To solve this problem, I would suggest modifying the SQL query to directly parse and extract the values from the JSON strings without using string manipulation functions. Here’s an updated code snippet that should work: create procedure StoreAnswers(@text varchar(max)) as begin insert into QuestionResponses(question, answer, state) select json_value(json.value('$.question'), 'nvarchar(50)') as question, json_value(json.value('$.answer'), 'nvarchar(50)') as answer, json_value(json.value('$.state'), 'nvarchar(100)') as state from (select replace(replace(replace(substring(@text, charindex('{', @text), len(@text)), 'answer: ', '"answer": '), 'question: ', '"question": '), 'state: ', '"state": ') as json from string_split(@text, char(10)) where value like '%{%}%') as jsons end; In this updated code snippet:

Oracle’s Aggregate Function Ordering Behavior Understanding the Limitations of Oracle’s Average Function with Group By Clauses In this article, we’ll delve into the intricacies of Oracle’s average function and its behavior when used within group by clauses. We’ll explore why ordering by avg can be finicky and what underlying data types might be contributing to these issues. The Problem: Incorrect Ordering When using an aggregate function like average in a group by clause, followed by an order by clause, the results may not always be sorted correctly.

Understanding Date Formats and Time Zones in R Date formats and time zones are essential concepts in programming, particularly when working with dates and times. In this article, we will explore how to convert a date column into a specific locale format using the R programming language. Introduction to Dates and Times in R R is a popular programming language for statistical computing and data visualization. It provides an extensive range of libraries and packages for data manipulation, analysis, and visualization.

Using Current Date in SQL Queries with Large Tables When working with large datasets, it’s essential to optimize your queries to ensure efficient performance and data retrieval. In this article, we’ll explore a way to write the value of the current date in each row per product ID without joining the same table again. Understanding the Problem Suppose you have a large table containing product information, including dates and corresponding values.

import numpy as np import time def diags2mtr(n, diags): mtr = np.zeros((n, n)) for i in range(len(diags)): row = max(1, i - n + 1) col = max(1, n - i) for j in range(len(diags[i])): mtr[row + j - 1, col + j - 1] = diags[i][j] return mtr def diags2mtrOmp(diags_matrix, diags_length): # Note: OpenMP requires a compiler that supports it # For example, with GCC: -fopenmp flag is needed nDiags = len(diags_matrix) n = diags_matrix.

Understanding iPhone Simulator Location Services Introduction When it comes to developing iOS apps, one of the essential features to consider is location services. The iPhone simulator provides an excellent way to test and debug your app’s location-related functionality in a controlled environment. However, users have reported issues with the simulator not being able to find their current location, even when they are connected to a WiFi network. In this article, we will delve into the reasons behind this behavior and explore the technical aspects of iPhone location services.

Seamless Dynamic Audio Loop on iPhone Overview Creating a seamless audio loop on an iPhone can be a challenging task, especially when dealing with multiple sound files and varying playback durations. In this article, we will explore different approaches to achieving this goal using Apple’s AVAudioPlayer API. Introduction The desire to create seamless audio loops is not unique to our specific use case. Many applications, such as music streaming services or video games, rely on dynamic audio looping to enhance the user experience.

Handling Multiple Categories for Min and Max Values in a SQL Query When dealing with large datasets, extracting specific information such as the minimum and maximum values can be a daunting task. In this article, we will explore how to extract min and max values from a table while also identifying their respective categories. Problem Description Consider a scenario where you have a table named Asset with columns Asset_Type and Asset_Value.

Understanding Context Managers in psycopg2: A Deeper Dive As a developer working with databases, you’re likely familiar with the importance of managing connections and cursors effectively. In Python’s popular psycopg2 library, context managers provide a convenient way to handle these resources. However, implementing them correctly can be tricky. In this article, we’ll delve into the world of context managers in psycopg2, exploring their purpose, benefits, and best practices. We’ll examine two examples provided by the question and answer, and break down the differences between them.

Understanding Missing Values in DataFrames and How to Create New Columns Missing values in dataframes can be a significant challenge for data scientists. In this article, we will explore how to identify missing values, create new columns based on these values, and fill them with meaningful information. What are Missing Values? In statistics, a missing value is an entry in a dataset that cannot be observed or recorded. These can occur due to various reasons such as: