4 key metrics to know when monitoring microservices applications running on Kubernetes

Understanding
how
microservice
applications
works
on
Kubernetes
is
important
in
software
development.
In
this
article,
we
will
discuss
why
observing
microservice
applications
on
Kubernetes
is
crucial
and
several
metrics
that
you
should
focus
on
as
part
of
your
observability
strategy.

Why
should
you
observe
microservice
health
running
on
Kubernetes
and
what
are
the
Kubernetes
metrics
you
should
monitor?

Consider
a
large
e-commerce
platform
that
utilizes
microservices
architecture
deployed
on
Kubernetes
clusters.
Each
microservice,
responsible
for
specific
functionalities
such
as
inventory
management,
order
processing
and
payment
handling,
operates
independently
and
communicates
with
others
via
APIs
which
are
critical
to
your
business/
service
growth.

In
such
a

complex
environment,
ensuring
seamless
operation
and
detecting
issues
proactively
becomes
imperative
and
can
be
challenging.

Observability
in
this
scenario
can
assist
with
real-time
insights
into
the
performance,
availability
and
interdependencies
of
these
microservices
and
the
Kubernetes
application.

Observability
is
essential
for
several
reasons:

• 
  Early
  detection
  of
  issues:
  Microservices
  are
  distributed
  and
  interconnected,
  making
  it
  challenging
  to
  identify
  issues
  when
  they
  arise.
  Observing
  their
  health
  allows
  you
  to
  detect
  problems
  early
  on,
  minimizing
  downtime
  and
  potential
  service
  disruptions.
  Using
  Instana
  you
  will
  get
  1-second
  granularity,
  which
  helps
  you
  detect
  problems
  faster
  than
  other
  solutions.
• 
  Reliability:
  Monitoring
  microservice
  health
  ensures
  that
  your
  application
  remains
  reliable.
  By
  tracking
  metrics
  such
  as
  response
  times,
  error
  rates
  and
  resource
  utilization,
  you
  can
  proactively
  address
  any
  performance
  issues
  before
  they
  impact
  users.
• 
  Scale
  efficiently:
  Kubernetes
  allows
  for
  dynamic
  scaling
  of
  microservices
  based
  on
  demand.
  Observing
  their
  health
  helps
  you
  make
  informed
  decisions
  about
  when
  and
  how
  to
  scale
  services
  to
  ensure
  optimal
  performance
  and
  resource
  utilization.
• 
  Meet
  SLAs:
  Many
  organizations
  have
  service-level
  agreements
  (SLAs)
  that
  define
  expected
  levels
  of
  service
  availability
  and
  performance.
  Observing
  microservice
  health
  helps
  you
  meet
  these
  SLAs
  by
  ensuring
  that
  your
  services
  are
  running
  smoothly
  and
  meeting
  performance
  targets.

By
monitoring
Kubernetes
health,
organizations
can
proactively
identify
and
address
issues,
optimize
resource
usage
and
maintain
optimal
cluster
performance.

These
are
the
key
metrics
that
can
be
measured:

Cluster
availability:

Monitoring
Kubernetes
cluster
availability
metrics
helps
ensure
that
the
clusters
are
up
and
running
and
are
healthy.
Metrics
such
as
cluster
uptime
and
pod
status
provide
insights
into
the
overall
health
of
the
cluster.
They
are
at
the
highest
and
most
important
layer
and
can
provide
complete
visibility
into
what’s
happening
in
your
environment.

Pod
metrics:

Monitoring
pod
health
metrics
such
as
pod
restarts,
pod
readiness
and
pod
eviction
help
identify
issues
with
individual
pods
and
ensures
that
applications
are
running
smoothly.
Monitoring
pod
health
enables
organizations
to
detect
and
troubleshoot
issues
quickly,
minimizing
downtime
and
ensuring
high
availability.

Service
availability:

Monitoring
service
availability
metrics
such
as
service
uptime,
service
response
time
and
service
error
rate
help
ensure
that
Kubernetes
services
are
available
and
responsive
to
users.
By
monitoring
service
availability,
organizations
can
detect
service
failures
or
degradation
and
take
proactive
measures
to
restore
service
availability
and
minimize
impact
on
users.

Nodes
health:

This
is
a
metric
that
shows
the
status
of
nodes
in
the
context
of
Kubernetes
cluster
metrics.
Some
other
important
metrics
include:

• kube_node_status_capacity:
  This
  metric
  indicates
  the
  available
  capacity
  for
  different
  resources
  on
  a
  node,
  helping
  you
  identify
  how
  much
  resources
  are
  available.
• kubelet_running_container_count:
  It
  tells
  you
  how
  many
  containers
  are
  currently
  running
  on
  a
  node.
• kubelet_runtime_operations_latency_microseconds:
  This
  metric
  measures
  the
  time
  it
  takes
  for
  each
  operation
  to
  complete,
  categorized
  by
  type,
  and
  it’s
  measured
  in
  microseconds.

Observability
by
the
numbers

IBM
Instana
can
monitor
your
microservice
application
running
on
Kubernetes.

IBM
Instana
is
a
fully
automated
real-time
observability
platform
that
contextualizes
performance
data.
It
lets
you
detect
problems
or
transactions
with
a
1-second
granularity
in
your
microservice
application.
Additionally,
you
get
100%
traces
that
allow
you
to
fix
issues
easily
if
there
are
any
when
running
your
microservices
on
Kubernetes.

Resources
to
get
started
on
observing
your
Kubernetes

If
you
want
to
have
full
visibility
and
be
more
proactive
in
solving
issue,
consider

Instana’s
new
self-hosted
standard
edition
is
a
comprehensive
solution
designed
for
all
levels
of
Kubernetes
usage.
Whether
you
are
a
beginner
or
an
advanced
Kubernetes
user,
Instana
Standard
Edition
monitoring
has
you
covered.
You
can

sign-up
for
a
free
account
today
and
start
monitoring
your
kubernetes
clusters
or
view
the
step-by-step
guide
below.

View
the
step-by-step
guide
today