jaeger VS skywalking

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.

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

Apache SkyWalking is an APM tool, focusing on microservices, Cloud Native apps, and Kuernetes architectures. It builds its architecture on four kinds of components:

Trying to play devil's advocate here.

> It needs at least a stable set of users, but maintaining a set of users is essentially managing the set of people onboarding and the set of people migrating off.

I could say that I don't care very much about how much users a piece of software has, only that it has enough information on how to use it and enough maintainers to patch any security vulnerabilities and do occasional releases with updated dependencies, as well as address any serious issues or bugs.

For example, Apache Skywalking is an APM solution that most people haven't even heard of (in contrast to something like Sentry), yet it fits those qualities and I see few to no issues with it: https://skywalking.apache.org/

> If you're shrinking then a competitor is providing better options, or your problem space has shifted.

Again, as a user, I might not care that Sentry or another piece of software is better in any number of ways than Apache Skywalking. Similarly, I might not care that something like PostgreSQL is more correct or has a large market share (at least on HN) in comparison to something like MariaDB/MySQL.

If a piece of software meets the needs of my project and won't effectively rot with time, then it's quite possibly good enough as it is, even if it's not the market leader. For my small project's APM needs Apache Skywalking is enough. For my CRUD database needs, something like MariaDB/MySQL will be okay until the time Sun burns out (or PostgreSQL if I'm feeling fancy, but even that's not one of the modern and hip solutions).

Ergo, those better options only become relevant once they're closer to being must haves than nice to haves. Same as how Docker Swarm might be enough for many, even if Kubernetes basically won in the "container wars" and has a way more active community. Swarm will only stop being an option for me once it hits EOL, at least for certain projects where simplicity is appreciated.

Then again, a counterpoint to my own argument here could be the story of LibreOffice and OpenOffice, where the latter was basically donated (instead of the rights to the name being given to the folks behind LibreOffice) and is now in decline while LibreOffice is flourishing - but at the same time they were so close to one another feature wise, that maybe it's not a good point, same as with Gogs and Gitea.

> Where's Java primarily used these days?

I've seen a lot of enterprise-y webdev projects use it for back end stuff (Dropwizard, Spring Boot, Vert.X, Quarkus) and in rare cases even front end (like Vaadin or JSF/PrimeFaces). The IDEs are pretty great, especially the ones by JetBrains, the tooling is pretty mature and boring, the performance is really good (memory usage aside) and the language itself is... okay.

Curiously, I wanted to run my own server for OIDC/OAuth2 authn/authz and to have common features like registration, password resets and social login available to me out of the box, for which I chose Keycloak: https://www.keycloak.org/

Surprise surprise, it's running Java under the hood. I wanted to integrate some of my services with their admin API, seems like the Java library is also updated pretty frequently: https://mvnrepository.com/artifact/org.keycloak/keycloak-adm... whereas ones I found for .NET feel like they're stagnating more: https://www.nuget.org/packages?q=keycloak (probably not a dealbreaker, though)

Then, I wanted to run an APM stack with Apache Skywalking (simpler to self-host than Sentry), which also turns out to be a Java app under the hood: https://skywalking.apache.org/

Also you occasionally see like bank auth libraries or e-signing libraries be offered in Java as well first and foremost, at least in my country (maybe PHP sometimes): https://www.eparaksts.lv/en/for_developers/Java_libraries and their app for getting certificates from the government issued eID cards also runs off of Java.

So while Java isn't exactly "hot" tech, it's used all over the place: even in some game engines, like jMonkeyEngine, or in infrastructure code where something like Go might actually be more comfortable to use.

> What should people use?

I recall Apache Skywalking being pretty good, especially for smaller/medium scale projects: https://skywalking.apache.org/

The architecture is simple, the performance is adequate, it doesn't make you spend days configuring it and it even supports various different data stores: https://skywalking.apache.org/docs/main/v9.0.0/en/setup/back...

The problems with it are that it isn't super popular (although has agents for most popular stacks), the docs could be slightly better and I recall them also working on a new UI so there is a little bit of churn: https://skywalking.apache.org/downloads/

Still better versus some of the other options when you need something that just works instead of spending a lot of time configuring something (even when that something might be superior in regards to the features): https://github.com/getsentry/self-hosted/blob/master/docker-...

Sentry is just the first thing that comes to mind (OpenTelemetry also isn't simpler due to how much it tries to do), but compare its complexity to Skywalking: https://github.com/apache/skywalking/blob/master/docker/dock...

I wish there was more self-hosted software like that out there, enough to address certain concerns in a simple way on day 1 and leave branching out to more complex options like OpenTelemetry once you have a separate team for that and the cash is rolling in.

Personally I've also used Apache Skywalking for a decent out of the box experience: https://skywalking.apache.org/

I've also heard good things about Sentry, though if you need to self-host it, then there's a bit of complexity to deal with: https://sentry.io/welcome/