Where can I find more detailed information regarding the schema parameter of the from_json function in Spark SQL? A coworker gave me a schema example that works, but to be honest, I just don't understand and it doesn't look like any of the examples I have found thus far. The documentation found here seems to be lacking.
JSON Files. Spark SQL can automatically infer the schema of a JSON dataset and load it as a Dataset[Row]. This conversion can be done using SparkSession.read.json() on either a Dataset[String], or a JSON file. Note that the file that is offered as a json file is not a typical JSON file.
Now by using from_json (Column jsonStringcolumn, StructType schema), you can convert JSON string on the Spark DataFrame column to a struct type. In order to do so, first, you need to create a StructType for the JSON string. import org.apache.spark.sql.types.{
Spark SQL can automatically infer the schema of a JSON dataset and load it as a Dataset [Row]. This conversion can be done using SparkSession.read.json () on either a Dataset [String], or a JSON file. Note that the file that is offered as a json file is not a typical JSON file. Each line must contain a separate, self-contained valid JSON object.
Write Spark DataFrame to JSON file Use the Spark DataFrameWriter object “write” method on DataFrame to write a JSON file. While writing a JSON file you can use several options. Spark DataFrameWriter also has a method mode () to specify SaveMode; the argument to this method either takes below string or a constant from SaveMode class.
In the link you shared the from_json
function uses this example:
SELECT from_json('{"a":1, "b":0.8}', 'a INT, b DOUBLE');
Spark SQL supports the vast majority of Hive features such as the defining TYPES
The example problem I was facing required me to parse the following JSON object:
{'data': [
{
"id":02938,
"price": 2938.0,
"quantity": 1
},
{
"id":123,
"price": 123.5,
"quantity": 2
}
]}
The corresponding Spark SQL query would look like this:
SELECT
from_json('{"data":[{"id":123, "quantity":2, "price":39.5}]}'),
'data array<struct<id:INT, quantity:INT, price:DOUBLE>>').data) AS product_details;
you can couple this with the
explode
function to extract each element into it's own column.
I recommend this post to learn more about constructing the types for your query.
Refer to this SO post for more examples https://stackoverflow.com/a/55432107/1500443
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