Tracing has always been a key use case for time series data. But admittedly, it’s also one that past versions of InfluxDB could not handle as well as we wanted. One of the roadblocks was the cardinality issue. Tracing data is, almost by definition, high cardinality data and prior to InfluxDB IOx, high cardinality data could affect query performance.
The much-anticipated release of Grafana Tempo 2.0, which we previewed at ObservabilityCON 2022, will represent a huge step forward for the distributed tracing backend. Among the biggest highlights will be TraceQL, a first-of-its-kind query language that makes it easier than ever to find the exact trace you’re looking for. There’s supposed to be a video here, but for some reason there isn’t. Either we entered the id wrong (oops!), or Vimeo is down.
It’s common in our everyday language to conflate seeing and understanding when the two are actually very different things. For example, if every day for the last few years we spoke briefly and wrote down the total number of Covid cases in the world, it would be easy to see some trends in the data—you would see the data. But if we present the same data drawn as a chart, it’s easy to understand where the spikes and dips are and when the situation got really bad.
After a period of beta testing, we're happy to announce the launch of our latest AppSignal for Node.js package. This package features six new integrations and uses the OpenTelemetry framework for reliable telemetry data collection. OpenTelemetry is an open standard that facilitates the instrumentation of standardized telemetry data collection. AppSignal is committed to using OpenTelemetry in new integrations, and our Node.js integration is the first to use the standard.
OpenTelemetry (OTel), the emerging industry standard for collecting observability data, recently announced the GA of its demo application – and this is good news for distributed tracing fans.
There is significant momentum around observability, as detailed in VMware’s 2022 State of Observability report, with almost all respondents stating that observability would benefit their organization. This is further validated by Gartner including observability in their Magic Quadrant for Application Performance Monitoring and Observability report for the first time this year.
OpenTelemetry (OTel), is an open source, CNCF (Cloud Native Computing Foundation) project that provides tools, APIs and SDKs for observability data collection (i.e, logs, metrics and traces) from cloud-native applications. Developers can use the data collected from OTel to monitor and analyze application health and performance. To leverage the data and its insights, you can export the data to external solutions, like APMs, open source Jaeger and Zipkin, Helios, and others.
AppSignal is a strong supporter of open-source technology. We owe so much of our modern world to the unseen, hard-working software developers who build and maintain the many technologies that make everything from reading this article to sending a message from your phone possible. That's why we're investing in OpenTelemetry, the open-source standard for telemetry data collection, rather than developing our own independent standard.
Here's how to configure the OpenTelemetry Community Demo application to send telemetry data to AppDynamics Cloud.
The drive for faster, more scalable services is on the rise. Our day-to-day lives depend on apps, from a food delivery app to have your favorite meal delivered, to your banking app to manage your accounts, to even apps to schedule doctor’s appointments. These apps need to be able to grow from not only a features standpoint but also in terms of user capacity. The scale and need for global reach drives increasing complexity for these high-demand cloud applications.
iOS and Android apps are often a common component of distributed applications, forming a key part of the software architecture. These mobile apps provide another way to access data and perform actions on various services, requiring tight integration between the apps and the components which serve the data and control it.
Today the Grafana Agent team is excited to announce the release of Grafana Agent v0.29.0. This September, we introduced a new way to easily run and configure Grafana Agent called Grafana Agent Flow, our new dynamic configuration runtime built on components. Within Flow, we are also embracing Grafana Labs’ big tent philosophy by introducing OpenTelemetry (OTel) Collector components and converters for traces, metrics, and logs in Agent v0.29.0.
When developers talk about the options OpenTelemetry opens up to them, one of the most powerful use cases is troubleshooting distributed architectures. With OTel data and insights, developers can identify bugs and solve a wide range of issues across various types of architecture and flows. These include asynchronous flows, flows with Lambda functions, and many more.
In the last few years, the usage of databases that charge by request, query, or insert—rather than by provisioned compute infrastructure (e.g., CPU, RAM, etc.)—has grown significantly. They’re popular for a lot of the same reasons that serverless compute functions are, as the cost will scale with your usage. No one is using your site? No problem: you’re not charged.
TL;DR: proposal (and a survey) to deprecate native Jaeger exporters in OpenTelemetry SDKs in favor of OTLP exporters.
At ObservabilityCON, we announced our first step towards launching a native integration between Grafana k6 load testing and Grafana Tempo tracing (k6 x Tempo) in Grafana Cloud. We created k6 x Tempo to help dev, testing, and operation teams analyze their performance test results more effectively and proactively improve the reliability of their business-critical applications.
Have you been meaning to learn about OpenTelemetry and the integration of all available application and service telemetry? If you like to learn things by doing; get ready to dive in and have some fun with OpenTelemetry and Splunk Observability Cloud. Quickly learn more about OpenTelemetry auto-instrumentation and collectors at your own pace with these walkthroughs and guides.
With serverless and containerized applications becoming a norm, workloads and integrations are spread across multiple cloud environments. As these apps become increasingly more distributed, monitoring also becomes more complicated with siloed and incomplete telemetry. This is where distributed tracing brings great value. It enables end-to-end visibility in your modern and complex application.
Distributed tracing is the ability to follow a request through a software system from beginning to end. While that may sound trivial, a single request can easily spawn multiple child requests to different microservices with modern distributed architectures. These, in turn, trigger further sub-requests, resulting in a complex web of transactions to service a single originating request.
One of the things that most excites me about what we at Helios are doing differently than anyone else is trace visualizations. While there are many ways to troubleshoot microservice architectures, a good visual overview goes a really long way to speeding up understanding and therefore accelerating time to a resolution. When your manager asks, “Why did that break down?” with Helios you can answer quickly with accurate data—this is the value of the Helios platform.
