Introduced in 1991, Python has grown to become a versatile and reliable programming language for modern computing requirements. Python is a powerful language used in web development, data science, software prototype creation, and much more. One of the best qualities of this language is it’s easy to learn and uniform across many use-cases.
A decade ago, DevOps teams were slow, lumbering behemoths with little automation and lots of manual review processes. As explained in the 2020 State of DevOps Report, new software releases were rare but required all hands on deck. Now, DevOps teams embrace Agile workflows and automation. They release often, with relatively few changes. High-quality DevOps change management is no longer a nice-to-have, it’s a must. For a lot of DevOps teams, this is easier said than done.
Maintaining product focus is the best way to guarantee a successful business. As the late great Steve Jobs put it: “if you keep an eye on the profits, you’re going to skimp on the product… but if you focus on making really great products, the profits will follow.” There are a wide variety of statistics available on how much time developers actually spend writing code, anywhere from 25% to 32%.
As our customers scale and utilize Coralogix for more teams and use cases, we decided to make their lives easier and allow them to set up their Coralogix account using declarative, infrastructure-as-code techniques. In addition to setting up Log Parsing Rules and Alerts through the Coralogix user interface and REST API, Coralogix users are now able to use modern, cloud-native infrastructure provisioning platforms.
Driving productivity of software development and delivery teams is critical for any organization. The six years of research by DevOps Research and Assessment (DORA) showcases the role easy-to-use tooling plays in driving this productivity and in turn a better work/life balance for the team. The research finds that highest performing teams are 1.5x more likely to have tools they consider easy to use.
Everyone’s software crashes. As an engineer, you don’t feel your users’ frustration unless they reach out to customer support, write bad reviews, or tweet about it. This feedback is often lacking relevant information to resolve the issue. In some cases, you can re-engage with the customer, but that process is time-consuming and inefficient. Another option would be to examine the crash reports, but sometimes they don’t give sufficient insight to fix the problem.
In this post, we’re going to talk about tips for securing the reliability of Loki’s write path (where Loki ingests logs). More succinctly, how can Loki ensure we don’t lose logs? This is a common starting point for those who have tried out the single binary Loki deployment and decided to build a more production-ready deployment. Now, let’s look at the two tools Loki uses to prevent log loss.
PromQL, short for Prometheus Querying Language, is the main way to query metrics within Prometheus. You can display an expression’s return either as a graph or export it using the HTTP API. PromQL uses three data types: scalars, range vectors, and instant vectors. It also uses strings, but only as literals. This intro will provide basic PromQL examples and concepts to understand as you get used to Prometheus queries.
Application Performance Monitoring (APM) refers to monitoring or managing the performance of your code, application dependencies, transaction times, & overall user experiences. It is an important technology that ensures the computer application programs are performing as expected. The ultimate goal of performance monitoring is to supply end users with a top quality end-user experience.
