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• Blurred lines between CI and CD, with other automation use cases needing accessibility |
• Open telemetry helps glue information from multiple tools in CI/CD pipelines |
• Need for data to verify pipeline performance and security in audit and supply chain security |
• Data structure for modeling CI and CD pipelines |
• Exposing data of CD processes as a goldmine for troubleshooting and maintenance |
• Benefits of sizing platforms and shortening build cycles |
• Interest in cost accounting, process optimization, and digital transformation |
• Capturing data on distributed traces using open telemetry |
• Unified view of CI and CD processes |
• Debate over standardizing tools or implementing different ones |
• Distributed trace culture providing overall visibility across phases |
• Dimensions of data collection and culture of abstraction |
• Requirements for a good CI-CD system with open telemetry integration |
• Perfect flow of a system with good open telemetry in the software development pipeline |
• Steps involved in processing code, including invocation of external services and tools like Maven or Gradle |
• Need for observability at each level of the pipeline |
• Distributed tracing and passing context through levels of systems |
• Understanding the beginning of a pipeline and its end result |
• Debate on whether deployment should be the last step in a CI or CD pipeline |
• Importance of reporting, post-processing, and accounting work after deployment |
• Use of external tools for various tasks in the pipeline |
• Discussion of instrumenting pipelines with Jenkins, Maven, and Ansible |
• Need to understand what spans to capture during pipeline execution to measure time taken for specific steps. |
• Understanding the importance of attribute extraction from pipeline execution |
• Capturing relevant attributes for troubleshooting and use cases |
• Associating attributes with teams for cost accounting and performance analysis |
• Using stages (CI build, QA, security) to group pipeline constructs for organizational grouping and data usefulness |
• Attributes for velocity and team performance in software delivery process |
• Inviting entire listener base to move latency sensitive workloads to the edge with compute at edge free for three months and up to $100,000 in credit |
• Overview of Fastly's edge cloud network and modern approach to serverless computing |
• Benefits of deploying complex logic at the edge, including unparalleled security, blazing fast computational speed, and real-time observability |
• Calculation or "spans" being worked out incorrectly when it comes to job allocation in agents, and how open telemetry can help CICD administrators solve this problem |
• Role of open telemetry in helping CICD administrators, who may be a shared role among many people |
• Security scanners and deployment |
• Complicated systems and scalability problems |
• CI/CD pipeline maintenance and troubleshooting difficulties |
• Limited observability of data in distributed systems |
• Need for assistance to quickly understand problem impact and scope |
• Importance of observability for slicing and dicing data in any dimension |
• Docker registry issues and their impact on CI/CD administrators |
• Provision of tools to help notify CI/CD administrators early of problems |
• Observability and its benefits in microservices architecture |
• Automated anomaly detection through machine learning and statistics |
• Benefits of observability for CI/CD administrators |
• Comparison of modern CI/CD systems as a mesh of asynchronous processes |
• Complexity of pipeline dependencies and interactions |
• Agent provisioning and synchronization issues |
• Caching in CI/CD systems and its impact on pipeline performance |
• Difficulty in tracing events and understanding pipeline flow |
• Managing outages and issues across multiple pipelines |
• Difficulty in caching system interaction |
• Complexity of pipeline systems and debugging challenges |
• Importance of caching for reducing costs |
• Simplifying complex systems for better throughput |
• Visibility and monitoring of cicd pipelines |
• Divergence between dev and ops teams' goals (stability vs. new features) |
• Need for observability to build confidence in change |
• Flaky tests in distributed systems |
• Challenges with testing distributed systems, including race conditions |
• Open Telemetry's potential to help with flaky tests |
• Existing solution for Go test using Open Telemetry |
• Opportunity to create a backbone for unit test results through Open Telemetry channels |
• Potential for dev/ops team to implement their own tool to process and share flaky test reports |
• Community-driven open source solution leveraging the flexibility of Open Telemetry |
• Expecting an emerging standard for open telemetry in CI/CD systems |
• Currently, almost no tools have built-in serial open telemetry instrumentation |
• Jenkins and Concourse CI are two platforms that provide native open telemetry instrumentation |
• Integrating open telemetry in Jenkins requires installing the Jenkins Open Telemetry plugin and connecting to an open telemetry endpoint backend (e.g. Elastic or Jaeger) |
• Auto CLI from Equinix Labs can be used to collect open telemetry data for systems without built-in support |
• Capturing health metrics and tracing pipeline execution using open telemetry |
• Two initiatives that came to mind for instrumenting CI/CD pipelines were using Honeycomb's small cli tool and Hotel CLI as a wrapper. |
• Hotel CLI can be used with tools like GitHub Actions, GitLab CI, and Jenkins, even if the platform itself is not instrumented. |
• A hackish method was mentioned of replacing the shell on agents with one that has Hotel CLI enabled by default. |
• An example of using Open Telemetry to create a shell wrapper that sends commands to Open Telemetry was discussed. |
• Running Jenkins in production was touched upon, and it was noted that elastic uses Kubernetes for their modern Jenkins platform. |
• Leverage flexibility of Docker containers to let development teams customize their build environment was recommended. |
• Modern Jenkins management and the elimination of "Jenkins Plugin Hell" |
• Running Jenkins and Kubernetes in production |
• Deploying Jenkins using configurations, code, and infrastructure as code |
• Packaging Jenkins into containers using tools like Helm charts |
• Local development and testing of CI pipelines |
• Deployment options for Jenkins in production, including use of Kubernetes and Helm charts |
• Jenkins configuration as code |
• Pipelines stored in repository with project |
• Agent definitions stored in repository |
• Entire Jenkins combination (server, login, config) as a single deliverable |
• Configuring Jenkins using Kubernetes API or targeting Jenkins master directly |
• Flexibility of deploying Jenkins and retrieving configurations without redeploying system |
• Fire Hydrant platform and its features |
• Incident response automation |
• Separating CI (Continuous Integration) from CD (Continuous Deployment) pipelines |
• Implementation of separate CI and CD tools (e.g. GetHub Actions, ArgoCD) |
• Discussion on automating deployment processes and supply chain security |
• KeylessH is currently used to watch images and automatically update when changes occur |
• A desire to decouple deployment and integration concerns, allowing for multiple copies of production environments |
• The use of CI/CD systems, with a focus on scalability concerns |
• Jenkins is commonly used for pipeline development, along with GitHub Actions |
• Pipeline libraries are created to implement common steps, such as deploying to Docker Hub or building Maven projects |
• Test frameworks can be built for these pipelines to ensure they function correctly |
• Jenkins plugin development and maintenance process |
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