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Category: Misc Languages

Building an Azure Function to Automate CHECKDB

Published 2020-09-11 by Kevin Feasel

Arun Sirpal shows us how to build an Azure Function:

The title is a mouthful and so is this post. In the past I have linked to blog posts from Microsoft that say consistency checks for Azure SQL Database is the responsibility of Microsoft. (https://azure.microsoft.com/en-gb/blog/data-integrity-in-azure-sql-database/)

However, Paul Randal got me thinking about his thoughts on it (via his insider email). Forming the core of this post. If you desire to run DBCC CHECKDB against Azure SQL Database (which I know people do) – how can you do this? There are many ways, but for this blog post – Enter Azure functions. There are many moving parts to this, but once setup and coded it is a very satisfying experience. Let’s dig in. I am NOT going to copy and paste every little element of the high-level guide from Microsoft, there is no point in that but I will show you the links that you need to setup the relevant function app project then the tailored bits around CHECKDB forms the bulk of this post.

This isn’t necessary to do, but if you want to learn how Azure Functions work, it’s a good example of working through the mechanics.

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Comparing Cassandra and DynamoDB

Published 2020-08-31 by Kevin Feasel

Lewis DiFelice compares and contrasts Cassandra with DynamoDB:

In this post, we’ll look at some of the key differences between Apache Cassandra (hereafter just Cassandra) and DynamoDB.

Both are distributed databases and have similar architecture, and both offer incredible scalability, reliability, and resilience. However, there are also differences,  and understanding the differences and cost benefits can help you determine the right solution for your application.

There’s some good info in this comparison.

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Mutation Testing in Action

Published 2020-08-28 by Kevin Feasel

Nathan Thompson walks us through a mutation testing experiment:

Since our hypothesis was that the implementation differences between Jasmine and Jest could affect the Mutation Score of our legacy and new test suites, we began by cataloging every bit of Jasmine-specific syntax in our legacy suite. We then compiled a list of roughly forty test files that we would target for Mutation Testing in order to cover the full syntax catalog. For each file we generated a Mutation Score for its legacy state, converted it to run in our new Jest setup, and generated a Mutation Score again. Our hope was that the new Jest framework would have a Mutation Score as good as or better than our legacy framework.

By limiting the scope of our test to just a few dozen files, we were able to run all mutations Stryker had to offer within a reasonable timeframe. However, the sheer size of our codebase and the sprawling dependency trees in any given feature presented other challenges to this work. As I mentioned before, Stryker copies the source code to be mutated into separate sandbox directories. By default, it copies the entire project into each sandbox, but that was too much for Node.js to handle in our repository:

In my undergrad days, I loved mutation testing mostly because of the terminology. I’m happy to see a proper implementation of mutation testing and I’m even happier to see that they have a .NET version.

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Querying Power BI from Visual Studio Code

Published 2020-08-25 by Kevin Feasel

Phil Seamark shows us how to write queries against Power BI using Visual Studio Code:

It’s helpful to understand there are two main client libraries for Analysis Services. A client library is what you can add to any new Visual Studio Code Project to provide objects, methods and functions relevant for the tool you are building.

Make sure you download the NetCore (.Net Core) versions of these libraries when working with Visual Studio Code. There are .Net Framework versions of these libraries that are more suited to use with the full Visual Studio product.

Read on for links to those libraries and a thorough demonstration.

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Generating Stored Procedure Mappings for Entity Framework Core

Published 2020-08-24 by Kevin Feasel

Erik Ejlskov Jensen takes us through stored procedure mapping with Entity Framework Core Power Tools:

In my previous post I showed how you can map and use stored procedures manually from EF Core, a process which involved quite a bit of code, and some changes to your derived DbContext.

With the latest release of EF Core Power Tools, you can opt-in to have any SQL Server stored procedures in your database made available to you.

Click through to learn how to do this.

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Domain Models: Purity vs Completeness

Published 2020-08-07 by Kevin Feasel

Vladimir Khorikov reflects on domain modeling:

This is an example of a rich domain model: all business rules (also known as domain logic) are located in the domain classes. There’s one such rule currently — that we can only assign to the user an email that belongs to the corporate domain of that user’s company. There’s no way for the client code to bypass this invariant — a hallmark of a highly encapsulated domain model.

We can also say that our domain model is complete. A complete domain model is a model that contains all the application’s domain logic. In other words, there’s no domain logic fragmentation.

Domain logic fragmentation is when the domain logic resides in layers other than the domain layer. In our example, the UserController (which belongs to the application services layer) doesn’t contain any such logic, it serves solely as a coordinator between the domain layer and the database.

Domain modeling doesn’t land on too many database administrators’ doorsteps, but I enjoyed the article.

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Custom Windows in Apache Flink

Published 2020-07-31 by Kevin Feasel

Alexander Fedulov walks us through window options with Apache Flink:

In the previous articles of the series, we described how you can achieve flexible stream partitioning based on dynamically-updated configurations (a set of fraud-detection rules) and how you can utilize Flink’s Broadcast mechanism to distribute processing configuration at runtime among the relevant operators. 

Following up directly where we left the discussion of the end-to-end solution last time, in this article we will describe how you can use the “Swiss knife” of Flink – the Process Function to create an implementation that is tailor-made to match your streaming business logic requirements. Our discussion will continue in the context of the Fraud Detection engine. We will also demonstrate how you can implement your own custom replacement for time windows for cases where the out-of-the-box windowing available from the DataStream API does not satisfy your requirements. In particular, we will look at the trade-offs that you can make when designing a solution which requires low-latency reactions to individual events.

This article will describe some high-level concepts that can be applied independently, but it is recommended that you review the material in part one and part two of the series as well as checkout the code base in order to make it easier to follow along.

It’s worth giving this a careful read.

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Creating Power BI Measures via Visual Studio Code

Published 2020-07-30 by Kevin Feasel

Phil Seamark goes one step further with TOM:

My last blog introduced the idea of using Microsoft Visual Studio Code to work with Power BI Models. For this article, I build on that idea by showing how you can use a TOM based script to automatically generate measures in your model Power BI (or Azure Analysis Services) model.

For simplicity, the example in this blog will do the following:

– Connect to an instance of Power BI Desktop
– Iterate through every Table in the model
– Iterate through every Column in the “current” table from the outer loop
– If the Column is numeric and not hidden, create a simple [Sum of <column>] measure

Read on for demonstration code and a walkthrough of the process.

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The Basics of Gremlin

Published 2020-07-21 by Kevin Feasel

Raul Gonzalez introduces us to Gremlin:

Graph databases in Cosmos DB benefit from the same features, like the SQL API, it is globally distributed, scales independently throughput and storage, provides guaranteed latency, automatic indexing and more. So when relational databases choke with certain queries, No-SQL databases come to play.

Gremlin is the query language used by Apache Tinkerpop and it is implemented in Azure Cosmos DB. This language enables us to transverse graphs and answer complex queries that would be otherwise very expensive to run in traditional relational database engines.

Read on for a detailed example.

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