I have written a stored procedure on SQL Server that takes as an input the following JSON post:
{"sentiment_results": {"polarity": -0.6, "subjectivity": 0.7, "emotions": {"anger": 0.08296050131320953, "disgust": 0.00219865539111197, "fear": 0.07708118110895157, "joy": 0.757244884967804, "surprise": 0.027166856452822685, "sadness": 0.05334791541099548}}, "sentiment_time": "2020-08-04T16:43:47.141948"}
...and is using the following script to enter the data on a database table (post_metric_score table -> one row for each datapoint)
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 1, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results')
WITH ([score] FLOAT '$.polarity')
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 2, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results')
WITH ([score] FLOAT '$.subjectivity')
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 3, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results.emotions')
WITH ([score] FLOAT '$.anger')
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 4, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results.emotions')
WITH ([score] FLOAT '$.disgust')
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 5, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results.emotions')
WITH ([score] FLOAT '$.fear')
INSERT INTO [STAGING].[post_metric_score]([post_id],[metric_id],[score])
SELECT @post_id, 6, try_convert(decimal(12, 8), [score])
FROM OPENJSON(@postJson, '$.sentiment_results.emotions')
WITH ([score] FLOAT '$.joy')
The script works fine but it uses a lot of CPU because is running the same insert query 6 times per JSON post.
Is there a way to simplify the script above so it does not have to run the insert statement multiple times?