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Google Cloud PlatformQuery JSON strings
This article describes the Databricks SQL operators you can use to query and transform semi-structured data stored as JSON strings.
Note
This feature lets you read semi-structured data without flattening the files. However, for optimal read query performance Databricks recommends that you extract nested columns with the correct data types.
You extract a column from fields containing JSON strings using the syntax <column-name>:<extraction-path>, where <column-name> is the string column name and <extraction-path> is the path to the field to extract. The returned results are strings.
Create a table with highly nested data
Run the following query to create a table with highly nested data. The examples in this article all reference this table.
CREATE TABLE store_data AS SELECT
'{
"store":{
"fruit": [
{"weight":8,"type":"apple"},
{"weight":9,"type":"pear"}
],
"basket":[
[1,2,{"b":"y","a":"x"}],
[3,4],
[5,6]
],
"book":[
{
"author":"Nigel Rees",
"title":"Sayings of the Century",
"category":"reference",
"price":8.95
},
{
"author":"Herman Melville",
"title":"Moby Dick",
"category":"fiction",
"price":8.99,
"isbn":"0-553-21311-3"
},
{
"author":"J. R. R. Tolkien",
"title":"The Lord of the Rings",
"category":"fiction",
"reader":[
{"age":25,"name":"bob"},
{"age":26,"name":"jack"}
],
"price":22.99,
"isbn":"0-395-19395-8"
}
],
"bicycle":{
"price":19.95,
"color":"red"
}
},
"owner":"amy",
"zip code":"94025",
"fb:testid":"1234"
}' as raw
Extract a top-level column
To extract a column, specify the name of the JSON field in your extraction path.
You can provide column names within brackets. Columns referenced inside brackets are matched case sensitively. The column name is also referenced case insensitively.
SELECT raw:owner, RAW:owner FROM store_data
+-------+-------+
| owner | owner |
+-------+-------+
| amy | amy |
+-------+-------+
-- References are case sensitive when you use brackets
SELECT raw:OWNER case_insensitive, raw:['OWNER'] case_sensitive FROM store_data
+------------------+----------------+
| case_insensitive | case_sensitive |
+------------------+----------------+
| amy | null |
+------------------+----------------+
Use backticks to escape spaces and special characters. The field names are matched case insensitively.
-- Use backticks to escape special characters. References are case insensitive when you use backticks.
-- Use brackets to make them case sensitive.
SELECT raw:`zip code`, raw:`Zip Code`, raw:['fb:testid'] FROM store_data
+----------+----------+-----------+
| zip code | Zip Code | fb:testid |
+----------+----------+-----------+
| 94025 | 94025 | 1234 |
+----------+----------+-----------+
Note
If a JSON record contains multiple columns that can match your extraction path due to case insensitive matching, you will receive an error asking you to use brackets. If you have matches of columns across rows, you will not receive any errors. The following will throw an error: {"foo":"bar", "Foo":"bar"}, and the following won’t throw an error:
{"foo":"bar"}
{"Foo":"bar"}
Extract nested fields
You specify nested fields through dot notation or using brackets. When you use brackets, columns are matched case sensitively.
-- Use dot notation
SELECT raw:store.bicycle FROM store_data
-- the column returned is a string
+------------------+
| bicycle |
+------------------+
| { |
| "price":19.95, |
| "color":"red" |
| } |
+------------------+
-- Use brackets
SELECT raw:store['bicycle'], raw:store['BICYCLE'] FROM store_data
+------------------+---------+
| bicycle | BICYCLE |
+------------------+---------+
| { | null |
| "price":19.95, | |
| "color":"red" | |
| } | |
+------------------+---------+
Extract values from arrays
You index elements in arrays with brackets. Indices are 0-based. You can use an asterisk (*) followed by dot or bracket notation to extract subfields from all elements in an array.
-- Index elements
SELECT raw:store.fruit[0], raw:store.fruit[1] FROM store_data
+------------------+-----------------+
| fruit | fruit |
+------------------+-----------------+
| { | { |
| "weight":8, | "weight":9, |
| "type":"apple" | "type":"pear" |
| } | } |
+------------------+-----------------+
-- Extract subfields from arrays
SELECT raw:store.book[*].isbn FROM store_data
+--------------------+
| isbn |
+--------------------+
| [ |
| null, |
| "0-553-21311-3", |
| "0-395-19395-8" |
| ] |
+--------------------+
-- Access arrays within arrays or structs within arrays
SELECT
raw:store.basket[*],
raw:store.basket[*][0] first_of_baskets,
raw:store.basket[0][*] first_basket,
raw:store.basket[*][*] all_elements_flattened,
raw:store.basket[0][2].b subfield
FROM store_data
+----------------------------+------------------+---------------------+---------------------------------+----------+
| basket | first_of_baskets | first_basket | all_elements_flattened | subfield |
+----------------------------+------------------+---------------------+---------------------------------+----------+
| [ | [ | [ | [1,2,{"b":"y","a":"x"},3,4,5,6] | y |
| [1,2,{"b":"y","a":"x"}], | 1, | 1, | | |
| [3,4], | 3, | 2, | | |
| [5,6] | 5 | {"b":"y","a":"x"} | | |
| ] | ] | ] | | |
+----------------------------+------------------+---------------------+---------------------------------+----------+
Cast values
You can use :: to cast values to basic data types. Use the from_json method to cast nested results into more complex data types, such as arrays or structs.
-- price is returned as a double, not a string
SELECT raw:store.bicycle.price::double FROM store_data
+------------------+
| price |
+------------------+
| 19.95 |
+------------------+
-- use from_json to cast into more complex types
SELECT from_json(raw:store.bicycle, 'price double, color string') bicycle FROM store_data
-- the column returned is a struct containing the columns price and color
+------------------+
| bicycle |
+------------------+
| { |
| "price":19.95, |
| "color":"red" |
| } |
+------------------+
SELECT from_json(raw:store.basket[*], 'array<array<string>>') baskets FROM store_data
-- the column returned is an array of string arrays
+------------------------------------------+
| basket |
+------------------------------------------+
| [ |
| ["1","2","{\"b\":\"y\",\"a\":\"x\"}]", |
| ["3","4"], |
| ["5","6"] |
| ] |
+------------------------------------------+
NULL behavior
When a JSON field exists with a null value, you will receive a SQL null value for that column, not a null text value.
select '{"key":null}':key is null sql_null, '{"key":null}':key == 'null' text_null
+-------------+-----------+
| sql_null | text_null |
+-------------+-----------+
| true | null |
+-------------+-----------+
Transform nested data using Spark SQL operators
Apache Spark has a number of built-in functions for working with complex and nested data. The following notebook contains examples.
Additionally, higher order functions provide many additional options when built-in Spark operators aren’t available for transforming data the way you want.