istio
jaeger
Our great sponsors
istio | jaeger | |
---|---|---|
87 | 94 | |
34,943 | 19,370 | |
1.3% | 1.3% | |
10.0 | 9.7 | |
6 days ago | 7 days ago | |
Go | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
istio
-
Improve your EKS cluster with Istio and Cilium : Better networking and security
Istio is a popular open-source service mesh framework that provides a comprehensive solution for managing, securing, and observing microservices-based applications running on Kubernetes.
-
Optimal JMX Exposure Strategy for Kubernetes Multi-Node Architecture
Leverage a service mesh like Istio or Linkerd to manage communication between microservices within the Kubernetes cluster. These service meshes can be configured to intercept JMX traffic and enforce access control policies. Benefits:
-
Open Source Ascendant: The Transformation of Software Development in 2024
Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.
-
Delving Deeper: Enriching Microservices with Golang with CloudWeGo
Consider the case of Bookinfo, a sample application provided by Istio, rewritten using CloudWeGo's Kitex for superior performance and extensibility.
-
How to Build & Deploy Scalable Microservices with NodeJS, TypeScript and Docker || A Comprehesive Guide
It is a dedicated infrastructure layer that manages service-to-service communication, providing features like load balancing, encryption, authentication, and monitoring. Istio deploys sidecar proxies alongside each microservice instance. These proxies handle communication, providing features like load balancing, service discovery, encryption, monitoring and authentication.
-
Caddy for Certs and Istio for Reverse Proxy
5Y old post that sounds like they've done similar here: Caddy Issue Istio Issue but doesn't cover much of the implementation
- Understanding Istio: A Beginner's Guide to Service Mesh
-
Developer’s Guide to Building Kubernetes Cloud Apps ☁️🚀
In a production environment there will be a load balancer setup with an Ingress Controller, Service Mesh or some type of Custom Router. This allows all traffic to be sent to the single load balancer IP address and then route the traffic to a service based on the Domain name or subpath. We are using a NGINX ingress controller but service meshes like Istio have been becoming the most popular solution to use as they offer more segmentation, security and granular control.
-
Progressive Delivery on AKS: A Step-by-Step Guide using Flagger with Istio and FluxCD
Flagger is a progressive delivery tool that enables a Kubernetes operator to automate the promotion or rollback of deployments based on metrics analysis. It supports a variety of metrics including Prometheus, Datadog, and New Relic to name a few. It also works well with Istio service mesh, and can implement progressive traffic splitting between primary and canary releases.
-
Implementing TLS in Kubernetes
End-to-end data encryption with a service mesh: Using an end-to-end data encryption mechanism with a service mesh like Istio, TLS can secure communication between different microservices within a Kubernetes cluster. This is a popular approach for modern, distributed microservice architectures.
jaeger
-
Observability with OpenTelemetry, Jaeger and Rails
Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/
-
Show HN: An open source performance monitoring tool
As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.
This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.
What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].
From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].
In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).
To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).
Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!
[1] https://github.com/highlight/highlight/blob/main/LICENSE
[2] https://www.jaegertracing.io
[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...
[4] https://www.highlight.io/docs/getting-started/native-opentel...
[5] https://static.highlight.io/assets/docs/gorm.png
[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...
[7] https://highlight.io/blog/clickhouse-materialized-views
[8] https://www.highlight.io/docs/getting-started/self-host/self...
[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...
-
Kubernetes Ingress Visibility
For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements
-
Migrating to OpenTelemetry
Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.
[1] https://www.jaegertracing.io/
-
The Road to GraphQL At Enterprise Scale
From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.
-
OpenTelemetry Exporters - Types and Configuration Steps
Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.
-
Fault Tolerance in Distributed Systems: Strategies and Case Studies
However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.
-
Observability in Action Part 3: Enhancing Your Codebase with OpenTelemetry
In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.
-
Building for Failure
The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.
-
Distributed Tracing and OpenTelemetry Guide
In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.
What are some alternatives?
osm - Open Service Mesh (OSM) is a lightweight, extensible, cloud native service mesh that allows users to uniformly manage, secure, and get out-of-the-box observability features for highly dynamic microservice environments.
Sentry - Developer-first error tracking and performance monitoring
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
skywalking - APM, Application Performance Monitoring System
anthos-service-mesh-packages - Packaged configuration for setting up a Kubernetes cluster with Anthos Service Mesh features enabled
prometheus - The Prometheus monitoring system and time series database.
crossplane - The Cloud Native Control Plane
signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool
falco - Cloud Native Runtime Security
Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.
kratos - Your ultimate Go microservices framework for the cloud-native era.
fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows