In this cloud-computing era, where every IT component is modularized, distributed yet interconnected, it becomes essential to know what’s going on in the back end of the infrastructure. All modern organizations gather and store log data to track activity occurring in applications, servers, computer systems, cloud services, and so on.
With hundreds and thousands of applications and machines running across your environment, the risk of component failures or malfunctioning becomes higher. A thorough analysis of system-generated logs tells you what went wrong in the application and what caused it.
In this article, we’ll walk you through every little detail you need to know about logs and their management.
What Are Logs?
In the computing context, a log message is a string of text containing contextual information in abundance derived from the source. A relevant portion of this information gets recorded through logging calls using a default mechanism or a logging system. At the same time, the remaining informational content is fetched from the operating system as a part of the log generation process.
In fact, many operating systems, application frameworks, and server programs have a built-in logging system automatically collecting data for your use in the future. One of the most commonly used logging standard protocols is syslog. This standard for message logging provides a dedicated framework/subsystem for generating, recording, filtering, and analyzing log entries. This saves you the effort of building your own logging system from scratch.
A typical log message written to a log file contains the following information:
- The date and time of the message logged (timestamp)
- The source of the log message (a service, app, or a channel) that generated it
- The type of the message indicated either by a symbol, icon or in simple text
- The content or text recorded in the log message
Shown below is a sample log message consisting of the elements listed above. It’s of the informational message type.
|2019/12/31 15:42:39.785||INFORMATION||Application XYZ_CRM |
Program RX has stopped
Discussed below are the types and various content fields in a log message.
Types of Logs
Log data can be transactions, errors, resource consumption statistics, events, performance metrics, security issues, network traffic, and more. But, there are specific categories of log messages under which they’re written and classified.
While logging, each message type is approved by the calling code. The following are the log message types based on severity codes and special cases:
|Log Message Type||Brief Description|
|Informational||This message type includes basic log entries of events. It records information about the tasks and operations performed by the server.|
|Error||Critical errors encountered by the operating system, server, application, etc. while in operation mode. For example, table corruption, configuration error, return codes for bad function, etc. |
Unmarked Errors: Communication channel or service error. Or an error encountered and logged while processing a message. Unmarked errors are displayed on the application dashboard or control panel of the system.
Marked Errors: Do not appear on the dashboard or the control panel of your machine.
|Warning||A warning message is the gray version of error, which indicates a problem is encountered but isn’t final. For example, failed attempts to invoke a service due to some connection error.|
|Verbose||This type logs a cluster of information or records, which is more than usual logging. E.g., A large log file dumped with function variables (VARs), arguments (args), etc.|
|Status||Status logs capture details of significant or critical events. E.g., Failure, Success, etc.|
|Success||When a message has been processed successfully.|
|Server Log||A message generated by the server.|
|Unqueued Message||Log messages received but not added to the messages queue waiting to be processed. Unqueued log entries could be messages (originals) modified by a log message filter or messages that could not be processed as they contained errors.|
|Resubmitted Message||Logged messages resubmitted for processing.|
|Resubmitted Unqueued Message||This type of log indicates unqueued messages resubmitted for further processing.|
|Acknowledgment Message||An Acknowledgement (ACK) entry is logged whenever the application successfully receives a message from the source channel or component.|
|Message Generated by Resubmission Operation||This is a log message created due to the resubmission of the previously generated message.|
|Message Generated by Resubmission Operation and then Resubmitted||This is a similar message type including log entries generated post the resubmission of the previous message. The only difference is this message itself gets resubmitted.|
|Bug||Log entries of actual bugs encountered. E.g., invalid params/parameters, array index out of bounds, etc.|
|Debugging||Log entry or message of internal debugging.|
|User||Custom messages, written by end-users.|
|Comment||A message type inserting user comments by executable code.|
|Regression Test Log||A log message recorded specifically and used only before or after regression testing.|
Now that we’re familiar with logs and their message types let’s get a detailed understanding of what log management is and why you should care.
What Is Log Management?
Every element of your software, application, or system writes logs and records them in a separate file. This log data keeps piling up and, by default, sits on a local hard drive or some storage place in the cloud. You can put these logs to use with the help of log management.
In general terms, log management is defined as a comprehensive process or workflow of handling log data captured in large volumes from various sources and storage locations. The idea behind this practice is to simplify the maintenance of applications or websites by using the technical information generated by them in the form of log messages.
Log management came into existence with the advancement of IT and the increasing volume of data capture every day.
According to Wikipedia, log management generally covers:
- Log collection
- Centralized log aggregation
- Long-term log storage and retention
- Log rotation
- Log analysis (in real-time and in bulk after storage)
- Log search and reporting.
Described below are some of the critical processes carried out in log management:
Log aggregation is the practice or method of collecting various log files scattered across different locations in a centralized place. This is done for further organizing log data stored in them to make the data easily searchable.
In this process, log files are imported from multiple sources, such as applications, databases, infrastructure, containers, etc.
Log File Parsing
It’s the method of slicing or fragmenting data into small digestible chunks of information to make log storage and management more effortless. Every log file captures different pieces of data and technical information. When parsing a log file, similar data structures are identified, and the information is grouped using those structures. For example, aggregation of logs containing timestamps from a certain timeframe or logs tracking the activities of a particular user.
Log analysis, on the other hand, is the act of pulling out computer-generated log data collected through the logging process to investigate the events occurring in your network systems, apps, operating systems, programming code, and all other devices producing logs. It’s a subset of log intelligence involving a thorough study of logs for efficient debugging, security, and compliance-related operations.
Here’s the list of some of the key drivers for log analysis:
- Troubleshooting of systems and devices
- Response to security incidents and potential threat activities
- Compliance with security policies, regulations, and audits
Another key advantage of performing log analysis for digital marketers is website performance optimization. The practice is used as a strategic approach to aggregate the website’s log data and transform it into detailed insights. These insights are beneficial in analyzing how your website is performing in terms of visibility across search engines, traffic, conversions, and other critical metrics. Log analysis uncovers the potential areas of improvements for your website, like search engine optimization (SEO). It also helps measure the impact of a digital campaign by revealing how well crawlers or bots are ranking your website on the search engines. Other benefits include:
- Understanding the user behavior to find out which web pages are performing and which are not relevant to your users.
- Identify which pages bots are able to crawl and which pages have not been indexed (i.e., made searchable).
- Make informed decisions by studying log files containing critical website information—events, patterns, transactions between a user and the website, and so on.
Why Log Management?
Today, most IT organizations encounter a consistently expanding surge of security threats. These risks and vulnerabilities keep changing and growing exponentially, making it difficult for IT security experts to outflank threats without any monitoring and operational intelligence in place. Besides, challenges still exist when you have to comply with industry regulations. And, should you fail to achieve compliance requirements, the business is at overall risk—from financial, security, and legal aspects.
Value-based data captured from day-to-day transactions across IT environments can be monitored and examined as a whole instead of viewing them in discrete, disparate silos. Log management gives you the ability to watch all your applications and systems simultaneously. This amplified visibility into your organization-wide activities and real-time events enhance the overall security posture. It increases the effectiveness of your IT pros to respond rapidly to anomalous events.
Security experts comprehended the advantages of centralized log management systems a while ago. The standard approach for hackers to mask their intrusion attack is by destroying or tampering with logs and damaging logging mechanisms. Having a protected/encrypted copy of sensitive log data in a central location ensures critical information is secured for post-event analysis. Also, the copy acts as proof that can be accessed for taking any subsequent legal action.
Logs helping to achieve specific mandates of regulatory compliances are called audit logs. A few popular compliances and regulations are:
- the Sarbanes Oxley Act (SOX),
- the Health Insurance Portability and Accountability Act (HIPAA),
- Payment Card Industry (PCI),
- the Gramm-Leach-Bliley Act (GLBA), Markets in Financial Instruments Directive (MiFID),
- Federal Information Security Management Act of 2002 (FISMA) and more.
Moreover, today’s complex networks and sophisticated systems require a more advanced, comprehensive log management setup. Gone are those days when setting up in-house, on-premises frameworks for logging data was simple and done for the sake of compliance purposes. The current scenario demands log management to be more than a simple data aggregation setup. It should have extended capabilities to enable you with data normalization, analysis, reporting, alerting, and secure log archival processes.
Besides, manually analyzing or managing diverse logs poses many challenges, especially when they’re located at disjointed file systems. Some of the commonly faced challenges:
- Volume: As your organization continues to grow, the volume log data also increases. Large enterprises generate logs in hundreds of gigabytes (GBs) per day. Collection, aggregation, and storage of data by simple, traditional methods at this scale is tedious.
- Velocity: Today’s modern, more advanced systems and devices produce logs at the speed of light, which makes data capturing and centralization challenging.
- Veracity or Accuracy: Log events collected or entered manually in plain text files or tables might not be accurate and contain errors. This gets tricky and challenging for systems monitoring and detecting critical incidents, intrusions, etc.
- Normalization or Standardization: Log messages are written to multiple files in different formats. For the ease of developers and IT pros to analyze the data in one single form, the normalization process has been designed to standardize the logs gathered from diverse sources into standard output. This practice becomes difficult and is not feasible when performed manually or using conventional approaches.
A reliable log management solution not only solves these challenges but also provides valuable insights into your IT operations. It enables efficient troubleshooting with data intelligence.
However, before investing in any ready-to-use log management solution, you must evaluate their capabilities based on your use-case scenarios. Security information and event management (SIEM), data retention capacity, and scalability are some of the features to look for.
Top 4 Best Log Management Tools
Listed below are the top tools and platforms considered to be the benchmark for log management solution.
SolarWinds® Loggly® is a unified and complete solution for all your log monitoring and analysis needs. This cloud-based platform acts as a single source of truth, where you can collect, organize, store, and investigate massive log data aggregated from your infrastructure.
You run a significant number of applications in public clouds (AWS and Azure) to leverage their flexibility to scale, low storage costs, high computing power, and more. Hence, it’s essential to keep a close watch on the environment since issues can occur anywhere.
Loggly provides advanced, interactive, and surround search capabilities with its dynamic Field Explorer. This feature helps you drill down into relevant log data to spot issues and root causes quickly. The instant and accurate response to your searches not only accelerates the troubleshooting process but also minimizes mean time to repair (MTTR). Now, you can gain actionable insights into all your services, apps, and cloud environments—AWS, Azure, and hybrid cloud. Diagnose unusual events or outliers across the stack through custom alerts and metrics.
Besides, identifying bottlenecks and source of failures for rapid resolution becomes easy with:
- customizable dashboards and shared visualizations
- contextual searching across aggregated log data
- extensive language support
- automated field investigation in real-time for intelligent correlation of data
Its powerful analytics based on logged exceptions and log formatting capabilities helps identify variations in data points, so you focus on logs needing attention. Another value-added service this tool delivers is the scalability to support your constant growth of IT operations and log maintenance requirements. It leverages the cost-effective Elastic Stack technology for massive data ingestion and flexible data retention as your IT infrastructure scales dynamically.
Loggly’s fast searching, custom alerts, and continuous log monitoring make it easier to resolve performance-related issues in AWS applications. It supports over 60 out-of-the-box integrations to provide you with end-to-end monitoring with performance metrics, logs, and traces that span across a wide array of AWS services. Getting started with Loggly is easy, as it doesn’t involve lengthy configurations and offers flexible pricing to meet varied needs. You’ll also find Loggly’s product documentation and tips and guides for logging highly helpful. Deep dive into the features and capabilities of SolarWinds Loggly by trying out a fully functional, free version now.
2. Sumo Logic
Sumo Logic has evolved as an enterprise-grade log management product, originally a SaaS version of Splunk. Like Loggly, it’s also a cloud-based solution collecting logs from any application or device in almost any format and transforming them into actionable insights and threat intelligence. Its centralized log management service stores and analyzes over 100 petabytes (PB) of log data every day.
The tool scans all your logs, identifies potential threats or breaches, and provides a consolidated view of abnormal patterns, outliers in your application and system behaviors, unusual events, and performance issues based on priority. This helps IT experts to proactively respond to these outliers and find out the root cause to prevent any security or performance impact on the business in the future.
However, Sumo Logic is most beneficial if your log management needs are tied to security and compliance. Its centralized platform enables you to improve the overall security posture and achieve compliance with built-in predictive analytics, pattern detection, outlier discovery, and anomaly detection capabilities. Besides, it pulls crucial data stuck in silos and turns them into unified logs and metrics, which can be analyzed to monitor the entire application stack in real time.
LogDNA is another efficient log management tool giving an aggregated view of all the log entries emitted by different parts of IT infrastructure, such as firewalls, routers, switches, operating systems, servers, etc. More importantly, the tool collects data from any system location and supports log management across any IT environment, architecture, and language.
This is a Kubernetes-based log management solution facilitating analysis of over 20 terabytes of log data per day. Its intelligent features like toggle timeline, custom filters, Google-like syntax, and more, make log search easier in Live Tail (to see a real-time feed of logs).
While LogDNA offers a free trial, it’s only for 14 days, which might not be enough for organizations willing to test the solution across their hybrid environment containing on-premises equipment, cloud, virtual machines (VMs), and more.
4. SolarWinds Papertrail
SolarWinds Papertrail™ is a popular log management tool among DevOps teams, known for its high scalability and customization features. As a hosted log management service, Papertrail simplifies initial installation and could be set up in minutes following a few simple steps. You can collect logs from a wide range of servers, systems, applications, and even cloud-based sources for centralized management and analysis.
Papertrail’s Event Viewer provides an intuitive approach to monitoring and analysis and is the core of the product. In this log viewer, you can tail logs and monitor events in real time. You can pause the live event stream at any time to focus on particular messages as per your convenience. The viewer also offers a simple and powerful search feature, supporting common search operators. To ensure quick troubleshooting, you can save important or frequently accessed searches. These saved searches can also be utilized for configuring alerts.
Papertrail also gives you a unique feature to access past messages by entering a specific time. Further, you can make certain important portions (e.g., User ID, IP, source code file name, etc.) of log messages clickable. This feature is extremely helpful during troubleshooting as when you click on the element, it’ll allow you to view all nearby messages related to the clicked element.
Papertrail’s command line interface is another useful feature to expedite troubleshooting and account management. Further, you can filter specific messages during troubleshooting to focus on significant patterns.
An important aspect of log management is to keep tabs on log volumes. Any spike in log volume could be an indication of some misconfiguration. Sometimes, the failure of a critical database can also flood the logging setup with error messages. To detect such spikes, you can set up alerts in Papertrail. You can receive these alerts over email or use popular collaboration tools like PagerDuty, Slack, and more. Learn more about Papertrail’s features here.
So which of these solutions is the best? When it comes to log management and monitoring, Loggly is hands down the most popular and capable product. Its easier setup, highly intuitive user interface, and support for a wide range of logging formats make log management a breeze. Also, being a cloud-based solution, it also allows easy scalability to meet your organization’s growing needs. Get started with Loggly’s free trial now!