kafka-go VS Grafana

There are two main libraries that people use to write clients Confluent Kafka and segment io kafka

Unlike the standard scripts, and many binary clients, it's a native, statically-compiled, binary. It uses segmentio/go-kafka internally, which means CGO can be disabled during compilation.

Scaling up our producers/consumers proved to be more cost-effective than adding more CPU/brokers to the Kafka cluster. To accomplish this, we used the segmentio/kafka-go client library which provides an excellent abstraction for interacting with the cluster and will handle data compression completely transparently.

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

I want to announce the first release of a kafkagosaur, a new Kafka client. It's built using WebAssembly and binds to the kafka-go library. The first release includes functionality to read and write Kafka messages and SASL support.

Use the Segment Kafka library, not Sarama, it’s much easier to use https://github.com/segmentio/kafka-go

Kafaka - kafka-go, Debezium Outbox

Segment learned quite some time ago that librdkafka-go has problems like these (and doesn’t support Contexts either), so they wrote a pure Go replacement instead. https://github.com/segmentio/kafka-go

In this article let's try to create closer to real world CQRS microservices with tracing and monitoring using: 🚀 Kafka as messages broker gRPC Go implementation of gRPC PostgreSQL as database Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB Web and API based SMTP testing Redis Type-safe Redis client for Golang swag Swagger for Go Echo web framework

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.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

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.

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

Grafana

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.

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:

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:

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.