Decoding the code

Mariusz Krzanowski blog

When retry never succeeds

Introduction

Software development is a craftsmanship in which we are trying to satisfy clients’ needs. Resiliency is one of those needs. We are aware that sometimes database connection can fail or we can have other transient errors like being chosen as a deadlock victim. In case of transient errors a good practice is to retry a failed process, to try to succeed in the next try. In .NET world many developers use  e.g. Polly https://github.com/App-vNext/Polly to add a retry policy for chosen methods.

Problem with IoC and Scoped Lifecycle

A bug we found a few months ago in the legacy code has proven that developers usually are focused on success scenarios and do not spend a lot of time on failure analysis in advance.

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Azure Functions V3 and disappearing function.json files

Introduction

When I started migration process to Azure Function 3.0 of an existing project, I have discovered small, but painful inconvenience. The problem was that all function.json files were permanently removed from output folder just before Azure Function Tools func.exe was started. I have to confess it was very annoying. There were suggestions e.g. on StackOverflow or other sites to run PowerShell which copies files later – after emulator is started, but it was not a solution I like.

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Is Liskov Substitiuton Principle violated by async pattern?

The async/await keywords change methods behavior. When async/await pattern is used, the method never throws an exception. The method always returns a task which can be eventually Faulted, Canceled or RunToCompletion. So why do I think about violating Liskov Substitution Principle?

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Exception filters to catch first thrown exception

Introduction

This post is inspired by the book ‘C# in depth’ by Jon Skeet and presentation ‘Internals of Exceptions’ made by Adam Furmanek. The topic I want to focus on is stack unwinding when an exception is caught.

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The time complexity trap in dictionaries

We developers often think about time complexity. We are familiar with the fact that time complexity is the main factor, which helps us create really powerful algorithms. When we need to store data requiring random access, we use hash sets or dictionaries. Why do we do this? Because we know, that time complexity is O(1) for that kind of stores.

When we use Dictionary<TKey,TValue> we think that access via key is really fast. We expect O(1) time complexity. We are right when we think about algorithms usually implemented inside dictionaries. The truth is that we are right only when we consider dictionary implementation only. What we are missing sometimes is the time complexity of two operations that must be calculated while accessing the value by the key. Those operations are:

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ConcurrentDictionary and race condition

Introduction

The goal of this article is to show the problem of race condition while execution of GetOrAdd(TKey, Func<TKey, TValue>) method. The described problem has a few solutions. I will focus on one I found at StackOverflow, and I will explain why this solution works.

Double execution problem

Not everyone realizes that  GetOrAdd(TKey, Func<TKey, TValue>) can call multiple times the delegate resolving value for a single key. It is easy to prove this thesis with the following code.

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Intensive cache miss

Introduction

It is common knowledge that using cache can speed a lot our applications. In this post I would like to focus on application design using cache. The simplest scenario using cache that I frequently see follows this algorithm.

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Idempotence in sending e-mail

When you build a distributed system it is difficult to design good transaction guarantee mechanism. If your communication is asynchronous, you communicate with remote services using some messaging system. I guess you already know that network is not reliable, so your message can be lost. To guarantee message delivery you have to resend it when confirmation is missing. There are solutions to guarantee idempotence for the service receiving data. What I want to share is my idea how to make sending e-mail service as close as possible to the idempotence solution.

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Review of Designing Data-Intensive Applications by Martin Kleppmann

Today I finished reading a book ‘Designing Data-Intensive Applications’ written by Martin Kleppmann. I would like to share with you my review of this book.

In my view designing and development of any distributed system is a hard work. My opinion is based on over a decade of experience in this subject. The problem is that all modern applications are distributed in some way. If a database is hosted on a different computer than a web server, there is a communication link. You have two services – database service and application service – which share an unreliable network as a communication channel. The browser hosting client code which connects to the web server creates distributed system as well. 

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Developing workflow without workflow engine

Disclaimer

Workflow engines are very advanced tools and I saw a lot of projects where they created great business values. They sometimes simplify development. In this article I do not argue that you should not use them. My goal is to show you that there is an alternative – transformation from workflow into set of services distributed in the future.

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