retry-go
opentracing-javascript
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retry-go | opentracing-javascript | |
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5 | 32 | |
2,217 | 1,090 | |
3.5% | - | |
5.7 | 1.6 | |
9 days ago | over 2 years ago | |
Go | TypeScript | |
MIT License | Apache License 2.0 |
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retry-go
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Retry operations with constant, delays and exponential backoff strategies
Why this instead of https://github.com/avast/retry-go or https://github.com/cenkalti/backoff ?
- Network Error Handling
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retry package for golang
What is that advantage of your package compared to other ones like https://github.com/avast/retry-go?
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Go, NATS, gRPC and PostgreSQL clean architecture microservice with monitoring and tracing π
processCreateEmail handling create email events, it's start tracing span, increase metrics counters, then unmarshal message data, and call usecase create method, if it fails, we retry for 3 times using retry-go, if it still fails, we check is the current message redelivered and if redelivery count > maxRedeliveryCount(it's up to your business logic, here is 3 times limit), handling error cases can be very different and depends on your service business logic, in this example used Dead Letter Queue approach.
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Go, Kafka, gRPC and MongoDB microservice with metrics and tracing π
Workers validate message body then call usecase, if it's returns error, try for retry, good library for retry is retry-go, if again fails, publish error message to very simple Dead Letter Queue as i said, didn't implement here any interesting business logic, so in real production we have to handle error cases in the better way. And after message success processed commit it.
opentracing-javascript
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Kotlin Spring WebFlux, R2DBC and Redisson microservice in k8s πβ¨π«
Spring Spring web framework Spring WebFlux Reactive REST Services Spring Data R2DBC a specification to integrate SQL databases using reactive drivers Redisson Redis Java Client Zipkin open source, end-to-end distributed tracing Spring Cloud Sleuth auto-configuration for distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus Kubernetes automating deployment, scaling, and management of containerized applications Docker and docker-compose Helm The package manager for Kubernetes Flywaydb for migrations
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Go and ElasticSearch full-text search microservice in k8sπβ¨π«
Elasticsearch client for Go RabbitMQ Go RabbitMQ Client Library Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus Echo web framework Kibana is user interface that lets you visualize your Elasticsearch Docker and docker-compose Kubernetes K8s Helm The package manager for Kubernetes
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Go EventSourcing and CQRS with PostgreSQL, Kafka, MongoDB and ElasticSearch πβ¨π«
PostgeSQL as event store database Kafka as messages broker gRPC Go implementation of gRPC Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB MongoDB database Elasticsearch Elasticsearch client for Go. Echo web framework Kibana Kibana is data visualization dashboard software for Elasticsearch Migrate for migrations
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OpenTelemetry vs OpenTracing - choosing one for instrumentation
OpenTracing was an open-source project aimed at providing vendor-neutral APIs and instrumentation for distributed tracing. In distributed cloud-native applications, it is difficult for engineering teams to see how requests are performing across services. And thatβs where distributed tracing comes into the picture.
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OpenTelemetry and Jaeger | Key concepts, features, and differences
OpenTracing is now archived, and it is suggested to migrate to OpenTelemetry.
- Microservice communication Diagram
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Go EventSourcing and CQRS microservice using EventStoreDB πβ‘οΈπ«
In this article let's try to create closer to real world Event Sourcing CQRS microservice using: ππ¨βπ»π EventStoreDB The database built for Event Sourcing gRPC Go implementation of gRPC MongoDB Web and API based SMTP testing Elasticsearch Elasticsearch client for Go. Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus swag Swagger for Go Echo web framework Kibana Kibana is user interface that lets you visualize your Elasticsearch
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Do Not Log
I agree; but I think it'll take years, if ever, to change this culture.
Logging is a byproduct of a past time; everything is a file, stdout is a file, lets persist that file, now we have multiple replicas, lets collect the file into a multi-terabyte searchable database.
The biggest downside: Its WILDLY expensive. Large orgs often have an entire team dedicated to maintaining logging (ELK) infrastructure. This price-tag inevitably leads to bikeshedding on backend teams about how to "reduce the amount we're logging" or "cleaning up the logs" or "structuring them to be more useful".
Outside of development, they are so rarely useful. Yet inevitably someone will say: "you're just not structuring your logs correctly." Maybe that's true; similarly, I don't find vim to be a highly productive editing experience. Maybe I just don't have the thousands of extensions it would take to make it so. Or maybe You're stuck in the past and ignoring two decades of tooling improvement. Both can be true.
I'm phrasing this as a false dichotomy, because in many teams: it is. Logging is easy; its built-in to most languages; so devs log. The information we need is in the system; its a needle in a haystack, but the needle is there. We log when a request comes in, when it hits pertinent functions, when its finished, how its finished, the manager says: "just look at the logs". Instead of "what better tooling can we make an investment in so future investigations of this nature don't take a full day."
For starters: Tracing. Tracing systems should be built-in to EVERY LANGUAGE, just like console.log. We have a standard [1] sponsored by the Linux Foundation and supported by every major trace ingestion system. This is not a problem of camps and proprietary systems; its a problem of culture. I should be able to call a nodejs stdlib function at startup, specify where I want traces to go, sampling rate, etc, and immediately get every single function call instrumented. Its literally just highly-structured-by-default logging! Dump the spans to stdout by default! Our log ingestion systems can read each line, determine if its a span, if so route to trace ingestion, otherwise route to log ingestion.
This is a critical step because it asserts that tracing is actually a very powerful tool that everyone needs to learn, like logging. Everyone knows about logging. Why? console.log. Its there, it gets used. Tracing right now is relegated to a subculture of "advanced diagnostics"; you gotta adopt a tracing provider, bring in dependencies, learn each implementation of OpenTracing, authenticate to send traces over HTTP... as a community, we should normalize just "dump traces like you dump logs, to stdout", have a formatter to make them nice to use in development, and now all that instrumentation work that any dev is capable of utilizing (just like console.log) "just works" in production.
[1] https://opentracing.io/
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I share my authentication server.
Service mesh - ssup2ket services run on service mesh for detailed traffic control and easy monitoring. Service mesh is applied through Istio. Istio uses OpenTracing for easy request tracing between multiple services.
- logging best practices
What are some alternatives?
kafka-go - Kafka library in Go
pgx - PostgreSQL driver and toolkit for Go
opentelemetry-specification - Specifications for OpenTelemetry
Echo - High performance, minimalist Go web framework
prometheus - The Prometheus monitoring system and time series database.
Gin - Gin is a HTTP web framework written in Go (Golang). It features a Martini-like API with much better performance -- up to 40 times faster. If you need smashing performance, get yourself some Gin.
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. π₯ π₯. π Open source Application Performance Monitoring (APM) & Observability tool
grpc-go - The Go language implementation of gRPC. HTTP/2 based RPC
opentelemetry-js - OpenTelemetry JavaScript Client
sarama - Sarama is a Go library for Apache Kafka. [Moved to: https://github.com/IBM/sarama]
apm-agent-nodejs - Elastic APM Node.js Agent