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(Operating Lambda: Debugging code – Part 1)[https://aws.amazon.com/blogs/compute/operating-lambda-debugging-code-part-1/]
(Operating Lambda: Debugging configurations – Part 2)[https://aws.amazon.com/blogs/compute/operating-lambda-debugging-configurations-part-2/]
(Operating Lambda: Debugging configurations – Part 3)[https://aws.amazon.com/blogs/compute/operating-lambda-debugging-integrations-part-3/

In the Operating Lambda series, I cover important topics for developers, architects, and systems administrators who are managing AWS Lambda-based applications. This three-part series discusses core debugging concepts for Lambda-based applications.

(Those pesky pull request reviews)[https://jessitron.com/2021/03/27/those-pesky-pull-request-reviews/]

They’re everywhere. In Slack: “hey, can I get a review on this?” In email: “Your review is requested!” In JIRA: “8 user stories In-Progress” (but code-complete). In your repository: 5 open pull requests. They’re slowing your delivery. They’re interrupting your developers.

How can we get people to review pull requests faster??

(Operating Lambda: Using CloudWatch Logs Insights)[https://aws.amazon.com/blogs/compute/operating-lambda-using-cloudwatch-logs-insights/]

In the Operating Lambda series, I cover important topics for developers, architects, and systems administrators who are managing AWS Lambda-based applications. This three-part series discusses monitoring and observability for Lambda-based applications and covers:

  • Using Amazon CloudWatch, CloudWatch Logs Insights, and AWS X-Ray to apply monitoring
    across services.
  • How existing monitoring concepts apply to Lambda-based applications.
  • Troubleshooting application issues in an example walkthrough.
    This post explains how to use CloudWatch Logs Insights in your serverless applications.

(CDK Lambda Deployment takes about a minute - how about sub second Function Code Deployment?)[https://aws-blog.de/2021/04/cdk-lambda-deployment-takes-about-a-minute-how-about-sub-second-function-code-deployment.html]

Creation of Lambda infrastructure with the CDK is really powerful. Updating the Function code is really slow. Here is a fix for that to get to a sub-second Lambda function deployment time.

(Best practices for developing cloud applications with AWS CDK)[https://aws.amazon.com/blogs/devops/best-practices-for-developing-cloud-applications-with-aws-cdk/]

In this post, we discuss strategies for organizing the development of complex cloud applications with large teams, using the AWS Cloud Development Kit (AWS CDK) as a central technology. AWS CDK allows developers and administrators to define their cloud applications using a familiar programming language, such as TypeScript, Python, Java, or C#. Applications are organized into stages, stacks, and constructs, which allows for modular design techniques in both runtime logic (such as AWS Lambda code or containerized services) and infrastructure components such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Relational Database Service (Amazon RDS) databases, and network infrastructure.

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Refinement Code Review

With the right environment, I can look a bit of code written six months ago, see some problems with how it's written, and quickly fix them. This may be because this code was flawed when it was written, or that changes in the code base since led to the code no longer being quite right. Whichever the cause, the important thing is to fix problems as soon as they start getting in our way. As soon as I have an understanding about the code that wasn't immediately apparent from reading it, I have the responsibility to (as Ward Cunningham so wonderfully said) take that understanding out of my head and put it into the code. That way the next reader won't have to work so hard.

After all, many problems that code reviews seek to remedy are problems that only become problems when the code is read in the future.

MONITORING REPLICATION: PG_STAT_REPLICATION

PostgreSQL replication (synchronous and asynchronous replication) is one of the most widespread features in the database community. Nowadays, people are building high-availability clusters or use replication to create read-only replicas to spread out the workload. What is important to note here is that if you are using replication, you must make sure that your clusters are properly monitored.

The purpose of this post is to explain some of the fundamentals, to make sure that your PostgreSQL clusters stay healthy.

POSTGRESQL: WHAT IS A CHECKPOINT?

Check some information about checkpoints, mix and max wal size, how they operate toghether and what they play on our day-to-day database workload.

Checkpoints are a core concept in PostgreSQL. However, many people don’t know what they actually are, nor do they understand how to tune checkpoints to reach maximum efficiency. This post will explain both checkpoints and checkpoint tuning, and will hopefully shed some light on these vital database internals.

Boos your user defined functions in PostgreSQL

Using the RDBMS only to store data is restricting the full potential of the database systems, which were designed for server-side processing and provide other options besides being a data container. Some of these options are stored procedures and functions that allow the user to write server-side code, using the principle of bringing computation to data, avoiding large datasets round trips and taking advantage of server resources. PostgreSQL allows programming inside the database since the beginning, with User Defined Functions (UDFs). These functions can be written in several languages like SQL, PL/pgsql, PL/Python, PL/Perl, and others. But the most common are the first two mentioned: SQL and PL/pgsql. However, there may be “anti-patterns” in your code within functions and they can affect performance. This blog will show the reader some simple tips, examples and explanations about increasing performance in server-side processing with User Defined Functions in PostgreSQL. It is also important to clarify that the intention of this post isn’t to discuss whether Business Logic should be placed, but only how you can take advantage of the resources of the database server.

The career-changing art of reading the docs

Don’t wait for knowledge to find you through years of inefficient trial and error. Go get it. And the most convenient, comprehensive place to grab it was there in front of you all along.

Read the docs.

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CQRS Is an Anti-Pattern for DDD

Are you interested in new ways to build better software systems? If you work with distributed systems or build any kind of web application, you most likely have heard of the new trends like using Domain-Driven Design with Event-Sourcing and Command Query Responsibility Segregation (CQRS). Well, they are not exactly brand new. However, they are now becoming increasingly popular.

nip.io/

Dead simple wildcard DNS for any IP Address

Stop editing your /etc/hosts file with custom hostname and IP address mappings.

The Tighten Test: 12 Steps to a Better Team

Twenty years ago today, Joel Spolsky (who later co-founded Stack Overflow) published The Joel Test: 12 Steps to Better Code listing 12 metrics for rating the quality of a software development team. The premise is simple: you get 1 point for each “yes” answer, for a total score of up to 12 points.

Architecture decision records, also known as ADRs, are a great way to document how and why a decision was reached within a codebase. We’ve started to adopt them within the mobile team here at GitHub, documenting decisions that affect the iOS codebase and Android codebase, as well as decisions that affect both mobile clients.

ADRs are not the most common within open source codebases, but have gained more popularity since ~2017 within long-lived, “evolutionary” codebases like those in more enterprise-y settings.

So why write an ADR? Why spend time documenting something when a decision has already been made?

https://free-for.dev/

This is a list of software (SaaS, PaaS, IaaS, etc.) and other offerings that have free tiers for developers.