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Alibaba Cloud Observability
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Alibaba Cloud Observability

Driving continuous progress in observability technology!

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Recent Articles

Latest from Alibaba Cloud Observability

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Alibaba Cloud Observability
Alibaba Cloud Observability
Jun 3, 2025 · Cloud Native

How PromQL Copilot Turns Natural Language into Precise Monitoring Queries

PromQL Copilot leverages Alibaba Cloud's observability platform and AI techniques to convert ambiguous natural‑language monitoring requests into accurate PromQL statements, addressing challenges of ambiguity, domain knowledge, and metric coverage while providing generation, explanation, diagnosis, and recommendation features for cloud‑native environments.

AICloud NativePromQL
0 likes · 12 min read
How PromQL Copilot Turns Natural Language into Precise Monitoring Queries
Alibaba Cloud Observability
Alibaba Cloud Observability
May 26, 2025 · Cloud Native

Why Observability 2.0 Is Redefining Cloud‑Native Monitoring and Data Pipelines

Observability 2.0 unifies logs, metrics and traces into a single platform, leveraging event‑centric wide‑key‑value models, AI‑driven anomaly detection, and cloud‑native elastic architectures to deliver faster system insight, lower downtime, scalable data pipelines, reduced costs, and improved developer experience across SLS services.

Cloud Nativecost optimizationperformance
0 likes · 12 min read
Why Observability 2.0 Is Redefining Cloud‑Native Monitoring and Data Pipelines
Alibaba Cloud Observability
Alibaba Cloud Observability
May 19, 2025 · Information Security

How Tool‑Poisoning Attacks Exploit MCP and What to Do About It

This article analyzes the security risks of the Model Context Protocol (MCP), demonstrates a tool‑poisoning attack that steals private keys via malicious tool descriptions, explores client‑side and server‑side threat vectors, and presents observability‑based mitigation using eBPF and LoongCollector.

AI model securityMCPcloud-native-security
0 likes · 23 min read
How Tool‑Poisoning Attacks Exploit MCP and What to Do About It
Alibaba Cloud Observability
Alibaba Cloud Observability
May 19, 2025 · Cloud Native

How LoongCollector Transforms Log Collection with High‑Performance Pipelines

LoongCollector, the 2025 evolution of iLogtail, introduces a fully redesigned pipeline architecture, hot‑reload isolation, significant CPU and memory reductions, and advanced monitoring, delivering up to 80% higher log‑collection throughput for cloud‑native environments while ensuring seamless upgrades and multi‑region fault tolerance.

Log Collectionobservabilitypipeline
0 likes · 14 min read
How LoongCollector Transforms Log Collection with High‑Performance Pipelines
Alibaba Cloud Observability
Alibaba Cloud Observability
May 12, 2025 · Cloud Native

Building a Cost‑Effective, High‑Availability Global Log Collection with Alibaba Cloud SLS

This article explains how enterprises can overcome the challenges of globally collecting logs by using Alibaba Cloud's high‑performance iLogtail/LoongCollector agents, selecting optimal network paths, applying cost‑saving strategies such as CloudLens diagnostics and LZ4 compression, and configuring multi‑region dual‑write for reliable, low‑cost log delivery.

Alibaba CloudLog CollectionSLS
0 likes · 24 min read
Building a Cost‑Effective, High‑Availability Global Log Collection with Alibaba Cloud SLS
Alibaba Cloud Observability
Alibaba Cloud Observability
Apr 14, 2025 · Cloud Native

How to Connect Grafana to Large Language Models with MCP (Model Context Protocol)

This guide shows how to use the Model Context Protocol (MCP) to build a lightweight server that links Grafana dashboards to large language models, covering MCP concepts, FastMCP setup, Python client implementation, environment preparation, and integration with Cherry Studio for seamless AI-driven data access.

AI IntegrationCloud NativeFastMCP
0 likes · 12 min read
How to Connect Grafana to Large Language Models with MCP (Model Context Protocol)
Alibaba Cloud Observability
Alibaba Cloud Observability
Apr 1, 2025 · Cloud Native

How We Boosted Multi-line Log Collection Speed from 90 MB/s to 350 MB/s

This article details a real‑world case where massive multi‑line log volumes overwhelmed iLogtail, explains the performance bottlenecks caused by full‑line regex matching, describes the switch to prefix‑only matching and IngestProcessor, and shows how these changes lifted throughput from under 100 MB/s to over 300 MB/s while halving CPU usage.

Log CollectioniLogtailperformance optimization
0 likes · 15 min read
How We Boosted Multi-line Log Collection Speed from 90 MB/s to 350 MB/s