By [Your Name], Professional Journalist and Editor
In today’s complex technological landscape, as software architectures evolve from monolithic to microservices and cloud-native, the demand for observability has become unprecedentedly crucial. Asa leading global online travel service platform, Ctrip faces the challenge of handling massive amounts of monitoring data and log processing, which places higher demands on platform governance and continuous stability.
At the upcoming QCon Shanghai, Zhou Xinyi, Director of Cloud-Native R&D at Ctrip, will deliver a keynote speech titled AI-Driven Observability Platform Architecture Upgrade Practices. In a pre-conference interview, ZhouXinyi shared Ctrip’s innovative solutions to address these challenges, particularly in the application of technical approaches such as data sampling, tiered storage, and unified monitoring agents. He delved into how to effectively govern massive data while balancing system performance and cost-effectiveness. Additionally, Zhou Xinyi shared Ctrip’s leading practices in AIOps, providing valuable technical insights into the observability challenges posed by cloud-native architectures.
Industry Transformation and Insight into the Current Situation
InfoQ: What do you consider the most prominent issues in Ctrip’scurrent observability platform? How do these issues specifically impact platform operations and decision-making?
Zhou Xinyi: As the complexity of Ctrip’s software systems and applications continues to increase, the volume of data generated by Ctrip’s observability platform is also growing rapidly. Ctrip currently has over 10,000 applications, with over 1 million instances (including physical machines, virtual machines, and containers), generating over 1 billion metrics data points per minute. The daily growth of logs generated by all applications and systems exceeds 1PB. Observability data includes logs, metrics, and tracing information. Howto effectively collect, store, process, and analyze this data has become a significant challenge, and it is currently the most prominent issue facing Ctrip’s observability platform. These issues have the following impact on platform operations and decision-making:
- Information Overload: The massive amount of data leads to information overload, making it difficult for operations personnel to extract valuable information. In severe cases, this can lead to key issues being masked, extending troubleshooting time.
- Performance Bottlenecks of the Observability Platform: Processing and storing massive amounts of data requires high-performance infrastructure, which also increases machine costs and operational complexity. Ifthe platform’s performance is insufficient, it can lead to data delays or loss, affecting the timeliness of monitoring data.
- Increased Costs: Daily log storage volume exceeds 1PB. Without effective governance, an additional 1PB of disk space is required for log storage every day.
InfoQ:As systems become increasingly complex, how is Ctrip’s monitoring and log data growing rapidly? What technical or management challenges have you encountered in managing this data?
Zhou Xinyi: The rapid growth of Ctrip’s monitoring and log data is driven by several factors:
- Microservices Architecture:Ctrip has transitioned to a microservices architecture, leading to a significant increase in the number of applications and instances. Each microservice generates its own set of monitoring data and logs.
- Cloud-Native Adoption: Ctrip has embraced cloud-native technologies, such as containers and serverless computing, which further contribute to thegrowth of monitoring and log data.
- Increased User Base and Transaction Volume: As Ctrip’s user base and transaction volume continue to grow, the amount of data generated by the platform also increases proportionally.
Ctrip’s Innovative Solutions
To address these challenges, Ctrip has implemented several innovative solutions:
- Data Sampling: Ctrip employs data sampling techniques to reduce the volume of data collected and stored. This involves selectively sampling data points based on predefined criteria, ensuring that critical information is captured while minimizing storage requirements.
- Tiered Storage: Ctrip utilizes tiered storage to optimize storage costs. High-frequency and critical data is stored in high-performance storage, while less frequently accessed data is stored in lower-cost storage tiers.
- Unified Monitoring Agent: Ctrip has developed a unified monitoring agent that collects data from various sources, including applications, infrastructure, and cloud services. This agent provides a consistent data format andsimplifies data collection and analysis.
AI-Driven Observability Platform
Ctrip has also leveraged AI to enhance its observability platform:
- AIOps: Ctrip employs AIOps techniques to automate routine tasks, such as anomaly detection, root cause analysis, and incident response. This frees up operations personnel to focuson more strategic tasks.
- Machine Learning: Ctrip utilizes machine learning algorithms to identify patterns and anomalies in monitoring data, enabling proactive problem identification and resolution.
Conclusion
Ctrip’s journey in building a robust and scalable observability platform demonstrates the importance of embracing innovative technologies and AI-driven approaches. By effectively managing massive data volumes, Ctrip ensures high-performance and reliable operations, ultimately providing a seamless and enjoyable experience for its users.
References:
- [Link to QCon Shanghai Website]
- [Link to Ctrip’s Official Website]
Note: This article is a samplebased on the provided information. You can expand upon this by adding more details, insights, and quotes from the interview. You can also include your own analysis and perspectives on the topic.
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