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WHAT IS LOG MONITORING? SEE WHY IT MATTERS IN A HYPERSCALE WORLD
Adrian Phillips Infrastructure July 15, 2022
IN THIS BLOG POST
* What are logs?
* What is log monitoring?
* How log monitoring facilitates log analytics
* Reap the benefits of log monitoring
* Log monitoring use cases
* Overcoming log monitoring challenges
* How Dynatrace unlocks the value of log monitoring
Log monitoring and management are now crucial as organizations adopt more
cloud-native technologies, containers, and microservices-based architectures.
In fact, the global log management market is expected to grow from $1.9 billion
in 2020 to $4.1 billion by 2026, according to numerous market research reports.
The increasing adoption of hyperscale cloud providers — such as Amazon Web
Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) — as well as
containerized microservices are driving this growth. But the flexibility of
these environments also makes them more complex. As such, this complexity brings
an exponential increase in the volume, velocity, and variety of logs.
To identify what’s happening in these increasingly complex environments — and,
more importantly, to harness their operational and business value — teams need a
smarter way to monitor and analyze logs.
Therefore, it’s critical to explore logs and log monitoring to understand why
they’re so critical for healthy cloud architectures.
WHAT ARE LOGS?
A log is a timestamped record of an event generated by an operating system,
application, server, or network device. Logs can include data about user inputs,
system processes, and hardware states.
Log files contain much of the data that makes a system observable — for example,
records of all events that occur throughout the operating system, network
devices, or pieces of software. Logs even record communication between users and
application systems. Logging is the practice of generating and storing logs for
later analysis.
WHAT IS LOG MONITORING?
Log monitoring is a process by which developers and administrators continuously
observe logs as they’re recorded. With log monitoring software, teams can
collect information and trigger alerts if something affects system performance
and health.
DevOps teams (or development and operations teams) often use a log monitoring
solution to ingest application, service, and system logs so they can detect
issues throughout the software delivery lifecycle (SDLC). Whether a situation
arises during development, testing, deployment, or in production, a log
monitoring solution detects conditions in real time to help teams troubleshoot
issues before they slow down development or affect customers.
But to determine root causes, teams must be able to analyze logs.
HOW LOG MONITORING FACILITATES LOG ANALYTICS
Log monitoring and log analytics are related — but different — concepts that
work in conjunction. Together, they ensure the health and optimal operation of
applications and core services.
Whereas log monitoring is the process of tracking logs, log analytics evaluates
logs in context to understand their significance. This includes troubleshooting
issues with software, services, applications, and any infrastructure with which
they interact. Such infrastructure includes multicloud platforms, container
environments, and data repositories.
Log monitoring and analytics work together to ensure applications are performing
optimally and to determine how systems can improve.
Log analytics also helps identify ways to make infrastructure environments more
predictable, efficient, and resilient. Together, they provide continuous value
to businesses by providing a window into issues and how to run systems
optimally.
REAP THE BENEFITS OF LOG MONITORING
Log monitoring helps teams to maintain situational awareness in cloud-native
environments. This practice provides myriad benefits, including the following:
* Faster incident response and resolution. Log monitoring helps teams respond
to incidents faster and discover issues before they affect end users.
* More IT automation. With clear insight into crucial system metrics, teams can
automate more processes and responses with greater precision.
* Optimized system performance. Log monitoring can reveal potential bottlenecks
and inefficient configurations so teams can fine-tune system performance.
* Increased collaboration. A single log monitoring solution benefits cloud
architects and operators so they can create more resilient multicloud
environments.
LOG MONITORING USE CASES
Anything connected to a network that generates a log of activity is a candidate
for log monitoring. As solutions evolve to use artificial intelligence, the
variety of use cases has extended beyond break-fix scenarios to address a wide
range of technology and business concerns.
These include the following:
* Infrastructure monitoring automatically tracks modern cloud infrastructure,
including the following:
* Hosts and virtual machines;
* Platform as a service, such as AWS, Azure, and GCP;
* Container platforms, such as Kubernetes, OpenShift, and Cloud Foundry;
* Network devices, process detection, resource utilization, and network usage
and performance;
* Third-party data and event integration; and
* Open source software.
* Application performance monitoring and monitoring of microservices discovers
dynamic microservices workloads running inside containers and detects and
pinpoints issues before they affect real users.
* Digital experience monitoring, including real-user monitoring, synthetic
monitoring, and mobile app monitoring, ensures that every application is
available, responsive, fast, and efficient across every channel.
* Application security automatically detects vulnerabilities across cloud and
Kubernetes environments.
* Business analytics provide real-time visibility into business key performance
indicators to improve IT and business collaboration.
* Cloud automation and orchestration for DevOps and site reliability
engineering teams build better-quality software faster by bringing
observability, automation, and intelligence to DevOps pipelines.
OVERCOMING LOG MONITORING CHALLENGES
In modern environments, turning the crush of incoming logs and data into
meaningful use cases can quickly become overwhelming. While log monitoring is
essential to IT operations, practicing it effectively in cloud-native
environments has some challenges.
One major challenge for organizations is a lack of end-to-end observability,
which enables users to measure an individual system’s current state based on the
data it generates. As environments use thousands of interdependent microservices
across multiple clouds, observability becomes increasingly difficult.
Organizations also struggle with inadequate context. Logs are often collected in
data silos, with no relationships between them, and aggregated in meaningless
ways. Without meaningful connections, you’re often looking for a few traces
among billions to know whether two alerts are related or how they affect users.
Too often, logging tools leave you clicking through data and poring through logs
to deduce root causes based on simple correlations. Lack of causation makes it
difficult to quantify the effect on users. It also makes it hard to determine
which optimization efforts are delivering performance improvements.
Additionally, log monitoring’s high cost and blind spots often plague
enterprises. To avoid the high data-ingest costs of traditional log monitoring
solutions, many organizations exclude large portions of their logs. This results
in minimal sampling. Although cold storage and rehydration can mitigate high
costs, it is inefficient and creates blind spots.
With the complexity of modern multicloud environments, traditional aggregation
and correlation approaches are inadequate. Teams need to quickly discover faulty
code, anomalies, and vulnerabilities. Too often, organizations implement
multiple disparate tools to address different problems at different phases,
which only compounds the complexity.
HOW DYNATRACE UNLOCKS THE VALUE OF LOG MONITORING
Logs are an essential part of the three fundamental pillars of observability:
metrics, logs, and traces. End-to-end observability is crucial for gaining
situational awareness into cloud-native architectures. But logs alone aren’t
enough. To attain true observability, organizations need the ability to
determine the context of an issue, both upstream and downstream. Equally
important is leveraging user experience data to understand what’s affected, the
root cause, and how it affects the business.
To overcome these challenges and to get the best intelligence for log
monitoring, organizations need to work with a solution that takes analytics to
the next level.
Using a real-time map of the software topology and deterministic AI, Dynatrace
helps DevOps teams automatically monitor and analyze all logs in context of
their upstream and downstream dependencies. This broad yet granular visibility
enables analysts to understand the business context of an issue. Using AI, teams
can automatically pinpoint an issue’s precise root cause down to a particular
line of code.
Unlike tools that rely solely on correlation and aggregation, the Dynatrace
AIOps platform enables teams to speed up and automate processes. As a single
source of truth, Dynatrace combines precise answers and automation to free teams
to optimize apps and processes. With a sole source of analytics, teams can
improve system reliability and drive better business outcomes.
To learn more about how Dynatrace can turn your organization’s logs into
critical insights, join us for the on-demand power demo, “Observe all your logs
in context with Dynatrace log monitoring.”
Watch webinar now!
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Adrian Phillips
The Author
Adrian Phillips leads product marketing for the Infrastructure Monitoring
solution at Dynatrace. His passion is helping customers transform the way they
work using data, AI, and cloud-based software. Adrian holds a degree in
journalism and loves being with his family or competing in disc golf tournaments
when he's home in Raleigh, NC.
LOOKING FOR ANSWERS?
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Disclaimer: The views expressed on this blog are my own and do not reflect the
views of Dynatrace LLC or its affiliates.
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