envoy VS jaeger

Apple also contributed[1] MPTCP support to Envoy Proxy.

[1]https://github.com/envoyproxy/envoy/pull/18780

After it finishes (it can take a few minutes), Docker-Compose automatically starts a cluster with two containers. One is an Envoy proxy (running on port 3000) that relays requests from outside the cluster to the other container (running on port 4000), which is our AR.IO gateway that will handle the requests.

Hi HN!

For the past few weeks we've been working on Proximal - a workflow engine that lets you quickly iterate on WebAssembly extensions for Envoy Proxy[0] (or other proxies) right on your local machine: https://github.com/apoxy-dev/proximal

This work is based on Proxy-WASM[1] extension ABI for Envoy (and other proxies like APISIX and Mosn[2]) which allows you to execute WebAssembly code on every API request a la Cloudflare Workers. As part of our wider effort at https://apoxy.dev to improve API glue code we built an experimentation / development platform and hope you will find it useful!

On the technical side this project packs Envoy itself, Envoy controller, REST API (for controlling the controller =)), React SPA, and Temporal server/worker (for orchestration) - all baked into a single Go binary. You can find more on architecture and limitations in the repository README[4].

This project is pretty early stage and we would appreciate community feedback!

Previous HN discussions on this topic:

* https://news.ycombinator.com/item?id=36113542

* https://news.ycombinator.com/item?id=22582276

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[0] https://www.envoyproxy.io/

[1] https://github.com/proxy-wasm/spec/blob/master/docs/WebAssem...

[2] https://apisix.apache.org/ https://mosn.io/

[3] https://github.com/apoxy-dev/proximal/blob/main/README.md#ar...

Hey HN,

We made an Envoy Proxy[0] playground so we could test out our Envoy configs directly in the browser. This is based on Julia's work with Nginx Playround[1] (we forked[2] that repo and added more Envoy to it). Check it out!

[0] - Envoy is a popular programmable proxy similar to Nginx or HAProxy that is popular with cloud-native setups: https://www.envoyproxy.io

Envoy is the proxy that does the heavy lifting. Istio is just a glorified configuration system. Even if you choose to use Istio you're still using Envoy.

You're spot-on about using iptables rules. There is an example here with a yaml configuration and some iptables commands: https://github.com/envoyproxy/envoy/blob/main/configs/origin...

You might be able to re-use some of that. It should be pretty easy to get metrics for outbound/inbound http requests, but I don't remember the exact yaml incantation.

I'm using the default RaspiOS Lite 64bits and as highlighted in this issue, the RaspiOS kernel does not support CONFIG_ARM64_VA_BITS_48, which makes cilium-envoy to fail building. As solution, I was told to use either Ubuntu as base OS or Traefik Ingress Controller, which is not configured in K3s.

I know envoy (https://www.envoyproxy.io/, https://www.envoyproxy.io/docs/envoy/latest/intro/arch_overview/security/jwt_authn_filter) can do this natively, I'm sure you could probably build something with nginx and its Lua scripting, not sure about traefik and caddy but I dont think they support that.

Envoy is a cloud native opensource proxy server. The Envoy proxy offers a variety of http filters to handle incoming requests.

Istio: By far the most popular service mesh. It is built on top of Envoy proxy, which many service meshes use.

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/

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...

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

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/

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.

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.

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.

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.

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.

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.