opentracing-javascript
elastic
opentracing-javascript | elastic | |
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32 | 21 | |
1,090 | 7,316 | |
- | - | |
1.6 | 0.0 | |
over 2 years ago | 2 months ago | |
TypeScript | Go | |
Apache License 2.0 | MIT 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
elastic
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How to include max_children in the Elasticsearch query
i am trying to generate the following query using github.com/olivere/elastic/v7
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Iβm a recent graduate and this is what is asked of me in my current (first) job. Please help me.
I think that https://olivere.github.io/elastic/ is a lot better as an API client
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Go and ElasticSearch full-text search microservice in k8sπβ¨π«
For Go available two good libraries for elasticsearch, the official Elasticsearch client and another one from community olivere elastic, both is good, but at this moment only the official client supports 8 version of elasticsearch and for serious production think it's the choice.
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How to add multiple conditions in elastic search in github.com/olivere/elastic/v7 library
You are using the Query DSL, which is documented here, and has examples for boolean combination queries, so if you want it built up in a more strongly-typed fashion, check the documentation for it: https://github.com/olivere/elastic/wiki/QueryDSL
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Go EventSourcing and CQRS with PostgreSQL, Kafka, MongoDB and ElasticSearch πβ¨π«
ElasticSearch repository implementation uses go-elasticsearch official library, another good one is olivere elastic but here it's not support 8 version which used for this project.
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Go EventSourcing and CQRS microservice using EventStoreDB πβ‘οΈπ«
In this project we have microservice working with EventStoreDB using oficial go client, for [projections (https://zimarev.com/blog/event-sourcing/projections/) used MongoDB and Elasticsearch for search, and communicate by gRPC and REST. Did not implement here any interesting business logic and didn't cover tests, because don't have enough time, the events list is very simple: create a new order, update shopping cart, pay, submit, cancel, change the delivery address, complete order, and of course in real-world better use more concrete and meaningfully events, but the target here is to show the idea and how it works. Event Sourcing can be implemented in different ways, used here EventStoreDB, but we can do it with PostgreSQL and Kafka for example. After trying both approaches, found EventStoreDB is a better solution because all required features are implemented out of the box, it is optimized and really very good engineers developing it.
- Where can I find go-elasticsearch examples?
- How to add current time into a field in ES?
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Elasticsearch in Go, Err: βthe client noticed that the server is not Elasticsearch and we do not support this unknown productβ
Relevant Discussion from the maintainer
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Any good resources to learn Elasticsearch with Golang?
I have only used the olivere library and I think it is as good as a Go library gets.
What are some alternatives?
kafka-go - Kafka library in Go
bleve - A modern text/numeric/geo-spatial/vector indexing library for go
opentelemetry-specification - Specifications for OpenTelemetry
go-elasticsearch-examples - Official golang elasticsearch driver examples
prometheus - The Prometheus monitoring system and time series database.
elastigo - A Go (golang) based Elasticsearch client library.
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
goes
opentelemetry-js - OpenTelemetry JavaScript Client
goriak - goriak - Go language driver for Riak KV
apm-agent-nodejs - Elastic APM Node.js Agent
elasticsql - convert sql to elasticsearch DSL in golang(go)