Five Guidelines for Building a Log Management System
When you're building a log management system, here are some things to keep in mind in order to enable better transparency, streamline operations, and support DevOps methods.
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Log management is not a new aspect of IT, but its importance has increased in recent years due to the emergence of the Cloud. The accelerated growth of machine-generated log data in complex, multi-tier, and dynamic cloud environments increases the need for solutions that enable better transparency, streamline operations, and support DevOps methods. Effective and efficient log management systems have become critical requirements in domains including IT operations, network security, and user behavior analysis.
Successful log management systems quickly identify and understand key data within massive amounts of of information, allowing DevOps engineers, site reliability engineers, and developers to respond to events and issues immediately.
Running an effective log analytics system, however, is not without its challenges. Correlating between logs generated by different layers or components to understand the cause of a problem and how it can be solved, for example, is no simple task. It’s impossible to undertake manually.
In light of such complications, we at log management platform Logz.io have created a process that will help you to build a powerful and efficient log management system.
1. Create a Strategy
Just like every other important aspect of IT management, log management needs a plan. First, determine the most important objectives of your logging systems. (For example, you may wish to monitor system latency to ensure low user churn rate.) Second, list the methods and tools that will be used as well as the administrators and users of those tools. Identify where the log data will reside and how the information will be secured. Third, incorporate your log management plan into your DevOps implementation efforts and create a policy for whenever you will add or update logs (for example, whenever new features or user workflows are released).
2. Structure Your Logs
System operators have two main obstacles to tackle in log management: understanding many different logging formats and locating relevant data in an effective manner. Structuring logs properly is crucial to overcoming both challenges. First, structured logs are easier for both humans and machines to read. Second, analyse your specific use case and ask the right questions so that you will be able to create the exact format structure that you need.. This will allow the data to be processed accurately by your log management system and will enable you to troubleshoot issues more quickly.
3. Centralize, Separate, and Secure
Collecting logs from all of the different parts of your system to a centralized data repository enables you to perform effective cross-system analyses. In addition, centralizing also grants developers access to the pool of data so that they can get the information that they need to fix bugs or address support issues within their own code and without disrupting the production environment. In the case of a security breach, logs can be the most important source of information, and having an isolated and secured repository will ensure their availability.
4. Correlate Data Sources
Monitoring and logging across all system components will give you a holistic view of what is occurring in your environment. t is much easier to identify the root cause of an issue by looking at the bigger picture. If you receive an alert in real-time that some of your users have not been able to log in, for example, you can see which specific database configurations are causing the malfunction in your log management system. High visibility into your system’s behavior also allows you to determine key usage metrics and developing trends.
5. Define the Context
Logging data without defining the specific context beforehand will only make it more difficult to reach valid conclusions. Instead, use unique identifiers to record user sessions, and trace their interactions all the way back to the requests from the database. In an e-commerce example, track a critical user’s activities to learn if the purchase process of a certain product is functioning correctly. Such an approach makes troubleshooting much more effective.
A Final Note
Every second matters when it comes to an end user that is losing patience over a feature that is not functioning and directly resulting in a loss of online revenue. You must be able to identify the issue, diagnose the root cause, and then solve the problem all within a very short amount of time.With the move to microservices that are based on virtual containers and situated in dynamic cloud environments, logs have become a precondition for operational success. If planned and implemented effectively, a log management system will enable the collection of valuable information that will inform your critical decisions and actions.
For More Information
In our own environment, we use the open source ELK Stack (Elasticsearch, Logstash, and Kibana) as our log management system. For those who might be interested, we have created some informational guides on AWS log analysis with ELK as well as on NGINX, IIS, and Apache log analysis with ELK.
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