Log monitoring

Logs are the unsung heroes of the IT world, capturing the critical details of system operations with meticulous precision. While these records might seem like endless data streams, they hold the keys to optimizing performance, enhancing security, and ensuring system reliability.

This guide will cover practical techniques for managing logs and the tools that simplify this essential task. We'll start with structured and unstructured logging fundamentals, explore what log monitoring is and why we need it, and learn how organizations use ClickHouse at the center of their log monitoring systems.

Logs are digital records that capture events occurring within computer systems, applications, or networks. Think of them as a system's diary - a chronological record of everything that happens, from routine operations to critical errors. Every time a user logs in, a file is accessed, or an error occurs, these events are recorded with crucial details like timestamps, event types, and relevant data.

The most common source of logs is the applications themselves. Modern applications are designed to emit logs automatically, documenting their operation, performance metrics, user interactions, and any issues encountered. These logs serve as breadcrumbs for developers and system administrators, helping them understand what's happening inside their systems and troubleshoot when things go wrong.

Logs can be generated and captured in various formats. Raw logs are often simple text files, with each line representing an event, implicitly following a specific format determined by the application or system generating them. For example, an HTTP web server might log each request with details like timestamp, IP address, requested URL, and response code in a predetermined order. An example of this type of log message is shown below:

192.168.1.1 - - [22/Jan/2025:12:00:00 +0000] "GET /index.html HTTP/1.1" 200 612 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"

Structured logging has become increasingly popular, especially with modern applications. This approach formats logs in a machine-readable structure, most commonly JSON. A JSON log entry might include clearly labeled fields for timestamp, severity level, message, and any relevant metadata. This structured format makes logs easier to parse, search, and analyze programmatically. An example of this type of log message is shown below:

{
  "timestamp": "2025-01-22T12:00:00Z",
  "level": "INFO",
  "message": "User logged in successfully.",
  "ip_address": "192.168.1.1",
  "user_id": 42,
  "method": "POST",
  "endpoint": "/login",
  "response_code": 200,
  "response_time_ms": 350,
  "user_agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
}

Logs generally fall into two main categories: system logs and application logs.

System logs provide information about events happening at the operating system (OS) level. These logs capture system-level events such as:

• Authentication attempts
• Connection requests
• Service and process starts/stops
• System configuration changes
• System errors
• Performance metrics
• Resource usage

Application logs, on the other hand, document events occurring at the software level. These logs are generated by specialized server software (like proxies or firewalls) and other software applications. Application logs typically include:

• Application-level authentication
• CRUD (Create, Read, Update, Delete) operations
• Application errors and exceptions
• Application-specific functions
• User interactions

Application logs are particularly valuable for developers and system administrators as they provide detailed insights into how their software behaves in production. For example, a web application might log every API request, database query, or user session, while a firewall might log all attempted connections and security-related events.
The combination of both system and application logs provides a comprehensive view of an organization's IT infrastructure, enabling effective monitoring, troubleshooting, and security analysis.

Log monitoring involves continuously capturing and observing log data to identify anomalies, issues, or patterns that require attention. While log monitoring could, at its most basic level, mean simply logging into a server and reviewing log files manually, modern systems require a far more sophisticated approach.

The complexity of today's systems has made traditional manual log monitoring impractical for several reasons. First, the rise of microservice architectures means applications generate an enormous volume of logs across numerous services and containers. Second, modern infrastructure treats servers as "cattle rather than pets," - meaning servers are regularly created and destroyed rather than carefully maintained individual machines. Furthermore, with the growing adoption of serverless architectures, there might not even be a persistent server to access in the traditional sense.

These challenges have led to the development of comprehensive log management systems that automatically collect, process, and analyze logged data from across the entire infrastructure. Modern log monitoring involves systematically gathering logs from all sources, centralizing them in a single system, and implementing automated tools to help identify and respond to issues immediately.

The goal is to transform raw log data into actionable insights, enabling teams to:

• Detect and respond to problems quickly
• Understand system behavior
• Track performance metrics
• Maintain security vigilance
• Ensure compliance requirements are met

This systematic approach to log monitoring has become essential for maintaining reliable and secure systems in today's complex technological landscape.

Log monitoring is essential for maintaining healthy, efficient systems and applications for several reasons.

System visibility is fundamental to understanding your infrastructure's health. Through log monitoring, teams gain real-time insights into system behavior and can track user activities and system operations. This comprehensive view allows organizations to understand exactly what's happening across their entire infrastructure at any given moment.

Service availability is another critical aspect that log monitoring supports. By continuously monitoring logs, teams can quickly detect and diagnose system outages when they occur. This rapid detection enables faster response times to critical issues, helping organizations maintain service level agreements (SLAs) and minimize system downtime. When systems experience issues, logs provide the necessary information to understand what went wrong and how to fix it.

Performance monitoring is equally essential for maintaining efficient operations. Through log monitoring, teams can track system performance metrics over time, identifying bottlenecks and potential issues before they become critical. This includes monitoring resource usage and throughput, helping teams understand when and where performance might be degrading. Organizations can take corrective action by catching performance issues early before users are significantly impacted.

Effective log monitoring makes problem detection and resolution straightforward. When issues arise, logs help pinpoint exactly which services or system components are failing or struggling. This detailed information helps teams identify the root cause of problems and implement solutions more quickly. Log monitoring can also reveal patterns that indicate potential future issues, enabling proactive maintenance and system optimization.

By implementing effective log monitoring, organizations can maintain reliable services, optimize performance, and resolve issues before they significantly impact users or business operations. This proactive approach to system management is essential in today's digital landscape, where system reliability and performance directly impact business success.

Organizations often begin their logging journey with out-of-the-box monitoring solutions. These tools provide immediate value through pre-built dashboards and visualizations, ready-to-use alerting systems, and built-in integrations with standard services. With quick setup, deployment times, and managed infrastructure, they offer a streamlined path to system observability.

Popular solutions include Datadog, Splunk, Graylog, New Relic, and Sumo Logic. These tools serve as excellent starting points for organizations beginning their observability journey, offering robust functionality without the need to build and maintain custom infrastructure.

As organizations grow, the volume of log data they produce can reach petabyte levels daily. Traditional log monitoring tools often become cost-prohibitive at this scale, leading companies to restrict log retention times. However, having access to extensive historical logs is crucial for engineers to investigate and resolve issues thoroughly.

ClickHouse offers an economical alternative with its robust capabilities for efficiently managing vast amounts of data.

Our journey at ClickHouse demonstrates this perfectly. When scaling our cloud service, we faced the same challenges many large organizations encounter with traditional logging solutions. Our experience building a 19 PiB logging platform that handles over 37 trillion rows while maintaining 6 months of retention provides a compelling example of what's possible with ClickHouse.

➡️ Read How we Built a 19 PiB Logging Platform with ClickHouse and Saved Millions

This transformation isn't unique to us. Didi, one of the world's largest mobility platforms serving over 450 million users, faced similar challenges with their Elasticsearch implementation. Their successful migration to ClickHouse for log storage resulted in a 30% reduction in hardware costs while handling petabyte-level daily log volumes. Their story provides another excellent example of ClickHouse's capabilities in handling enterprise-scale log monitoring.

➡️ Read Didi Migrates from Elasticsearch to ClickHouse for a new Generation Log Storage System

Trip.com's migration to ClickHouse for its logging platform provides another compelling case study of successful large-scale log management. After facing significant challenges with its 4PB Elasticsearch deployment, including cluster instability, performance degradation, and high costs, Trip.com successfully migrated to ClickHouse and scaled to over 40PB of data.

The results were remarkable: they achieved 50% storage space savings, 4-30 times faster query performance with P90 queries under 300ms, and significantly lower total cost of ownership. Their success stemmed from ClickHouse's columnar storage design, efficient compression, SQL-based querying, and superior scalability. The platform now handles massive concurrent workloads while maintaining longer retention periods, demonstrating ClickHouse's capability to manage enterprise-scale logging requirements cost-effectively and efficiently.

➡️ Read How trip.com migrated from Elasticsearch and built a 50PB logging solution with ClickHouse