dtm VS prometheus

- using a distributed transaction framework (like https://github.com/dtm-labs/dtm)

But in this article, we introduce a workflow pattern in github.com/dtm-labs/dtm. Under this pattern, a mixture of XA, SAGA and TCC can be applied to different branches in a single distributed transactions, allowing users to customize most of the contents of a distributed transaction, providing great flexibility.

The open source distributed transaction framework https://github.com/dtm-labs/dtm has a two-stage message pattern inside that handles this problem very well. The following is an example of the use of an interbank transfer operation.

The above Sub-transaction Barrier technique, when used in conjunction with the distributed transaction framework https://github.com/dtm-labs/dtm, has been made available in several language SDKs, with the following example code in Go.

Maybe you can take a look at github.com/dtm-labs/dtm which provide built-in TCC pattern

Distributed XA transactions can solve the above business problem. This article presents a solution based on dtm-labs/dtm. DTM is a popular distributed transaction framework which supports XA, Saga, OutBox, and TCC patterns.

Github: https://github.com/dtm-labs/dtm

This article gives an example of implementing a distributed transaction across multiple store engines, Mysql, Redis and Mongo. This example is based on the Distributed Transaction Framework https://github.com/dtm-labs/dtm and will hopefully help to solve your problems in data consistency across microservices.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.