retry-go
Grafana
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retry-go | Grafana | |
---|---|---|
5 | 379 | |
2,217 | 60,279 | |
3.5% | 1.5% | |
5.7 | 10.0 | |
8 days ago | 5 days ago | |
Go | TypeScript | |
MIT License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
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 👋
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 👋
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.
Grafana
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.
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Monitoring, Observability, and Telemetry Explained
Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.
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4 facets of API monitoring you should implement
Prometheus: Open-source monitoring system. Often used together with Grafana.
- Grafana: Open and composable observability and data visualization platform
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The Mechanics of Silicon Valley Pump and Dump Schemes
Grafana
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Reverse engineering the Grafana API to get the data from a dashboard
Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.
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Building an Observability Stack with Docker
So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:
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How to collect metrics from node.js applications in PM2 with exporting to Prometheus
In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:
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Root Cause Chronicles: Quivering Queue
Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.
What are some alternatives?
kafka-go - Kafka library in Go
Thingsboard - Open-source IoT Platform - Device management, data collection, processing and visualization.
pgx - PostgreSQL driver and toolkit for Go
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
Echo - High performance, minimalist Go web framework
Heimdall - An Application dashboard and launcher
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.
Wazuh - Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
grpc-go - The Go language implementation of gRPC. HTTP/2 based RPC
Thingspeak - ThingSpeak is an open source “Internet of Things” application and API to store and retrieve data from things using HTTP over the Internet or via a Local Area Network. With ThingSpeak, you can create sensor logging applications, location tracking applications, and a social network of things with status updates.
sarama - Sarama is a Go library for Apache Kafka. [Moved to: https://github.com/IBM/sarama]
uptime-kuma - A fancy self-hosted monitoring tool