dapr VS jaeger

Dapr provides a set of building blocks that abstract concepts commonly used in distributed systems. This includes secured synchronous and asynchronous communication between services, caching, workflows, resiliency, secret management and much more. Not having to implement these features yourself eliminates boilerplate, reduce complexity and allows you to focus on developing your business features.

Diagrid Catalyst is a Developer API platform providing a brand-new approach to distributed application development. Using the Catalyst APIs, powered by the Dapr open source project, developers can overcome the complexity of rewriting common software patterns and achieve higher productivity by offloading infrastructure concerns from their code to Catalyst.

The following two examples are open-source projects maintained by Fermyon with contributions from companies like Microsoft and SUSE. The first is Spin, which allows us to use WebAssembly to create Serverless applications. The second, SpinKube, combines some of the topics I'm most excited about these days: WebAssembly and Kubernetes Operators :) The official website says, "By running applications in the Wasm abstraction layer, SpinKube offers developers a more powerful, efficient, and scalable way to optimize application delivery on Kubernetes." By the way, this post shows how to integrate SpinKube with Dapr, another technology I'm very interested in, and I should write some posts soon.

Speaking of this has anyone had much experience with Dapr (https://dapr.io/) before?

I always thought this was a particularly interesting approach from Microsoft where they use this pattern to essentially take the complexity of micro services and instead try and keep it as simple as a normal .NET application but (and I think this is the clever part) in both a vendor and language neutral way.

But all of a sudden it means you can start removing all kinds of cruft and random SDKs from your codebase and push almost all of your interactions with the outside world into something like this .

Azure Container Apps hosting of Azure Functions is a way to host Azure Functions directly in Container Apps - additionally to App Service with and without containers. This offering also adds some Container Apps built-in capabilities like the Dapr microservices framework which would allow for mixing microservices workloads on the same environment with Functions.

Having containers is nice but everything (well ... nearly everything 😉) gets better with Dapr as an outstanding tool for app development in the container-based area. Here we go what might be worth a look:

Anyone using or planing to use darp Distributed application platform runtime as a microservices platform? https://dapr.io/

A CNCF project, the Distributed Application Runtime (Dapr) provides APIs that simplify microservice connectivity. Whether your communication pattern is service to service invocation or pub/sub messaging, Dapr helps you write resilient and secured microservices. Essentially, it provides a new way to build microservices by using the reusable blocks implemented as sidecars.

Dapr is a runtime that implements common patterns such as pub/sub, state storage, etc. It runs as a sidecar to your app. Your app then interfaces with it using an sdk or http calls to use said patterns instead of implementing those patterns directly yourself. Seems pretty cool to me, but you can find out more at https://dapr.io/.

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.