Logging vs Metrics vs Tracing: What's the Difference?

Modern software systems demand robust monitoring to ensure health and functionality. Observability, encompassing logging, metrics, and tracing, goes beyond mere issue detection to ensure system integrity, whether in monolithic or microservice architectures.

While these tools serve distinct functions and outcomes, their combined use is essential for a comprehensive understanding and troubleshooting of distributed systems, where failures often result from complex interactions rather than isolated events.

This article delves into how logs, metrics, and traces each contribute to system monitoring, facilitating deeper analysis and end-to-end request tracking to pinpoint and resolve failures.

What are events?

Events are anything that is deemed important for business, security, or troubleshooting reasons and can range from user actions, system errors, configuration changes, security breaches, network traffic, performance metrics, to software updates and service statuses.

Some common examples of events include:

• Completing a batch processing job.
• Triggering scheduled tasks or cron jobs.
• Changes in user permissions or roles.
• Receiving an incoming HTTP request.
• Database transactions like creating, updating, or deleting records.
• Executing a function.
• Registering or authenticating a user.
• Initiating or terminating a background process.
• Writing data to a file.
• Making an HTTP request to some API.

Every event carries its own set of contextual information. For instance, a database query may include details like the query statement, the execution time, the transaction that initiated the query, and the number of affected rows.

Similarly, a user login attempt might capture the user's ID, the time of attempt, the success or failure status, and the device used for the attempt. When a service interacts with an external API, the context might consist of the endpoint URL, the payload sent, the response received, and the duration of the call.

To manage and make sense of this information, three primary strategies are employed: tracing to track the flow of requests through the system; logging to record discrete events or states; and metrics to aggregate numerical data points over time for monitoring and analysis.

These techniques help in condensing the data into more manageable forms, facilitating more effective system oversight and decision-making.

Let's talk about logging first.

What are logs?

Logs are an historical record of the various events that occur within a software application, system, or network. They are chronologically ordered so that they can provide a comprehensive timeline of activities, errors, and incidents, enabling you to understand what happened, when it happened, and, often, why it happened.

They also provide the highest amount of detail regarding individual events in the system because you can selectively record as much context as you want, but this can also be a downside as excessive logging can consume too much resources and hamper the performance of the application.

Historically, logs were unstructured which made them a pain to work with as you'll typically have to write custom parsing logic to extract the information you're interesting in.

127.0.0.1 alice Alice [06/May/2021:11:26:42 +0200] "GET / HTTP/1.1" 200 3477

Nowadays, though, logging has become more sophisticated with structured formats like JSON at its fore front which trades of some verboseness and storage space for consistent and unambiguous parsing that works everywhere JSON is supported.

{
  "ip_address": "127.0.0.1",
  "user_identifier": "alice",
  "user_authentication": "Alice",
  "timestamp": "06/May/2021:11:26:42 +0200",
  "request_method": "GET",
  "request_url": "/",
  "protocol": "HTTP/1.1",
  "status_code": 200,
  "response_size": 3477
}

Applications generate a variety of logs, each serving a distinct purpose, including but not limited to:

• Debug logs: Designed specifically for troubleshooting, these logs provide extensive detail to facilitate quick resolution of issues. Given their voluminous nature, debug logs are typically generated in limited scenarios, such as during a debugging session, rather than continuously, to avoid overwhelming storage and processing capacities.

• Error logs: These logs document the occurrence of unexpected errors within the application's operations. They capture critical information about the error condition, such as the type of error, the location within the application where it occurred, and a timestamp. This helps in diagnosing issues and preventing future occurrences.

• Audit logs: These logs track and record sequential actions or events, offering a transparent trail of activities including user actions, system changes, or data access, making them invaluable for compliance and security.

• Application logs: These are records of significant events at the application level, encompassing business transactions, state changes, and other events critical to the business logic of the application. They're pivotal for understanding the application's behavior in the context of business operations.

• Security logs: Focused on security-related events, these logs are essential for detecting, understanding, and responding to security incidents. They record login attempts, access control activities, and any other actions that could affect the application's security posture.

Note that all logs should not be treated the same way. For example, audit and security logs may be subject to regulatory requirements dictating how long they must be retained. In contrast, debug logs are purged more frequently to conserve storage resources.

Logging benefits and use cases

One of the primary benefits of logs is their role in troubleshooting and debugging. When something goes wrong, logs provide the historical context needed to understand the sequence of events leading up to an issue which effectively decreases the Mean Time To Acknowledgement (MTTA), a crucial metric for evaluating incident response efficiency.

Essentially, by shortening the time to detect an issue, log data allows responders to start addressing the problem sooner, which ultimately minimizes application downtime and reduces the incident's impact on end-users.

Security and compliance is another area where logs play a critical role. By recording all attempts to access the system, successful or otherwise, these logs can help identify patterns of malicious activity, unauthorized access attempts, and potential vulnerabilities within the system.

Logging is also commonly used to gain insights into user behavior, application performance, and usage patterns. This information is invaluable for strategic decisions, such as where to allocate resources for development, how to improve user experience, and which features or services are most valued by users.

Logging challenges and limitations

The biggest concern with logging is often the sheer volume of data generated, especially in large or complex systems. This abundance can overwhelm storage capacities and complicate the process of searching for specific, useful information amidst the noise.

The management of log data also requires sophisticated tools and strategies for collection, storage, analysis, and archiving, which can often impose significant costs and complexity. It's not uncommon for

Another challenge is ensuring the security and privacy of log data. Since logs often contain sensitive information, they must be transmitted and stored securely to ensure private data isn't leaked to the outside world.

The impact of logging on system performance can also not be overlooked. Detailed logging can significantly consume resources, affecting application efficiency. Balancing the need for detailed logs while maintaining system efficiency requires careful tuning and consideration.

Finally, interpreting log data can be challenging due to the lack of standardization across different systems and applications. The skills required to analyze and derive meaningful insights from log data are highly specialized, creating a skills gap that must be addressed through training or hiring.

What are metrics?

Metrics focus on aggregating numerical data over time from various events, intentionally omitting detailed context to maintain manageable levels of resource consumption. For instance, metrics can include the following:

• Number of HTTP requests processed.
• The total time spent processing requests.
• The number of requests being handled concurrently.
• The number of errors encountered.
• CPU, memory, and disk usage.

Context is not entirely disregarded in metrics as its often useful to differentiate metrics by some property, although this requires careful consideration to avoid high cardinality.

High-cardinality data, like email addresses or transaction IDs, are typically avoided in metrics due to their vast and unpredictable range but low-cardinality data such as Boolean values, response time buckets are much more manageable.

Metrics are particularly useful for pinpointing performance bottlenecks within an application's subsystems by tracking latency and throughput, which logs, given their detailed and context-rich nature, might struggle to do efficiently.

Once a potential issue is identified through metrics, logs can then provide the detailed context needed to drill down into specific problematic requests or events.

This illustrates the complementary roles of logs and metrics: metrics offer a high-level overview with limited context, ideal for monitoring and performance analysis across broad system components, while logs provide deep, contextual details for thorough investigation of specific events.

Balancing the use of both allows for effective system monitoring and debugging while keeping data processing demands in check.

Metrics benefits and use cases

Metrics offer a more efficient means of generating alerts compared to logs because querying a time-series database is typically much faster than querying log data.

They are also instrumental in performance optimization and tuning. By tracking response times, throughput, and resource utilization, you can identify bottlenecks and inefficiencies within your systems, helping you make informed decisions about where to allocate resources for maximum impact.

Another critical use case for metrics is in business intelligence and decision-making. Numbers related to user engagement, feature usage, and transaction volumes offer valuable insights into customer behavior and preferences.

Finally, metrics are fundamental in incident response and post-mortem analysis. When an incident occurs, metrics can help in quickly identifying the scope and impact. After resolving the issue, a detailed analysis of the relevant metrics can reveal the root cause and inform strategies to prevent future occurrences.

Metrics challenges and limitations

A significant challenge of metrics is the selection and definition of meaningful metrics. Identifying which metrics are truly indicative of system health and performance can be difficult, and there's often a risk of focusing on easy-to-measure metrics that do not accurately reflect the system's state.

It's also necessary to be mindful of the use of a wide variety of labels. An extensive assortment of labels can lead to increased storage and querying overheads, impacting performance.

While metrics excel at providing quantitative data, they don't offer the qualitative insights needed to fully understand an issue. For instance, a spike in error rates could be identified through metrics, but without correlating logs or traces, the underlying cause might remain elusive.

There's also the challenge of time lag in metric reporting and analysis. Since metrics are typically aggregated and reported over intervals, they can delays in detecting and responding to issues. In fast-moving environments, even a small delay can have significant consequences.

What is tracing?

In tracing, not every single event is scrutinized; instead, a selective approach is employed, focusing on specific events or transactions, such as every hundredth one that traverses designated functions. This selective observation records the execution path and duration of these functions, offering insights into the program's operational efficiency and identifying latency sources.

Some tracing methodologies extend beyond capturing mere snapshots at critical junctures to meticulously tracking and timing every subordinate function call emanating from a targeted function.

For instance, by sampling a fraction of user HTTP requests, it becomes possible to analyze the time allocations for interactions with underlying systems like databases and caches, highlighting the impact of various scenarios like cache hits and misses on performance.

Distributed tracing advances this concept by facilitating traceability across multiple processes and services. It employs unique identifiers for requests that traverse through remote procedure calls (RPCs), enabling the aggregation of trace data from disparate processes and servers.

A distributed trace consists of several spans, where each span acts as the fundamental element, capturing a specific operation within a distributed system. This operation might range from an HTTP request to a database call or the processing of a message from a queue.

Tracing provides insight into the origin and nature of issues by revealing:

• The specific function involved.
• The duration of the function's execution.
• The parameters that were passed to it.
• The extent of the function's execution reached by the user.

Tracing benefits and use cases

Tracing is critical in understanding and optimizing complex software systems, especially in environments characterized by distributed architectures. Through selective monitoring, where only a fraction of events are observed, tracing provides insights into the operational dynamics of an application without overwhelming the system with data.

By focusing on specific functions or requests of interest and tracking their execution time, tracing offers a granular view of application performance. It allows you to understand the intricate paths requests take, including interactions with databases and caches, and how these interactions influence overall response times.

Distributed tracing elevates the utility of tracing by enabling it to function across multiple processes and services. By tagging requests with unique identifiers and tracking these as they traverse through various parts of the system, distributed tracing provides a cohesive view of a request's journey across service boundaries.

This capability is indispensable in diagnosing issues within microservice architectures, where requests often pass through numerous, loosely coupled services. Tools like OpenZipkin and Jaeger are at the forefront of facilitating distributed tracing, offering the means to aggregate and analyze trace data from across a distributed system.

Tracing challenges and limitations

Implementing tracing comes with benefits and significant challenges, including the choice between manual and auto-instrumentation. Manual instrumentation offers full control over what is traced at the cost of increased development time, while auto-instrumentation simplifies coding but might lack detail.

Tools like OpenTelemetry can help in collecting trace data efficiently. Another challenge is managing the volume and storage cost of data, especially in high-transaction environments like SaaS applications.

To optimize storage costs and maintain effective troubleshooting capabilities, decisions on data collection can be made at the start (head-based) or end (tail-based) of transactions, balancing the need for detailed tracing with cost considerations. Tail-based decisions allow for capturing data on problematic transactions, providing visibility without overwhelming storage.

Which should you choose?

Choosing between logging, tracing, and metrics involves understanding the strengths and limitations of each method and considering the specific needs of your system or application. Here's a framework for making that decision:

• Metrics are ideal for recording event occurrences, item counts, action durations, or reporting the status of resources like CPU and memory.
• Logs are more suited for capturing detailed narratives of events, especially for documenting errors, warnings, or unusual occurrences that may also be highlighted by metrics.
• Traces offer a window into the journey of a request as it moves through various services in a microservices setup, requiring a unique identifier for each trace to ensure traceability.

In most cases, a combination of logging, tracing, and metrics is the best approach, leveraging the strengths of each to provide a comprehensive view of your system's health and performance.

For example, you can detect a problem through metrics, diagnose it with logs and use tracing to understand the flow of requests that led to the issue.

Final thoughts

As software systems continue to grow in complexity, identifying and remedying inefficiencies, glitches, and faults grows increasingly challenging.

However, by integrating logging, metrics, and tracing, you can gain a comprehensive perspective essential for achieving your observability objectives.

Metrics offer quantitative insights into different aspects of your system, logs provide a chronological record of specific events, and tracing reveals the path requests take through your system.

Ultimately, the true value of telemetry data lies not in its collection but in its application and interpretation, so you also need to be well versed in how to use these data once its collected.

Thanks for reading!

Article by

Ayooluwa Isaiah

Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.

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