►
Description
When planning a microservices architecture orchestrated by Kubernetes, it is necessary to use a Kubernetes native messaging broker to manage the high load of messaging in the system. Kubernetes native messaging broker provides scaling, robustness and security, low DevOps maintenance and transparent connectivity with the cloud native ecosystem.
This webinar will review how to implement message queue broker as part of the Kubernetes clusters in a variety of scenarios such as, hybrid cloud deployment for flexibility or graduate migration from legacy environment to Kubernetes. It will cover what patterns and use cases can be applied.
A
As
people
join,
they
can
catch
up
with
the
recording,
so
I'd
like
to
thank
everybody
who's
here
today.
This
is
CN
CF
webinar
and
we're
going
to
be
talking
about
the
need
for
a
kubernetes
native
message:
queue
broker,
qmq
now
I'm
Alex,
Alice,
I'm,
the
founder
of
open
vats
and
inlets
the
cloud
native
tunnel
and
as
a
sense,
if
ambassador
I'm
going
to
be
moderating.
A
Do
have
a
few
housekeeping
items
and
then
we'll
hand
it
back
over,
and
basically
this
is
not
your
regular
Zune
call.
This
is
a
webinar,
and
so
as
attendee
you
don't
get
to
talk
and
be
in
the
video,
but
you
can
drop
your
messages
into
the
Q&A
box
and
you
should
see
that
at
the
bottom
right
now
we
also
have
chat,
and
so
what
we'll
do
is,
as
Lia
presents,
is
occasionally
interrupt
and
ask
some
of
the
questions
as
we're
going
through.
A
A
So
please
just
don't
say
anything
that
you
wouldn't
say
in
public
that
might
be
in
violation
of
the
code
of
conduct
and
that
you
know
we
just
need
to
be
respectful
of
the
participants
and
the
presenters
and
with
that
I'm
going
to
hand
it
back
over
to
Leon
we're
going
to
kick
off
the
presentation,
the
need
for
Cuba
Nettie's
native
message
queue
broker.
So
thank
you.
B
B
In
some
broad-
and
you
know
some
kind
of
basic
understanding
when
we
move
into
kubernetes
and
starting
to
build
micro
services,
most
of
the
most
of
the
solutions
that
you
have
and
service
to
service
point-to-point
connectivity
means
that
you
can
use
resting
to
freeze
or
geo
pc,
or
even
your
some
kind
of
service
match
that.
Actually,
you
deploy
a
data
plan,
and
actually
you
hardly
in
how
in
how
we
connect
between
between
services,
and
it
gives
some
kind
of
complexity
to
your
architecture.
B
What
you
going
to
do
with
service
discovery,
how
you
can
reach
other
services,
what
you
are
going
to
do
with
versioning,
if
you
changing,
for
example,
your
API
definition
between
services.
So
to
this
to
this
challenge-
and
this
is
not
it's
not
new-
a
messaging
broker
or
queue
is
one
way
to
work
with
that.
Actually
that
all
the
services,
no
message
queue
all
the
message
broker
and
address
and
now
can
communicate
between
them
through
the
message
queue
broker
and
allow
a
lot
of
flexibility
and
endless
possibilities
for
my
high
tech
chure
point
of
view.
B
So
if
you
would
like,
if,
when
you
put
inside
inside
your
cluster
and
a
messaging
and
a
messaging
broker,
you
started
to
gain
some
some
advantages
compared
to
a
different
solution.
For
example,
I
will
talk
about
how,
when
you
going
to,
when
you
put
some
kind
of
message,
broker
outside
of
your
cluster,
but
when
you
put
it
inside
first,
you
started
to
gain
from
the
benefits
of
kubernetes.
If
you,
for
example,
am
using
tracing
matrix.
B
U
and
it's
embedded
in
your
cluster
you
can
enjoy,
for
example,
end-to-end
tracing
between
services,
also
from
security
point
of
view.
Everything
is
inside
a
cluster.
Third,
it's
also
the
ability
to
replicate
and
build
clusters
and
mini
clusters.
Put
them
on
the
edge,
for
example,
means
that
if
you,
your
attacker,
is
using
some
kind
of
messaging
messaging
cue
broker
capabilities.
You
gain
that
the
kubernetes,
the
kubernetes
benefits,
also
with
your
with
your
architecture.
B
So
from
from
IT
perspective,
it
will
be,
for
example,
if
you
need
to
deploy
and
the
whole
back-end
or
to
deploy
some
kind
of
architecture
when
a
native
cue
message
queue,
for
example,
he
has
an
operator,
you
can
build
it
through.
Your
pipeline,
we
all
see
ICT,
you
can
deploy
it
and
very
quick.
You
can
scan
you
can
down
scale
up
and
down.
You
can
use
whatever
kind
of
controls
that
you
want
to.
You
want
to
use
and
also
its
unified.
B
You
don't
pressure
the
workflow
of
what
you
and
deployment
now
some
some
of
the
some
of
the
solution.
Today,
M
is
trying
sorry.
Some
of
the
solution
today
are
not
built
into
kubernetes.
If
you
talk
about
traditional
one
like
Kafka
or
M,
rabbit
or
any
kind
of
other,
we
call
it.
We
call
it
legacy
one.
What
we
saw
that
it's
many
many
companies,
many
solution
act,
is
putting
it
outside
of
the
cluster.
B
B
In
the
rotation
of
you'll
TLS
certificates,
it's
also
possible
to
do
it
with
some
kind
of
entity
that
actually
it's
outside
of
the
cluster,
and
actually
it's
you
open
and
expose
your
security
domains
outside
of
the
cluster.
You
double
the
traffic.
You
need
an
additional
environment
maintenance.
It's
sometimes.
B
Some
kind
of
additional
over
that
you
need
and,
of
course
you
can
use
it,
you
can
deploy
and
you
can
deploy
on
caf-co
rabbit
or
something
like
that
inside
your
cluster,
but
think
about
for
that,
for
example,
putting
a
cluster
on
the
edge
okay
in
on
the
edge.
You
have
limited
resources.
You
have
some
some
other
challenges
that
actually
a
big
solution
like
after
that.
You
need
five
six
nodes
in
only
only
to
support
very
simple
one
and-
and
it's
not
so
tightly
integrated
inside
kubernetes.
So
it's
also
kind
of
challenges
challenge
to
do
so.
B
B
Will
another
another
option?
Another
cool
thing
is
that
is
once
you
have
a
message
broker
in
star
cluster
used
you
you
can
now
start
and
use
the
message
broker
as
a
k22.
Many
other
services
that
you
now
we
don't
need
to
write
interface
to
that,
for
example,
if
you
have
a
cache
like
Redis,
I
will
show
it
in
a
minute
or
you
have
some
kind
of
log
or
for
like
elastic
or
you
have
a
databases.
You
have
some
like
that.
B
If
you
have
services
that
need
now,
this
kind
of
this
kind
of,
if
you
have
some
application,
that
these
kind
of
services
for
them
instead
to
to
connect
them
directly
or
managed
directly,
and
you
can
use
the
cube
message
broker
inside
in
order
to
route
messages
and
connected
between
them
with
connectors
or
any
kind
of
interface
between
them.
I
will
give
another
a
very
good
example
and
I'm
going
to
show
it
in
a
minute.
For
example,
you
have
an
API
and
you
have
an
API.
Does
it
connect
to
a
database?
B
Typical
typical
solution
is
we'll
have
like
a
container
deployment
of
this
API
that
will
have
the
database
connection
inside
it
connect
directly
to
a
database
and
this
it's
potentially
problem.
Instead,
if
you
can
decouple
them
with
the
message
broker,
you
can
connect
the
API
directly
to
the
message
broker
and
the
message
broker
and
another
service
would
connect.
Also
do
the
message
broker
and
this
service
will
handle
all
the
connectivity
and
all
the
requests
for
this
database,
and
then
you
can
scale.
B
Sorry
five
typical
use
cases,
and
that
is
most
of
usage,
of
a
message
broker
and
beside
kubernetes.
The
first
one
is
a
multi
stage.
Pipeline
multi
stage
pipeline
data
processing
pipeline
is
very
very
common
scenario
that
you
have
some
kind
of
pipeline:
a
work
of
a
moving
object
of
data
that
need
to
be
processed
by
different
processor.
For
example,
if
you.
C
B
Em
to
pew
and
some
some
predefined
work
to
do
you
have
it
the
first
stage
of
processes
that
take
it
from
the
cube,
do
some
kind
of
processing
and
move
it
to
another
stage
on
the
cube,
for
example,
in
security
system,
you
have
multiple
cleaners,
for
example,
that
need
to
clean
the
data
as
a
pipeline,
so
using
the
multistage
data
processing
pipeline
with
processors.
It's
a
typical
usage
of
of
a
message
queue,
and
here
is
the
queue
because
mainly
we're
going
to
talk
about
in
patterns
of
messaging
and
the
patterns
of
messaging.
B
That
is
we're
going
to
talk
about
them.
Our
cues
are
our
PCs
are
streaming
real-time,
non,
real-time
and
all
the
application
around
them.
One
one
important
master
one
important
thing
to
see
is
also
take
Billy
to
live
like
some
clock,
a
dead
letter
message
you,
if
you
familiar
with
Amazon
experience
or
something
like
that
you
are
able
to,
for
example,
if
you
frame
any
stage
to
process
some
kind
of
message,
it
will
a
message.
B
C
B
Is
is
very,
very
familiar.
Actually,
the
most
of
the
usage
of
killing
is
like
job
and
tasks
distributed,
kill
that
you
have
many
many
many
producers,
that
are
you
sending
tasks
and
job
at
going
through
and
ascending
to
a
messaging
queue
and
on
the
other
hand,
some
workers
are
taking
this
kind
of
information
from
the
pure
end
processing
as
different
from
the
different
from
their
other
other
M
from
their
previous
use
case
M
there.
The
previous
case
use
some
cat
like
on
a
serial
path
of
moving
object
between
and
processor.
B
A
B
Of
course
you
can
limit.
For
example,
you
can
limit
the
amount
of
from
your
project.
Workers
can
be
any
amount
of
subscribers
that
can
be
in
parallel
or
one
you
can
do.
For
example,
you
can
do
some
kind
of
multi
casting
of
messages
between
them,
so
you
have
a
very
good
control.
How
you
can
throttle,
then
the
messaging
and
between
them,
and
also
you
can
control
what
happen.
For
example,
if
you,
if
you
need
deletion,
expiration
delay
of
messages.
B
A
A
B
B
B
All
this
messaging
patterns
and
support
metrics
from
Mattel's
everything
is
built-in,
and
if
you
don't
need
to
write
almost
any
line
of
code,
you
don't
need
to
build
another
another
business
logic
on
top
of
it
and
it's
very
small
of
30
mix
of
container,
and
it
can
be
blood
anywhere
and
has
its
own
written
in,
go
here's
its
own
capabilities,
so
you
can
use
F,
Kafka
and
Robert,
but
you
need
also
some
a
lot
of
knowledge
of
them.
From
DevOps
and
DevOps
capabilities
you
can
run.
Is
there
no
Pareto?
B
You
don't
even
need
to
configure
it.
Only
one
exists.
Simple
example
is
there's
no
configuration
in
Cuban.
Queue
means
that
you
don't
need
to
define
no
pues,
no
egg.
No,
it
changes.
Zero
configuration
means
that
you
can
up
and
it's
running
and
when
you
send
a
message,
you
open
the
killed,
open,
open
topic.
If
you
receive
any
doing
everything
it
for
you
and
it's
very
flexible.
It's.
A
B
No
problem,
so
this
is
the
distributed
queue.
Another
one
is
this
three
messaging
process
is
very
similar,
very,
for
example,
in
88
type
of
applications
such
that
you
need
to
process
a
lot
of
a
lot,
a
lot
of
messages
and
starting
to
route
them
to
a
different
different
service
like
pipeline
data
stores
and
machine
learning.
Another
another
very,
quite
interesting
part
is
very
well.
Is
the
ability
to
pops
up
in
real-time
messaging
is
fast
that
you
phones
up.
We
need
to
distribute
it
a
lot
of
data,
also
in
and
out,
fan-in
and
fan-out.
B
Also
that
you
can
measure
this
with
a
lot
of
data
and
the
most
I
think
a
very
common
one
is,
for
example,
this
one,
and
this
is
that
what
we
call
the
application
decouple
and
micro
services,
and
this
means
that
you
can
now
have
the
the
message
queue
as
a
message
broker
and
that
handling
all
the
small
pieces
and
all
the
surfaces
are
called
the
connectivity
between
and
all
the
services,
and
this
brings
me
to
actually
show
you
a
real
example.
Real-Life
example
of
such
such
such
such
an
application.
A
Sure
I
mean
if
you've
got
if
you
got
a
dementia,
get
ready
could
start
with
that,
so
this
is
kind
of
going
now.
If
this
slide,
what
would
happen
if
you
had
a
kubernetes
cluster
on
premises
and
a
Cuban
X
cluster
on
Amazon
and
kicky
Bank,
you
somehow
fed
right
between
the
two
of
them.
That's
one
of
the
questions.
Yes,.
B
A
B
A
A
B
Actually,
it's
a
great
question
because
we
found
out
in
the
beginning
that,
first
of
all,
yes,
you
decide
how
much
volume
you
want
and
it
will
create
a
PVC
for
you,
Pacific
volume
claim
this
is
a
stateful
set
means
that
it's
maintained
some
consistency
between
all
the
poles.
What
we
saw
over
more
than
two
years
of
operation
of
of
Cuban
view
is
that
more
and
more
user
that
the
user
are
not
using
the
persistency,
because
it's
starting
to
move
to
other
and
messaging
authors
that
don't
need
persistence.
B
It
means
that,
if
you
must
have
persisted
because
you
don't
you
don't
need
to
be
you
not
willing
to
lose
one
message
and
when
I'm
not
willing
to
use
one
message,
which
means
that
when
all
your
cluster
is
down
all
the
all
the
message
queue
is
done.
All
the
nodes
are
down
means
that
if
you
have
three
sides
of
3
or
5,
even
without
persist,
the
volume
defined
you
actually
have
all
all
the
messages
means
that
you're
not
losing
it.
B
Only
if
you're
going
to
wipe
up
your
cluster
without
persistent
and
all
something
really
bad
happening,
you're
going
to
lose
you're
not
going
to
use
persistent
volume.
But
if
it's
important
for
you,
yes,
no
problem
when
you,
when
you
define
your
cluster,
you
can
set
any
amount
of
volume,
you
be
anything
it
will
take
the
claim
and
create
for
you,
okay,.
A
B
B
Let's
say
have
some
kind
of
some
kind
of
a
typical
application.
What
we
call
it
a
user
domain
user
domain
is
some
kind
of
some
kind
of
solution
or
what
we
call
it.
Micro,
Soviet
architecture
for
day
domain
users
domain
means
that
it's
like
an
API
and
any
web
interface.
That
I
would
like
to
create
a
user
guide
verification
code.
B
I
would
like
to
do
some
kind
of
verification,
and
we
got
to
do
not
login
and
logout
very
small,
typical,
a
typical
typical
application
of
of
how
you
manage
users
or
supplies,
so
I
will
show
you
some
kind
of
architecture
that
we
quickly
build
and
show
that
the
capabilities
of
what
I'm
talking
about
what
we
have
here
is
services
and
the
services
as
we
live
in
kind
of
Web
API,
and
we
have
some
kind
of
a
web
server
I'm
going
to
show
you
in
a
minute
how
it
works.
This
is
the
the
web.
B
B
In
some
other
service,
we
have
a
cache
for
Redis
and
I
will
show,
show
you
in
a
minute
some
how
it
works.
Also,
we
have
a
possible
sequel.
This
will
be
our
users
database
and
we
have
also
elastic
search
that
actually
will
do
something
like
an
auditing
and
log
all
the
messages
that
happening
in
this
architecture.
Very
simple
and
also
I'm,
going
to
show
you
how
it
looks
like
in
inside
of
the
messaging
broker
and
the
capabilities
that
you
can
see
with
it.
B
So
so
the
first
thing
I'm
going
to
do
is
do
a
login
and
the
log
in
will
have
we
involve
some
kind
of
going
to
directly
to
the
man
database
and
do
some
kind.
What
we
call
a
query
like
an
RPC.
He
will
send
a
query
to
the
database
and
ask
if
this
user
exists.
If
not,
he
will
create
it
and
send
it
back
for
a
verification.
B
Then
he
will
send
you
some
kind
of
a
token,
and
then
you
need
to
do
some
kind
of
verification
means
that
again
he
will
ask
and
in
sense
of
kind
of
a
command
to
the
database
and
the
verification,
then
we're
going
to
do
a
login
here.
It
would
be
much
more
interesting
here.
First
of
all,
he
will
see
in
the
cache
if
this
user
has
already
logged
in.
If,
yes,
he
will
actually
take
and
do
all
the
work
from
the
cache.
If
not,
he
will
work
from
the
database
now.
B
B
Then,
if
you
are
going
to
do
logout
what
he
will
do,
if
you
will
mob
that
the
users
log
out
and
also
clean
the
cache
so
means
that
the
next
time
he
would
do
login
he
we
need
to
do
again
again,
the
full
login
and
again,
all
the
messages
will
be
logged
out
automatically
to
and
to
the
elasticsearch.
Okay,
so
give
me
one
second
I'm
going
to
share
the
screen.
I.
B
B
Now
we
go
again
to
the
database
and
I
will
show
them
in
how,
in
real
time
it's
happened,
and
then
we
can
do
log
out
if,
for
example,
blue
logo,
they
can
do
very
quickly
again
the
log
in
sorry,
and
it
would
be
much
much
faster
than
because
going
now
to
the
cache
and
I
can
do
now
log
out
and
no
gain
again,
more
or
less.
This
is
the
this.
Is
that
the
flow
for
example?
B
We
can
see
some
kind
of
angles
that
happen
if
you,
if
I,
do
a
log
in
log
out
or
something
that
I'm
already
logged
out
I
will
be
already
logged
out.
If
I'm
going
to
do
some
kind
of
better
notification,
we
have
better.
If
occasion,
if
I
have
some
some
bad
registration
already
already
exist.
So
this
is
like
a
simple,
typical
application,
and
now
what
I'm
going
to
do?
I'm
going
to
switch
to
some
kind
of
a
nice
thing
that
you're
going
to
show
you.
B
B
B
So,
what
I'm
going
to
do
here,
I'm
going
to
do
the
same
same
flow
with
different
information
and
I,
will
show
you
exactly
what's
going
on
in
each
each
each
each
channel
for
the
first
one
I'm
going
to
shoot
the
monitor
the
monitoring
for
the
elastic
cell
record,
the
history
one.
Now
it's
connected
now
I'm
going
to
go
into
the
users.
Channel
theses
will
be
with
the
database
and
the
third
one
I'm
going
to
show
you
for
the
cash
this
is
going
to
the
cash.
What
is
the
it's
doing
now?
B
A
A
B
A
B
B
B
What
we
saw
here
is
a
lot
of
a
lot
of
messages,
but
what
we
can
see
is
that
the
message
and,
for
example,
going
through
and
the
post
Chris
one
executed.
This
is
the
query
and
say
I
get
through
and
here
in
outside
we
can
see
again.
This
is
the
base64,
because
actually
we
see
the
data
on
side
and
on
side
of
the
of
the
message
broker,
but
let's
continue
and
and
again,
but
what
I'm
going
to
show?
What
want
to
see
is
what's
happening
with
that
with
the
cash
okay.
B
B
B
So
this
is.
This
is
a
typical
application
of
multi-service
how
you
can
use
kubernetes
message:
broker
cuban
queue
with
the
events
and
wiry
commands
capabilities,
so
maybe
before
I'm,
going
to
the
next
and
use
case
and
also
some
kind
of
a
demo
very
interesting,
interesting
one.
If
you
have
more
question
yeah.
A
A
Mean
if
you
have
a
Twitter
account
for
Kibum
key,
maybe
you
could
send
it
out
and
focus
in
follow
you
I'll,
find
the
link
for
Kibum
can
put
it
into
the
chant
and
is
asking
about
you
know
what
is
the
sort
of
maximum
fruit?
Has
this
been
like
compared
to
other
products?
And
you
know
what
what
kind
of
the
word
is
rmq?
What
are
you
getting
in
terms
of
requests
per
second.
B
We
have
installation
of
given
queuing
in
financial
services
that
they're
shooting
billions
of
billions
of
messages
per
loop
in
our
because
you
need
to
push
a
lot
of
that
of
quotes
and
and
and
data
in
our
test.
You
can
get
with
the
course
with
the
proper
and
hardware
in
memory,
and
the
memo
is
footprint
is
very,
very
small.
You
can
get
810
million
a
message
per
second
again,
it's
depend
on
your
on
your
heart
of
specification,
but
problem
to
get
very
high
throughput
and
again
depends
on
your
pattern.
B
A
B
If
some
kind
of
what
we
called
migration
as
most,
we
saw
also
with
many
companies
that
we
work
is
they
have
an
on-prem
installation
of
old
systems
like
MSM,
cue
from
something.net
or
if
you
have
also
even
Kafka
or
something
like
that,
and
and
it's
you
would
like
to
start
moving
your
infrastructure
to
kubernetes
once
you,
for
example,
if
you're
going
to
use
it.
This
is
some
kind
of
real
case
that
we
had
is
we
wanted
to
move
from
MSM
q?
B
Ms
up,
you
base
system
that
doing
financial
trading
and
the
tron
on-prem,
and
when
we
wanted
to
move
to
a
kubernetes,
there
is
no
MSM
q
capabilities
inside
kubernetes
means
that
we
need
to.
We
need
some
kind
of
solution,
and
what
we
are
doing
here
is
actually
doing
something
called
bridging
means
that
we
put
some
kind
of
bridge
on
the
old
prem
on
Prem
side
and
I'm,
going
to
show
you
it,
and
this
bridge
from
one
hand
connect
to
the
legacy
legacy
system.
B
This
one
is
a
demo
that
we
did
two
to
Microsoft
as
your
team
that
we
showed
in
the
capability
how
to
migrate
and
full
dotnet
and
base
architecture
to
full
kubernetes,
and
that's
actually
user
immersive
view.
And
what
we
have
here
is
some
kind
of
financial
data
and
application
that
that
has
from
one
hand
some
kind
of
generate
of
Kools
and
have
some
kind
of
command
that
sending
to
MSM
q.
B
B
B
Make
sure
that
this
is
the
screen?
Yes,
okay,
whatever
what
we
see
here
is
like
a
very
small
and
example.
Of
course
you
have
some
like
a
for
exchange.
M
client
that
sending
getting
getting
quotes
the
high
throughput
from
such
and
such
a
high
tech
chure.
So
what
happened
is
that
this
is
the
front
end.
Okay,
now
I'm
going
to
make
going
to
see
and
I
can
switch
to
this
one.
You
see
some
kind.
This
is
the
legacy
one.
B
And
we
have
some
kind
of
message:
it
message
worker.
Actually
this
is
this
is
like,
like
a
bridge
connecting
to
connecting
to
to
a
cubicle
just
sitting
on
a
KS
and
in
the
I
guess.
In
a
case,
we
have
a
Cuban
piu
cluster
with
the
service
that
actually,
you
can
present
this
now.
What
nice
thing
about
it
is
that
we
it's
not
only
the
streaming
of
that.
Okay
I
can
stop
it's
like
a
command
that
I'm
sending
to
the
two-day-old
ultra
and
can
resume
this
information.
B
C
A
B
Transactional
and
you
message
me
that
when
you
receive
and
message
it's
very
similar
to
Amazon
experience,
you
can
get
a
message
from
a
queue
and
hold
it
for
a
specific
time
that
you
want,
and
then
you
can
acknowledge,
reject,
rerouted
or
if
it's
not
processed
correctly,
you
can
throw
it
or
you
can
send
it
to
some
kind
of
debt
that
thank
you
and
then
kill
the
chain
of
the
transaction.
Now
there
is
a
ability
to
do
some
kind.
What
we
call
it
a
chain
of
transactional
means
that
you
can
what
you
can
do
it
is.
B
You
can
have
one
you
can
help
focus
on
the
first
process
so
that
taking
the
first
method,
you
can
hold
it
send
to
another
one
another
one,
another
one,
and
then
you
can
have
them.
If
some
some
someone
is
failing,
you
can
actually
send
back
a
notification
and
everything
would
be
canceled.
So
this
is
also
possible
architecture
and
very
easy
to
implement.
We
have
done
couple
of
time
and
show
it
to
other
custom
how
to
do
it
very
good,
very
easy.
A
B
A
B
You
have
a
persistent
volume,
is
going
up
and
take
it
from
there
persistent
volume
and
rebuild
rebuild
and
the
logs
and
what
you
need
to
do.
It's
it's
based
on
rough
protocol.
If
you
don't
have
persistent
volume,
we
you
love,
you
know
you're
losing
it,
but
it's
a
stateful
set.
So
you
get
you
gaining
all
the
benefit
of
its
have
no
dependency.
You
don't
need
for
instability.
Zookeeper!
You
don't
need
other
other
dependencies
that
you
need
to
install
before
it.
It's
one
container
bridge.
No,
you
know
three
five
and
that's
it.
A
B
You
have
couple
options,
helm
so
to
be
a
Pareto
in
the
next
week
and
also
you
can
use
the
CLI
and
then
you
can
very
in
very
quick
way.
You
can
install
and
manage
with
the
CLI
all
that
means
that
and
keep
in
mind
you
can
install
update
you.
Can
the
nice
thing
about
to
see
a
like
that
you
can
actually
work
with
it
and
and
develop
with.
It
means
that
you
don't
need
you
can
send
messages,
you
can
see
what
going
on
between
them
and
if
you
I
can
even
show
how
very
quickly.
B
One
example
is
when
you
have
a
cluster
a
remote
cluster
and
you
would
like
to
connect
and
work
with
us.
You
typically
need
to
do
both
holding
a
lot
of
hustle.
The
Kuban
pusat
yield
is
the
the
combined
line
at
the
CLI
you,
you
can
do
very
quickly
something
called
cluster
proc
change.
Actually,
what
you
going
to
do
is
going
to
automatically
put
forward
all
the
ports
to
your
locals
and
you
can
actually
develop
and
work,
as
is
in
your
local
host
and
very
easy
and
very
easy
to
do.
B
A
C
B
Yeah,
you
can
connect
between
clusters
and
what
we
have
done
is
something
like,
like
there's
another
component
of
the
cold
gateway
that
you
can
install
it
and
now
each
cluster
connect
to
this
can
connect
to
this
gateway
and
message
between
between
clusters,
and
this
is
one
option.
If
there's
another
option
for
hard
connectivity,
you
can
actually
build
some
kind
of
very
small
connector
that
will
help
you
see
connectivity
between
and
between
cluster.
But
again
it's
it's.
B
One
of
the
things
that
we
are
really
like
to
do
and
cooperate
is,
is
to
hear
feedback
and
things
that
we
need,
maybe
to
add,
do
it.
One
of
the
example
is,
for
example,
authorization.
This
is
very
unique
feature
of
cuban
queue.
You
can
upload
some
kind
of
authorization
file
that
will
can
pair
specific
per
resource
like
in
this
access
layer.
Access
control
is
that
you
can
allow
service
per
pattern
per
channel
per
specifically
to
allow
or
not.
B
Not
to
allow
access
to
its
access
to
to
the
message
broker
together
with
JWT
token
authentication
and
also
some
kind
of
multi
custom
built,
for
example,
you
can
send
a
message
and
to
event
the
same
as
such
case.
You
can
set
to
event
and
then
multiple
to
also
to
a
queue
or
different
queues.
In
the
same
message.
You
don't
need
to
set
couple
of
them.
You
know
two
different
services.
You
can
even
wrestle.
You
can
say:
ok,
please
send
it
to
as
event
to
this
channel.
B
A
B
So,
just
to
give
you
some
kind
of
Cuban
view
current
countries,
the
close
source
projects,
it
would
be
open
source
soon
and
we're
going
to
oppress
us
that
what
we
call
the
community
side
of
it
and
of
the
pubic
view
it's
free
and
it
will
be
always
be
free
and
currently,
even
today,
it's
free,
you
can
use
it.
You
can
download
it.
You
can
start
very
quickly,
will
take
you
5
seconds
to
install
it,
and
you
can
use
the
link
on
our
website
the
QuickStart.
B
You
can
see
that
we
use
Excel
or
so
from
licensing
point
of
view.
It's
free
to
you
to
you
as
much
as
you
want,
and
there
is
a
an
Enterprise
version,
additional
features
that
you
can
you
can
use
and
the
enterprise
also
already
open
source.
You
can
have
and
say,
I
get
the
code
it's
play
with
this,
and
this
is
already
deployed
and
production
already
forms
to
use
runs
already
in
many
clusters,
mainly
in
the
financial
in
financial
applications.