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Today, we discuss C# code quality and a variety of errors by the example of CMS DotNetNuke. We're going to dig into its source code. The source code is available on GitHub. You're going to need a cup of coffee. DotNetNuke. DotNetNuke is an open-source content management system (CMS) written mainly in C#.
This lets you build your SLOs around the indicators that matter to you and your customers—critical metrics related to availability, failure rates, request response times, or select logs and business events. While the SLO management web UI and API are already available, the dashboard tile will be released within the next weeks.
What developers want Developers want to own their code in a distributed, ephemeral, cloud, microservices-based environment. This ownership starts with understanding how their code behaves in all environments, resolving issues, and writing and optimizing code in a high-quality, secure, and timely manner.
Onboarding teams using self-service Kubernetes selectors is one of the best examples of how Dynatrace embraces cloud native technologies to increase automation, reduce bureaucracy, and encourage agility. The following example drives the point home. Embracing cloud native best practices to increase automation. Putting it all together.
While selecting a Kubernetes segment, the selector provides a dynamic list of available resources. Segments can implement variables to dynamically provide, for example, a list of entities to users, such as available Kubernetes clusters, for unmatched flexibility and dynamic segmentation. What are Dynatrace Segments?
Automatic data capture and display: More data, including span attributes, is available for out-of-the-box analysis, with no additional configuration necessary. To pinpoint the exact requests that are failing, the e-commerce team filters by excluding successful HTTP 200 status codes. You can even walk through the same example above.
It automates tasks such as provisioning and scaling Dynatrace monitoring components, updating configurations, and ensuring the health and availability of your monitoring infrastructure. Dynatrace code modules, enabled via Dynatrace webhook, provide distributed tracing and code-level visibility for applications deployed on Kubernetes.
All-new Dynatrace code-level vulnerability detection All-new Dynatrace code-level vulnerability detection evaluates all requests passing through your applications to identify vulnerabilities. The deep insights into application code provided by OneAgent® help track potentially vulnerable data flow within an application.
You can select any trigger thats available for standard workflows, including schedules, problem triggers, customer event triggers, or on-demand triggers. For this example, we go to Simple Workflows and select Trigger > Davis event trigger to find these out-of-memory errors. Go to Workflows and start creating a new workflow.
In this example use case, we’re investigating a high CPU load issue in an application using OpenTelemetry. Dynatrace OpenPipeline is configured to ingest logs from our environment, including the AdService example service. Dynatrace Live Debugger will be generally available (GA) within the next 90 days.
The IT world is rife with jargon — and “as code” is no exception. “As code” means simplifying complex and time-consuming tasks by automating some, or all, of their processes. Today, the composable nature of code enables skilled IT teams to create and customize automated solutions capable of improving efficiency.
Most of these leverage the unique capability of Dynatrace OneAgent® to extract business data from in-flight application payloads — without writing any code. Most of the use cases in these two broad categories benefit from the flexibility that comes from multiple available sources of business data.
Certain service-level objective examples can help organizations get started on measuring and delivering metrics that matter. Teams can build on these SLO examples to improve application performance and reliability. In this post, I’ll lay out five SLO examples that every DevOps and SRE team should consider.
Key components of GitOps are declarative infrastructure as code, orchestration, and observability. Many observability solutions don’t support an “as code” approach. Dynatrace enables software intelligence as code. Observability is required for effective collaboration and automation.
A natural solution is to make flows configurable using configuration files, so variants can be defined without changing the code. Unlike parameters, configs can be used more widely in your flow code, particularly, they can be used in step or flow level decorators as well as to set defaults for parameters.
For example, it supports string and numerical values, enabling a multitude of different use cases. To achieve the best visual outcome, we recommend experimenting with the available customization options. Use color coding to tell a story. For example, set the value range for CPU consumption from 0% to 100%.
Thanks to centralized configuration, near real-time availability of all services, and potential cost optimization, metric streaming is now the future of performance data distribution. tenant_url – URL of your Dynatrace SaaS environment, for example [link]. Here you can see an example of a created stack.
By adding Flutter support, we’re giving you more freedom to choose what best fits your use case and available resources. When you set up user actions in your code, OneAgent automatically links associated web requests to those user actions. Let Dynatrace Davis AI alert you to detected anomalies in usage patterns.
Both categories share common requirements, such as high throughput and high availability. Eventually Consistent Global Counter While some users may accept the limitations of a Best-Effort counter, others opt for precise counts, durability and global availability.
To provide automated feedback for developers, the concept of quality gates for static code analysis in continuous integration is widely adopted throughout the industry. The developer must pause their current engineering work to address the reported issue and consider the code changes they worked on a few days or weeks prior.
And by “sample” we mean “an example”, like food samples in your local grocery store. One of the main reasons this feature exists is just like with food samples, to give you “a taste” of the production quality ETL code that you could encounter inside the Netflix data ecosystem. This is one way to build trust with our internal user base.
The automated extraction of ownership information, for example, from Kubernetes annotations, is therefore essential. Keeping ownership teams and their properties up to date is essential, as is having the right contact information available when needed. An example via Monaco can be found in this public GitHub repository.
To make this possible, the application code should be instrumented with telemetry data for deep insights, including: Metrics to find out how the behavior of a system has changed over time. And because Dynatrace can consume CloudWatch metrics, almost all your AWS usage information is available to you within Dynatrace.
In a MySQL master-slave high availability (HA) setup, it is important to continuously monitor the health of the master and slave servers so you can detect potential issues and take corrective actions. If the exit code indicates a failure, the return code from MySQL will tell us the failure reason.
It automates tasks such as provisioning and scaling Dynatrace monitoring components, updating configurations, and ensuring the health and availability of the monitoring infrastructure. It also detects new containers and injects OneAgent code modules into application pods.
Dynatrace Configuration as Code enables complete automation of the Dynatrace platform’s configuration, ensuring that software is secure and reliable. With Configuration as Code, developers can manage their observability and security tasks with config files that can be developed alongside source code conveniently and at scale.
This is one example of the many use cases we’re exploring. For example, it can help DevOps and platform engineering teams write code snippets by drawing on information from software libraries. It highlights the potential of GPT technology to drive “information democracy” even further.
More accurately, DOMContentLoaded signifies that all blocking and defer and type=module code has finished running. before the app’s key functionality is available, with almost half waiting over 3.5s! Even More Insights While DOMContentLoaded tells us when our defer red code finished running—which is great!—it log ( window.
Typically, the attackers attempt to exploit some weakness in the vendor’s development or delivery life cycle and attempt to inject malicious code before an application is signed and certified. It all starts with the code. Dynatrace has established a secure SDL, which automatically enforces that all developed code is reviewed by a peer.
The Clouds app provides a view of all available cloud-native services. Logs in context, along with other details, are instantly available after selecting a resource. The reasons are easy to find, looking at the latest improvements that went live along with the general availability of the Logs app.
Code changes are often required to refine observability data. This results in site reliability engineers nudging development teams to add resource attributes, endpoints, and tokens to their source code. This example is a good starting point for exploratory analysis with context-aware Dynatrace Davis insights.
Lack of data: In many cases, available data may not be enough to make informed decisions due to data silos, distribution, or negligence. Easy workflow creation and maintenance Dynatrace Workflows provides workflow automation capabilities with an intuitive drag-and-drop UI and a no-code, low-code approach.
For example, you might transform the count-based metric “form submissions” into a rate to report form submissions per hour. Examples of metric calculations. Consider the example 75% enrollment-in-autopay goal as an SLO. Here are other examples to spur your imagination: Convert currencies. Presentation matters.
There are two different approaches to GraphQL development; schema-first and code-first development. The code in your service only implements this schema. With code-first development , you don’t have a schema file. Instead, the schema gets generated at runtime based on definitions in code. Let’s start with a simple schema.
Managing Auto-Instrumentation in Pods The Operator automatically injects and configures auto-instrumentation for your applications, which enables you to collect telemetry data without modifying your source code. There are two versions available: v1alpha1 : apiVersion: opentelemetry.io/v1alpha1 spec.containers[*].name}'
For example, optimizing resource utilization for greater scale and lower cost and driving insights to increase adoption of cloud-native serverless services. Using an interactive no/low code editor, you can create workflows or configure them as code. This workflow uses the Dynatrace Site Reliability Guardian application.
Especially when new security vulnerabilities are detected, collaboration with the right people—for example, the team responsible for a vulnerable service—is vital to keeping negative impact low and MTTR at a minimum. Creation of ITSM tickets (for example, Jira and ServiceNow) and assignment to the right teams.
Dynatrace stores transaction data (for example, PurePaths and code-level traces) on disk for 10 days by default. Increased storage space availability. For example, when retention times are set to 5 days, only 2 days of older data is compressed; or when set to 10 days, 7 days of older data is compressed.
A trivial performance optimisation to help speed up third-party or other-origin requests is to preconnect them: hint that the browser should preemptively open a full connection ( DNS , TCP , TLS ) to the origin in question, for example: In the right circumstances, this simple, single line of HTML can make pages hundreds of milliseconds faster !
Part 1 of this series starts will cover the key ingredients needed for successful DevOps use to deliver better software faster, followed by a short overview of GitHub Actions and example use cases related to deployment and release monitoring. Example #1 – Deploy application code to Kubernetes.
You only need to write platform-specific code where it’s necessary, for example, to implement a native UI or when working with platform-specific APIs. Almost 50% of the production code in our Android and iOS apps is decoupled from the underlying platform. Debugging Kotlin source code from Xcode.
To further enrich log data for automated observability, it’s necessary to dynamically tie logs to distributed traces on the code level, user sessions in the app front-end, and the topology of your IT landscape. If a more granular rule is present on the host level, that rule will precede any blanket rule on, for example, the tenant level.
Part one also provided an overview of Dynatrace’s Cloud Automation solution, Microsoft’s GitHub Actions , and open-source examples you can use and extend related to deployment and release monitoring. Example #3 – Automate Monitoring configuration as code. Monitoring as Code workflow example. Blog series use cases.
Similar to the observability desired for a request being processed by your digital services, it’s necessary to comprehend the metrics, traces, logs, and events associated with a code change from development through to production. Code : The branch for the new feature in a GitHub repository is merged into the main branch.
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