garden VS jaeger

Hey there! Have you tried garden.io for caching? We also cache tests. Pretty much anything that's possible to cache. We're open source at https://github.com/garden-io/garden

To add to what's already been said: If you think about it, CI pipelines are typically a complete description of how your system is built, tested, and deployed.

Which is pretty fantastic except for how walled off they are. You can't really re-use these descriptions for e.g. development, they're not vendor agnostic, and they only way to run them is by pushing your code.

Maybe it's a silly analogy but it's almost like being a web dev that doesn't have a browser and needs to send their code to a friend who can tell them if that font size looks good.

I think we're way over due for freeing these "blueprints" of our system from the confines of CI and making them portable and flexible. And containers are the technology that's enabling that.

Full disclaimer (as always): I work at Garden[0] where we're also solving that problem but taking a slightly different approach to Dagger (it's still a DAG). Garden config is declarative and the jobs (we call them actions) have a semantic meaning. You can e.g. have a Build action of type container or a Deploy action of type Helm and Garden will figure out what to do with it.

[0] https://github.com/garden-io/garden

Yes, there's us over at https://github.com/garden-io/garden! We're big believers in pipelines that run anywhere. I even made a short little video that should give you the gist. [1]

Some of the short-list of differences: we use YAML for our configuration language, Dagger can use full-fat languages to define its pipelines. Our feature scope is broader: you can use us to vend IDP-like stacks to your developers if you're a Platform Team; we make development with remote Kubernetes clusters very easy, including all the remote image builds; and we have a number of integrations so you can bring your IaC tool of choice (Pulumi, Terraform) into your pipeline and set up service -> infra dependencies.

[1] https://www.youtube.com/watch?v=JFnan6s2cDg

Me too. In fact Garden (dev tooling for the Kubernetes)[0] is a Berlin start-up with three Icelandic founders.

And if I'm not mistaken, two of us worked briefly with @halldorel (above commenter) at an earlier Icelandic start-up. It's a small world (if you're Icelandic).

[0] https://garden.io

There are dedicated tools just for that. Apart from skaffold check also tilt.dev, garden.io, devspace.sh, okteto.com

Would appreciate any insights on garden.io. Thanks.

Telepresence, tilt, garden.io, okteto, skaffold etc.

- https://garden.io/

haha, do my pictures give off a strong not-web-dev vibe? Either way your right, I'm focusing on devxp and automation for kubernetes. Because my work is open source you can see it here https://github.com/garden-io/garden (btw we're also hiring another open core dev like me)

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.