A few weeks back, I initiated a call for blog posts about pgBadger for the tenth installment of the monthly PGSQL Phriday series. It was an enjoyable experience seeing the entries come in over the week. I extend my heartfelt gratitude to all who participated. You will find a brief summary of all these insightful blog posts below.
Henrietta (Hettie) Dombrovskaya - Hettie notes how pgBadger has been pivotal in diagnosing slow database performance, focusing on metrics such as the frequency and duration of queries. She argues that slow performance often results from suboptimal database access patterns due to the use of ORM (Object-Relational Mapping), leading to an excessive number of database calls. pgBadger can highlight these multiple database calls, enabling Hettie to identify queries that are executed quickly but too frequently. Finally, Hettie expresses a wish for a feature in pgBadger to track repeating sequences of SQL statements, which would aid in understanding an application's behavior.
Anthony Nowocien - Anthony appreciates how it turns raw log data into an understandable web report and provides insights on statistics, events, sessions, and query performance. Anthony points out that pgBadger is beneficial for giving high-level overviews, managing lengthy log files, and visually presenting data, which aids in communication with non-technical individuals.
He also acknowledges a few limitations, including potential performance issues and limited chart functionality. Despite these, he strongly recommends pgBadger for PostgreSQL performance analysis, as its effectiveness depends on the quality of the logs it parses.
I particularly loved his delightful reference to the "Very Hungry Caterpillar" book.
Laetitia Avrot - While she recognizes its utility for routine monitoring, she finds it lacks flexibility for the complex problems she often encounters. She prefers using a CSV output for Postgres logs and querying them as tables using Foreign Data Wrapper (FDW). This approach allows her more control over her log analysis. Despite her personal preferences, she recommends pgBadger for DBAs who need an overview of their system's performance.
Alicja Kucharczyk – In my own blog post, I discuss how pgBadger has transformed my approach to Postgres troubleshooting. I recount my first encounter with pgBadger at my debut IT job and its valuable assistance in solving Postgres issues ever since. When troubleshooting, I rely on four main components: system views snapshots, OS level metrics, basic instance data, and pgBadger itself. These elements enable me to efficiently resolve most issues I encounter.
I deep-dive into different sections of the pgBadger report, focusing on the 'VACUUMS' and 'Temp Files' tabs that have proven most useful. I share how I interpret these reports, spot irregularities, and adjust settings to optimize performance. The 'Top' and 'Events' tabs are also key in identifying time-consuming queries and monitoring for any warnings or errors.
I want to extend a special thank you to Ryan Booz for his fantastic work organizing and hosting the event, and to Gilles Darold for his tireless efforts in creating and maintaining the incredibly powerful pgBadger tool. Your dedication and hard work have been instrumental in our ability to explore, learn, and share these insights about pgBadger.
