I have this Gaint Array of (dicts) loaded from a Json in a date partitioned big query external table with table structure as below as
Field name
Type.
Mode
meta
Record
Nullable
Messages
String
Repeated
date
Integer
Nullable
Every "Messages" Field is in its own row/record in my Bigquery table (New_line_delimited_Json)
I am trying to parse the "messages" field/column to extract some fields Key1 and Key2 which happens to be inside an Array (of dicts). For sake of simplicity ,below is the snippet of json of which "messages" is a field that I am trying to unnest/explode.
Ignore this schema;updated schema below***
[
{
"meta": {
"table": "FEED",
"source": "CP1"
},
"Messages": [
"{
"Key1":"2022-01-10",
"Key2":"H21257061"
}"
],
"date": "20220110"
},
{
"meta": {
"table": "FEED",
"source": "CP1"
},
"Messages": [
"{
"Key1":"2022-01-11",
"Key2":"H21257062"
}"
],
"date": "20220111"
}
]
updated schema on 01/17
{
"meta": {
"table": "FEED",
"source": "CP1"
},
"Messages": [
"{
"Key1":"2022-01-10",
"Key2":"H21257061"
}",
"{
"Key1":"2022-01-10",
"Key2":"H21257062"
}"
],
"date": "20220110"
},
updated schema representation on 01/17:
so far I have tried this but I am getting sql output of key1 and Key2 as Nulls
WITH table AS (SELECT Messages as array_column FROM `project.dataset.table` )
SELECT
json_extract_scalar(flattened_array, '$.Messages.key1') as key1,
json_extract_scalar(flattened_array, '$.Messages.key2') as key2
FROM table t
CROSS JOIN UNNEST(t.array_column) AS flattened_array
Still a little ambiguous so I assume below correctly represents your table (at least it matches the structure/schema in your question)
If my assumption correct - consider below approach
select * except(id) from (
select to_json_string(t) id, kv[offset(0)] as key, kv[safe_offset(1)] as value
from your_table t,
t.messages as message,
unnest([struct( split(translate(message, '"', ''), ':') as kv)])
)
pivot (min(value) for key in ('Key1', 'Key2'))
If / when applied to above sample data - output is
Edit: Trying to help you further - Ok so, looks like your table looks like below
In this case - try below (quite light modification of previous version)
select * except(id) from (
select to_json_string(t) id, kv[offset(0)] as key, kv[safe_offset(1)] as value
from your_table t,
unnest(regexp_extract_all(messages, r'"[^"]+":"[^"]+"')) as message,
unnest([struct( split(translate(message, '"', ''), ':') as kv)])
)
pivot (min(value) for key in ('Key1', 'Key2'))
with output
But obviously, I would use below simplest approach
select
json_extract_scalar(messages, '$.Key1') as Key1,
json_extract_scalar(messages, '$.Key2') as Key2
from your_table
SELECT
JSON_QUERY(message,"$.Key1") as Key1,
JSON_QUERY(message,"$.Key2") as Key2
FROM
`project.dataset.table` as table
CROSS JOIN UNNEST(table.Messages) as message
CROSS JOIN for flattening the array,which will return a row for each message.
After that “JSON_QUERY” to extract the needed values from the JSON string.
Related
I have a JSON array in Snowflake, where I need to parse into a table, i.e., convert all information into a row. Assume my table includes two columns: id and json_tag column.
An example row is as below:
{ [
json_tag
[
{
"key": "app.name",
"value": "myapp1"
},
{
"key": "device.name",
"value": "myiPhone11"
},
{
"key": "iOS.dist",
"value": "latestDist5"
}
]
This is not a standard example, another example can have 5 or even 6 with new key names. An example is below:
{ [
json_tag
[
{
"key": "app.name",
"value": "myapp2"
},
{
"key": "app.cost",
"value": "$2.99"
},
{
"key": "device.name",
"value": "myiPhoneX"
},
{
"key": "device.color",
"value": "gold"
},
{
"key": "iOS.dist",
"value": "latestDist4.9"
}
]
What I want is working on a table (without creating a new one) where the json row is split into columns as below:
id app.name app.cost device.name device.color iOS.dist
1 myapp1 null myiPhone11 null latestDist5
2 myapp2 $2.99 myiPhoneX gold latestDist4.9
I tried the following snippet:
with parsed_tb as (
select id,
to_variant(parse_json(json_tag)) as parsed_json_tag
from mytable
)
select parsed_json_tag[0]:value::varchar as app_name,
parsed_json_tag[1]:value::varchar as app_cost,
parsed_json_tag[2]:value::varchar as device_name
from tb;
As you can imagine, the snippet above does not work when there is no app.cost in key values or every row differs in number of keys and values.
I tried lateral flatten command in Snowflake, but out creates many rows and I cannot figure out how to put them in columns in the same row. I also tried using recursive command, and could not achieve it.
So my question is:
How can I access a key by its name rather than slicing an array as I do above? - this would solve my problem I guess.
If #1 solution I imagine does not fix, how can I attain the table above?
I have found a solution to this problem with the help of following thread:
Mysql, reshape data from long / tall to wide
Solution is first parsing json column with unique identifier, and then lateral flattening and then grouping the results by unique identifier [formatting table from long to wide as given in the link above].
So for the examples I provided above, the solution would be as follows:
with tb as (
select id,
to_variant(parse_json(json_tags)) as parsed_json_tags
from mytable
),
flattened as (
select id,
VALUE:value::string as value,
VALUE:key::string as key
from mytable, lateral flatten(input => (tb.parsed_json_tags))
)
select id,
MAX( IFF( key='app.name', value, NULL ) ) AS app_name,
MAX( IFF( key='app.cost', value, NULL ) ) AS app_cost,
MAX( IFF( key='device.name', value, NULL ) ) AS device_name,
MAX( IFF( key='device.color', value, NULL ) ) AS device_color,
MAX( IFF( key='iOS.dist', value, NULL ) ) AS ios_dist
from flattened
group by 1;
I have a requirement around this data where I need to extract array elements but I still want to keep them grouped, which means I can not use unnest function. Below is the sample data:
[
{ "emp_id": 8291828, "name": "bruce", },
{ "emp_id": 8291823, "name": "Rolli" }
]
My data is in the same format as above,i.e. (array(row(emp_id varchar, name varchar))) what I need is to get rid of the array, so that data look like
{ "emp_id": 8291828, "name": "bruce", },
{ "emp_id": 8291823, "name": "Rolli" }
Would appreciate if anyone can help me on this.
You could use element_at If you have a sequence table (1,2,3, ..).
with numbers as
(
select * from
(
Values
(1),(2),(3)
) as x(i)
)
,emp as
(
select *
from (
values
(ARRAY[cast(ROW(8291828,'bruce') as row(emp_id bigint, name varchar)), cast(row(8291823,'Rolli') as row(emp_id bigint, name varchar))])
) as emp (records)
)
select
element_at(emp.records,i) record
from numbers n
cross join emp
where n.i <= cardinality(emp.records);
I have the below sample JSON:
{
"Id1": {
"name": "Item1.jpg",
"Status": "Approved"
},
"Id2": {
"name": "Item2.jpg",
"Status": "Approved"
}
}
and I am trying to get the following output:
_key name Status
Id1 Item1.jpg Approved
Id2 Item2.jpg Approved
Is there any way I can achieve this in Snowflake using SQL?
You should use Snowflake's VARIANT data type in any column holding JSON data. Let's break this down step by step:
create temporary table FOO(v variant); -- Temp table to hold the JSON. Often you'll see a variant column simply called "V"
-- Insert into the variant column. Parse the JSON because variants don't hold string types. They hold semi-structured types.
insert into FOO select parse_json('{"Id1": {"name": "Item1.jpg", "Status": "Approved"}, "Id2": {"name": "Item2.jpg", "Status": "Approved"}}');
-- See how it looks in its raw state
select * from FOO;
-- Flatten the top-level JSON. The flatten function breaks down the JSON into several usable columns
select * from foo, lateral flatten(input => (foo.v)) ;
-- Now traverse the JSON using the column name and : to get to the property you want. Cast to string using ::string.
-- If you must have exact case on your column names, you need to double quote them.
select KEY as "_key",
VALUE:name::string as "name",
VALUE:Status::string as "Status"
from FOO, lateral flatten(input => (FOO.V)) ;
I'm trying to read the column which type is json, values in column look like this
column1
---------------------------------------------
"[{'name': 'Kate', 'position': 'painter'}]"
Im using this query, but all I get is null, what can I do to get the values for each keys?
SELECT
column1 ->> 'name' AS name
FROM
table1;
Then you use jsonb_pretty that Returns from_json as indented JSON text.
select jsonb_pretty('[{"name": "Kate", "position": "painter"}]');
Output display you:
jsonb_pretty
-------------------------------
[ +
{ +
"name": "Kate", +
"position": "painter"+
} +
]
so in your case you use
SELECT jsonb_pretty(column1) AS name FROM table1;
Use json_array_elements function:
SELECT json_array_elements(t) -> 'name'
FROM table1;
I am trying to remove a column from a BigQuery table and I've followed the instructions as stated here:
https://cloud.google.com/bigquery/docs/manually-changing-schemas#deleting_a_column_from_a_table_schema
This did not work directly as the column I'm trying to remove is nested twice in a struct. The following SO questions are relevant but none of them solve this exact case.
Single nested field:
BigQuery select * except nested column
Double nested field (solution has all fields in the schema enumerated, which is not useful for me as my schema is huge):
BigQuery: select * replace from multiple nested column
I've tried adapting the above solutions and I think I'm close but can't quite get it to work.
This one will remove the field, but returns only the nested field, not the whole table (for the examples I want to remove a.b.field_name. See the example schema at the end):
SELECT AS STRUCT * EXCEPT(a), a.* REPLACE (
(SELECT AS STRUCT a.b.* EXCEPT (field_name)) AS b
)
FROM `table`
This next attempt gives me an error: Scalar subquery produced more than one element:
WITH a_tmp AS (
SELECT AS STRUCT a.* REPLACE (
(SELECT AS STRUCT a.b.* EXCEPT (field_name)) AS b
)
FROM `table`
)
SELECT * REPLACE (
(SELECT AS STRUCT a.* FROM a_tmp) AS a
)
FROM `table`
Is there a generalised way to solve this? Or am I forced to use the enumerated solution in the 2nd link?
Example Schema:
[
{
"name": "a",
"type": "RECORD",
"fields": [
{
"name": "b",
"type": "RECORD"
"fields": [
{
"name": "field_name",
"type": "STRING"
},
{
"name": "other_field_name".
"type": "STRING"
}
]
},
]
}
]
I would like the final schema to be the same but without field_name.
Below is for BigQuery Standard SQL
#standardSQL
SELECT * REPLACE(
(SELECT AS STRUCT(SELECT AS STRUCT a.b.* EXCEPT (field_name)) b)
AS a)
FROM `project.dataset.table`
you can test, play with it using dummy data as below
#standardSQL
WITH `project.dataset.table` AS (
SELECT STRUCT<b STRUCT<field_name STRING, other_field_name STRING>>(STRUCT('1', '2')) a
)
SELECT * REPLACE(
(SELECT AS STRUCT(SELECT AS STRUCT a.b.* EXCEPT (field_name)) b)
AS a)
FROM `project.dataset.table`