross Apply ( Select account_id = max(case when value like 'account_id:%' then stuff(value,1,11,'') end )
,resource_type = max(case when value like 'resource_type:%' then stuff(value,1,14,'') end )
,resource_name = max(case when value like 'resource_name:%' then stuff(value,1,14,'') end )
from string_split(resource_type,',')
)B
Results
account_id resource_type resource_name
224526257458 buckets camp-stage-artifactory
535533456241 buckets tni-prod-diva-backups
369798452057 buckets 369798452057-s3-manifests
460085747812 buckets vessel-incident-report-nonprod-accesslogs
Unfortunately, the values inside the arrays are not valid JSON. You can patch them up by adding {} to the beginning/end, and adding " on either side of : and ,.
DECLARE #json nvarchar(max) = N'{
"start_date": "2021-12-01 00:00:00+00:00",
"end_date": "2021-12-31 23:59:59+00:00",
"resource_type": "all",
"records": [
{
"directconnect_connections": [
"account_id:227148359287,resource_type:directconnect_connections,resource_name:''dxcon-fh40evn5''",
"account_id:401311080156,resource_type:directconnect_connections,resource_name:''dxcon-ffxgf6kh''",
"account_id:401311080156,resource_type:directconnect_connections,resource_name:''dxcon-fg5j5v6o''",
"account_id:227148359287,resource_type:directconnect_connections,resource_name:''dxcon-fgvfo1ej''"
]
},
{
"virtual_interfaces": [
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:''dxvif-fgvj25vt''",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:''dxvif-fgbw5gs0''",
"account_id:401311080156,resource_type:virtual_interfaces,resource_name:''dxvif-ffnosohr''",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:''dxvif-fg18bdhl''",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:''dxvif-ffmf6h64''",
"account_id:390251991779,resource_type:virtual_interfaces,resource_name:''dxvif-fgkxjhcj''",
"account_id:227148359287,resource_type:virtual_interfaces,resource_name:''dxvif-ffp6kl3f''"
]
}
]
}';
SELECT
j4.account_id,
j4.resource_type,
TRIM('''' FROM j4.resource_name) resource_name
FROM OPENJSON(#json, '$.records') j1
CROSS APPLY OPENJSON(j1.value) j2
CROSS APPLY OPENJSON(j2.value) j3
CROSS APPLY OPENJSON('{"' + REPLACE(REPLACE(j3.value, ':', '":"'), ',', '","') + '"}')
WITH (
account_id bigint,
resource_type varchar(20),
resource_name varchar(100)
) j4;
db<>fiddle
The first three calls to OPENJSON have no schema, so the resultset is three columns: key value and type. In the case of arrays (j1 and j3), key is the index into the array. In the case of single objects (j2), key is each property name.
I'm trying to retrieve the health value from Snowflake semi structured data in a variant column called extra from table X.
An example of the code can be seen below:
[
{
"party":
"[{\"class\":\"Farmer\",\"gender\":\"Female\",\"ethnicity\":\"NativeAmerican\",\"health\":2},
{\"class\":\"Adventurer\",\"gender\":\"Male\",\"ethnicity\":\"White\",\"health\":3},
{\"class\":\"Farmer\",\"gender\":\"Male\",\"ethnicity\":\"White\",\"health\":0},
{\"class\":\"Banker\",\"gender\":\"Female\",\"ethnicity\":\"White\",\"health\":0}
}
]
I have tried reading the Snowflake documentation from https://community.snowflake.com/s/article/querying-semi-structured-data
I have also tried the following queries to flatten the query:
SELECT result.value:health AS PartyHealth
FROM X
WHERE value = 'Trail'
AND name = 'Completed'
AND PartyHealth > 0,
TABLE(FLATTEN(X, 'party')) result
AND
SELECT [0]['party'][0]['health'] AS Health
FROM X
WHERE value = 'Trail'
AND name = 'Completed'
AND PH > 0;
I am trying to retrieve the health value from table X from column extra which contains the the variant party, which has 4 repeating values [0-3]. Im not sure how to do this is someone able to tell me how to query semi structured data in Snowflake, considering the documentation doesn't make much sense?
First, the JSON value you posted seems wrong formatted (might be a copy paste issue).
Here's an example that works:
first your JSON formatted:
[{ "party": [ {"class":"Farmer","gender":"Female","ethnicity":"NativeAmerican","health":2}, {"class":"Adventurer","gender":"Male","ethnicity":"White","health":3}, {"class":"Farmer","gender":"Male","ethnicity":"White","health":0}, {"class":"Banker","gender":"Female","ethnicity":"White","health":0} ] }]
create a table to test:
CREATE OR REPLACE TABLE myvariant (v variant);
insert the JSON value into this table:
INSERT INTO myvariant SELECT PARSE_JSON('[{ "party": [ {"class":"Farmer","gender":"Female","ethnicity":"NativeAmerican","health":2}, {"class":"Adventurer","gender":"Male","ethnicity":"White","health":3}, {"class":"Farmer","gender":"Male","ethnicity":"White","health":0}, {"class":"Banker","gender":"Female","ethnicity":"White","health":0} ] }]');
now, to select a value you start from column name, in my case v, and as your JSON is an array inside, I specify first value [0], and from there expand, so something like this:
SELECT v[0]:party[0].health FROM myvariant;
Above gives me:
For the other rows you can simply do:
SELECT v[0]:party[1].health FROM myvariant;
SELECT v[0]:party[2].health FROM myvariant;
SELECT v[0]:party[3].health FROM myvariant;
Another option might be to make the data more like a table ... I find it easier to work with than the JSON :-)
Code at bottom - just copy/paste and it runs in Snowflake returning screenshot below.
Key Doco is Lateral Flatten
SELECT d4.path, d4.value
from
lateral flatten(input=>PARSE_JSON('[{ "party": [ {"class":"Farmer","gender":"Female","ethnicity":"NativeAmerican","health":2}, {"class":"Adventurer","gender":"Male","ethnicity":"White","health":3}, {"class":"Farmer","gender":"Male","ethnicity":"White","health":0}, {"class":"Banker","gender":"Female","ethnicity":"White","health":0} ] }]') ) as d ,
lateral flatten(input=> value) as d2 ,
lateral flatten(input=> d2.value) as d3 ,
lateral flatten(input=> d3.value) as d4
How can I remove an object from an array, based on the value of one of the object's keys?
The array is nested within a parent object.
Here's a sample structure:
{
"foo1": [ { "bar1": 123, "bar2": 456 }, { "bar1": 789, "bar2": 42 } ],
"foo2": [ "some other stuff" ]
}
Can I remove an array element based on the value of bar1?
I can query based on the bar1 value using: columnname #> '{ "foo1": [ { "bar1": 123 } ]}', but I've had no luck finding a way to remove { "bar1": 123, "bar2": 456 } from foo1 while keeping everything else intact.
Thanks
Running PostgreSQL 9.6
Assuming that you want to search for a specific object with an inner object of a certain value, and that this specific object can appear anywhere in the array, you need to unpack the document and each of the arrays, test the inner sub-documents for containment and delete as appropriate, then re-assemble the array and the JSON document (untested):
SELECT id, jsonb_build_object(key, jarray)
FROM (
SELECT foo.id, foo.key, jsonb_build_array(bar.value) AS jarray
FROM ( SELECT id, key, value
FROM my_table, jsonb_each(jdoc) ) foo,
jsonb_array_elements(foo.value) AS bar (value)
WHERE NOT bar.value #> '{"bar1": 123}'::jsonb
GROUP BY 1, 2 ) x
GROUP BY 1;
Now, this may seem a little dense, so picked apart you get:
SELECT id, key, value
FROM my_table, jsonb_each(jdoc)
This uses a lateral join on your table to take the JSON document jdoc and turn it into a set of rows foo(id, key, value) where the value contains the array. The id is the primary key of your table.
Then we get:
SELECT foo.id, foo.key, jsonb_build_array(bar.value) AS jarray
FROM foo, -- abbreviated from above
jsonb_array_elements(foo.value) AS bar (value)
WHERE NOT bar.value #> '{"bar1": 123}'::jsonb
GROUP BY 1, 2
This uses another lateral join to unpack the arrays into bar(value) rows. These objects can now be searched with the containment operator to remove the objects from the result set: WHERE NOT bar.value #> '{"bar1": 123}'::jsonb. In the select list the arrays are re-assembled by id and key but now without the offending sub-documents.
Finally, in the main query the JSON documents are re-assembled:
SELECT id, jsonb_build_object(key, jarray)
FROM x -- from above
GROUP BY 1;
The important thing to understand is that PostgreSQL JSON functions only operate on the level of the JSON document that you can explicitly indicate. Usually that is the top level of the document, unless you have an explicit path to some level in the document (like {foo1, 0, bar1}, but you don't have that). At that level of operation you can then unpack to do your processing such as removing objects.
I need data from a variety of tables and below is the only way I know to do it (I just know the basics). The query below works fine but shows duplicates. I need to know how to remove those.
SELECT DISTINCT
a.int_order_id, a.trans_id, a.wtn,a.isp_id,
d.first_name, d.middle_initial, d.last_name,
d.company_name, d.emaiL,
a.ilec_lob, a.node_type_id, a.cddd,
a.isp_ban, a.tos_version, a.isp_ckt_id,
a.isp_circuit_type, a.atm_vpi, a.atm_vci,
a.frs_dlci, b.order_create_date, b.pon,
b.order_status_id, e.trans_status_id,
e.description, c.STREET_NUMBER,
c.STREET_NUMBER_SUFFIX, c.DIRECTIONAL_ID,
c.street_name, c.thoroughfare_id,
c.street_suffix, c.address_line1, c.address_line2,
c.unit_type_id, c.unit_value, c.city, c.state_id, c.zip
FROM
VZEXTRACT1.vvov_os_ord_dsl A
JOIN
VZEXTRACT1.vvov_os_order_details B ON a.int_order_id = b.int_order_id
JOIN
VZEXTRACT1.vvov_os_ord_address C ON b.int_order_id = c.int_order_id
JOIN
vzextract1.vvov_os_ord_contact D ON c.int_order_id = d.int_order_id
JOIN
VZEXTRACT1.vv0v_trans_status E On b.order_status_id = e.trans_status_id
WHERE
a.isp_id NOT IN (657,500)
AND B.ORDER_CREATE_DATE >= to_date('01-may-15')
AND B.ORDER_CREATE_DATE < to_date('30-JUL-15')
When you want to remove duplicate rows you will have to use
Distinct : It will consider distinct of all selected columns so you will have to find out because of which column you are getting duplicate rows even if you have used distinct
Group by : You can use group by clause to get distinct rows. You can use aggregate function for column causing duplicate rows from point 1 and avoid duplicates.
over(partition by) : you can also use this clause for column causing duplicates. Like you concat values of such column ,
wm_concat(my_col)over(partition by id)