Distributed Logging System Design | Centralized Logging | Systems Design Interview

Summary

The transcript discusses designing a centralized logging system for a large-scale company. It covers functional requirements like visibility of logs from all services and user search capabilities. The system should be highly available, scalable, and handle high traffic. The speaker estimates log data volume and suggests using tools like Elasticsearch for search and S3 for storage. They also discuss using agents like Fluentd or Logstash for log collection and Apache Flink for stream processing, emphasizing the importance of minimal delay and data availability.

Takeaways

• 🔍 The main objective is to design a centralized logging system for a company operating at a large scale.
• 📋 Functional requirements include visibility of logs from all services, support for free text search, and time-ordered logs.
• ⏲️ The system must handle logs in real-time with minimal delay, ideally less than a minute.
• 💡 Scalability and high availability are essential due to traffic bursts and high log volume.
• 📦 The system must store logs efficiently, estimating the storage requirement to handle petabytes of data.
• 🗄️ A cold storage solution, such as S3, can be used for long-term log storage, particularly for compliance like GDPR.
• 🛠️ Agents like FluentD or FluentBit will run on the services, sending logs to a message queue for processing.
• ⚙️ The design should support log enrichment and filtering at the agent level to reduce data processing at the source.
• 📊 Elasticsearch will be used for indexing and querying logs, with S3 storing older or archived logs.
• 🛡️ There should be mechanisms like Apache Flink for partitioning logs and ensuring exactly-once processing, enhancing performance and fault tolerance.

Q & A

What are the key functional requirements for a centralized logging system discussed in the script?

The key functional requirements include: (1) logs from all services should be available and visible in the centralized logging system, (2) users should be able to perform free-text search to retrieve logs, and (3) logs should be presented in time order, ensuring chronological visibility.

What non-functional requirements (NFRs) are considered essential for the centralized logging system?

The NFRs discussed include: (1) high availability to ensure the system is always accessible, (2) scalability to handle bursts of traffic and high volumes of log data, and (3) logs should be available quickly, ideally with a maximum delay of 1 minute.

Why is scalability a critical factor in designing a centralized logging system?

Scalability is crucial because the system needs to handle large volumes of log data that fluctuate based on traffic patterns. For example, high traffic periods may generate more logs, requiring the system to accommodate spikes in log ingestion and storage.

What estimation techniques are used to evaluate the log storage requirements?

The speaker estimates storage requirements by considering the number of log lines generated per second, the average size of each log line (e.g., 1KB), and then multiplying by the number of services, time intervals, and storage duration (e.g., for GDPR compliance). This results in storage requirements ranging from terabytes to petabytes over a year.

What role do agents play in the centralized logging architecture?

Agents are deployed on services to read logs and send them to a message queue (e.g., Apache Flink). These agents can also process and enrich logs by adding metadata or filtering data before sending them to the logging system.

How is Apache Flink used in this logging system?

Apache Flink is used for partitioning log data based on machine ID and log type, ensuring that logs are processed efficiently. It also ensures message checkpointing, so no logs are lost, and handles stream processing to make logs available quickly, within the target delay of 1 minute.

What are some of the common agents used in centralized logging systems?

Common agents mentioned include Fluentd and Fluent Bit, which are lightweight and efficient for reading and forwarding logs. Logstash is another option, but it is considered heavier and might impact service performance.

What purpose does S3 serve in this architecture?

S3 is used as a cold storage tier for older logs that are no longer needed for immediate search or analysis. Logs that are beyond the retention period in Elasticsearch (e.g., 7 days) are archived in S3 for longer-term storage, ensuring cost-effective retention.

What is the role of Elasticsearch in this architecture?

Elasticsearch is used for indexing and enabling efficient search and retrieval of logs. The search APIs access Elasticsearch to provide quick results for free-text queries and logs from recent time periods.

How can this design be enhanced for senior engineers?

Senior engineers can enhance the design by focusing on advanced aspects like optimizing agent performance, improving partitioning strategies in Apache Flink, and ensuring the system can handle exactly-once delivery guarantees. Additionally, integrating out-of-the-box solutions like Kibana for visualization or managed cloud services like AWS OpenSearch can further streamline the system.