A log refers to a digital file that records and stores activity within software applications or systems. Log management is a practice that involves gathering, processing, storing, and analyzing data from heterogeneous systems to boost system performance, improve resource management, pin-point issues, enhance compliance, and improve security.
Log Management and DevOps
Logging in today’s DevOps landscape has become more difficult than ever. DevOps needs visibility into workloads that security, development, and operations teams collaboratively manage and provide. The emergence of cloud-based services, as well as the rapid maturity of microservices-based architecture, ensure that alongside legacy systems, DevOps teams have more systems to manage.
More logs are produced, hence more log data is generated. DevOps teams are required to deal with logging structures with greater variation. As such, these teams require approaches to upholding visibility and security that can keep up with fast-paced and greatly dynamic environments.
Modern log management solutions uphold DevOps culture by going beyond being able to ingest, query, provide alerts on, and visualize data to also provide collaborative functionality. Log management allows developers to monitor and observe the behavior of applications prior to their release to production in continuous integration and continuous development (CI/CD) pipelines. This allows developers to catch preventable issues before deployment and production.
As a result, occasions where development teams would need to constantly troubleshoot or redesign their solutions while in production are reduced. This creates a polished and more seamless user experience and develops a culture of building more user-friendly products and services.
Also read: Re-Architecting Log Management for DevOps
Uses of Log Management in DevOps
Modern log management systems go beyond just using logs to generate insights into the performance of applications and services they support. They automatically aggregate, index, and analyze logs to provide insights and present them to CI/CD pipelines and other utilities.
These log management systems also automate code deployment to ensure repeatable steps in this process can be performed without human oversight and to improve DevOps performance.
Leveraging machine learning capabilities
Historical log data can empower DevOps teams to examine user engagements and uncover anomalies. Additionally, machine learning capabilities, like time-series anomaly detection (TSAD), can provide greater visibility for DevOps teams when logging data like application traffic.
Offering software teams modern performance and analysis capabilities
Today’s software teams require log management solutions that provide performance and analysis capabilities as a base for observability. Such solutions offer DevOps teams a platform supporting an ever-growing, open-source market to ensure their products are part of an ecosystem that future-proofs them.
They also store important log-based metrics to eliminate the need to crunch large volumes of data from scratch to uncover aggregate trends.
Monitoring applications in real-time
In addition to usage in CI/CD, DevOps uses log management to monitor applications in real time to reduce the gap between DevOps and the end users. For instance, mappable data includes customer satisfaction scores.
Software application and infrastructure troubleshooting
The standard use case of log management in DevOps is managing the issues facing the applications and infrastructure through alerts from logs and metrics.
Log Management Benefits
Increased complexity of systems and applications ensures the scope and difficulty of operations also increase. Log management equips DevOps teams with the ability to uncover trends across the infrastructure of an enterprise and to anticipate issues. Consequently, they are always equipped to prevent urgent issues that would disrupt processes and to ease operations.
Improved resource usage
As logs offer more metadata to sift through and visualize compared to metrics, you can derive greater actionable insights from them. These insights can inform users on how to better allocate their resources to prevent system overloads and bottlenecks.
Anomalies may be indicators of attacks. Logging allows security teams to gather, analyze, and aggregate log data to improve the effectiveness and comprehensiveness of security information and event management (SIEM) services.
User experience challenges such as slow response times and lacking a response entirely can be dissected through log management. Log management shows you requests at each level to identify which requests are failing and an understanding of why they are failing.
Also read: Top DevOps Trends to Watch in 2022
Challenges of Log Management
Handling increasingly complex systems
The complexity of today’s systems is amplified by their increasingly distributed nature. Each tool or application generates streams of logs. As much as these logs may provide valuable information, they may also be verbose and redundant.
The redundant information complicates the process of finding valuable information. Furthermore, this challenge is amplified in log management systems with underdeveloped searching and reporting features.
IT burden and automation
Log management carried out manually is tedious, inefficient, and time consuming. The use of automated log management tools eases the burden of IT professionals. However, automated log management tools may also fall short of being an ideal solution, as they work within set parameters.
Therefore, as challenges and threats to data evolve daily, preset parameters may not account for these changes. As such, a dedicated human may still be required to discern what to automate and to take up manually.
The latency between when data enters a system and when data is available in search results and visualizations can be caused by the high computational cost of indexing within log files. Furthermore, the process of indexing data by the log management system may contribute to increased latency.
Today’s data explosion is heaping pressure on organizations to successfully capture, store, organize, analyze, and derive value from large volumes of data. This adds complexity to log management, as it is expected to manage large quantities of data while providing accurate and prompt insights.
Implementing Log Management Efficiently
Prioritize automation tools
The soaring distributed nature of today’s systems coupled with their real-time requirements is challenging log management to adopt automated tools to handle various recurring tasks such as data collection. This automation reduces strain on resources in DevOps teams and promotes overall efficiency.
Leveraging the cloud
DevOps teams, and organizations as a whole, should consider cloud-based log management solutions to keep abreast with the dynamic data landscape of today. Teams can enjoy the flexibility and scalability offered by these cloud-based solutions, as they can scale up or down based on their development and operations needs.
Use a centralized system
Centralization of log management improves both data access and security of an organization. Central storage of data improves the efficiency of anomaly detection and response. It also defines access control, as it helps in controlling and managing the visibility of data. For example, it eliminates the exposure of various teams to logs outside their scope of responsibility. This approach also ensures personally identifiable information is only accessed by the correct parties.
As application development becomes more rapid, containerized applications are becoming more rampant due to their greater reliability and cost-effectiveness. The importance of log management to the development, support, and usage of mission-critical applications means that logs should also be compartmentalized. This provides a consistent experience across all environments and allows DevOps teams to individualize development, staging, and production environments.
Consider log management as a driver
Considering log management as an afterthought may undermine how you leverage log data and ultimately dilute the quality of the business decisions derived from the data. As such, log management should be considered a driver of observability and should be viewed as truly essential to DevOps.
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