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A
Hello,
thank
you
for
joining
me
for
this
webinar
on
how
to
enable
powerful
connectivity
between
eight
sources
and
kubernetes
backend.
My
name
is
the
olabad.
I
am
the
co-founder
and
the
cto
of
cuban
q.
Why
we
are
here,
as
many
of
you
already
know,
that
kubernetes
has
become
the
de
facto
standard
for
deploying
container-based
workloads,
it's
so
popular
and
fully
featured
that
kubernetes
has
evolved
to
allow
clusters
to
exist
on
across
multiple
clouds
and
even
to
run
on
edge
computing
devices,
even
small
one,
but
kubernetes
isn't
inherently
aware
of
location.
A
A
What
I
mean
is
that
sometimes,
when
you
are
develop
a
system
on
your
back
end
with
a
lot
of
powerful
resources
like
network
and
cpu
and
memory,
this
is
not
the
case
once
you
need
to
connect
to
some
kind
of
edge
devices
that
the
resources
characteristics
are
not
the
same
as
in
your
local
network.
So
one
of
the
one
of
the
enablers
to
do
such
architecture
is
using
a
messaging
platform,
that's
sitting
on
top
of
kubernetes,
as
is
enabling
to
do
communication
and
hierarchy
connectivity
between
all
the
components
in
your
architecture.
A
A
Okay,
so
qmq
platform
components
has
four
main
components.
The
first
of
all
is
the
main
component.
This
is
that
and
qb
cluster.
This
is
an
enterprise-grade
message
broker
and
message
queue.
It's
very
scalable
natively
to
kubernetes
it's
highly
available
and
very
secure.
On
top
of
it,
we
have
three
components
that
together
are
forming
the
ecosystem
of
cubic
loop
platform.
The
first
one
is
cubic.
You
targets
is
a
container
based
targets,
a
connector
that
allows
connecting
to
about
1775
different
services
from
cuban
queue
to
them.
A
Services,
like
databases,
cache
other
messaging
system
file,
system,
storage,
such
things.
The
second
component
is
chemical
sources.
This
is
actually
the
other
side
of
of
cuban
queue
targets.
This
is
allowed
to
ingest
data
into
human
hue.
We
will
use
this
as
one
of
the
examples
in
the
use
case,
it's
allowed
to
bring
data
into
qmq
and
then
spread
it
between
other
services
or
even
used
to
different
connectors
like
human
kill
target.
Qmq
bridges
is
more
the
interconnectivity
between
cube
mq
clusters.
A
It's
enabled
to
transfer
data
between
one
chemical
cluster
to
another
one
or
to
replicate
or
to
aggregate
or
to
do
any
kind
of
transformation
between
20kmp
cluster
is
also
enabled
to
do
cross
clouds
or
cross
availability
zones,
or
something
like
that
data
between
two
two
or
even
more
cubicle
cluster.
We
start
by
a
little
bit
discussed
about
what
is
keeping
queue
message
broker.
What
is
the
main
features
and
later
on?
We
see
some
features
that
will
help
us
in
our
use
case.
First
of
all,
cuban
queue
is
deployed
within
operator
for
full
life
cycle
operation.
A
This
is
very
important
because
you
will
you
want
to
have
the
ability
to
do
a
live
roll-up
of
upgrades
as
day
two
operations.
So
it's
the
problem
with
operator.
It's
very
fast.
It's
written
in
goal
is
small
lightweight
docker
container.
It
supports
actually
two
main
messaging
families,
the
asynchronous
one
and
the
synchronous
one
in
the
asynchronous
one
we're
talking
about
durability.
A
Fifa
cubase
is
like
send
and
forget
type
of
messaging
type,
with
a
publish
subscribe
event.
This
is
also
a
secret
asynchronous
messaging
platform,
and
we
have
also
pops
up
with
persistent
means
that,
instead
of
like
in
any
memory
website
for
event
or
something
like
that,
we
have-
and
we
have
probably
subscribed
with
some
persistent
and
we
called
event
star
and
in
the
family
of
synchronous
messaging.
We
have
the
rpc
commanded
query
messaging
patterns.
A
One
of
the
main
features
of
qmq
is
the
transport
layer,
qmq
support,
grpc
and
rest
and
websocket
transport
layer
with
tls
and
support
for
both
rpc
and
stream
mode.
This
is
very
important.
We
discuss
about
how
why
it's
so
important,
supporting
streaming
in
kubernetes
messaging
system,
mainly
when
we
working
with
a
very
low
bandwidth
devices
like
in
edge
location,
also,
chemicals,
support
access,
control,
utilization
and
authentication.
A
We
have
multicasting
and
smart
routing.
We
will
touch
on
a
little
bit
later
and
one
of
the
key
features
of
cuban
queue
is
almost
no
need
for
messaging
configuration
needed
no
need
to
set
cues
exchanges,
nothing!
Actually
you
are
setting
it
up,
sending
a
message
and
that's
it.
Cubano
has
support
for
dot
net
java
python
go
and
load
sdk.
This
is
sit
on
top
of
glpc
protobuf
and
also,
of
course,
we
have
rest
interface
interface
for
other
framework
that
don't
have
the
support
for
grpc.
Let's
talk
about
the
queue
messaging
pattern.
A
The
key
messaging
pattern
is
very
similar
to.
If
you
familiar
with
the
amazon
sqs,
it's
a
fifo
based
order,
a
preserved
message
queue
exactly
one
massive
delivery
guarantee
cassette
batch
and
receiving
has
expiration
level
of
messaging.
You
can
set
delay
to
processing
messaging.
You
have
we
have
that
letter
cues
long,
pawning,
streaming
of
skills
in
out
we
have
peak
messaging
means
that
you
can
look
on
the
on
the
queue
see
what
kind
of
message
you
are
waiting
for
processing
and
then
you
can
decide
what
what
you
want
to
do.
A
You
can
do
all
you
can
do
change
your
message.
Message:
visibility,
reject
messages.
You
can
specific
ack
messages
you
can
resend
messaging
to
different
to
different
cue.
Also,
pull
and
push
modes
depend
on
your
architecture
in
the
events
pop
sub
messaging
pattern.
It's
a
it's
a
real-time
messaging
button,
very,
very
fast
when
I
say
very
fast,
we're
talking
about
millions
of
millions
of
messaging
per
second.
This
is
in
memory.
A
Has
some
consumer
group
support
with
a
wildcard
support,
has
load
balancing
between
tumors
suppose
it's
only
one
semester,
delivery
guarantee
means
that
if
you
didn't
consume,
the
message
is
lost.
As
I
said,
wildcat
petitions
and
it's
not
persistent.
The
event
store:
it's
like
the
events,
though
the
events
messaging
button,
but
it's
now.
A
If
you
want
later
on,
observing
support,
you
can
do
you
can
connect
and
ask
for
messaging,
starting
from
the
last
message
that
was
sent
or
the
first
from
the
actually
the
queue
support,
message:
sequence,
timestamp
time,
duration,
you
can
play
according
to
what
you
want
to
do.
The
rpc
query
and
command
message
patterns
is
the
synchronous
part
of
human
view,
connectivity
mode,
it's
mainly
for
connection
to
a
real
time
like
a
database.
A
If
you
have
a
command
or
you
have
a
query
that
you
want
to
send
a
message
to
a
database
and
return
some
kind
of
query
back
with
the
data.
It
support.
Two
actually
two
sub
message
pattern.
What
one
we
call
command.
The
second
called
query
command.
It's
like
a
web
book
you're
sending
a
message
to
some
kind
of
service
and
you
get
a
message
back.
If
it's
work
or
not.
If
not,
what
is
there?
The
query
is
more
sending
to
a
database
getting
back
a
data
pack
so
together.
A
This
gives
you
full
view
of
qmq
messaging
capabilities.
What
is
the
advantage
using
cubone
q
over
other
solutions?
First
of
all,
it
was
designed
and
optimized
to
work
on
kubernetes
with
seamless
integration
with
other
kubernetes
components.
This
means
metrics.
This
means
working
with
the
service
mesh.
This
means
that
you
can
run
everywhere.
You
can
run
on
a
cloud
can
run
on-prem.
It
can
run
on
each
device
it
can
run
with
in
cluster.
A
Together
it
can
be
run
standalone.
You
can
run
on
even
a
drone
supporting
from
arm
7
64
to
very
high
and
powerful
workloads
on
very
powerful
cpus
and
memory.
Has
all
the
messaging
pattern
run
anywhere
very
low,
recall
resource
cpu
we
have
tested
the
docker
content
is
about
40
meg,
so
it's
it's
very,
very
small.
You
can.
You
can
develop
and
build
the
complete
architecture
with
cube
and
q
other
components
like
target
bridges
and
sources.
We
will
see
them
down
in
in
our
demo
and
discuss
it
about
it.
Now.
A
It's
enterprise
already
out
of
the
box,
no
need
for
dedicated
persistent
volume.
It's
again
depends
on
your
usage
of
your
messaging
patterns
and
I
would
like
to
say
the
importance
of
grpc
interface.
It's
more
performance,
less
latency,
it's
a
it's
a
unified
api
and
one
of
the
biggest
advantage.
That
is
that
in
when
you're,
using
kubernetes
and
using
messaging,
the
opening
and
the
close
connection,
all
the
time
is
very
highly
cost.
This
means
that
using
qmq
with
the
streaming
capabilities,
you
are
open
once
and
you
can
stream
data
as
much
as
you
you
want
this.
A
This
gives
you
low,
latency
and
also
less
but
consume
less
of
resources
to
handle
such
transfer
data
between
endpoints,
okay,
let's
see
the
first
connector
of
qbmq
messaging
framework.
The
first
one
is
the
cubic
feel
targets.
Cubicle
targets
enable
and
allows
to
build
the
message-based
microservice
architecture
on
kubernetes,
with
minimal
efforts
and
without
developing
connectivity
interface
between
cubing
queue,
method
broker
and
external
system,
such
database
cache
messaging
and
rest
based
api.
When
you're
building
a
micro
services
based
on
messaging
platform,
you
need
the
intel
connectivity
between
other
services.
A
For
example,
you
have
an
api
and
you
have
a
database
that
need
to
get
information
from
this
database
into
api,
or
we
want
to
save
the
some
data
on
cache
or
we
want
to
say
we
want
to
take
the
data
and
do
some
kind
of
a
queuing
in
order
to
process
later
human
crew
targets.
Give
you
this
ability
to
connect
cubemq
to
other
services.
A
We
have
all
the
cache
there
in
aws.
You
can
see
a
large
service
that
we
were
supporting
from
stores
that
all
the
database,
all
the
messaging
storage
and
also
in
azure.
So
so
the
next
thing
we're
going
to
discuss
is
human
fuel
sources.
Human
q
sources
is
the
actually
the
other
side
of
qbq
targets.
It's
allowed
to
ingest
data
inside
into
a
cuban
queue
and
enable
you
to,
for
example,
form
some
kind
of
ingesting
component
inside
to
your
back
end.
A
It
supports
other
messaging
components
like
property
people,
also
like
a
file
storage,
and
we
see
it
later
on
in
our
demo
how
it
works.
It's
mainly
working
together
with
tmq
targets
and
more
for
also
for,
if
you
want
to
migrate
and
old
services
like
if
you
have
services
in
rabbit
mq
and
want
to
move
to
kubernetes-
and
you
want
to
have
to
to
still
have
connectivity
to
a
rabbit
in
your
outside
of
kubernetes,
so
you
can
use
the
sources
and
the
targets
from
that
support.
A
Rabbiting
queue
in
order
to
form
some
kind
of
migration
paths
for
them.
Human
queue
source
is
also
an
open
source
project.
You
can
look
it
into
the
github,
and
here
we
we
have
all
the
sources
that
we
support
from
the
from
http,
like
we
have
like
an
api
gateway.
This
is
a
very
interesting
connector.
We
you,
you
can
put
human
fuel
sources
as
an
api
gateway
in
front
of
your
user
and
you
can
absorb
data
inside
and
it
you
can
place
it
on
a
queue
or
to
process.
A
A
To
conclude
the
ecosystem
components
of
cubemq.
I
want
to
discuss
about
the
bridge
connector.
The
bridge
connector
allowed
to
connect,
allows
to
connect
between
human-q
clusters.
There's
several
connectivity,
and
I
will
show
you
in
in
a
second
in
in
the
github
repository,
but
the
main
idea
is
that
you
will
the
ability
to
interconnect
between
qpmq
clusters,
no
matter
where
they
are.
If
it's
on
other
regions,
other
availability
zones
other
in
the
cloud
or
in
your
on-prem,
it
depends
on
and
how
you
want
to
connect
between
them
back
to
github.
A
Cuban
cube
bridge
is
also
an
open
source
project
that
we
have
about.
Four
topologies
one
is
the
bridge,
is
one
to
one.
Connectivity
between
clusters
means
that
I
can
connect
from
cluster
a
to
cluster
b.
We
have
replication
means
that
you
we
have
a
way
that
you,
a
data
in
one
cluster,
can
replicate
the
same
data
to
different
cluster.
A
This
is
very
appealing
to
if
you
have
some
kind
of
ingestion
of
analytics
or
many
many
of
forms
of
flow
streams
of
data
that
you
want
to
consume
in
in
several
kubernetes
in
cubicle
clusters,
and
we
have
some
aggregation
means
that
you
can
collect
many
collect
a
lot
of
data
from
many
in
your
cluster
and
send
it
to
another
cluster
and
what
we
call
a
transform
that
it's
like
a
mix
between
replication
and
aggregation
together.
A
This
concludes
the
cuban
queue
platform
component
description
and
in
this
point
I
would
like
to
show
you
a
use
case
of
using
cuban
queue
and
the
component
how
to
move
data
between
edge
and
s3
buckets
in
aws.
The
use
case
that
I'm
going
to
show
you
is
taking
from
one
of
our
clients
that
is
multinational
technology
company.
A
A
The
research
is
done
by
other
services
that
digesting
this
file
and
producing
some
outputs
sending
back
to
the
clients
with
some
information
they
are
currently
using
ibm
and
q
because
they
are
on
the
vm
type
world
and
when
they
want
to
move
they
want,
they
are
currently
moving
to
kubernetes
and
they
need
a
solution
that
will
be
container
based
and
it
will
be
more
robust
and
much
faster
than
what
they
have
today.
A
They
have
also
a
cloud
and
on-prem
solution
that
needs
to
be
support,
which
means
that
it's
not
only
from
edge
to
aws.
It's
also
for
bridging
between
two
location
of
on-prem
and
a
cloud
I'm
going
to
divide
the
demo
to
four
step
before
going
to
start
the
steps,
I'm
going
to
talk
about
briefly
we're
going
to
use
the
cuban
queue
built-in
deploy
building
tool.
This
is
an
online
building
tool
that
will
help
us
to
configure
all
the
components
and
to
deploy
them
very
quickly
to
a
kubernetes
cluster.
A
So
we're
going
to
use
this
tool
to
follow
configuration
and
for
the
steps
I'm
going
to
do
like
this
in
step,
one
we're
going
to
deploy
a
cuban
queue
cluster
on
a
remote
kubernetes
cluster
on
gcp
google
cloud
platform,
we're
going
to
add
a
cube
mq
targets
that
from
one
end,
will
be
connected
to
the
local
cuban
queue
cluster
and
on
the
other
side
it
would
be
send
and
save
files
on
the
s3
buckets
on
aws,
the
second
one.
The
second
step
will
be
deploying
we
could.
A
A
The
third
step
will
be
configuration
of
cubic
you
sources.
This
will
be
a
standalone
cuban
queue
sources,
application
that,
from
one
hand,
will
be
listening
to
the
local
local
files
that
we
will
send
put
there
later
on
some
files
and
on
the
other
side,
will
send
the
files
that
he
is
listening
and
take
from
this
file.
Folder
to
a
queue
in
qmq
that
will
be
later
on
will
be
sent
to
the
s3
on
the
remote
side.
A
Step
four
will
be
moving
some
files.
So
let's
do
it
step
one
in
step,
one
we're
going
to
create
a
cuban
queue
cluster
on
a
remote
kubernetes
cluster,
in
this
case
we're
going
to
deploy
on
gcp.
For
this
I'm
going
to
use
our
build
and
deploy
tool.
As
we
can
see
here.
This
is
the
cuban
cube
built-in
deploy
management
console.
This
is
a
web
application.
A
This
application
allows
you
to
configure
all
the
qmq
components
and
be
able,
with
cube,
ctl
command
line,
to
deploy
yaml
files
into
kubernetes
cluster,
and
then
you
can
create
your
architecture
as
you
desired
so
step.
One
is
creating
cuban
queue
cluster
on
gcp,
kubernetes
cluster
and
also
adding
cuban
queue
targets
that
points
to
s3
buckets
that
will
take
files
from
a
specific
topic
or
channel.
We
call
going
to
call
it
s3
and
save
it
as
a
file
on
aws
s3.
A
Clicking
on
get
cluster
we're
going
to
add,
and
here
we're
going
to
create
a
put
cuban
key
with
a
demo
cuban
queue
namespace
demo
very
important
is
that
we're
going
to
expose
the
cuban
queue
cluster
outside
in
order
that
the
bridge
in
step,
2
and
step
3
will
be
able
to
connect
directly
to
to
the
cuban
queue
cluster.
That's
running
on
gcp
we're
going
to
put
the
grpc
interface
with
load
balancer,
that's
it
and
do
save
and
we're
going
to
do
a
deploy
here.
A
When
I
do
click
on
deploy,
I
can
get
the
manifest,
and
here
I
get
two
manifests
that
I
can
play
with.
One
is
the
initialization
of
all
cuban
queue,
clds
and
definition
all
blacks,
everything
and
the
second
one
is
the
the
the
yama
that
that
is
representing
the
cuban
queue
cluster.
So
I'm
going
to
start
within
it.
I
can
click
it
and
go
to
there
now
console
this
one.
A
A
A
A
So
here
what
we're
going
to
do
we're
going
to
deploy
the
cuban
queue
targets
container
into
the
same
namespace?
That's
running
cuban
queue
cluster,
and
here
we're
going
to
select
a
connector
from
s3
type.
A
Then
we
can
see
that
we
have
here-
and
here
we
have
two
sides,
what
we
call
the
soul
side
and
the
target
side.
The
sound
side
is
from
where
you're,
taking
the
information
to
be
running
on
s3
target
side.
So
from
here
we're
going
to
select
the
queue
and
here
we're
going
to
select
that
grpc
service.
Others
here
is
giving
you
cluster
grpc
cuban
cuban
cuban
queue,
but
here
is
qmq
demo
this
this
is
the
namespace
and
the
channel
is
s3
here,
I'm
going
to
put
the
aws.
A
Aws
keys
and
all
the
information
and
then
do
save
here
are
getting
the
information
of
targets.
Of
course,
I
can
add
more
more
targets
if
I
want,
but
here
you
can
do
the
manifest,
but
what
I
want
that
it
will
run
on
the
namespace,
so
I'm
setting
the
namespace
demo
getting
the
manifest
the
same
again.
A
Created
we
can
see
if
I
want
the
parts
that
got
targets
created.
Okay,
another
cool
tool
is
keep
mq
ctr.
This
is
the
command
line
of
cuban
queue.
We
can
even
look
into
the
container
with
the
logs
we
can
do.
Q
mq
ctl
get
cons
logs.
A
You
will
select,
which
one
and
we're
going
to
use
this
one,
and
you
can
see
that
it's
already
initialized.
So
this
concludes
step
one
in
step.
Two
we're
going
to
create
a
cube.
It
could
first
a
kubernetes
cluster
on
a
local
machine
like
an
on
edge
device,
we're
going
to
use
k3d
as
the
kubernetes
distribution.
This
is
a
daemon
for
k3s
for
windows.
A
Then
we're
going
to
create
a
bridge
between
the
local
cubemq
cluster,
that's
sitting
on
the
head
side
and
pointed
to
the
remote
kubernetes
clutter,
that's
sitting
on
gcp
that
we
configured
in
step
one.
So
what
we're
going
to
do?
First,
we're
going
to
create
a
cluster
for
the
edge
with
k3d,
okay,
3d
cluster,
create.
A
A
A
I
did
the
mistake,
so
let's
do
this
one.
Let's
see
that
everything
is
running
so
now
we're
going
to
run
to
again.
Okay,
our
build
build,
deploy
tool
here,
we're
going
to
create
a
cluster
here.
I
can
delete
one
and
then
I
will
create
a
simple
cluster
and
with
the
default
keyboard,
queue
namespace
to
save,
deploy
and
here
since
we're
starting
fresh.
A
A
Up
and
running
all
of
them,
you
can
see
here
343
and
now
we
will
add
a
bridge,
a
keeping
queue
bridges
and
the
role
of
this
bridge
is
to
bridge
between
the
local
cuban
curriculus,
that
is
sitting
on
our
local
edge
kubernetes
cluster
to
the
gcp
kubernetes
cluster,
with
the
qbmq
cluster.
That's
sitting
there.
In
order
to
do
this,
we
will
add
a
bridge
in
this
menu
cuban
cube
readers.
A
A
We
will
select
q,
we
will
select
s3,
and
here
the
target
would
be
the
remote
one,
the
cube
in
q1,
that's
sitting
on
gcp,
and
here
we're
going
to
use
the
the
address
that
we
recorded
before
with
the
load
balancer
that
we
exposed
in
this
cuban
queue-
and
this
will
be
this
one
with
this
ip
address.
Second,.
A
A
A
A
A
This,
in
this,
in
this
case,
is
a
windows
one
for
one
hand,
this
cuboq
sources
will
listen
to
a
local
folder
that
we're
going
to
set
up
and
we'll
send
the
messages
that
the
files
that
in
this
folder
to
the
local
cubing
queue
cluster,
that's
sitting
on
the
edge
device
here,
we're
going
to
do
it
in
two
sub
steps.
A
So
we
will
start
again
by
let's
put
this
one:
click
with
sources
here
here:
we're
going
to
add
a
source
that
it's
a
file
system,
source
file
system
source,
and
here
we're
going
to
have
this
setting
the
setting
here
will
be
what
will
be
the
source.
Folder
names
means
that
what
is
the
local
one
here?
We're
going
to
use
the
e
demo
s3
to
this
file?
Sorry
to
this
folder
we're
going
to
upload
an
image,
and
then
this
is
what
we
want
to
see
on
the
s3
buckets
we're
going
to
set
the
buckets.
A
A
Nothing
is
here,
and
the
targets
here
will
be
a
local
one,
so
we're
going
to
do
this
one
and
also
a
local
host
again.
This
is
because
we
are
running
it
as
a
standalone
application.
It's
not
running
on
a
container.
This
is
another
advantage
of
cubic
connectors
that
can
run
also
as
a
connector
and
also
as
a
windows
or
linux
or
any
kind
of
other
other
file
in
architecture.
A
Now,
since
we
are
not
running
on
kubernetes,
we
need
only
the
read
only
the
the
url
of
the
configuration
here
we're
going
to
use
going
to
the
folder
of
cuban
queue,
some
resources,
sorry,
cd
sources
and
we're
going
to
run,
giving
queue
sources
and
get
here
we're
going
to
take
only
this
one.
But
before
we're
going
to
run
it,
I'm
going
to
do
some
poll
folding
cuban
queue
ctl
send
cluster
proxy.
A
You
will
go,
do
for
all
the
ports,
and
here
we're
going
to
run
it
and
it's
going
to
start
running
it
will
try
to
connect
to
to
keep
mq,
and
here
we
it's
connected
and
running.
So
this
is
step
three
step.