This document discusses declarative configuration management using Kubernetes and Helm. It begins with an overview of DevOps and configuration management processes. It then provides background on tools like Puppet, Chef, and Ansible before introducing Kubernetes as an operating system for microservices and Helm as a package management system. Key points covered include how Helm works by generating Kubernetes YAML configurations and using Tiller to apply them in the cluster. The document also notes that the Helm chart repository is large and actively maintained on GitHub. It concludes by acknowledging issues with Golang and configuration tools while also noting the presenter does not really care to criticize them.
This document discusses declarative configuration management using Kubernetes and Helm. It begins with an overview of DevOps and configuration management processes. It then provides background on tools like Puppet, Chef, and Ansible before introducing Kubernetes as an operating system for microservices and Helm as a package management system. Key points covered include how Helm works by generating Kubernetes YAML configurations and using Tiller to apply them in the cluster. The document also notes that the Helm chart repository is large and actively maintained on GitHub. It concludes by acknowledging issues with Golang and configuration tools while also noting the presenter does not really care to criticize them.
This document discusses configuration management (CM) tools like Chef, Puppet, and Ansible and container orchestration tools like Docker and Kubernetes. It provides an overview of what each tool is used for. Helm is introduced as a package manager and configuration management tool for Kubernetes that uses templates. While template usage is noted as a potential issue, Helm is described as having a large community and being a de facto standard in the Kubernetes world. Alternatives to Helm are also mentioned. In the conclusions, Helm is said to be an improvement over Ansible and that the author's personal experiences have been financially beneficial.
The document discusses Ansible and its shortcomings. It proposes alternatives like Stonic and s1onique that were intended to improve on Ansible but also faced issues. The document suggests rewriting Ansible in different languages like Haskell, Kotlin, Rust to address its problems in enforcing a clearly defined desired state. It concludes by questioning the use of configuration management systems and expressing fatigue with the topic.
This document discusses profiling Python performance. It begins by acknowledging that Python can be slow due to the Global Interpreter Lock (GIL) and lack of a just-in-time (JIT) compiler. It then demonstrates how to profile a Python program using the pyflame tool to collect stack samples and convert them to a flamegraph for analysis. The document shows pyflame being used to profile a simple Sanic web application under load testing. It finds that pyflame has surprisingly little overhead and concludes that while Python has limitations, it is not inherently slow when profiled and optimized properly.
This study analyzed over 50 million code repositories on GitHub to evaluate the relationship between programming languages and code quality metrics like bugs, complexity, and duplication. The findings suggest that languages like Python, PHP, and Java tend to have lower code quality than languages like TypeScript, Rust, and Go as measured by several metrics. The results provide insights for both language designers and developers on how languages and practices impact code quality attributes at large scale.
This MariaDB workshop covers database concepts like tables, queries, indexes, and transactions. It discusses performance monitoring using the slow query log and tools like Percona Toolkit and Anemometer. Replication in MariaDB is explained, including traditional master-slave replication and Galera clusters. Hands-on exercises reinforce key concepts.
The document discusses open data and mining GitHub for the greater good. It defines open data as any data available under an open license. It notes that GitHub is the largest social network of IT and CS professionals and contains lots of open source code. While the data on GitHub Archive is available, it questions whether it is truly open since it requires use of Google BigQuery. The document encourages mining GitHub to help work for the greater good.