apisix VS opentelemetry-go

Through this intelligent caching mechanism, APISIX efficiently utilizes system resources when handling a large volume of requests, thereby improving overall system performance and stability. APISIX, with its advanced LRU cache, provides developers with a reliable and efficient API gateway solution, facilitating smooth communication with external services.

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.

Default configuration

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.

Our first approach was to implement a separate SDK for each independent technology stack. We decided to use OpenTelemetry which is widely adopted and covers most of our needs.

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.

OpenTelemetry

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

You can follow this process with any large token AI system like Claude by identifying tracing data relevant to the code you are working on, using it as context to prompt OpenAI or other LLMs. Generally, you’d generate tracing data by implementing OpenTelemetry (aka OTEL) libraries into your application, adding spans to your functions with Jaeger, or using commercial SaaS tools like Honeycomb and Datadog.

While many programming languages provide robust support for Open Telemetry, this instance focuses on Golang. It's important to note that, in the current context, the logs SDK for Golang is not implemented. For future reference consult the list of supported languages and explore the Open Telemetry repositories. Always prioritize the main repository and its contrib repository, housing extensions and instrumentation libraries crucial to the Open Telemetry framework. Stay updated with the latest developments to ensure seamless integration and enhanced functionality.

OneUptime (https://github.com/oneuptime/oneuptime) is the open-source alternative to DataDog. It's 100% free and you can self-host it on your VM / server / cloud or you can use SaaS at https://oneuptime.com

NEW UPDATES (since we last posted to HN): We now support OpenTelemetry (https://opentelemetry.io/) natively which will help you to monitor, observe and debug any app, service, database or stack.

OpenTelemetry is the fastest growing Cloud Native Computing Foundation (CNCF) project. It standardizes the instrumentation and collection of traces, metrics, and logs from applications, and is supported by all the major observability projects, languages, and tools. One standard to rule them all!

OpenTelemetry is a collection of tools, APIs, and SDKs. Use it to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) to help you analyze your software’s performance and behavior. https://opentelemetry.io/

Tracetest uses your existing OpenTelemetry traces to power trace-based testing with assertions against your trace data at every point of the request transaction. You only need to point Tracetest to your existing trace data source, or send traces to Tracetest directly!