Logs are records of events or messages triggered by processing, conditions, or parameters. They collect valuable information about system events, users, data, and applications across an organization and the cloud. Logs are often stored and maintained using log management systems for debugging, tracing, and deciphering issues, but also for auditing and compliance. This article discusses the importance of logs, the different types of logs, services or events that generate logs, and the importance of effective log management for your organization.
What are logs?
A log is a record of an event, or a message generated by a system, process, or application. A log captures information about an event, including the time of occurrence, and it contains details about the event. Logs are files or messages transmitted to a log management system for ingestion, storage, and auditing purposes. DevOps, NetOps, managed services, security, developers, and associated teams can use these logs to trace, analyze, and remedy issues and for compliance auditing.
Log data can be stored as plain text, JavaScript Object Notation (JSON), or standardized formats such as Common Log Format (CLF) or Extended Log Format (ELF). Structured log formats enable log management systems to identify, tag, store, and retrieve information stored in logs. Consider the following example of a log record generated in CLF, which is often used by web servers, and which contains standardized details of a server request.
The following string represents a single sample log record:
127.0.0.1 user-identifier john [20/Jan/2020:21:32:14 -0700] “GET /apache_pb.gif HTTP/1.0” 200 4782
The table below identifies the information contained within this sample log record.
| Log snippet | Description |
|---|---|
| 127.0.0.1 | The IP address of the client (the remote host) that made the request to the server |
| user-identifier | The Ident protocol (also known as Identification Protocol or Ident) of the client |
| John | The userid (user identification) of the person that is requesting the document |
| [20/Jan/2020:21:32:14 -0700] | The date, time, and time zone that the request was attempted; by default, this is in the strftime format of %d/%b/%Y:%H:%M:%S %z |
| “GET /apache_pb.gif HTTP/1.0” | The client’s request line; GET refers to the method, apache_pb.gif is the resource that was requested, and HTTP/1.0 is the HTTP protocol |
| 200 | The HTTP status code that was returned to the client after the request; 2xx is a successful response, 3xx is a redirection, 4xx is a client error, and 5xx is a server error |
| 4782 | The size of the object, measured in bytes, that was returned to the client in question |
What are the different types of logs, and what processes or services generate logs?
Do not confuse log formats with log types, which contain different information or messages depending on their use or purpose. Systems, computers, and environments produce logs of varying types, the most common of which are shown below.
| Log type | Description |
|---|---|
| Application log | A record of what an application is doing, including errors and warnings |
| Access log | A record of every attempt to access a given server or service; this can be helpful for understanding login attempts and user identities |
| Network log | A record of traffic flow across networks, including IP addresses and more |
| System log | Consists of operating system–level records tracking hardware and kernel events |
| Transaction log | A record, especially for databases, for tracking changes for rollback or auditing |
| Security log | Contains messages or alerts of suspicious activity |
| Audit log | A record kept for regulatory compliance or reporting (for more information and the difference between regular logs and audit logs, see the audit logging overview) |
Various processes and services generate logs. For example, your computer creates logs related to user sign-ins, software installations, operating system updates, and other significant events. Internet activity on a web browser generates logs that track domains, cookies, or files on local storage. Anti-virus and security software create logs that reflect scanning across files, storage, and devices. Complex enterprise-scale operations, including cloud-based systems, generate logs that track API requests, data operations, user sign-in attempts, and more.
Why is logging important?
One of the most important reasons teams collect and manage logs is to troubleshoot issues. Without a record of what happened, where, and why, DevOps, NetOps, managed services, and other teams cannot diagnose or remedy a problem. Teams might not even be aware of issues without properly analyzing logs.
Compliance is another reason to collect logs. Businesses are responsible for maintaining some level of compliance depending on their industry, customer or regulatory agreements, or legislation. An example of regulatory requirements might state that an organization must hold onto logs of customer transactions for the past 10 years. Without logs, a business or organization cannot provide evidence of compliance or the means to audit their compliance.
Another benefit of logging is to gain insight into resources. Logs provide a detailed view into processes, transactions, user access, CPU and memory usage, and other details. Examples of log usage include error logging, user tracking, and a record of updates installed on a system.
Security is another important reason to collect and store logs. Logs offer details on sign-in attempts, user tracking, and access control messages, and they provide a trail for specific user requests through an application or process. Logging for security is also critical in case a business undergoes an audit. For example, cloud providers can provide logs of every single API call made to their services. Logging is also helpful when tracing user actions or activity when accessing sensitive data.
What is log management, and what are the benefits?
A log management system is a solution that gathers, sorts, and stores log data and event logs from a variety of sources in one centralized location. Without log management, teams can quickly become overwhelmed with the amount of messaging produced by operations. Considering the volume of logs generated by complex processing, systems’ pipelines, and applications, teams can benefit from a log management system, such as Datadog’s Log Management.
Log management systems turn logs into powerful resources that can be managed, archived, and queried. Log management systems or platforms consist of several functions, as shown below.
| Log management function | Description |
|---|---|
| Collection | Logs are ingested, and their details are parsed into an information-retrieval framework. |
| Enrichment | Log details are tagged via log pipelines to correlate information by service, message, errors, and other elements. |
| Storage | After parsing and tagging, logs are stored in an archive. |
| Querying | Log records can be searched based on keywords, meta tags, filters, dates, and other components. |
| Routing | Logs can be forwarded to custom destinations, such as audit folders or managed services. |
What use cases are applicable for log management systems?
Organizations of all sizes can benefit from collecting and analyzing logs. As computing grows in complexity, the number of logs generated increases, sometimes hundreds or thousands of records a minute. The table below describes some circumstances in which log management can benefit an organization.
| Use case | Description |
|---|---|
| Performance optimization | By analyzing log data, teams can identify performance bottlenecks, optimize resource utilization, and improve system efficiency. For example, log data can help identify slow queries or high resource consumption. |
| Capacity planning | Log management and analytics can be used for capacity planning by analyzing resource usage trends. This can help with predicting future resource needs and scaling infrastructure accordingly. |
| User behavior analysis | Logs can provide insights into user behaviors, such as login patterns, access patterns, and usage of specific features. This information can be used to enhance user experience and security. |
| Application development and testing | Developers can use log data to debug applications, to track the performance of new features, and to ensure that applications are functioning as expected. |
| Real-time monitoring | Some log management tools offer real-time monitoring capabilities, allowing IT teams to track system performance and security in real time. This can help in quickly identifying and addressing issues. |
What shifts or changes in the industry affect logging and log management?
Log management has been an essential part of computing for decades, from text files tracking the output of individual machines and programs to the introduction of syslog tracking between early networked systems. As software has grown more complex, more networked, and more distributed across cloud services, log volume has increased by orders of magnitude, and the difficulty in interpreting logs has increased as well.
As more organizations move to and operate from the cloud, log management has evolved to require highly sophisticated, cloud-native platforms. Cloud services can include serverless compute, data storage and management, user identity and authentication, and other services that connect through APIs and across HTTP requests. Due to this complexity in today’s cloud environments, logs are not just debugging tools; they are mission-critical assets for security, reliability, compliance, and business intelligence.
Datadog Log Management simplifies log monitoring and intelligence by ingesting, storing, and analyzing logs across your organization’s cloud environment. Integrations allow teams to build customized pipelines across services to process logs based on host or service. Alerting can create parameterized filters and queries for log events that aid in troubleshooting, tracing, and debugging complex cloud integrations and services.
What challenges do organizations face in implementing logging and log management best practices?
One significant challenge for logging is correlating and interpreting logs from multiple services, which may generate logs in different languages or formats. The data contained within logs should be standardized to be properly analyzed. Log management systems enhance correlation and interpretation from disparate sources by incorporating custom rules and integrations directly at the ingestion and parsing steps. Datadog Log Management consolidates all logs from multiple sources into one unified platform that correlates logs with metrics, traces, and all other observability data.
What features should users look for when choosing a log management solution?
Consider the following features when exploring a log management solution:
Centralized log ingestion and aggregation: Logs should be collected, tagged, and consolidated from various sources to form a master source. Through the log management solution, logs should be able to be enriched with custom reference data to add context.
Scalable, cost-effective log storage with flexible retention options: With a log management solution, log retention should be flexible enough to fulfill your organization’s use cases while reducing the number of places logs need to be stored.
Granular log indexing and routing controls: The log management solution should include centralized routing of processed logs to third-party destinations, such as a data lake or a Security Information and Event Management (SIEM) vendor.
Powerful search and query capabilities: The log management solution should provide capabilities to search, filter, and analyze logs without a complex query language. Log data should be presented via dashboards and drag-and-drop data visualizations for easy consumption.
Correlation across observability data (such as metrics, logs, and traces): The log management solution should establish logs, metrics, and tracing as part of your organization’s foundation for maintaining observability. Read Datadog’s Knowledge Center article, “What Is Observability & How Does it Work?” to review the three pillars of observability.
Real-time monitoring and alerting: The log management solution should provide automated threat detection rules, identify common threats and attacks, and provide insights to automatically identify anomalies.
Learn more
- Learn more about the fundamentals of logging by reading “Getting Started with Logs.”
- Review the concepts of log management by reading “Log Management.”