<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Oleksandr Hrebeniuk on foojay.io - Friends of OpenJDK</title><link>https://foojayio.github.io/website/today/author/oleksandr-hrebeniuk/</link><description>Articles written by Oleksandr Hrebeniuk on foojay.io - Friends of OpenJDK</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 07 Jun 2024 07:17:58 +0000</lastBuildDate><atom:link href="https://foojayio.github.io/website/today/author/oleksandr-hrebeniuk/index.xml" rel="self" type="application/rss+xml"/><item><title>Identify Dependencies in Codebases During Microservice Migration</title><link>https://foojayio.github.io/website/today/how-to-identify-dependencies-in-your-codebase-during-microservices-migration/</link><pubDate>Fri, 07 Jun 2024 07:17:58 +0000</pubDate><guid>https://foojayio.github.io/website/today/how-to-identify-dependencies-in-your-codebase-during-microservices-migration/</guid><description>&lt;blockquote&gt;
&lt;p&gt;Migration from a monolithic architecture to microservices presents challenges, particularly in identifying and managing dependencies within the codebase. By analyzing the application&amp;rsquo;s training data, we can uncover the seams and threads that bind the application together, discovering a safer, more iterative path to refactor our code.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;&lt;strong&gt;Table of Contents&lt;/strong&gt;&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;Collecting tracing data to understand code dependencies&lt;/li&gt;
&lt;li&gt;Using Digma Analytic Insights to identify dependencies in your codebase:&lt;/li&gt;
&lt;li&gt;Code Nexus Point Insight&lt;/li&gt;
&lt;li&gt;Top Usage Insight&lt;/li&gt;
&lt;li&gt;Discovering Dead Code&lt;/li&gt;
&lt;li&gt;Duration Breakdown Insight&lt;/li&gt;
&lt;li&gt;Conclusion: Identifying Dependencies in Your Codebase During Microservices Migration&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;Migration from a monolithic architecture to microservices can be crucial for scalability, flexibility, and development velocity. However, this transformation has challenges, particularly when trying to identify and manage dependencies within the codebase. Many teams optimistically embark on an application-wide modernization epic, thinking of making short work of refactoring their codebase to be more modular. Unfortunately, many of these initiatives quickly lose the initial momentum, as it becomes apparent that untangling the Gordian monolith knot is proving to be an uphill struggle.&lt;/p&gt;</description></item><item><title>SQL Query Optimization: How to Identify and Optimize Slow SQL Queries</title><link>https://foojayio.github.io/website/today/sql-query-optimization-how-to-identify-and-optimize-slow-sql-queries/</link><pubDate>Thu, 09 May 2024 21:44:39 +0000</pubDate><guid>https://foojayio.github.io/website/today/sql-query-optimization-how-to-identify-and-optimize-slow-sql-queries/</guid><description>&lt;p&gt;&lt;strong&gt;In this article, we want to share our experience with fellow developers and offer insights using real-life examples on how to identify and optimize slow SQL queries, especially when working with relational database management systems like PostgreSQL, MySQL, MSSQL, Oracle, etc.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;If you are a developer who doesn&amp;rsquo;t use database ORMs like Hibernate that much, you are no stranger to the concept of SQL query optimization. As developers, we know that a slow SQL query can reduce our application&amp;rsquo;s efficiency and performance, leaving our users with a bad experience.&lt;/p&gt;</description></item></channel></rss>