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Category: JSON

Invoke External REST Endpoints from Azure SQL DB

Rob Farley is impressed:

This internal procedure is new in Azure SQL DB in 2022. I think it presents a significant change to the way we do things in the world of SQL, and makes some other tools a whole lot more useful as well.

sp_invoke_external_rest_endpoint lets me send data to a REST API from within a stored procedure. Invoking an HTTP REST endpoint – as simple as that. And while I know you’re probably thinking, “But I can send data to a REST API from anywhere – why do I need to do it from within a stored procedure?”, I want to describe a few scenarios to you.

I like having the functionality, though would want to control how frequently my teams would use it. The reason is that this potentially makes your database the a domain boundary (when thinking in domain-driven design concepts).

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OPENJSON Performance and Schemas

Dave Mason has a new blog theme and a post on OPENJSON performance:

Support for JSON data has been around in SQL Server for a while now, starting with SQL 2016. The OPENJSON rowset function is the built-in function that allows you to natively convert JSON text into a set of rows and columns. There are two options for using OPENJSON: with the default schema or with an explicit schema. There are performance implications for each, which I’ll review with some examples.

Dave has some nice tips for people working with JSON data in SQL Server.

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Using JSON_PATH_EXISTS() in SQL Server

Hasan Savran shows how the JSON_PATH_EXISTS() function works in SQL Server:

Schemas can easily change if you save your data in JSON format. It is very easy to add or remove properties from JSON documents. When the data model changes quickly, you might need to worry about if the property you are looking for exists in the documents. If the path you are looking for does not exist in some documents, you need to handle the exception in some way. JSON_PATH_EXISTS comes to your help in situations like that. It tests whether a specified path exists in the input JSON.

Read on for the syntax and examples of use.

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Constructing JSON Objects in SQL Server

Hasan Savran checks out a couple of functions new to SQL Server 2022:

JSON Functions are introduced to SQL Server in version 2016. Saving JSON documents and retrieving documents using JSON Functions brings many possibilities to SQL Server. It is great to see that Microsoft continues to add different functions to the original JSON functions set.

    Today, I will explain two new JSON functions which are available in SQL Server 2022 and Azure SQL Database. 

Read on to learn more about these functions.

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JSON Enhancements in Azure SQL DB and SQL Server 2022

Umchandar Jayachandran has an announcement:

Today, we are announcing the public preview of JSON enhancements in Azure SQL Database and SQL Server 2022 CTP 2.0. This preview contains an enhancement to ISJSON function and three new JSON functions – JSON_PATH_EXISTS, JSON_OBJECT and JSON_ARRAY. Currently, the ISJSON function allows you to test if a string value contains a valid JSON object or array. The new optional json_type_constraint parameter in ISJSON function can now be used to test conformance of JSON documents to the IETF RFC 8259 specification. This capability allows you to test for strings that contain a JSON value, scalar, object, or array. This functionality is like the IS JSON predicate in the ANSI SQL standard. The new JSON_PATH_EXISTS function allows you to test for the existence of a specific SQL/JSON path expression in a JSON document. This functionality is like the JSON_EXISTS predicate in the ANSI SQL standard. The new ANSI SQL compatible JSON value constructors – JSON_OBJECT and JSON_ARRAY functions allow you to construct JSON object or array from SQL data.

Even if you don’t store data in JSON format, there are good reasons why you might need to accept data in JSON format (or emit data in JSON format), especially when working with languages like R and Python.

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Building posexplode() in the Serverless SQL Pool

Jovan Popvic rides to the rescue with JSON:

The array cells are pivoted and returned as simple scalar columns. Now you can simply use WHERE or GROUP BY clauses to filter or summarize information by array element values. Another very useful piece of information might be the index of every element (generated as pos column).

Spark enables you to use the posexplode() function on every array cell. The posexplode() function will transform a single array element into a set of rows where each row represents one value in the array and the index of that array element. As a result, one row with the array containing three elements will be transformed into three rows containing scalar cells. This flattened/normalized representation is much easier for the analysis.

Once the array is flattened and normalized, you can easily analyze the data and find how much people knowing SQL or Java.

Read on to see how you can implement the equivalent of POSEXPLODE() using OPENJSON() in the Azure Synapse Analytics serverless SQL pool.

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Ordered String Splitting with OPENJSON

Aaron Bertrand splits and cares about sort order:

Last year, I wrote about replacing all your CLR or custom string splitting functions with native calls to STRING_SPLIT. As I work on a project migrating several Microsoft SQL Server instances to Linux, I am encountering one of the roadblocks I mentioned last time: the need to provide an element in the output to indicate the order of the elements in the input string. This means STRING_SPLIT in its current form is out, because 1) it offers no such column; and, 2) the results are not guaranteed to be returned in any specific order. Are there other ways to achieve this functionality at scale and without CLR?

As Koen mentions in the comments, you can now get STRING_SPLIT with a sort parameter, but Aaron’s response is also valid: not everybody will have access to that today, so it still makes sense to understand the options.

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Compressing JSON in SQL Server

Randolph West has a recommendation:

I’ll also pre-emptively note that if this table was simply an append-only archive table, the row size would not really matter. Unfortunately, this table participates in thousands of transactions per day, and as the original developers used Entity Framework and didn’t think much of using NVARCHAR(MAX), the entire row is coming over the wire into the application each time it is queried.

As I’ve written previously about this kind of thing, this is not a good design pattern. Using the VARBINARY(MAX) data type with COMPRESS in the INSERT/UPDATE queries — and DECOMPRESS in the SELECT queries — is a much better design pattern and dramatically reduces the amount of data transferred over the network. Additionally, SQL Server needs significantly less space to store, maintain, and back up this compressed data.

Read on to see the likely benefits from doing this. I’d say that if your main purpose of storing the JSON is just to pass a blob back and forth, then yes, do compress. If you’re frequently shredding these sorts of large documents within SQL Server…well, probably time for a better data model.

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A Cross-Platform Comparison of JSON in Relational Databases

Lukas Eder gives us some information on SQL/JSON and where different relational database management systems are in their JSON journies:

Building (dogfooding) on top of our own SQL/JSON API has revealed a lot of caveats of the various SQL/JSON implementations across vendors, and to be frank, it’s been a bit of a sobering experience. Despite there now being the ISO/IEC TR 19075:6 standard (mostly driven by Oracle this time), many vendors have already implemented some kind of JSON support, and it looks differently in all dialects – to the extent where writing vendor agnostic SQL/JSON is almost impossible with hand written native SQL. You’ll need an API like jOOQ or any other abstraction to standardise the different dialects.

Click through for the survey.

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JSON Patching in Cosmos DB

Hasan Savran does some JSON modification:

JSON is a quite common format for data in these days. NoSQL databases save data in JSON format. Even Relational Databases like SQL Server give you option to save or retrieve data in JSON format. When we need to update a property in a JSON document, we need to update the whole JSON document. This can be a problem specially if document size is larger. This is where JSON Patch feature comes in. JSON Patch is a format that let you update JSON document partially. 

     You can do more than updating a property. You can add, remove, replace, move, or test a value by using this format. Let’s look at its details by using the following JSON document as the source.

Read on to see how patching works with Cosmos DB in particular.

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