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From YouTube: Kubernetes in the Datacenter: Squarespace’s Journey Towards Self-Service Infrastructure
Description
Kubernetes in the Datacenter: Squarespace’s Journey Towards Self-Service Infrastructure [I] - Kevin Lynch, Squarespace
As Squarespace’s engineering organization evolved, microservices became an obvious solution to quickly deliver new features and improve infrastructure reliability. We encountered significant challenges in our transition to a microservice-based architecture. Each new service increased the operations burden to provision and maintain a growing fleet of servers, frequently slowing the process of adding new services and scaling existing services in our datacenters.
I’ll discuss how we used Kubernetes to containerize our microservice ecosystem and solve those challenges. To effectively work with ephemeral Kubernetes pods, we replaced Graphite with Prometheus and Sensu with AlertManager to monitor service health rather than individual instances. We discovered massive performance issues containerizing our Java services and worked around JVM complexities. To ease our transition from virtualization to containerization, services running inside and outside of Kubernetes must seamlessly discover each other with Consul and communicate with each other. Thanks to Calico, BGP, and our Leaf-Spine Layer 3 network topology, we efficiently route pod network traffic with the rest of our network.
About Kevin Lynch
Kevin Lynch is a Staff Engineer on the Infrastructure Engineering team at Squarespace. He focuses his efforts on eliminating the complexities of datacenters with the help of automation. He received his BSc and MSc degrees in Computer Science from Drexel University. During his time there he worked on a variety of different projects including processor design, source code transformation, and autonomic computing.
Join us for KubeCon + CloudNativeCon in Barcelona May 20 - 23, Shanghai June 24 - 26, and San Diego November 18 - 21! Learn more at https://kubecon.io. The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy and all of the other CNCF-hosted projects.
A
Hello,
my
name
is
Kevin
Lynch
and
I'm
going
to
be
telling
you
about
kubernetes
in
the
data
center
I'm
going
to
be
telling
you
about
square
spaces,
journey
towards
self-service
infrastructure.
I've
been
working
at
Squarespace
for
about
three
years,
so
I've
seen
our
entire
transition
from
a
monolith,
application
to
micro
services
and
on
to
kubernetes.
A
Just
some
background
for
those
of
you
who
may
not
be
familiar.
Squarespace
was
founded
in
2003
and
we
provide
a
platform
for
people
to
host
their
websites,
their
commerce
platform
and
we
host
domains
for
them
as
well,
and
we
have
over
a
million
customers
and
going
back
to
that,
founded
in
2003
means
that
we
run
out
of
data
centers.
We
predate
AWS
publicly,
so
we
have
three
data
centers
over
5,000
hosts
in
these
data
centers
and
for
scale
comparisons.
We
have
about
40
million
metrics
that
we
generate
every
minute.
So
there's
a
lot.
A
That's
going
on
in
these
data
centers
all
the
time
back
in
2013,
we
were
less
than
50
engineers
and
we
had
a
traditional
stack
where
we
had
a
monolithic
application,
and
you
know
there
was
some
background
jobs
and
they
communicated
to
a
database.
A
lot
of
the
world
looked
like
this
back
then,
and
these
these
engineers,
you
know
they
needed
their
goal,
was
to
introduce
new
features
to
build
the
product
and
to
grow
fast
and
really
what
ended
up
happening
was.
You
know
whatever
works
was
was
what
we
went
with.
A
You
know
we
grew
a
little
bit
then
in
2014
you
know
we
had
about
75
engineers
and
we
realized
that
whatever
works
wasn't
really
working
for
us
anymore.
You
know
engineers
wanted
to
introduce
new
features,
but
there
ended
up
being
too
much
firefighting.
You
know
people
were
unsure
of
the
reliability
and
stability
of
the
code.
They
were
introducing
because
there
was
so
much
going
on
in
these
monolithic
applications.
So
like
a
lot
of
companies
at
that
time,
you
know
we
started
down
that
microservices
route
and
it
was
great.
A
However,
you
know
2016,
we
have
a
hundred
plus
engineers,
you
know
the
platform
was
scalable
and
reliable
and
developers
could
move
faster
and
Squarespace
could
move
faster.
Because
of
that
you
know
we
had
all
these
services
that
were
communicating
with
each
other
and
taking
traffic
from
from
the
internet,
and
all
of
these
services
wasn't
really
working
for
operations.
A
So
this
is
our
typical
workflow
for
provisioning,
new
machines
in
in
our
data
centers,
and
whether
it's
iron
or
virtualized
it.
It
started
with
a
very
manual
process
of
finding
resources.
You
know
CPU
RAM
disk,
wherever
we
could
create
this
virtual
machine,
we'd
have
to
assign
make
sure
we
could
assign
networking
we'd
have
to
make
sure
the
networking
worked.
A
We
had
to
add
all
of
these
hosts
who
are
in
ansible
inventory,
and
then
we
could
kick
off
an
automated
process
where
you
know
we
would
update
DNS
and
and
pixie
boot
the
VM
and
install
the
OS
and
configure
the
OS
with
all
of
these
dependencies
that
we've
built
up
we'd
have
to
configure
monitoring
for
them
and
install
all
the
application,
the
dependencies
as
well,
and
finally,
we
could
install
the
application.
This
took
this
process
at
best
took
about
30
minutes
because
of
all
of
the
manual
operations
and
in
the
pixie
booting
oftentimes.
A
A
It
was
always
difficult
to
find
all
these
resources
was
very
slow
to
provisioning
scale
and
because
we're
running
our
data
centers,
we
do
have
you
know
pets
in
this
in
this
environment,
every
machine
has
is
a
special
case
right
and
we
were
trying
to
shoehorn
the
this
micro
service
cattle
mentality
into
all
of
these.
These
at
these
physical
pets
that
we
love
and
also
this
system,
was
way
too
complex
for
any
new
engineers
that
came
on
board
whether
they
were
developer,
development
focused
or
operations
focused.
A
So
we
had
to
come
up
with
a
better
solution
in
2017.
We
have
about
200
engineers
a
little
over
that
and
we
started
down
this
this
path
of
self-service
infrastructure.
Finally,
with
with
the
help
of
self-service
compute,
networking,
metrics
and
storage,
we're
able
operations
is
finally
able
to
move
as
fast
as
our
developers
and
sports
players
can
actually
move
as
fast
as
we
want
it
to
so
for
self-service,
compute,
we
use
kubernetes.
You
know
we
all
love
containers,
that's
why
we're
here
and
it's
very
simple.
A
You
know
you
just
say:
keep
control
apply
and
in
some
description
of
what
service
you
want
applied
and
it
just
works.
However,
there's
there's
a
lot
of
pain
points
that
we
ran
into
that.
We
were
aware
of
at
the
time
to
take
a
step
back
our
our
service
definitions
that
we
deploy
into
kubernetes
closely
match.
What
we
deploy
is
is
a
service
on
a
VM.
A
We
have
a
Java
spring
boot
based
service
model,
which
relies
on
a
lot
of
the
Netflix
utilities,
for
service
discovery
and
in
routing
of
requests.
We
use
console
for
service
discovery
and
key
values.
We
use
fluent
D
for
logging
to
ship
all
of
these
service
to
ship
off
the
or
logs
to
a
centralized,
elq
stack
and.
A
We
also
assign
resources
to
these
Java
services
very
similarly
to
how
we
assign
them
to
our
VM.
So
each
Java
services
on
is
typically
given
two
cores
or
four
gigs
of
and
four
gigs
of
RAM.
We
do.
We
do
adjust
this
per
for
certain
services
as
well,
but
this
is
the
default
that
we
provision
for
all
of
them.
A
This
is
implemented
in
kubernetes
in
linux
by
calculating
the
number
of
shares
that
are
assigned
to
this
based
off
of
the
cpu
request.
X,
1024
and
kubernetes
is
aware
of
all
of
the
available
shares
based
off
of
the
total
number
of
cores
on
a
system
on
over
a
kubernetes
nodes.
We
run
64
core
boxes,
so
this
would
be
assigned
to
about
65,000
shares
for
the
entire
box.
A
Now
each
service
would
then
be
throttled
based
off
of
the
limit,
so
it's
given
an
allotment
of
the
CPU
limit
times,
100
milliseconds
and
that's
calculated
over
a
hundred
milliseconds.
So
if
you
wanted
to
us
to
assign
a
container
roughly
two
cores,
it
would
turn
into
a
CPU
limit
of
2
of
200
milliseconds
over
100
milliseconds.
So
you
could
have
two
concurrent
threads
operating
at
the
same
time.
A
So
we
had
to
figure
out
why
this
was
happening
so
the
the
JVM
we
when
we
analyzed
this,
we
saw
that
there
were
64
garbage,
collector
threads
128,
dead
e
threads
for
doing
HTTP
traffic,
and
then
there
were
64
KVM
for
queuing
threads
for
parallel
operations.
These
numbers
seemed
strange.
They
seemed
to
match
the
number
of
cores,
so
we
had
to
figure
out
what
was
going
on.
A
So
all
of
the
libraries
in
in
Java
are
a
lot
of
them
are
configured
by
based
on
the
number
of
available
processors.
Keti.
Does
this
the
JVM?
Does
this
and
configures
the
420
employees
pulls
in
the
DC
thread
pools
and
then
there
could
be
various
other
libraries
that
would
access
this
to
automatically
tune
and
scale
the
number
of
threads
for
a
given
operation
and
the
JVM
is
able
to
detect
this
by
running
a
assist
control
call
to
get
the
number
of
online
processors.
A
A
Java
container,
that
would
was
able
to
calculate
all
these
resources
and
thankfully,
because
the
C
groups
is
mounted
inside
of
every
container
and
provides
the
values
of
all
of
these
we're
able
to
pretty
much
reverse-engineer
the
number
of
cores
that
are
assigned
to
this
by
just
looking
at
the
the
number
of
quota
microseconds
available
and
dividing
that
by
the
by
the
period,
and
then
we
automatically
tune
the
JV
by
passing
in
KVM
flags.
Where
were
explicitly
setting
this.
However,
this
doesn't
solve
all
the
problems
we
were.
A
All
they
need
to
say
is
I
want
a
Java
service
with
two
cores
and
they're
able
to
get
this
now
going
back
to
how
we
configure
our
how
our
Java
services
look
like
I
said
we
use
spring
boot
for
the
core
container
and
we
use
all
of
the
Netflix
utilities
for
doing
you
know
we
use
Netflix
ribbon
for
doing
automatic
retries
or
we
could
do
client-side
load
balancing
directly
in
the
application.
So
every
Java
service
is
shipped
with
all
of
this
logic,
and
then
we
can
do
circuit
breaking
with
history
and
service
discovery
with
console.
A
However,
we
were
in
a
world
where
we
wanted
to
to
have
our
VM
infrastructure
still
communicate
with
our
with
our
pods
that
were
deploying
into
kubernetes,
and
you
know,
each
of
the
the
console
agents
need
to
communicate
with
each
other
for
discovery.
The
Java
services
will
ultimately
need
to
communicate
with
each
other
directly.
So
we
leverage
calico
for
this
kubernetes
provides
a
pluggable
container
network
interface
and
there
are
many
options:
there's
flannel
calico
weave,
cube
net
VX,
LAN,
probably
a
lot
more
than
I'm
aware
of
on
this
list.
A
We
ended
up
going
with
calculate
because
it
gave
us
a
few
benefits
for
running
in
our
data
center.
Calcio
provides
Software,
Defined
Networking.
We
can
use
that
to
configure
nap
Network
policy,
IP
tables
access
rules,
and
it
gives
us
the
benefit
of
not
needing
network
overlays
in
our
data
centers.
So
that
means
we
don't
have
any
performance
impact
from
doing
encapsulation.
It
eliminates
any
MTU
overhead
that
we
may
have,
and
it
also
gives
us
the
ability
to
have
seamless,
ingress
and
egress
because
of
our
network
setup.
A
So
in
our
data
center,
we've
deployed
what
we
what's
referred
to
as
a
layer,
three
kloss
topology.
So
this
is
a
spine
and
leaf
architecture,
and
all
of
these
Leafs
are
layer,
three
networking,
so
each
each
of
those
each
rack
only
communicates
with
each
other
rack
over
layer
three,
and
this
makes
it
very
simple
to
to
understand,
because
the
spines
aren't
doing
any
any
processing
and
all
of
the
Leafs
are
their
own
layer
two
domains.
A
So
any
any
mac
addresses
don't
really
aren't
seen
by
any
other
racks,
and
this
makes
it
really
easy
to
scale
out
and
predictable
and
consistent
for
any
network
communication,
because
anything
communicating
in
one
rack
is
going
to
have
at
most
two
hops
to
get
to
another
rack
and
it.
This
also
gives
us
the
ability
of
having
anycast
support
as
well.
A
So,
like
I
said,
each
of
these
has
their
own
bgp
domain,
which
means
they
have
their
own
layer,
3
network
slice.
So
we
can
assign
networking
a
slash
24
now
to
each
of
these,
and
we
can
also
anycast
the
same
IP
across
all
of
these.
So
this
makes
it
a
lot
easier
to
to
to
have
services
communicate
with
just
any
any
service
IP,
and
it
will
be
routed
to
any
healthy
instance
that
can
respond
to
that.
A
So
we
use
calcio
to
do
bgp
peering
directly
with
these
leaf
switches,
and
we,
as
we
pair
that
with
all
of
our
kubernetes
nodes
as
well,
and
this
allows
us
to
seamlessly
communicate
whether
it's
vm
running
on
on
a
rack
in
a
different
leaf
switch.
It
can
communicate
with
any
pod,
that's
running
in
kubernetes.
A
So
this
is
a
typical
kubernetes
architecture,
where
we
have
semesters
and
some
nodes
running
some
pod
workers
using
calcio
we're
able
to
announce
directly
the
pod
IPS-
and
these
are
representatives,
slash
26
subnets-
that
Calico
assigns
directly
to
these,
and
the
calculation
would
then
pick
these
up
and
announce
them
to
the
to
the
top
of
racks,
which
likewise
we're
able
to
announce
directly
the
service
IP
range.
So
all
we
have
to
do
is
add
these
to
the
loopback
interface
and
tell
Calico
to
announce
these
and
all
of
a
sudden.
A
A
A
So
this
gives
us
a
really
powerful
way
for
our
developers
to
have
self-service
networking
without
any
interaction
with
us,
and
then
we
can
leverage
the
network
policies
as
well
to
automatically
assign
firewall
rules,
for
instance,
if
we
want
for
these
services
as
well,
we
the
other
benefit
of
this,
is
we
can
also
rely
on
this
for
federating
as
well,
because
we
have
two
data.
You
know
multiple
data
centers.
We
can
seamlessly
communicate
from
the
pod
network
from
one
data
center
to
another
data
center
and
using
the
Federation
API.
We
can
then
control
both
of
them.
A
So
the
other,
the
other
self-service
tool
that
we
decided
to
to
use
was
Prometheus.
Historically,
we
use
graphite
for
all
of
our
VM
based
metrics.
This
had
some
problems.
Unfortunately,
the
application
and
collecti
is
are
sending
metrics
either
from
the
application
or
from
the
from
the
system,
and
there's
forwarding
this
to
graphite.
A
Unfortunately,
we
can
easily
run
into
scenarios
where
the
application
developer
just
added
a
new
endpoint
to
generate
metrics,
and
we
have
this
exponential
explosion
of
metrics
as
we
deploy
them
to
all
of
our
services,
and
all
of
these
are
aggregated.
All
these
metrics
are
aggregated
in
graphite,
where
each
metric
ends
up
becoming
its
own
file,
so
which
is
slow
to
create
on
all
on
in
this
cluster
and
also
really
slow
expensive
to
to
clean
up.
A
Graphite
also
has
some
other
problems
as
well,
like
there's
loss
of
position
when
it
does
aggregation,
aggregated
roll-ups
of
all
of
these
metrics
as
well,
and
it's
it's
kind
of
inefficient
to
do
aggregated
calculations
across
all
of
these.
We
then
use
sensu
to
actually
do
a
learning
based
off
of
off
of
the
sensor,
client
that
runs
on
the
host
and
we
can
also
trigger.
We
can
also
have
that
query
graphite
as
well.
A
However,
we
were
running
into
some
problems
with
this.
The
the
application
and
in
the
system
are
very
tightly
coupled.
It
then
becomes
really
difficult
for
us
to
route
alerts
to
it.
What
happens
when
the
application
goes
down?
Who
do
we
route
the
alert
to
the
application
dev?
What
happens
when
the
system
goes
when
the
whole
host
goes
down?
Do
we
route
that
to
the
application
developer?
Do
we
route
that
to
the
team
that's
in
charge
of
running
that
host
and
what
happens
if
the
whole
hypervisor
goes
down?
Who
do
we
alert?
A
So
we
really
want
these,
our
application
level
alerts
to
be
service
level
based.
However,
when
we
pair
sensitive
with
graphite,
it
becomes
really
confusing
to
create,
because
we
had
this
central
I,
this
other
repository,
where
devs
would
have
to
go
into
and
add
all
of
these
checks
and
the
checks
were
actually
really
expensive
because
doing
all
these
aggregated
checks
on
graphite
is
painful.
A
So
in
comes
Prometheus
Prometheus
ties
in
really
well
with
our
kubernetes
infrastructure.
It
gives
us
the
ability
to
do
automatic
discovery
of
all
of
our
containers,
whether
it's
using
the
kubernetes
api
to
communicate
with
pods
or
it
could
communicate
with
console
and
discover
an
AVM
based
application
as
well.
There's
no
loss
of
precision
because
it
just
keeps
appending
metrics
and
it's
really
great
at
storing
tag
data.
So
we
can
store
a
metric
space
with
tags
of
whatever
the
service
are
running.
A
What
hod
they're
running
on,
which
endpoint
we're
using
to
collect
these
metrics
from
and
so
forth,
and
it's
really
really
great
for
these
efficient
for
these
ephemeral
instances.
So
when
a
new
pod
comes
up,
aw
is
doing
is
modifying
the
tags
that
are
four
that
are
being
generated
for
that
for
that
metric.
So
all
of
those
would
be
aggregated
and
ultimately,
the
same
Prometheus
file.
A
So
we
used
two
Prometheus
operator
for
this,
so
we
assign
each
team
that
wants
to
deploy
services
to
kubernetes
urn
namespace.
We
also
give
them
their
own
Prometheus
instance
as
well.
So
this
gives
us
a
few
benefits,
because
we
are
able
to
then
separate
out
each
team's
names,
each
team's
Prometheus
metric
collector,
and
that
that
gives
us
the
ability,
for
you
know
when
one
of
these
Prometheus
goes
down,
we
don't
lose
metrics
for
everything.
A
We
just
would
lose
that
those
metrics
temporarily
for
that
one
team,
so
the
Prometheus
operator
controls
all
of
these
Prometheus
instances
and
they'll
look
at
they'll
collect
metrics
from
all
of
those
team
services,
and
we
provide
a
centralized
alert
manager
which
is
also
configured
through
the
Prometheus
operator.
That
would
then
route
the
alerts
to
pager
duty.
So
at
that
point
all
the
service
owners
have
to
do
is
define
their
own
alerts
for
all
these
services
and
those
alerts
are
the
Prometheus
alert
manager
specification
that
look
like
this?
All
we
have
to
do.
A
All
they
have
to
do
is
provide
a
service
level
style
check
where,
for
instance,
if
we
wanted
to
look
at
the
error
rates
for
a
given
service,
all
you
have
to
do
is
look
at
the
response.
The
check
for
response
codes
of
500-
and
if
that
is
you,
know
high
for
five
minutes,
then
we
would
just
send
an
alert
to
whatever
team.
That
is
with
a
page
that
says
this
is
critical.
Please
look
at
this,
so
the
final,
the
final
tool
that
we
provide
for
our
developers
is
self-service
storage.
A
Historically,
we
have
a
centralized
NFS
cluster
for
all
of
our
files,
and
we
were
running
into
a
lot
of
problems
where
it
was
very
difficult
to
spin
up
a
new
VM
based
service
that
would
have
access
to
this.
All
the
access
controls
were
manual
manual
manually
configured
in
the
NFS
cluster,
and
we
could
also
often
run
into
issues
depending
on
the
application.
With
you
know,
things
like
file,
locking
issues
where
you
know
some
service
would
be
locking
a
file.
A
It
would
die
and
wouldn't
be
able
to
come
up
again,
because
that
that
file
was
locked
on
NFS.
We
also
would
leverage
ESX
local
storage
as
well.
However,
this
had
some
problems
of
you
know
slow
migrations.
If
we
needed
to
spin
up
a
if
we
needed
to
move
the
VM
elsewhere,
we
would
have
to
transfer
all
of
the
data
from
one
host
to
another
and
there
was
no
replication.
So
in
order
to
really
provide
self-service
storage
infrastructure,
we
needed
something
else.
That's
where
SEF
comes
in
Stef
gives
us
the
ability
to
deploy
on
commodity
hardware.
A
So
all
we,
so
we
don't
need
these
expensive,
NFS
machines
and
all
we
have
to
do
is
scale
out
the
system
whenever
we
need
more
storage,
set,
provides
automatic
replication
and
it
provides
multiple
access
patterns
as
well.
So
we
can
access
either
block
storage
directly,
we
can
access.
You
know.
Sefa
fast
eviction
is
very
similar
to
NFS
and
we
can
also
access
an
object
store.
It
provides
an
s3
compatible
API.
A
A
The
RBD
provisionary
would
then
detect
the
creation
of
this
of
this
pod
of
this
persistent
volume
claim
and
then
create
the
block
device
and
then
allow
the
stateful
set
to
then
mount
this.
This
persistent
volume,
which
is
then
mapped
directly
to
the
to
that
block
device,
and
it
will
manage
the
the
lifecycle
of
that
as
well.
So
when
you
get
rid
of
the
stateful
set,
the
block
storage
is
cleaned
up
as
well.
So
we
rely
on
this
for
some
tools,
so
we
have
Prometheus.
All
of
the
Prometheus
instances
are
relying
on
these
purpose.
A
A
They've
been
running
great
for
us
so
far.
Likewise,
we
can
rely
on
this
sefa
FS
shared
storage.
For
this.
This
gives
us
the
ability
of
having
multiple
services
access,
the
same
NFS
pool
by
creating
just
a
single
PVC
that
any
service
instance
is
able
to
access,
and
then
the
Ceph
FS
provisioner
would
create
a
shared
mount
that
all
these
are
able
to
access
and
share
data
across.
A
A
A
A
Yeah
so
we
run
the
databases
in
kubernetes,
so
we
have
some
stateful
sets
for
and
we've
got
some
staples.
That's
for
Postgres
and,
like
I
said,
like
I
said
earlier,
all
they
have
to
do
is
define
that
PVC
and
then
they
have
shared
storage.
So
then,
when
the
pod
goes
down,
it's
just
migrated
to
another
host
and
comes
back
online
with
the
same
data.
A
A
So
we
don't
abstract
that
we
are
right
now.
The
service
developers
are
writing
the
the
yam.
Well,
the
kubernetes
yeah
mellow
descriptions,
but
we
do
have
a
generator
tool
for
the
new
services
that
will
generate
an
entire
spring
beautification
and,
along
with
that,
we
generate
all
of
the
the
prescribed
deployment
and
and
alerts
for
all
those
services.
A
A
A
Yeah
spring
brute
we're
not
relying
on
console
for
the
configuration
itself
so
we're
using
so
someone
there
deployed
is
VMs.
We
just
use
a
just
a
regular
configure
mo
file
that
we've
deployed
to
each
of
those
hosts.
Along
with
that
code,
we
can
also
use
environment
variables
as
well,
which
is
what
we
use
in
the
kubernetes
environment.
If,
if
there's
any
any
information
that
the
service
once
itself,
it
would
communicate
with
the
key
value
store
directly
with
console
yeah.
A
So
we
haven't,
we
haven't,
had
the
need
yet
to
really
like
squeeze
performance
out
of
it.
So
we
haven't
gone
down
that
path.
Yet
we
just
wanted
to
make
sure
that
there
was
no
performance
loss
moving
to
kubernetes,
so
I
think
that's
something
we're
going
to
be
exploring
later
on,
like
really
how
small
can
we
can
we
get
all
of
these
services
and
and
and
what's
the
trade-off
between
instance,
size
and
number
of
instances,
because
you
know
we're
dealing
with
the
JVM
which
isn't
the
friendliest
of
beasts.
A
Now
the
the
memory
side
seemed
fine,
because
we
were
well
I
mean
we
pass
we
pass
in
very.
Similarly.
In
that
base
container
we
calculate
the
number
of
the
the
amount
of
RAM
that's
assigned
to
that,
and
then
we
scale
the
heap
appropriately
so
I
think
it's
it's
tunable,
but
I
think
by
default
we
give
the
heap
50%
and
allow
50%
of
non
heap,
but
that's
tunable
per
service
as
well.