jaeger VS validator

Compare jaeger vs validator and see what are their differences.

validator

:100:Go Struct and Field validation, including Cross Field, Cross Struct, Map, Slice and Array diving (by go-playground)
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jaeger validator
94 68
19,279 15,361
1.4% 2.6%
9.7 7.4
about 11 hours ago 13 days ago
Go Go
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

jaeger

Posts with mentions or reviews of jaeger. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-01.
  • Show HN: An open source performance monitoring tool
    2 projects | news.ycombinator.com | 1 Feb 2024
    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
    2 projects | /r/kubernetes | 10 Dec 2023
    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
    8 projects | news.ycombinator.com | 16 Nov 2023
    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
    6 projects | dev.to | 8 Nov 2023
    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
    5 projects | dev.to | 30 Oct 2023
    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
    4 projects | dev.to | 18 Oct 2023
    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
    3 projects | dev.to | 17 Oct 2023
    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.
  • Distributed Tracing and OpenTelemetry Guide
    5 projects | dev.to | 28 Sep 2023
    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.
  • Event-Driven Architecture 101
    3 projects | dev.to | 25 Sep 2023
    For example, without investment, visibility into system behavior as a whole can be much more difficult in an event-driven system. Investing in something like Open Telemetry and a service catalog is a good idea.  Getting started with these things are relatively simple, but if you want to store your traces somewhere that are searchable, you are going to have to either pay for a SaaS tool that ingests them or you are going to have to run and maintain an open source tool capable of this such as Jaeger. For service cataloging, Backstage is becoming a very popular option.   Depending on the capabilities and the capacity of your engineering team, this might be a good option and many companies do have platform teams that provide tooling such as this. With the average salary of a platform Engineer being ~$144k, companies should think carefully on whether the benefits of an EDA are going to outweigh the cost. We will dig deeper into this in part 2 and 3 of the series.
  • The Complete Microservices Guide
    17 projects | dev.to | 21 Sep 2023
    Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

validator

Posts with mentions or reviews of validator. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-03.
  • API completa em Golang - Parte 7
    3 projects | dev.to | 3 Feb 2024
  • API completa em Golang - Parte 3
    2 projects | dev.to | 16 Dec 2023
  • Is there any equivalent to pydantic, serde, etc?
    8 projects | /r/golang | 6 Dec 2023
    go-playground/validator
    8 projects | /r/golang | 6 Dec 2023
    Go uses zero values to provide sensible default values. It's a design choice. With a quick Google you'll find several libraries such as https://github.com/go-playground/validator or https://github.com/asaskevich/govalidator. I use validator whenever I need to ensure any JSON I unmarshalled is correct.
  • API completa em Golang - Parte 1
    8 projects | dev.to | 1 Dec 2023
  • Yet another validator 0.9.5
    2 projects | /r/golang | 5 Sep 2023
    Now it has most of the Playground validator's common checks and a few own tricks.
  • Openapi server generation
    3 projects | /r/golang | 25 Aug 2023
    In Go I've found this package - https://github.com/go-playground/validator. It seems popular in the community, but it is tag-based. It looks like if I wanted to use it - I would have to basically duplicate structs.
  • Request Validations in Go REST API
    6 projects | /r/golang | 21 May 2023
    I use https://github.com/go-playground/validator, but honestly, I am not a fan. I just haven’t found anything better.
  • Tools besides Go for a newbie
    36 projects | /r/golang | 26 Mar 2023
    IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here
  • popularity behind pydantic
    2 projects | /r/Python | 24 Mar 2023
    I work in Go now, it's crazy poor in that regard - let's just mention for instance "zero values" ( so things can remain uninitialized with a default value you can't choose ), recurring questions around "empty vs null vs not set", and everyone using go-playground/validator where you attach rules as comments ( "tags" really, but it's barely the same thing ) that are interpreted at runtime, extremely cumbersome to extend. And all that with an insane amount of boilerplate and footguns. But what really takes the cake: if you dare saying it's extremely weak you'll get shut down by the community. You're supposed to praise it, and indeed, hate python ( you know, that toy language that didn't evolve since 2008 ).

What are some alternatives?

When comparing jaeger and validator you can also consider the following projects:

Sentry - Developer-first error tracking and performance monitoring

skywalking - APM, Application Performance Monitoring System

ozzo-validation - An idiomatic Go (golang) validation package. Supports configurable and extensible validation rules (validators) using normal language constructs instead of error-prone struct tags.

prometheus - The Prometheus monitoring system and time series database.

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

govalidator - [Go] Package of validators and sanitizers for strings, numerics, slices and structs

Pinpoint - APM, (Application Performance Management) tool for large-scale distributed systems.

grpc-go - The Go language implementation of gRPC. HTTP/2 based RPC

fluent-bit - Fast and Lightweight Logs and Metrics processor for Linux, BSD, OSX and Windows

hypertrace - An open source distributed tracing & observability platform

VictoriaMetrics - VictoriaMetrics: fast, cost-effective monitoring solution and time series database

Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.