Steve Bigelow: It's hard to fix something you can't see
if your car breaks down a mechanic can pop the
hood and look inside. But what if an application
misbehaves or an organization's cloud network goes
down, how do you track and troubleshoot problems.
This is where the concept of observability comes
into play. By helping the responsible team gain
insight into what's going on with the system, so
they can identify the cause of a problem, and then
fix it. The challenge of observability is not so
much determining the internal state of a system
from observations, but rather collecting the right
observations. Here, we'll go over what
organizations need to know about observability and
how it impacts business. But for a deeper dive,
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observability. It sounds straightforward enough,
but observability is actually rather complex. For
one thing, observability is reactive in that it
prioritizes existing critical data. But
observability is also proactive where necessary,
meaning it includes techniques to add visibility
to areas where it might be lacking. Visibility is
the ability to peel back the layers of a system or
infrastructure to gain useful information.
monitoring tools and practices play a vital role
in this, providing the scope and depth needed to
gather health security hardware, host operating
system, and other data needed to gain visibility.
Visibility requires both policies and tools.
Visibility policies guide what data is gathered
policy also defines how data is secured and
retained. Good policies can help prevent an
organization from gathering everything and keeping
it forever, which as you might imagine, can turn
data and analytics into an unmanageable swamp.
Remember, more isn't always better. Visibility is
about seeing what's important, not seeing
everything. The tools for visibility are the
software applications used to gather and aggregate
data from systems, networks, applications and
platforms. In any discussion of observability,
you'll likely hear a reference to three external
measures, sometimes called the three pillars of
observability. These pillars are the three primary
source data types, logs, metrics, and traces. logs
or the first pillar refers to records of events
typically in text form. Some applications will log
what the developer believes represents critical
information. log information tends to be historic
or retrospective, often used to establish context
in operations management. Metrics. The second
pillar refers to real time operating data that's
usually accessed through an API using a poll or
polling strategy, or might be accessed as a
generated event or telemetry, a push or
notification for example, because metrics are
event driven, most fault management tasks are
driven from metrics and traces. The third pillar
of observability are records of information,
pathways or workflows. These pathways or workflows
are designed to follow a unit of work like a
transaction through the sequence of processes that
application logic directs it to follow. Some trace
data might be available from workflow processes,
such as service buses, or cloud native micro
services and service meshing, but it might be
necessary to incorporate trace tools into the
software development process to gain full
visibility. All three pillars, logs, metrics, and
traces are vital to observability. But each has
certain limitations. Logs, for example, can be
challenging to sort and aggregate in order to draw
meaningful conclusions or relationships. metrics
can be hard to tag and sort. And traces often
produce enormous amounts of data. Some of that
data may be necessary to gain observability, but a
lot of it might not be. So how do you overcome
these limitations and difficulties, it helps to
focus on goals. By this I mean, it helps to set
the business objectives and then set observability
goals that align with those objectives. For
example, if the business is concerned with latency
or throughput, then set appropriate latency or
throughput goals, and then use the three pillars,
logs, metrics and traces to help reach those
goals. The primary benefit observability is
improvements to the user experience. Specifically,
proper use of observability can improve
application availability and performance.
observability practices may also reduce operations
costs with observability problems can be solved
faster, and that means spending less staff time
and other resources on them. But observability
comes with some challenges too. One challenge is
something called accidental invisibility. That's
when data sources aren't properly filtered or
structured. This can cause important information
or a critical condition to be missed because it's
hidden from view. Another headache is when there's
a lack of important source data. This happens
especially at the application level. And with the
tracing of workflows, it can also be difficult to
assemble the right information, and interpret
what's available when the same type of data comes
in different formats from different sources.
That's why an organized strategy for structuring
information into a standard form is essential to
observability. You can minimize challenges and
reap the benefits of observability. by beginning
with a plan. The first step is to identify the
specific benefits desired, then link each benefit
to the type of data that would be needed to
achieve that benefit. It's important that these
links between benefit desired and data needed.
Consider the data you have available through
monitoring and telemetry. But it's equally
important to identify what information is not
currently gathered or is being gathered in the
system that isn't contributing its data for
observability analysis. After you have an
observability plan in place, the next step is to
create an observability architecture. This is a
diagram of the relationship between the source
data and how the data is presented to operations
personnel, AI systems, machine learning systems,
and so on. It's essential to identify all data
sources along with the information that each
source is expected to contribute. The architecture
diagram should also identify the tools that
collect and present the information, the tools for
data analysis and filtering, and the tools for
data presentation. The final step and
implementation is to choose your observability
toolkit or platform. And observability toolkit is
a set of monitoring tools are features that
support observability but need a human operator or
a separate software layer to support collective
analysis. toolkits usually require customization,
but they can also accommodate your existing
software and data sources. And observability
platform is an integrated software application
that collects information, performs analysis, and
presents actionable results to operations users, a
platform might still require customization to
accommodate all the data sources available. It
might also constrain the way data is integrated,
ultimately, which observability tool or platform
you choose depends on your organization's needs,
data sources, and budget. Achieving observability
isn't easy. It requires ingesting and sorting
through an enormous amount of data, and then
performing analytics so you get clear actionable
information. The sheer volume of raw data,
especially from multiple sources, makes analytics
difficult and resulting output as little value if
it doesn't actually tell the business anything it
wants to know. To up the odds that your
observability initiative will be effective. I'd
like to leave you with five suggestions. First,
set goals for observability. Understand what's
being observed and why and what the intended
benefits are for the business. Second, curate the
data. By this I mean, be sure the data that's
generated and ingested is relevant to the goals
set for your observability initiative, review the
data sources, consider adding context or altering
data collection to benefit observability. This
might require aggregating or rolling up some data
so you can more easily see trends in a time
series.
Third, seek meaningful and actionable outputs.
Details are easily lost in the noise of daily
business. So look for meaningful data to produce
actionable outputs. Fourth, configure outputs
appropriately configured reporting, alerting and
dashboards so they prove I'd meaningful and
actionable outputs. For example, rather than
setting static alerting thresholds, configure time
parameters that might forego an alert if the
parameter returns to normal within a given time,
which can cut down on noise. And finally, be sure
to consider who's receiving the observability
conclusions. For example, reports might go to one
admin non critical alerts might go to another
admin and critical alerts might be directed to a
third. Take the time to be sure the right people
see the right outputs and nothing important is
inadvertently ignored.