apisix VS prometheus

The main issue is that priority is documented in the config-default.yaml file, while the phase is buried in the code. Worse, some plugins run across different phases. For example, let's check the proxy proxy-rewrite plugin and, more precisely, the functions defined there:

// references: // https://github.com/tetratelabs/proxy-wasm-go-sdk/tree/main/examples // https://github.com/apache/apisix/blob/master/t/wasm/ package main import ( "github.com/tetratelabs/proxy-wasm-go-sdk/proxywasm" "github.com/tetratelabs/proxy-wasm-go-sdk/proxywasm/types" "github.com/valyala/fastjson" ) func main() { proxywasm.SetVMContext(&vmContext{}) } // each plugin has its own VMContext. // it is responsible for creating multiple PluginContexts for each route. type vmContext struct { types.DefaultVMContext } // each route has its own PluginContext. // it corresponds to one instance of the plugin. func (*vmContext) NewPluginContext(contextID uint32) types.PluginContext { return &pluginContext{} } type header struct { Name string Value string } type pluginContext struct { types.DefaultPluginContext Headers []header } func (ctx *pluginContext) OnPluginStart(pluginConfigurationSize int) types.OnPluginStartStatus { data, err := proxywasm.GetPluginConfiguration() if err != nil { proxywasm.LogErrorf("error reading plugin configuration: %v", err) return types.OnPluginStartStatusFailed } var p fastjson.Parser v, err := p.ParseBytes(data) if err != nil { proxywasm.LogErrorf("error decoding plugin configuration: %v", err) return types.OnPluginStartStatusFailed } headers := v.GetArray("headers") ctx.Headers = make([]header, len(headers)) for i, hdr := range headers { ctx.Headers[i] = header{ Name: string(hdr.GetStringBytes("name")), Value: string(hdr.GetStringBytes("value")), } } return types.OnPluginStartStatusOK } // each HTTP request to a route has its own HTTPContext func (ctx *pluginContext) NewHttpContext(contextID uint32) types.HttpContext { return &httpContext{parent: ctx} } type httpContext struct { types.DefaultHttpContext parent *pluginContext } func (ctx *httpContext) OnHttpResponseHeaders(numHeaders int, endOfStream bool) types.Action { plugin := ctx.parent for _, hdr := range plugin.Headers { proxywasm.ReplaceHttpResponseHeader(hdr.Name, hdr.Value) } return types.ActionContinue }

API7 takes Apache APISIX as its robust foundation, which is open-source and has an active community with over 600 contributors all over the world. The nature of open source allows users to examine the source code, which promotes transparency. This transparency helps users understand how APISIX works, verify its security, and identify and fix any potential vulnerabilities or bugs.

But the best part is that the libraries mentioned here and Apache APISIX are entirely open source, meaning you can look under the hood and modify things yourself.

4. Plugin definition: It is a really important part of plugin implementation that we define as a table with properties for the version, priority, name, and schema. The name and schema are the plugin's name and schema defined earlier. The version and priority are used by APISIX to manage the plugin. The version typically refers to the version that is currently in use like API versioning. If you publish and update your plugin logic, it is going to be 1.1 (You can set any version you wish). But you need to be very careful in choosing priority. The priority field defines in which order and phase your plugin should be executed. For example, the 'ip-restriction' plugin, with a priority of 3000, will be executed before the 'example-plugin', which has a priority of 0. This is due to the higher priority value of the 'ip-restriction' plugin. If you're developing your own plugin, make sure that you followed the order of plugins not to mess up the order of existing plugins. You can check the order of existing plugins in the config-default.yaml file and open the Apache APISIX Plugin Development Guide to determine.

Their use of etcd was a hard pass for me; I don't need more etcd in my life

Apache APISIX Apache APISIX is an open source, dynamic, real-time, high-performance cloud native API gateway. APISIX provides rich traffic management features such as load balancing, dynamic upstream, canary release, circuit breaking, authentication, observability, and more. Official website https://apisix.apache.org/ GitHub projects APISIX (the core): https://github.com/apache/apisix GitHub - apache/apisix: The Cloud-Native API Gateway GitHub - apache/apisix-dashboard: Dashboard for Apache APISIX GitHub - apache/apisix-website: Apache APISIX Website GitHub - apache/apisix-docker: the docker for Apache APISIX GitHub - apache/apisix-go-plugin-runner: Go Plugin Runner for APISIX GitHub - apache/apisix-java-plugin-runner: APISIX Plugin Runner in Java GitHub - apache/apisix-python-plugin-runner: Apache APISIX Python plugin runner GitHub - apache/apisix-helm-chart: Apache APISIX Helm Chart GitHub - apache/apisix-ingress-controller: ingress controller for K8s

Grafana configuration. Most of it comes from the configuration provided by APISIX.

Indeed Many members in the Slack channel reported that they came to APISIX because its feature-rich, check its README, please: https://github.com/apache/apisix

FlatBuffers does the trick as a better replacement to ProtoBuf when running in some resource-critical devices. We were working on a project that leverage Arrow IPC (internally FlatBuffers) and shared memory to collect metrics on edge devices with limit CPU and memory, hopefully we can open source it soon.

But in this case the format is defined by [Prometheus](https://github.com/prometheus/prometheus/blob/main/prompb/re...) and we just adopt that.

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

After that, you will set up a metrics server container. It will use Prometheus.io, an open-source monitoring and alerting toolkit designed to collect, store, and query time series data, making it a tool for monitoring your systems' performance and health through metrics.