#opentelemetry

[mp3monsterme]

Checking your OpenTelemetry pipeline with Telemetrygen

Testing OpenTelemetry configuration pipelines without resorting to instrumented applications, particularly for traces, can be a bit of a pain. Typically, you just want to validate you can get an exported/generated signal through your pipeline, which may not be the OpenTelemetry Collector (e.g., FluentBit or commercial solutions such as DataDog). This led to the creation of Tracegen, and then the…

[govindhtech]

Opentelemetry vs Prometheus: Opentelemetry Overview

Opentelemetry vs Prometheus

Prometheus monitors, stores, and visualises metrics but does not keep logs or support traces for root cause analysis. The application cases of Prometheus are more limited than OpenTelemetry.

Programming language-agnostic integrations allow OpenTelemetry to track more complicated metrics than Prometheus. Automated instrumentation models make OTel more scalable and extensible than the Prometheus. OpenTelemetry requires a back-end infrastructure and no storage solution, unlike Prometheus.

Quick summary Prometheus calculates cumulative measurements as a total, whereas OpenTelemetry uses deltas. Prometheus stores short-term data and metrics, whereas OTel may be used with a storage solution. OpenTelemetry uses a consolidated API to send or pull metrics, logs, and traces and transform them into a single language, unlike Prometheus. Prometheus pulls data from hosts to collect and store time-series metrics. OTel can translate measurements and is language agonistic, providing developers additional options. Data and metrics are aggregated by Prometheus using PromQL. Web-visualized metrics and customisable alarms are provided by Prometheus. Integration with visualisation tools is required for OpenTelemetry. OTel represents metric values as integers instead of floating-point numbers, which are more precise and understandable. Prometheus cannot use integer metrics. Your organization’s demands will determine which option is best. OpenTelemetry may be better for complex contexts with dispersed systems, data holistic comprehension, and flexibility. This also applies to log and trace monitoring.

Prometheus may be suited for monitoring specific systems or processes using alerting, storage, and visualisation models.

Prometheus and OpenTelemetry Application performance monitoring and optimisation are crucial for software developers and companies. Enterprises have more data to collect and analyse as they deploy more applications. Without the correct tools for monitoring, optimising, storing, and contextualising data, it’s useless.

Monitoring and observability solutions can improve application health by discovering issues before they happen, highlighting bottlenecks, dispersing network traffic, and more. These capabilities reduce application downtime, improve performance, and enhance user experience.

App monitoring tools OpenTelemetry and the Prometheus are open-source Cloud Native Computing Foundation (CNCF) initiatives. An organization’s goals and application specifications determine which data and functions need which solutions. Before using OpenTelemetry or Prometheus, you should know their main distinctions and what they offer.

Java Opentelemetry OTel exports these three forms of telemetry data to Prometheus and other back ends. This lets developers chose their analysis tools and avoids vendor or back-end lock-in. OpenTelemetry integrates with many platforms, including Prometheus, to increase observability. Its flexibility increases because OTel supports Java, Python, JavaScript, and Go. Developers and IT staff may monitor performance from any browser or location.

Its ability to gather and export data across multiple applications and standardise the collecting procedure make OpenTelemetry strong. OTel enhances distributed system and the microservice observability.

For application monitoring, OpenTelemetry and Prometheus integrate and operate well together. The DevOps and IT teams can use OpenTelemetry and Prometheus to collect and transform information for performance insights.

Opentelemetry-demo OpenTelemetry (OTel) helps generate, collect, export, and manage telemetry data including logs, metrics, and traces in one place. OTel was founded by OpenCensus and OpenTracing to standardise data gathering through APIs, SDKs, frameworks, and integrations. OTel lets you build monitoring outputs into your code to ease data processing and export data to the right back end.

Telemetry data helps determine system health and performance. Optimised observability speeds up troubleshooting, improves system reliability, reduces latency, and reduces application downtime.

Opentelemetry architecture APIs OpenTelemetry APIs uniformly translate programming languages. This lets APIs capture telemetry data. These APIs help standardise OpenTelemetry measurements.

SDKs Software development tools. Frameworks, code libraries, and debuggers are software development building elements. OTel SDKs implement OpenTelemetry APIs and provide telemetry data generation and collection tools.

OpenTelemetry collector The OpenTelemetry collector accepts, processes, and exports telemetry data. Set OTel collectors to filter specified data types to the back end.

Instrumentation library OTel offers cross-platform instrumentation. The instrumentation libraries let OTel integrate with any programming language.

Opentelemetry collector contrib Telemetry data including metrics, logs, and traces can be collected without modifying code or metadata using the OpenTelemetry protocol (OTLP).

Metrics A high-level overview of system performance and health is provided via metrics. Developers, IT, and business management teams decide what metrics to track to fulfil business goals for application performance. A team may measure network traffic, latency, and CPU storage. You may also track application performance trends with metrics.

Logs Logs record programme or application events. DevOps teams can monitor component properties with logs. Historical data might demonstrate performance, thresholds exceeded, and errors. Logs track application ecosystem health.

Traces Traces provide a broader picture of application performance than logs and aid optimisation. They track a request through the application stack and are more focused than logs. Traces let developers pinpoint when mistakes or bottlenecks occur, how long they remain, and how they effect the user journey. This data improves microservice management and application performance.

What’s Prometheus? Application metrics are collected and organised using Prometheus, a monitoring and alerting toolkit. SoundCloud created the Prometheus server before making it open-source.

End-to-end time-series data monitoring is possible using Prometheus. Time-series metrics capture regular data, such as monthly sales or daily application traffic. Visibility into this data reveals patterns, trends, and business planning projections. Prometheus collects application metrics for dedicated functions that DevOps teams want to monitor after integration with a host.

Using PromQL, Prometheus metrics offer data points with the metric name, label, timestamp, and value. For better visualisation, PromQL lets developers and IT departments aggregate data metrics into histograms, graphs, and dashboards. Enterprise databases and exporters are accessible to Prometheus. Application exporters pull metrics from apps and endpoints.

Prometheus tracks four metrics Counters Counters measure increasing numerical values. Counters count completed tasks, faults, and processes or microservices.

Gauges Gauges measure numerical data that fluctuate due to external variables. They can monitor CPU, memory, temperature, and queue size.

Histograms Events like request duration and answer size are measured via histograms. They split the range of these measurements into buckets and count how many fall into each bucket.

Summaries Summaries assess request durations and response size like histograms, but they also count and total all observed values.

Prometheus’ data-driven dashboards and graphs are also useful.

Benefits of Prometheus Prometheus provides real-time application monitoring for accurate insights and fast troubleshooting. It also permits function-specific thresholds. When certain thresholds are hit or exceeded, warnings might speed up problem resolution. Prometheus stores and provides analytics teams with massive amounts of metrics data. It stores data for instant examination, not long-term storage. Prometheus typically stores data for two to fifteen days.

Prometheus works perfectly with Kubernetes, an open-source container orchestration technology for scheduling, managing, and scaling containerised workloads. Kubernetes lets companies create hybrid and multicloud systems with many services and microservices. These complicated systems gain full-stack observability and oversight with Prometheus and Kubernetes.

Grafana Opentelemetry Grafana, a powerful visualisation tool, works with Prometheus to create dashboards, charts, graphs, and alerts. Grafana can visualise metrics with Prometheus. The compatibility between these platforms makes complex data easier to share between teams.

Integration of OpenTelemetry with Prometheus No need to choose OpenTelemetry and Prometheus are compatible. Prometheus data models support OpenTelemetry metrics and OTel SDKs may gather them. Together, these systems provide the best of both worlds and enhanced monitoring. As an example:

When combined, OTel and Prometheus monitor complex systems and deliver real-time application insights. OTel’s tracing and monitoring technologies work with Prometheus’ alerting. Prometheus handles big data. This capability and OTel’s ability to combine metrics, traces, and logs into one interface improve system and application scalability. PromQL can generate visualisation models using OpenTelemetry data. To provide additional monitoring tools, OpenTelemetry and Prometheus interface with IBM Instana and Turbonomic. Instana’s connection map, upstream/downstream service connection, and full-stack visibility let OTel monitor all services. They give the same wonderful experience with OTel data as with all other data sources, providing you the context you need to swiftly detect and address application problems. Turbonomic automates real-time data-driven resourcing choices using Prometheus’ data monitoring capabilities. These optimised integrations boost application ecosystem health and performance.

Read more on Govindhtech.com

[kubernetesframework]

How to Test Service APIs

When you're developing applications, especially when doing so with microservices architecture, API testing is paramount. APIs are an integral part of modern software applications. They provide incredible value, making devices "smart" and ensuring connectivity.

No matter the purpose of an app, it needs reliable APIs to function properly. Service API testing is a process that analyzes multiple endpoints to identify bugs or inconsistencies in the expected behavior. Whether the API connects to databases or web services, issues can render your entire app useless.

Testing is integral to the development process, ensuring all data access goes smoothly. But how do you test service APIs?

Taking Advantage of Kubernetes Local Development

One of the best ways to test service APIs is to use a staging Kubernetes cluster. Local development allows teams to work in isolation in special lightweight environments. These environments mimic real-world operating conditions. However, they're separate from the live application.

Using local testing environments is beneficial for many reasons. One of the biggest is that you can perform all the testing you need before merging, ensuring that your application can continue running smoothly for users. Adding new features and joining code is always a daunting process because there's the risk that issues with the code you add could bring a live application to a screeching halt.

Errors and bugs can have a rippling effect, creating service disruptions that negatively impact the app's performance and the brand's overall reputation.

With Kubernetes local development, your team can work on new features and code changes without affecting what's already available to users. You can create a brand-new testing environment, making it easy to highlight issues that need addressing before the merge. The result is more confident updates and fewer application-crashing problems.

This approach is perfect for testing service APIs. In those lightweight simulated environments, you can perform functionality testing to ensure that the API does what it should, reliability testing to see if it can perform consistently, load testing to check that it can handle a substantial number of calls, security testing to define requirements and more.

Read a similar article about Kubernetes API testing here at this page.

[virtualizationhowto]

SigNoz: Free and Open Source Syslog server with OpenTelemetry

SigNoz: Free and Open Source Syslog server with OpenTelemetry @signozhq #homelab #SigNozOpenSourceAlternative #DatadogVsSigNoz #MonitorApplicationsWithSigNoz #ApplicationPerformanceManagementTools #DistributedTracingWithSigNoz #MetricsAndDashboards

I am always on the lookout for new free and open-source tools in the home lab and production environments. One really excellent tool discovered recently is a tool called SigNoz. SigNoz is a free and open-source syslog server and observability program that provides an open-source alternative to Datadog, Relic, and others. Let’s look at SigNoz and see some of the features it offers. We will also…

[hackernewsrobot]

OpenTelemetry Tracing in < 200 lines of code

https://jeremymorrell.dev/blog/minimal-js-tracing/

[ericvanderburg]

Telemetry Pipelines Workshop: Integrating Fluent Bit With OpenTelemetry, Part 1

http://securitytc.com/TCRLwP

[strategictech]

Instrumenting a React App Using OpenTelemetry

Learn how to get started with OpenTelemetry in a React app with basic and auto-instrumentation, as well as adding custom spans and metrics.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[netcode-hub]

Master Tracing in Microservices 🔍 | Complete OpenTelemetry & Honeycomb Tutorial for Synchronous & Asynchronous Systems https://youtu.be/0qOfgj2e0og

[tumnikkeimatome]

AWS OpenSearchにObservability機能を実装する方法 - アプリやインフラのメトリクス・ログ・トレースの統合分析を実現

AWS OpenSearchのObservability機能概要 AWS OpenSearchは、アプリケーションやインフラストラクチャの監視・分析を行うための包括的なObservability機能を提供しています。 メトリクス、ログ、トレースという3つの主要なObservabilityシグナルを単一のソリューションで統合分析できる点が特徴です。 OpenTelemetry、Fluentd、Fluent Bit、Logstashなど、様々なオープンソースのデータ収集ツールをサポートしており、柔軟なデータ収集が可能です。 Observability実装の基本ステップ: AWS OpenSearchで始める包括的な監視体制 AWS…

[mp3monsterme]

Observability in Action - Book Review

With the Christmas holidays happening, things slowed down enough to sit and catch up on some reading – which included reading Cloud Observability in Action by Michael Hausenblas from Manning. You could ask – why would I read a book about a domain you’ve written about (Logging In Action with Fluentd) and have an active book in development (Fluent Bit with Kubernetes)? The truth is, it’s good to…

[kennak]

著者の言いたいことは理解できますが、クローズドソースの可観測性プラットフォームによるベンダー ロックインは、特に大規模な組織にとっては重大な課題です。 Datadog Agent などの特定のツールを使用して数百または数千のアプリケーションをインストルメント化すると、エンジニアリングに多大な時間を投資しなければ、そのツールから切り離すことはほぼ不可能になります。 プラットフォーム エンジニアリングのプロフェッショナル サービスの分野では、この問題が頻繁に発生します。 企業は、特に自社製品への支出に関する Datadog の不透明な性質に関して、大規模な可観測性プラットフォームのロックインにうんざりし始めています。 OTEL の約束の 1 つは、組織がベンダー固有のエージェントを OTEL コレクターに置き換えて、エンド オブザーバビリティ プラットフォームの柔軟性を実現できることです。 可観測性パイプライン (EdgeDelta や Cribl など) と併用すると、収集したテレメトリ データを再処理し、必要に応じて Splunk などの別のプラットフォームに送信できます。 その結果、ある可観測性プラットフォームから別の可観測性プラットフォームへの切り替えの悩みが少し軽減されます。 皮肉なことに、Splunk でさえこれを認識しており、OTEL 標準の背後に実質的なサポートを置いています。 OTEL は完璧とは程遠く、これらの目標の中には少し高尚なものもあるかもしれませんが、多くの大規模組織がこれらの理由から OTEL を採用していると言えます。

OpenTelemetry の問題 | ハッカーニュース

[craigbrownphd]

Open Source Elastic's OpenTelemetry SDK for .NET

https://www.infoq.com/news/2024/04/elastics-open-telemetry-net/?utm_campaign=infoq_content&utm_source=dlvr.it&utm_medium=tumblr&utm_term=AI%2C%20ML%20%26%20Data%20Engineering-news

[b2bcybersecurity]

Daten: Vollständige Transparenz und Kontrolle der Pipeline

Eine neue Technologie bietet Unternehmen eine einzige Pipeline zur Verwaltung und Kontrolle ihrer gesammelten Daten im Petabyte-Bereich. Das ermöglicht ihnen zuverlässige und kosteneffiziente Analysen und Automatisierungen. Moderne Clouds erzeugen eine enorme Menge und Vielfalt an Daten. Stakeholder in Unternehmen wünschen sich mehr datengestützte Erkenntnisse und Automatisierung, um bessere Entscheidungen zu treffen, die Produktivität zu steigern und Kosten zu senken. Die Schaffung einer einheitlichen und benutzerfreundlichen Umgebung für Datenanalysen und -automatisierung ist jedoch aufgrund der Komplexität und Vielfalt moderner Cloud-Architekturen und der Vielzahl an Überwachungs- und Analysetools in Unternehmen schwierig. Datenpipelines, Analysen und Automatisierung erfordern hohe Sicherheitsstandards Darüber hinaus müssen Unternehmen sicherstellen, dass ihre Datenpipelines, Analysen und Automatisierungen mit Sicherheits- und Datenschutzstandards wie der DSGVO übereinstimmen. Daher benötigen Unternehmen Transparenz und Kontrolle über ihre Datenpipelines, während sie gleichzeitig die Kosten kontrollieren und den Wert ihrer bestehenden Datenanalyse- und Automatisierungslösungen maximieren müssen. Dynatrace OpenPipeline gibt Geschäfts-, Entwicklungs-, Security- und Betriebsteams vollständige Transparenz und Kontrolle über ihre Data Ingestion, wobei der Kontext der Daten und der Cloud-Umgebungen, aus denen sie stammen, erhalten bleibt. Die Lösung ermöglicht diesen Teams das Sammeln, Konvergieren, Weiterleiten, Anreichern, Deduplizieren, Filtern, Maskieren und Transformieren von Observability-, Sicherheits- und Geschäftsereignisdaten aus beliebigen Quellen – einschließlich Dynatrace® OneAgent, Dynatrace APIs und OpenTelemetry – mit anpassbaren Aufbewahrungszeiten für einzelne Anwendungsfälle. Auf diese Weise können Unternehmen das ständig wachsende Volumen und die Vielfalt der Daten aus ihren hybriden und Multi-Cloud-Ökosystemen verwalten und mehr Teams in die Lage versetzen, auf die KI-gestützten Antworten und Automatisierungen der Dynatrace-Plattform zuzugreifen, ohne zusätzliche Tools zu benötigen. Vorteile der Zusammenarbeit mit anderen Kerntechnologien der Dynatrace-Plattform Dynatrace OpenPipeline arbeitet mit anderen Kerntechnologien der Dynatrace-Plattform zusammen, darunter das Grail Data Lakehouse, die Smartscape-Topologie und die hypermodale KI von Davis. Dies bietet die folgenden Vorteile: - Datenanalyse im Petabyte-Maßstab: Nutzt zum Patent angemeldete Stream-Processing-Algorithmen, um einen drastisch erhöhten Datendurchsatz im Petabyte-Maßstab zu erreichen. - Einheitliche Datenerfassung: Diese ermöglicht Teams die Erfassung von Observability-, Sicherheits- und Geschäftsereignisdaten aus beliebigen Quellen und in beliebigen Formaten, einschließlich Dynatrace OneAgent, Dynatrace APIs, Open-Source-Frameworks wie OpenTelemetry und anderen Telemetriesignalen. - Echtzeit-Datenanalyse bei der Erfassung: Dies ermöglicht es Teams, unstrukturierte Daten, wie zum Beispiel Protokolle, in strukturierte und nutzbare Formate zu konvertieren – etwa die Umwandlung von Rohdaten in Zeitreihen, die Berechnung von Metriken oder die Erstellung von Geschäftsereignissen aus Log Lines – und zwar direkt bei der Erfassung. - Vollständiger Datenkontext: Der Kontext heterogener Datenpunkte – einschließlich Metriken, Traces, Logs, Verhalten, Geschäftsereignissen, Schwachstellen, Bedrohungen, Lebenszyklusereignissen und vielen anderen – wird beibehalten und spiegelt die verschiedenen Teile des Cloud-Ökosystems wider, aus denen sie stammen. - Datenschutz- und Sicherheitskontrollen: Benutzer haben die Kontrolle darüber, welche Daten sie analysieren, speichern oder von der Analyse ausschließen. Die Lösung umfasst vollständig anpassbare Sicherheits- und Datenschutzkontrollen, um die spezifischen Anforderungen und gesetzlichen Vorschriften der Kunden zu erfüllen, wie zum Beispiel ein automatisches und rollenbasiertes Verbergen von personenbezogenen Daten. - Kosteneffizientes Datenmanagement: Dies hilft Teams, die Erfassung doppelter Daten zu vermeiden und den Speicherplatzbedarf zu reduzieren, indem Daten in brauchbare Formate umgewandelt werden (z. B. von XML in JSON) und es Teams ermöglicht wird, unnötige Felder zu entfernen, ohne dass Erkenntnisse, Kontext oder Analyseflexibilität verloren gehen. Fünf- bis zehnmal schnellere Verarbeitung der Daten „OpenPipeline ist eine leistungsstarke Ergänzung der Dynatrace-Plattform“, so Bernd Greifeneder, CTO bei Dynatrace. „Sie bereichert, konvergiert und kontextualisiert die heterogenen Observability-, Sicherheits- und Geschäftsdaten, die aus den Clouds stammen, und bietet einheitliche Analysen für diese Daten und die Services, die sie repräsentieren. Wie beim Grail Data Lakehouse haben wir OpenPipeline für Analysen im Petabyte-Bereich entwickelt. OpenPipeline arbeitet mit der hypermodalen KI Davis von Dynatrace zusammen, um aussagekräftige Erkenntnisse aus den Daten zu extrahieren und so eine robuste Analyse und zuverlässige Automatisierung zu ermöglichen. Unseren internen Tests zufolge ermöglicht OpenPipeline powered by Davis AI unseren Kunden eine fünf- bis zehnmal schnellere Datenverarbeitung als vergleichbare Technologien. Die Zusammenführung und Kontextualisierung von Daten innerhalb von Dynatrace erleichtert die Einhaltung gesetzlicher Vorschriften und die Durchführung von Audits, während mehr Teams innerhalb von Unternehmen einen unmittelbaren Einblick in die Leistung und Sicherheit ihrer digitalen Dienste erhalten.“   Passende Artikel zum Thema   Lesen Sie den ganzen Artikel

[holyjak]

An interesting project - OSS, cloud-native time-series DB in Rust, at v0.7 with 1.0 expected in August. Can be deployed as a single binary on an IoT device, or a cluster for the metrics and soon logs of your app. Paid by their cloud offering. Highlights: smart indices (created/destroyed on the fly w.r.t usage), efficiency, separate storage and compute (hi, Datomic!), SQL and Prometheus QL (=> can be used as a drop-in replacement for Prometheus in Grafana), distributed and parallel-processing query engine, can be used as storage for Prometheus. Write via a gRPC (Go, Java clients), InfluxDB HTTP Line write protocol, OpenTSDB HTTP put, ingest OpenTelemetry metrics via OTLP/HTTP, serve as a Vector sink,. Extend queries with Python.

[hackernewsrobot]

DataDog asked OpenTelemetry contributor to kill pull request

https://github.com/open-telemetry/opentelemetry-collector-contrib/pull/5836 Comments