Synthetic Monitoring for CI CD Pipelines
In any ML project, after you define the business use case and establish the success criteria, the process of delivering an ML model to production involves the following steps. These steps can be completed manually or can be completed by an automatic pipeline. Therefore, many businesses are investing in their data science teams and ML capabilities to develop predictive models that can deliver business value to their users. We all practice monitoring and observability in our Production environment. That’s how we know that our system runs well, that our environment is stable, and in case of issue – to root-cause and remediate quickly and efficiently.
With the increased adoption of cloud technologies, the growing trend is to move the DevOps tasks to the cloud. Cloud service providers like Azure and AWS provide a full suite of services to manage all the required DevOps tasks using their respective platforms. In an agile context, each development, whether bug fix or feature improvement, falls into the CI/CD pipeline before deploying to production. The primary goal of a CI/CD pipeline is to automate the software development lifecycle . On the Output tab of the stack, choose QSAnalysisURL and QSDashboardURL to open dashboard and analysis.
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Tekton is a community-driven project hosted by the Continuous Delivery Foundation . Tekton’s standardized approach to CI/CD tooling and processes is applicable across multiple vendors, programming languages, and deployment environments. Splunk On-Call integrates metrics, logs and your monitoring toolset into a single source of truth that allows on-call teams to quickly fix problems.
This article explains CI/CD security, the challenges, and best practices to secure your software production pipeline. A fully managed deployment service can automate software deployments to a variety of endpoints and even your on-premise environment. Testing prediction service performance, which involves load testing the service to capture metrics such asqueries per seconds and model latency.
By automating the process, the objective is to minimize human error and maintain a consistent process for how software is released. Tools that are included in the pipeline could include compiling code, unit tests, code analysis, security, and binaries creation. For containerized environments, this pipeline would also include packaging the code into a container image to be deployed across a hybrid cloud. By automating CI/CD throughout development, testing, production, and monitoring phases of the software development lifecycle, organizations are able to develop higher quality code, faster. Although it’s possible to manually execute each of the steps of a CI/CD pipeline, the true value of CI/CD pipelines is realized through automation. For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system.
This is a guest blog post from Chris Tozzi, Senior Editor of content and a DevOps Analyst at Fixate IO. Chris Tozzi has worked as a journalist and Linux systems administrator. He has particular interests in open source, agile infrastructure, and networking. He is Senior Editor of content and a DevOps Analyst at Fixate IO. This posting does not necessarily represent Splunk’s position, strategies, or opinion.
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Prometheus is the golden standard for monitoring and I’ll follow that path for our backend, whether it’s your own instance of Prometheus or a Prometheus-compatible solution such as Logz.io Infrastructure Monitoring. Let’s see how to monitor metrics from the Jenkins servers and the environment, following the same flow. Alerts can be defined using any of the data fields collected on the “Collect” step, ci/cd pipeline monitoring and could be complex conditions such as “if sum of failures goes above X or average duration goes above Y – dispatch an alert”. Essentially, anything you can state as a Lucene query in Kibana, you can also automate as an alert. We’ve built this alerting mechanism on top of Elasticsearch and OpenSearch as part of our Log Management service, and you can use other supporting alerting mechanisms as well.
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Step 4: Executing the pipeline
Visualizing logs exclusively in Kibana involves a simpler setup that doesn’t require access to Elasticsearch from the Jenkins Controller. This is because the Jenkins pipeline build console displays a hyperlink to the Kibana logs visualization screen instead of displaying the logs in the Jenkins UI. The Jenkins OpenTelemetry Plugin provides pipeline log storage in Elasticsearch while enabling you to visualize the logs in Kibana and continue to display them through the Jenkins pipeline build console.
Follow these steps to create an alarm that monitors the state of the canary job. When you reach the step to select metrics, make sure you select CloudWatchSynthetics, your canary, and the SuccessPercent metric, as shown in the following two figures. However, you can use theOpenTelemetry Collector Span Metrics Processorto derive pipeline execution traces into KPI metrics like throughput and the error rate of pipelines.
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- This helps to ensure that the pipeline is running smoothly and that issues are addressed quickly.
- I’ll use Jenkins as the reference tool, as many know this popular open source project, and as in my company we’ve used it extensively.
- Day 2 Operations for GKE Tools and guidance for effective GKE management and monitoring.
- You want to be able to understand how your application will behave for all of your users, and you can only do that effectively if you perform synthetic monitoring for a wide variety of user profiles and use cases.
Add service context to enable seamless transition between log monitoring, infrastructure monitoring and APM. One of the best known open source tools for CI/CD is the automation server Jenkins. Jenkins is designed to handle anything from a simple CI server to a complete CD hub. Like code coverage, monitoring the number of defects is useful for alerting you to a general upward trend, which can indicate that bugs are getting out of hand.
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Where CI leaves off, continuous delivery kicks in with automated testing and deployment. Not only does CD reduce the amount of “hands on” time ops pros need to spend on delivery and deployment, it also enables teams to drastically reduce the number of tools required to manage the lifecycle. The process of delivering an application involves several stages such as development, testing, and production monitoring. With the Splunk platform, real-time visibility and understanding can be achieved throughout all of these stages. Splunk provides a powerful platform for CI/CD pipeline monitoring, allowing teams to gain deep insights into pipeline performance, troubleshoot issues quickly, and optimize their development processes. Splunk can ingest data from a wide range of sources, including logs, metrics, and events generated by CI/CD pipeline tools and processes.