APO Observability Platform

website·Indexed 32 days ago

An observability platform designed for rapid fault localization and troubleshooting. APO utilizes APO-OneAgent for log and metric collection, integrating with Grafana Alloy, Vector, ClickHouse, and VictoriaMetrics. It provides support for monitoring middleware such as MySQL, Kafka, Redis, Elasticsearch, and RabbitMQ, and offers specialized configurations for Kubernetes environments to capture fault-scene logs and traces.

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What's inside APO

Overview of eBPF technology
eBPF (extended Berkeley Packet Filter) is a framework that allows users to write and insert small, highly optimized programs (eBPF programs) into the Linux kernel to extend and control kernel behavior without changing kernel source code or rebooting the system. It acts as a general-purpose execution engine that enables the efficient and secure execution of specific code based on system or program events.
Key characteristics include:
- Security: Programs run in a restricted virtual machine to ensure system stability.
- Flexibility: Programs can be dynamically loaded, unloaded, and updated.
- Performance: Optimized for high-efficiency execution within the kernel.
- Observability: Capable of capturing and analyzing runtime events for troubleshooting and performance tuning.
Technical components include bytecode instructions, storage objects, and Helper functions. Before execution, bytecode must be validated by the BPF Verifier. In kernels with BPF JIT (Just-In-Time) mode enabled, bytecode is translated directly into native machine instructions for the kernel to execute.
https://kindlingx.com/blog/%E4%BB%80%E4%B9%88%E6%98%AFeBPF/
Overview of APO Components and Agents
APO consists of two main components: APO-server (server-side) and APO-one-agent (user-side). The APO-one-agent collects information from business applications, clusters, and hosts in Kubernetes or virtual machine environments.
APO-one-agent includes the following default instrumentation probes:
- Java: opentelemetry-java-instrumentation v2.8.0
- Python: opentelemetry-python 1.27.0/0.48b0
- NodeJs: opentelemetry-js v1.24.1/0.51.1
- Go: Grafana Beyla v1.8.4
https://kindlingx.com/docs/APO%20%E5%90%91%E5%AF%BC%E5%BC%8F%E5%8F%AF%E8%A7%82%E6%B5%8B%E6%80%A7%E4%B8%AD%E5%BF%83/%E5%AE%89%E8%A3%85%E6%89%8B%E5%86%8C/%E5%AE%89%E8%A3%85%20APO-OneAgent/%E4%BC%A0%E7%BB%9F%E6%9C%8D%E5%8A%A1%E5%99%A8%E5%AE%89%E8%A3%85%20OneAgent
Overview of APO Observability Platform
APO is an open-source observability platform that integrates eBPF technology with the OpenTelemetry ecosystem to provide efficient monitoring of distributed systems with low data storage costs. It is designed to be 'one-click install' and 'out-of-the-box' to simplify deployment and troubleshooting for users without deep technical backgrounds.
https://kindlingx.com/blog/APO%20v1.0.0%20%E6%AD%A3%E5%BC%8F%E5%8F%91%E5%B8%83%EF%BC%81
Overview of Syncause AI Agent for Fault Analysis
Syncause is an AI Agent designed to reshape observability by automating the complex process of correlating fragmented data during system failures. It integrates data from various observability tools (such as Prometheus, Jaeger, Grafana, ELK, and commercial APM tools) to provide a unified analysis interface via a conversational AI.
https://kindlingx.com/blog/%E6%88%91%E4%BB%AC%E8%BF%99%E6%A0%B7%E5%81%9A%E3%80%8C%E6%95%85%E9%9A%9C%E5%88%86%E6%9E%90AI%E6%99%BA%E8%83%BD%E4%BD%93%E3%80%8D%EF%BC%8C%E9%82%80%E8%AF%B7%E4%BD%A0%E6%9D%A5%E8%AF%95%E8%AF%95
Overview of Grafana Alloy
Grafana Alloy is an open-source distribution of the OpenTelemetry (OTel) Collector. It features built-in Prometheus pipelines and supports metrics, logs, traces, and profiling. It is designed as a replacement for Grafana Agent and is fully compatible with OTel Collector, Prometheus Agent, and Promtail. It can be deployed in local, cloud, or hybrid environments and integrates with the Grafana LGTM stack or other compatible backends.
https://kindlingx.com/blog/tags/%E4%BD%BF%E7%94%A8%E5%9C%BA%E6%99%AF
Overview of APO Observability Platform Architecture
APO is an observability platform consisting of three main monitoring modules: Trace, Metrics, and Logs. It is composed of two primary components:
- APO-server: The server-side system containing frontend/backend components, databases, and collectors.
- APO-one-agent: The collection agent that gathers observability data from various programming languages and business environments and sends it to the server.
The log monitoring system is built on ClickHouse for high-performance column queries and uses iLogtail for lightweight collection across physical machines, VMs, and Kubernetes. It utilizes Vector to manage data flow and ensure ClickHouse stability, replacing the need for Kafka in many scenarios.
https://kindlingx.com/docs/APO%20%E5%90%91%E5%AF%BC%E5%BC%8F%E5%8F%AF%E8%A7%82%E6%B5%8B%E6%80%A7%E4%B8%AD%E5%BF%83/%E5%AE%89%E8%A3%85%E6%89%8B%E5%86%8C/%E5%BF%AB%E9%80%9F%E9%83%A8%E7%BD%B2/%E7%8B%AC%E7%AB%8B%E4%BD%BF%E7%94%A8%20APO%20%E6%97%A5%E5%BF%97%E6%A8%A1%E5%9D%97%E5%AE%9E%E7%8E%B0%E6%97%A5%E5%BF%97%E7%9B%91%E6%8E%A7%E5%8A%9F%E8%83%BD
Overview of OneAgent observability data collection
OneAgent is used to collect observability data—including trace, log, and metric data—from monitored environments such as traditional servers, containers, and Kubernetes. It integrates several technologies to achieve this:
- OpenTelemetry Probes: Automatically installed via Linux Preload and Kubernetes Webhook mechanisms (requires application restart to take effect).
- eBPF: Used to collect Polaris causal metrics and implement a retrospective sampling algorithm for trace data.
- ilogtail: Collects fault scene logs, preserving them based on the results of the retrospective sampling algorithm.
- Grafana Alloy: Collects metrics and can be extended via configuration to collect various other metric types.
- node-agent: Provides network latency and process status metrics.
Data Flow: Trace and log data are sent directly to the OpenTelemetry Collector. Metric data is collected by Alloy and then forwarded to the OpenTelemetry Collector.
https://kindlingx.com/docs/APO%20%E5%90%91%E5%AF%BC%E5%BC%8F%E5%8F%AF%E8%A7%82%E6%B5%8B%E6%80%A7%E4%B8%AD%E5%BF%83/%E6%8A%80%E6%9C%AF%E5%8E%9F%E7%90%86/OneAgent
Overview of APO Metrics Collection Architecture
APO provides a highly integrated Metrics collection system that replaces traditional Prometheus/Exporter setups. It uses the following stack:
- Collection: APO-one-agent utilizes an embedded Grafana Alloy to collect host, Kubernetes cluster, container, and network RTT (Round Trip Time) metrics.
- Transmission: Data is transmitted via OTEL Collector using the OTEL protocol, ensuring compatibility across clouds and clusters.
- Storage: Metrics are stored in VictoriaMetrics, which offers higher performance and lower resource consumption than standard Prometheus.
- Visualization: An integrated Grafana instance provides pre-configured dashboards for Kubernetes, containers, nodes, and network resources.
https://kindlingx.com/docs/APO%20%E5%90%91%E5%AF%BC%E5%BC%8F%E5%8F%AF%E8%A7%82%E6%B5%8B%E6%80%A7%E4%B8%AD%E5%BF%83/%E5%AE%89%E8%A3%85%E6%89%8B%E5%86%8C/%E5%BF%AB%E9%80%9F%E9%83%A8%E7%BD%B2/%E7%8B%AC%E7%AB%8B%E4%BD%BF%E7%94%A8%20APO%20Metrics%20%E6%A8%A1%E5%9D%97%E5%AE%9E%E7%8E%B0%20Metrics%20%E6%95%B0%E6%8D%AE%E7%9B%91%E6%8E%A7%E5%8A%9F%E8%83%BD
Overview of OneAgent Design Goals
OneAgent is designed to minimize user configuration and accelerate data collection for Traces and Logs. It addresses common pain points such as non-standard log directories, the need for language-specific agents for Tracing, and the configuration overhead of modifying images or using init containers in Kubernetes.
https://kindlingx.com/blog/tags/apo
Overview of Grafana Alloy
Grafana Alloy is an open-source distribution of the OpenTelemetry (OTel) Collector that includes built-in Prometheus pipelines. It supports metrics, logs, traces, and profiling. It is designed as a replacement for Grafana Agent and is fully compatible with OTel Collector, Prometheus Agent, and Promtail. It can be deployed in local, cloud, or hybrid environments and integrates with the Grafana LGTM stack or other compatible backends.
https://kindlingx.com/blog/APO%E4%BD%BF%E7%94%A8%E5%9C%BA%E6%99%AF%E4%B9%8B%EF%BC%9A%20%E7%BB%9F%E4%B8%80%E7%9A%84%E6%8C%87%E6%A0%87%E9%87%87%E9%9B%86%E5%B1%95%E7%A4%BA
Overview of the North Star Troubleshooting Metric System
The North Star Troubleshooting Metric System is a set of standardized, directional metrics designed to identify the root cause of production issues and provide clear guidance during the troubleshooting process. It aims to replace subjective, experience-based troubleshooting with a standardized workflow that can be implemented in actual production environments to achieve predictable recovery cycles (e.g., 1-5-10 targets).
https://kindlingx.com/blog/%E4%BB%80%E4%B9%88%E6%98%AF%E5%8C%97%E6%9E%81%E6%98%9F%E6%8E%92%E9%9A%9C%E6%8C%87%E6%A0%87%E4%BD%93%E7%B3%BB
Overview of APO Logging Features
APO uses a modified version of ilogtail for log collection and Vector for log structuring. It provides three primary capabilities:
1. Log Metrics: Statistics on total log volume and error log counts.
2. Fault Scene Logs: Automatically collects logs during slow or error traces and writes them to ClickHouse, correlating them via K8S or PID information.
3. Full Logs: Supports custom log parsing rules in the APO interface to structure logs into columns for faster querying, along with full-text search and context viewing.
https://kindlingx.com/blog/tags/fluent