opentracing-javascript
Redis
opentracing-javascript | Redis | |
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32 | 32 | |
1,090 | 19,322 | |
- | 0.9% | |
1.6 | 8.8 | |
over 2 years ago | 5 days ago | |
TypeScript | Go | |
Apache License 2.0 | BSD 2-clause "Simplified" License |
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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
Redis
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Using IAM authentication for Redis on AWS
MemoryDB documentation has an example for a Java application with the Lettuce client. The process is similar for other languages, but you still need to implement it. So, let's learn how to do it for a Go application with the widely used go-redis client.
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Unexpected behavior from Redis cluster client - Keys not being found even if they exist in the cluster
We have setup a redis cluster with 3 master, and 3 slave nodes using redis-go package (https://github.com/redis/go-redis).
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Building RESTful API with Hexagonal Architecture in Go
For building the RESTful Point of Sale service API, I've considered and selected a combination of technologies that would work seamlessly together. For handling HTTP requests and responses, using the Gin HTTP web framework would make sense because I think it seems complete and popular among Go community too. To ensure data integrity and persistence, I'm using PostgreSQL database with pgx as the database driver, the reason I choose PostgreSQL because it is the most popular relational database to use in production and offers efficient Go integration. I'm also implementing caching using Redis with go-redis client library, which provides powerful in-memory data storage capabilities.
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Authentication system using Golang and Sveltekit - Initialization and setup
Following the completion of the series β Secure and performant full-stack authentication system using rust (actix-web) and sveltekit and Secure and performant full-stack authentication system using Python (Django) and SvelteKit β I felt I should keep the streak by building an equivalent system in PURE go with very minimal external dependencies. We won't use any fancy web framework apart from httprouter and other basic dependencies including a database driver (pq), and redis client. As usual, we'll be using SvelteKit at the front end, favouring JSDoc instead of TypeScript. The combination is ecstatic!
- Go linter and helper for the OpenTelemetry SDK
- Redis with golang
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Tools besides Go for a newbie
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
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Should I reuse the connection on Redis or close it after every use?
Asynq uses https://github.com/go-redis/redis in order to connect to Redis. Whenever you create a client using go-redis, the client internally manages a connection pool, so when you need to execute a command in Redis the client just retrieves a connection from the pool and uses it. After using it, the connection is released and it goes back to the pool (no need to say that the Redis client is thread-safe).
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a tool for quickly creating web and microservice code
Caching component go-redis ristretto
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Storage Layer π¦
First thing first, we will install Redis client for Golang
What are some alternatives?
kafka-go - Kafka library in Go
redigo - Go client for Redis
opentelemetry-specification - Specifications for OpenTelemetry
riot - Go Open Source, Distributed, Simple and efficient Search Engine; Warning: This is V1 and beta version, because of big memory consume, and the V2 will be rewrite all code.
prometheus - The Prometheus monitoring system and time series database.
Hiredis - Minimalistic C client for Redis >= 1.2
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
mongo-go-driver - The Official Golang driver for MongoDB
opentelemetry-js - OpenTelemetry JavaScript Client
Go-NATS-Streaming-gRPC-PostgreSQL - Go Nats Streaming gRPC PostgerSQL emails microservice
apm-agent-nodejs - Elastic APM Node.js Agent
mgo - Go Doc Dot Org