Matrix Operations with JSON

Phil Factor takes a look at using JSON to perform memoization:

For the SQL Server developer, matrices are probably most valuable for solving more complex string-searching problems, using Dynamic Programming. Once you get into the mindset of this sort of technique, a number of seemingly-intractable problems become easier.  Here are fifty common data structure problems that can be solved using Dynamic programming. Until SQL Server 2017, these were hard to do in SQL because of the lack of support for this style of programming.  Memoization, one of the principles behind the technique is easy to do in SQL but it is very tricky to convert existing procedural algorithms to use table variables. It is usually easier and quicker to use strings as pseudo-variables as I did  with Edit Distance and the Levenshtein algorithmthe longest common subsequence, and  the Longest Common Substring. The problem with doing this is that the code to fetch the array values can be very difficult to decypher or debug. JSON can do it very easily with path array references.

The results aren’t fantastic but the code is easier at least.

Searching Complex JSON With SQL Server

Kevin Feasel

2019-02-27

JSON

Bert Wagner gives us a way that you can quickly search through complicated JSON:

Computed column indexes make querying JSON data fast and efficient, especially when the schema of the JSON data is the same throughout a table.

It’s also possible to break out a well-known complex JSON structure into multiple SQL Server tables.

However, what happens if you have different JSON structures being stored in each row of your database and you want to write efficient search queries against all of the rows of your complex JSON strings?

Bert’s solution is an example of a phenomenon I’ve noticed in relational databases: sometimes, the best solution is not the most straightforward. The most straightforward solution is to take the JSON as-is, but that hits a wall as Bert shows. Reshaping the data leads to much better performance…as long as you’re able to afford the time needed to reshape and don’t have JSON changing that frequently.

Azure SQL Managed Instance Inventory Analysis

Kevin Feasel

2018-10-03

Cloud, JSON

Jovan Popovic shows us how to use the Azure CLI plus JSON support in SQL Server to manage a list of Azure SQL Managed Instances:

Sometime you would need to know how many Managed Instance you have created in Azure cloud. Although you can find all information about the Azure SQL Managed Instances in Azure portal or API (ARM, PowerShell, Azure CLI), sometime it is hard to list all instances and search them using some criteria. In this post you will see how easily you can load list of your Managed Instances and build inventory of your resources.

Problem

Imagine that you have a large number of Managed Instances and you need to know how many instances you have, in what regions, subnets, and virtual networks they are placed, how much compute and storage is allocated to each of them, etc. Analyzing inventory of Managed Instances might be hard if you just use PowerShell.

Click through for the solution.

Formatting Queries As JSON With FOR JSON

Kevin Feasel

2018-08-23

JSON

Eduardo Pivaral shows off the FOR JSON functionality in SQL Server 2016 and later:

For most of real-world applications, the JSON AUTO will not give you the control you could need over your file format, for having more control over it, you must use the JSON PATH option, along with the ROOT option as follows:

SELECT TOP 10 id, dataVarchar, dataNumeric, dataInt, dataDate
FROM [dbo].[MyTestTable]
FOR JSON PATH, ROOT('TestTable')

Eduardo has several examples along these lines.

HASHBYTES On FOR JSON PATH Data

Kevin Feasel

2018-07-16

JSON, T-SQL

Greg Low walks us through a mechanism to check whether data has changed:

In a previous post, I wrote about how to determine if a set of incoming values for a row are different to all the existing values in the row, using T-SQL in SQL Server.

I later remembered that I’d seen a message by Adam Machanic a while back, talking about how FOR JSON PATH might be useful for this, so I did a little more playing around with it.

If you are using SQL Server 2016 or later, I suspect this is a really good option.

Click through to see an example using the WideWorldImporters database.

Flattening JSON Data With Databricks

Ivan Vazharov gives us a Databricks notebook to parse and flatten JSON using PySpark:

With Databricks you get:

  • An easy way to infer the JSON schema and avoid creating it manually
  • Subtle changes in the JSON schema won’t break things
  • The ability to explode nested lists into rows in a very easy way (see the Notebook below)
  • Speed!

Following is an example Databricks Notebook (Python) demonstrating the above claims. The JSON sample consists of an imaginary JSON result set, which contains a list of car models within a list of car vendors within a list of people. We want to flatten this result into a dataframe.

Click through for the notebook.

JSON Output And SSIS

Stacia Varga works around an oddity in the way SSIS reads JSON outputs:

What happened? The T-SQL produces the correct results in SQL Server Management Studio (SSMS). However, in SSIS, the same T-SQL statement in an OLE DB Source in a Data Flow Task produces two rows of data which adds a line feed into the flat file and renders the JSON unusable.

The problem is visible even before sending output to the flat file.

Click the link to see how Stacia solves this problem.

JSON Data In SSIS

Stacia Varga shows a few methods for handling JSON data in SQL Server Integration Services:

And then I had to write about it in my book Introducing Microsoft SQL Server 2016 (which is free to download) when JSON support was added to SQL Server 2016. But I still didn’t have clients using JSON. It was interesting to me that I could use SQL Server to work with JSON data, but it was still theoretical to me rather than practical.

Therefore, I never thought much about how I would handle it in SQL Server Integration Services (SSIS). I just didn’t have a reason.

Until now. This seems to be the year that I am bumping into JSON left and right. It’s everywhere!

Read on for those methods as well as Stacia’s recommendation.

Loading JSON-Based Data Into SQL Server From .NET

Chris Koester has a quick example demonstrating one way take JSON data from .NET code and load it into SQL Server:

Next we need to create a stored procedure that will accept JSON text as a parameter and insert it into the table. Two important points here:

  • JSON text must use the NVARCHAR(MAX) data type in SQL Server in order to support the JSON functions.

  • The OPENJSON function is used to convert the JSON text into a rowset, which is then inserted into the previously created table.

The whole process is quite easy; check it out.

Using JSON_MODIFY To Modify Existing JSON

Jovan Popovic shows off the JSON_MODIFY function in SQL Server:

Recently I found this question on stack overflow. The problem was in appending a new JSON object to the existing JSON array:

UPDATE TheTable
SET TheJSON = JSON_MODIFY(TheJSON, 'append $', N'{"id": 3, "name": "Three"}')
WHERE Condition = 1;

JSON_MODIFY function should take the array value from TheJSON column (the first argument), append the third argument into the first argument, and write the appended array back in TheJSON column.

However, the unexpected results in this case is the fact that JSON_MODIFY didn’t appended a JSON object {"id": 3, "name": "Three"}to the array. Instead, JSON_MODIFY appended a new JSON string literal  "{\"id\": 3, \"name\": \"Three\"}" to the end of the array.

This might be surprising result if you don’t know how JSON_MODIFY function works.

Read on to see how JSON_MODIFY works and why this doesn’t quite do what the poster thought.

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