►
From YouTube: OpenShift Commons Briefing #38: Intro to Hazelcast In-Memory Distributed Computing on OpenShift
Description
Chris Engelbert, Manager of Developer Relations at Hazelcast will present to the OpenShift Commons how to deploy the leading open source in-memory data grid, Hazelcast, on the leading open source platform-as-a-service, OpenShift by Red Hat.
Chris will present the methodology for setting up Hazelcast Discovery Service Provider Interface with Docker and Kubernetes on the OpenShift platform.
You can read more more about deploying Hazelcast on OpenShift here: http://blog.hazelcast.com/openshift/
For the latest information on OpenShift 3.2 and available briefings, please visit http://commons.openshift.org or subscribe to the OpenShift Blog (https://blog.openshift.com).
The OpenShift Commons exists to provide a platform for customers, partners, developers and other open source technology initiatives to collaborate, share and accelerate the pace of innovation and adoption of OpenShift globally.
The OpenShift Commons represents a new open collaborative community model designed to facilitate communication and sharing of best practices, feedback and development across the many open source initiatives that integrate with OpenShift. The best way to get involved is to join the conversation today at http://commons.openshift.org
A
A
Well,
welcome
to
this
open
shift,
Commons
briefing
today.
We're
really
pleased
to
have
hazel
Cass
Chris
Engelbert
with
us
to
talk
about
in-memory,
distributed
computing
using
hazel
cast
and
we're
becoming
streaming
live
from
a
couple
of
hotel
rooms.
So
we're
just
going
to
say
up
front.
There
may
be
a
few
breaks
in
the
Wi-Fi
where
he
is
in
Spain
and
where
I
am
in
Texas.
So
today
is
going
to
be
interesting.
We're
gonna
have
the
session
live
streaming
now
with
on
blue
jeans,
it'll
also
be
available
to
be
watched
afterwards
on
YouTube.
A
If
you
have
questions,
there's
a
chat
box
here,
I
just
ask
that
you
type
them
in
there
and
we're
gonna.
Let
Chris
introduce
himself
and
his
topic
and
if
you
have
questions
I,
think
there'll
be
one
little
pause
that
he
takes
when
he
tries
to
upload
on
Wi-Fi
a
docker
image
to
docker
hub
or
something
like
that.
B
B
B
I'm
Chris
Engelbert,
coming
from
Germany
being
raised
in
one
of
the
biggest
industry
areas
in
Germany,
currently
I'm
working
as
manager
of
developer
relations
at
heiser
cast
I
actually
joined
in
November
2013,
as
one
of
the
core
developers
moved
on
to
a
big
cool
stuff,
doing
conferences
traveling
the
world
like
all
the
good
stuff,
you
can
do
a
book
over
the
world
for
people
that
love
to
use.
Twitter,
you
see
my
Twitter
handle
down
in
the
slides.
I
know
you're
not
supposed
to
have
your
Twitter
handle
on
every
slide,
but
I
don't
care.
B
If
you
really
want
you
have
your
Twitter
timeline.
Burstered.
Just
follow
me
and
you're
good
to
go.
I
would
say:
I'm
a
Twitter
I'm,
a
Java
passionate
that
means
I'm,
part
of
the
Java
JCP,
the
Java
community
process.
Bad
actually
was
the
Java
language
and
the
JVM
forward,
and
just
today
we
got
the
proposal
for
Java
9,
so
there
will
be
fun
looking
for
that.
B
But
apart
from
that,
I'm
really
interested
in
seeing
that
Java
is
one
of
where
I'm
really
interested
to
see
that
javis
is
one
of
the
most
used
and
most
interesting
language
and
it's
still
growing,
and
that
is
good.
Apart
from
that.
Well,
everything
about
performance,
garbage
collection,
all
those
kind
of
benchmark,
fairytales
I
mean
we
all
know
benchmarks
are
not
real.
B
Everything
in
those
kinds
of
things
is
exactly
what
I
normally
do.
So
what
are
we
gonna
talk
about
today,
I
already
kind
of
gave
the
introduction
we're
talking
about
in-memory
computing,
in-memory
means
fast
and
and
and
fast
storage,
but
we
do
this
with
hazy
casts
without
shift
and
I
think
that
most
of
the
viewers
from
the
open
shift
block
or
the
open
shift
briefings
probably
know
how
you
shift
free
works.
It
actually
works
in
communities
and
docker.
B
So
far,
so
good
so
docker
as
I
said,
you
probably
all
know
that
docker
is
meant
to
to
just
build
an
image.
You
ship
it
somewhere,
you
run
it
and
it
just
magically
works.
It
pretty
much
works
wherever
you
are
docker
Heights,
all
that
kind
of
weird
that
you
normally
have
to
do
in
deployment
and
it
just
magically
works.
For
you.
Automatic
career
neat
is
on
the
other
side.
I
stole
this
from
from
reg
sang
one
of
the
evangelists
from
from
Google
and
I
really
thought
that
picture
kubernetes
actually
manages
the
cluster
for
you.
B
B
He
just
started
hello
world
like
think
a
hundred
times
in
the
Google
cloud,
pretty
cool
OpenShift
now
take
stalker
and
kubernetes,
and
some
some
internal
parts
and
and
just
moves
all
that
together,
and
that
is
the
interesting
thing,
because
OpenShift
itself
gives
you
this
amazing
infrastructure
that
automatic
hand
automatically
handles
all
the
different
parts
of
deployment
strategies
of
how
to
distribute
your
your
images
and
how
to
scale
up
and
scale
down
and
at
heiser
cast.
We
thought
that
is
cool
and
we
definitely
need
to
be
part
of
that.
B
So
the
interesting
thing
is
what
is
Heiser
cast
and
I
think
that
is
where
most
of
the
people
probably
don't
know
yet
so
in
hazal
cast
is
a
so-called
in-memory
data
grid
and
a
real
lovely
picture,
because
we're
not
going
to
have
a
laser,
but
it's
pretty
close,
so
in
memory
data
grid
means
we're
storing,
first
and
foremost,
it
means
we're
storing
data
in
memory.
I
think
you
got
that
part.
The
other
part
is
data
grid.
That
means
you
can
store
data.
B
You
can
compute
data,
it's
not
just
caching
or
those
simple
solutions,
but
you
can
really
do
a
lot
of
things.
A
lot
of
people
think
an
in-memory
data
grid
might
be
something
like
an
in-memory
database.
It's
kind
of
close
just
remove
the
persistency
pardon
you're,
pretty
close
to
what
it
is.
So,
as
I
said,
or
as
I
already
said,
there's
one
important
thing:
when
you
have
a
cluster
running
and
you
store
data,
there
are
two
different
ways.
So
that's
let's
take
the
other
one.
First,
the
first
one
is
data.
B
B
Requests
to
every
node
and
I
will
give
you
a
speed-up,
but
it
means
you,
you
have
the
same
data
and
that
means
you're
wasting
a
lot
of
memory,
so
the
other
option
is
data
distribution,
and
that
is
what
Hayes
accounts
does
that
means
if
we
have
this
grille
and
it's
big
pyramid
of
nuts
in
the
middle,
what
we
actually
do
we
take
subsets
of
those
nuts
and
distribute
it
in
the
cluster.
That
is
cool
for
a
couple
of
reasons.
B
First
of
all,
you
have
like
a
huge,
shared
Java
heap,
because
your
your
your
almost
linear
scale
up
the
the
heap
in
the
different
JVMs.
The
other
reason
is:
if
we
have
a
request,
a
query
or
whatever
you
want
to
send
to
the
data,
we
actually
send
it
to
all
the
nodes,
everybody's
working
with
little
data
set
in
parallel,
and
that
is
the
cool
part,
because
that
means
it
speeds
up
your
queries
massively.
B
So
let's
get
through
them.
As
I
said,
it
speeds
up
data
and
speeding
up
data
actually
means
we
want,
or
were
you
you
start
to
use.
Caching
and
in-memory
data
grids
and
stuff
is
when
you
want
to
have
fast
delivery.
Caching,
caches
are
the
most
typical
solution
or
the
most
typical
use
case,
but
not
the
only
one.
We
actually
have
customers
using
haze'
cast
as
their
primary
first
and
foremost
primary
data
store.
B
They
still
pluck
some
database
behind
that
to
make
sure
that
if
they
have
to
restart
the
cluster
for
some
reason
that
they
can
restart
and
really
read
the
data,
but
they
use
hazelnuts
for
pretty
much
everything
in
their
application.
Only
knows
about
haze'
cast
and
haze'
cast
knows
about
how
to
write
to
this
underlying
database
stuff.
B
So
why
do
we
for
in
memory
when
we
want
to
have
fast,
fast
delivery?
So
I
have
this
nice
or
I?
Have
this
nice
numbers
here
and
I?
Think
everybody
knows
that
l1
l2
these
days,
even
l3
and
l4
caches
are
the
fastest
memory
you
can
get,
but
unfortunately
those
things
are
still
pretty
small,
even
though
you
have
one
of
the
examines
abuse.
B
It's
still
just
like
64,
a
max
or
or
anything
like
that,
I'm,
not
sure
what
the
current
values
the
the
interesting
thing
is
main
memory,
so
we're
at
hundred
nanoseconds
access
time
for
main
memory,
and
that
is
pretty
fast.
It
actually
is
faster
than
than
a
gigabit
sorry,
so
it
actually
faster
is
faster
than
the
gigabit
network.
B
Still
a
gigabit
network
is
amazingly
fast
if
you
look
at
other
things
like
spinning
disk,
which
is
ten
times
as
slow,
sorry,
a
thousand
times
as
slow
or
even
sending
a
network
packet
to
TCP
tech
package
from
California,
Nahum,
Amsterdam
and
back
to
California.
That
is
a
hundred
50
millisecond
latency.
You
really
don't
want
those
things
in
your
cluster,
true,
even
even
if
we
look
at
Network
compared
to
spinning
disk,
so
there's
one
0.01
millisecond
access
time
over
a
gigabit
network,
but
there's
0.15,
millisecond
so
15
times
as
slow
as
the
network.
B
That
means
our
gigabit
networks
are
so
fast
these
days
that
even
some
of
the
fastest
SSDs
can't
really
keep
up
on
sending
data
a
fast
enough.
That's
that's
a
very
interesting
thing
and
haze'
cast
itself
has
a
test
lab.
We
have
our
own
test
lab
at
an
machines,
786
gigabytes
of
ram
24
cores
each
I
think
something
like
that,
and
we
have
a
gigabit
network
card.
We
have
a
10
gigabit
and
we
40
gigabit
and
I
promise
whenever
you
think
that
network
is
slow,
go
for
Solar,
Flare,
40,
gigabit
network
arts.
B
Those
things
are
so
amazingly
fast.
We
got
round
trips
times
in
microseconds.
That
is
absolutely
amazing.
So
network
is
not
the
problem
these
days
anymore
and
the
other
thing
so
net
network
is
because
we
have
a
cluster
right.
Main
memory
is
because
we
have
an
in-memory
data
store,
I'm,
not
sure
if
you
can
rate
that
and
I
think
my
mouse
curve
that
doesn't
work.
So
this
this
chart
actually
shows
the
the
the
way
that
the
price
and
the
size
of
memory
moved
between
1980
I,
think
everybody
knows
most
good
c64
times
and
they
claimed
640.
B
Kilobytes
might
be
enough
for
everybody,
even
though
I
think
it's
not
true
that
that
bill
gates
gates
actually
sat
that,
but
it's
it
was
amazingly
expensive
to
go
over
this.
This
640
kilobytes,
so
a
megabyte
of
RAM
in
in
1980,
was
over
6
grand
over
6
grand,
whereas
today
it's
less
than
half
a
cent,
that's
actually
pretty
impressive,
or
it's
about
half
a
cent.
It
kind
of
stabilized
at
that
level
where
it
is
in
2013,
but
the
memory
size
is
growing
so
that
these
are
home,
computers
and
I.
Think
16
gigabytes.
B
These
days
is
not
uncommon.
My
home
computer
is
32
I'm
a
gamer,
so
I'll
probably
need
some
some
more,
but
looking
at
at
server
hardware
Amazon,
just
yesterday,
launched
I
think
it
free
terabyte,
VM
free
terabyte
VM.
That
means
they're
not
running
a
single
VM
on
a
single
system.
They
probably
run
10
or
20,
or
maybe
a
hundred.
That
means
they
must
have
machines
with
Ferdie
and
300
terabytes
of
RAM
I.
B
Don't
know
how
they
do
that,
but
that's,
but
even
as
as
a
smaller
company,
as
I
said,
we
have
10
machines,
786
gig,
each
that's
close
to
a
terabyte
of
RAM
and
I
think
on
the
HP
website.
At
the
moment
you
can
buy
2
terabytes
right
from
the
store
without
any
kind
of
specialization.
That's
absolutely
amazing!
So
I
already
said
that
one
or
two
of
the
main
use
cases
for
hazel
castes
are
caching
and
primary
storage.
Caching
is
interesting
because
that
is
where
most
our
customers
start
from.
They
need
a
distributed.
B
Cache
both
replication,
caches
or
eh.
Cache
doesn't
work
for
them
anymore,
because
they
have
too
much
data,
and
then
they
look
for
distributed
caches
and
hazel
caste
can
do
that
for
you
and
it
does
it
very
well.
So
that
is
where
most
people
come
in.
They
need
a
caching
solution
and
then
they
figure
out
way.
I
can
do
way
more
with
hazel
caste.
It's
not
only
a
cache.
What
a
lot
of
people
think
in
the
first
place,
but
then
they
start
to
use
it
as
a
primary
storage
or
asset.
B
Your
application
is
only
working
against
hazel
cast
and
doesn't
know
about
anything
like
a
database
or
anything
in
between
or
under
behind
that.
So
that
is
the
second
use
case,
and
then
they
figure
out
there's
even
more
so
hazel
cast
also
offers
things
like
lists
and
sets
and
queues
and
also
cool
things
for
distributed
computing
like
a
distributed
executor
service
and
the
interesting
thing
about
hazel
cast
what
we
always
try
to
do.
We
try
to
make
it
as
simple
for
Java
developers
as
possible,
and
that
actually
means
hey
sokka's
implements
those
features.
B
This
said
queue
executor
service
based
on
top
of
the
the
Java
standard
libraries,
so
we
implement
the
Java
collection,
API
and
the
Java
concurrency
API
so
including
semaphores
and
locks,
and
all
that
stuff
right
distributed
for
you
transparently,
distribute
your
application.
Pretty
much
doesn't
know
about
it,
because
you're
working
against
the
official
API,
that's
the
cool
part
haze
across,
tries
to
be
as
simple
as
possible,
and
even
though
we
have
a
thing
well,
I
think
they
will
be
next
slide.
So
hey
the
cast
is
itself
is
all
about
scaling
out.
B
So
that
means,
instead
of
always
buying
a
bigger
machine.
There
is
always
a
company
that
can
sell
you
a
machine
that
is
twice
as
fast,
but
triple
s.
Costiy,
hey
Lucas
goes
the
other
way
same
way
as
a
lot
of
other
no
sequel
solutions
out
there,
and
that
means
you
take
cheap
hardware
or
virtual
machines
or
whatever,
and
you
take
a
lot
of
em
and
that
actually
gives
you
a
speed
improvement
over
just
scaling
out
your
single
machine.
B
So
how
does
that
ply
together?
So,
as
I
said,
we
have
a
haze
across
cluster
for
hazel
cast
that
actually
is
a
peer-to-peer
network.
So
I
found
this
picture,
which
seems
to
have
every
node
connected
I
like
that,
and
you
have
your
cluster
running.
You
have
your
application
in
there,
but
then
you
have
this
problem
right.
You
either
want
to
update
hazel
cast
or
you
want
to
app
update
your
own
application.
B
All
people
know
that
what
we
have
to
do
is
we
want
to
roll
it
out
somewhere
and
rolling
it
out
actually
means
we
first
have
to
package
everything
up.
That
means
our
application
code
pays
the
cost,
libraries,
more
libraries,
even
more
libraries,
and
when
we
haven't
stopped
all
the
libraries,
yet
we
have
even
more
libraries,
and
that
is
still
ok.
So
now
we
have
like
a
huge
jar:
a
war
file,
an
ear
file,
whatever
is
out
there,
and
we
want
to
deploy
this.
So
how
did
this
work
before
we
had
all
those
cool
things?
B
Well,
somebody
actually
had
to
move
your
your
war
or
your
your
deployment
unit
to
these
servers,
and
that
often
was
a
manual
step,
not
saying
that
this
is
true
for
all
companies
still,
but
believe
me,
you
will
surprised,
bear
still
love
companies
doing
this,
at
least
in
a
semi-automatic
way.
Pretty
scary,
though
so
we
try
to
deploy
it
and
when
you
actually
manage
to
deploy,
you
can
see
yourself
as
deployment
hero
and
that
actually
was
true
after
every
deployment
and
I'm
speaking
as
well.
B
From
my
own
experience
and
from
my
own
past,
we
did
this
at
a
couple
of
companies
every
deployment.
There
was
a
special
stand
up
in
the
morning,
just
to
see
that
everything
is
good.
We
had
to
be
early
in
the
company
just
to
do
a
deployment
or
just
to
know
that
the
deploy,
if
the
deployment
goes
wrong,
that
somebody
is
there
not
what
we
want.
So
what
often
have
happened
is
some
deployments
failed.
B
That
was
that
was
why
people
was
bare
in
the
morning
and
we
actually
had
to
either
roll
back
or
quite
fast
fix
it
and
try
another
roll
back
another
deployment
and
hope
that
this
one
works
and
I
think
everybody
knows
how
those
fast
I
need
to
fix
it
right
now
things
work
or
even
not
work.
So
hey
the
cast
started
from
a
customer's
request
to
say:
okay:
let's
do
it
differently.
This
customer
wanted
to
deploy
on
docker
or
as
a
docker
image.
B
So
he
asked
us
and
we
said
well,
you
know
that's
actually
a
quite
good
idea.
You're,
probably
right
about
that.
So
with
3/6
I
just
started
to
have
official
docker
images
and
those
are
all
on
docker
hub.
You
can
just
download
them
and
what
we
added
now
or
what
we're
adding
437
is
automatic
deployment
or
out-of-the-box
deployment
for
openshift.
B
This
talk
will
actually
show
a
slightly
more
manual
version
because
it's
not
yet
completely
done,
but
it
will
be
whenever
Hays
occur,
three
7s
out
so
Diane
and
I.
We
just
talked
about
it
and
we're
trying
to
get
this
out
as
fast
as
possible,
probably
by
updating
blog
posts
and
whatever.
So
that
is
where
hazel
cast
and
docker
kind
of
Hazel
cast
docker
and
open
shift
comes
together
and
why?
Why
do
we
choose
openshift?
B
It's
it's
pretty
simple.
The
main
reason
is
openshift
is
easy
for
DevOps
and
for
infrastructure
teams
to
manage,
and
it's
actually
quite
easy.
If
you
have
a
template,
we
see
that
in
a
bit
to
run
haze'
cost
to
scale
a
hazal
cast
right
on
top
of
OpenShift,
and
that
is
absolutely
impressive
and
amazing
and
I
think
it's
a
good
work.
So
let's
have
some
fun
with
OpenShift
and
I
actually
was
clever
enough
to
to
prepare
all
the
slides
for
the
case
that
my
life
life
demo
doesn't
work
but
we're
trying
it
life
life
first.
B
So,
let's
see
so
that
is
the
haze'
cast
opal
shift
project
for
now.
That's
all
my
I
see
that's
all
my
github
account
for
now.
As
I
said,
this
is
still
a
draft
version.
That's
not
completely
done
yet,
but
we're
working
on
that
we're
getting
it
ready
whenever
it
is
needed.
So
I
already
cloned
bad
repository
and
you
see,
while
I
was
trying
to
get
all
this
stuff
running
on
open
shift.
I
basically
collected
all
my
commands
I
ever
found.
B
It's
actually
quite
interesting
because
a
lot
of
most
commands
you
can
only
find
using
Stack
Overflow,
and
you
wonder
yourself
where
people
get
this
idea
from.
So
we
have
this
repository
right
here.
As
I
said,
it's
just
cloned.
If
you
look
here,
it's
set
status,
perfect,
okay.
So
let's
look
through
some
of
the
B
files,
so
I
chose
the
most
simplistic
way.
I
think
that
is
actually
possible.
I
use
a
maven
pump
file
to
build
my
docker
image
and,
in
this
case
I
give
my
docker
image
a
name.
I
call
notarios
bats.
B
A
B
It
again
right:
yes,
it's
there,
okay!
So
let's,
let's
start
again
as
I
said,
it's
a
dog,
it's
just
a
maven
pom
file
and
I
use,
one
of
the
smallest
Java
8
images.
I
could
find
and,
as
I
said,
this
is
pretty
important.
The
standard
port
fur
for
hezekiah
is
5
701,
that's
a
TCP
port
and
we
just
tell
docker
hey.
B
So
we
will
see
how
that
comes
together.
It
basically
installs
all
that
stuff
to
optical
cast.
Nothing
important
here,
I
think
so,
let's
build
this
first
and
I
actually
have
wonder
why
why
it
happened,
but
we'll
figure
it
out
come
on
clear,
so
what
we
actually
do
as
we
build
it
first,
that
is
when
the
door
lock
in
actually
builds
the
docker
file
itself
and
it
doesn't
work
for
oh
right.
It
can't
work
because
I
have
to
use
the
docker
terminal
to
I.
Remember.
B
A
B
Live
demos
are
fun,
yeah
life
demos
of
the
best.
Let's
try
it
again
and
I'm
pretty
sure
it
works
this
time
and
it
seems
like
wow
build
success.
I
knew
works
so
there
we
go.
That
means
we
build
a
new
image.
We
have
attack
for
latest
and
the
next
step
is
actually
to
tell
maven
or
be
the
doctor
plucking
and
maven
to
upload
it
docker
push,
and
that
will
be
interesting
because
I
have
no
idea.
If
that
is
going
to
work,
it
totally
does
not.
B
A
B
Looks
good
so
far
so
well,
I
think
we're
we're
on
the
safe
side
on
that
page.
So
what
I
actually
missed
various
ahead
cast
XML
and
that
is
where
you
configure
how
haze'
cast
will
do
IP
discovery
or
in
this
case
member
discovery,
so
what
we
or
what
I
did
for
for
openshift
and
kubernetes
itself?
There
is
a
hazy
cast
career
needed
discovery
strategy.
That
means
it
will
ask,
become
Anita's,
DNS,
service
or
rest
service
for
other
members
based
on.
B
In
this
case,
a
service
named
haze
cast
opal
shift
and
in
the
inside
the
namespace
default
and
I
think
that
might
might
be
wrong,
but
we'll
figure
it
out
I
think
the
namespace
is
actually
wrong,
but
we'll
see
so,
let's
keep
going
here.
I
downloaded
the
vagrant
file
for
open
shift
and
I
know
it's
not
the
right
one,
especially
I
think
it's
a
we
shift.
Webinar
I,
think
that
is
the
right
one.
Yeah
I
know
it's
not
the
latest
version.
B
So
that
will
be
quite
quick.
This
I
think
just
just
installed.
This
would
start
pinging
into
the
dark
local
docker
repository,
that's
at
least
what
I
understand
from
what
it
does,
but
the
next
step
will
actually
pretty
slow,
and
that
is
where
said
we
can
do
something
else
like
looking
up
a
question,
for
example,
so
mom
give
me
that
or
you
can
already
searched
for
question
I'm
just
going
to
to
write
the
second
line.
Whenever
that
one
is
finished
now
we
can
do
multitasking
so
that
works.
C
B
A
C
A
B
B
So,
yes,
you
have
to
write
it
on
your
own,
but
whenever
you
plug
it
into
hazel
castle
or
whenever
you
tell
hazel
custody,
is
that
everything
else
is
completely
transparent.
That
means
in
the
best
case,
there
is
one
or
two
people
in
the
company
that
actually
know
about
there's
some
magic
going
on
and
in
the
in
the
backend.
C
Noticed
that
the
the
discovery
method
you
were
showing
in
there
that
hazel
cast
crew
brunette
ease
discovery
strategy
yeah,
we
have
OpenShift
installations
in
two
different
data,
centers
and
they're
completely
different
installs,
but
a
single
application
might
be
deploying
into
each
of
those
environments
sort
of
for
dr
purposes.
So
if
one
of
the
data
centers
is
destroyed,
their
app
is
still
running
in
the
other
data
center.
Being
that
they're
completely
independent
OpenShift
environments.
C
B
C
B
Important
question
is
on:
how
do
you
configure
your
kubernetes
discovery
to
work
or
not
not
behave
discovery?
How
do
you
do
the
the
service
discovery
part
on
communities?
If
you
have
one
one
master
thing
for
for
that,
it
will
work.
If
you
have
completely
independent
data
centers,
it
won't
work.
In
this
case,
you
should
probably
anyways
use
Hayes
cast
van
replication
to
connect
those
two
independent
clusters.
B
We
have
Vaughn
replication
in
the
enterprise
version.
We
configure
the
different
endpoints
and
the
important
thing
is
as
thoughtful
the
reason
that
Hayes
Acastus
data
distribution
or
data
partitioning
you
don't
want
to
have
a
low
latency
connect
between
different
data
centers.
So
that
would
mean
that
statistically
half
of
your
data
is
in
the
other
data
center
and
your
whole
cluster
will
will
will
get
the
penalty
of
this
low
latency
connect.
B
B
I'm
now
in
B
and
B
OpenShift
vagrance,
based
of
JVM
JB
I'm,
a
virtual
machine,
so
I
used
vagrant
SSH
to
connect
to
this
to
this
internal
docker
image
and
I.
Do
a
sudo
su,
so
I'll
get
some
root
rights
because
I
now
doing
some
things
on
an
open
shift.
You
probably
all
know
and
probably
better
than
me
well
I,
lock
in
I,
lock
myself
in
and
to
be
hate,
the
open
shift,
configuration
and
I'm-
probably
not
doing
it,
be
a
right
way.
That's
the
only
way.
I
figured
out
actually
works
for
me.
B
So
if
you
know
better
just
interrupt
me
and
I'm
creating
a
new
project,
I'm
really
happy
to
figure
out.
There
is
another
way
to
create
a
new
project,
but
I,
don't
think
so,
though
I'm
creating
a
new
project
called
hezekiah
lustre
and
I'm,
actually
joining
that
one
or
switching
to
that
one
Hisle
cast
plaster
and
we're
now
working
on
the
head,
cast
lustre
project
and
now
comes
the
longest
part
and
I.
Think
I,
just
gonna
copy
that
from
my
slides,
because
I'm
lazy,
where
you
are
where
you
are
there,
you
go.
B
Perfect,
so
what
I
just
did
I
downloaded
the
openshift
phaser
cast
deployment
template
well
I'm,
not
a
VI
guy.
You
figure
that
out
so
be
the
openshift
or
behave
cast
template
configures
or
defines
how
hast
cast
works
in
openshift.
What
is
deployed,
what
what
images
most
specifically
and
I
think
the
image
is
somewhere?
Where
is
it
down
here?
So
there
is
V
hast
cast
openshift
latest
image
and
I
hope
that
active
will
work
the
other
way
around
down
low
in
a
bit.
B
It
also
configures
or
defines
a
couple
of
environment
variables.
We
see,
there's
a
service
DNS
and
service
name
and
namespace
and
a
couple
of
things
we
are
configured
per
default.
It
will
always
use
ipv4
to
connect
internally
and
I'm
honest
here,
I
never
tried,
ipv6
v6
so
far,
but
I
think
it
work
at
least
from
hazel,
caste
and
again
the
configuration
for
the
part.
B
We
know
that
there
is
only
just
one
is
a
caste
version,
or
instance
running
in
each
container,
so
that
works
apart
from
that
we
are
configuring,
a
replication
controller,
so
the
thing
that
actually
makes
sure
that
there
is
so
much
replicas
or
so
much
instances
of
this
hay-zu
cast
pot
running
that
we
just
deploy
pretty
full.
We
always
configure
or
always
deploy
free
instances.
You
can
happen
downscale
this
at
runtime
image,
I
think
that's,
basically
it
and
there
is
some
more
configuration,
especially
to
make
names
nice.
So,
let's
see
so
we
already
we
downloaded.
B
C
B
Far
and
what
I
actually
like
about
be
beggaring,
the
mermen
that
it
configures
proxy
country,
proxy
setups
for
chorus,
so
that
is
nice.
So
again,
it's
admin,
admin,
we're
just
logging
into
the
openshift
console
and,
as
I
said,
it's
probably
not
the
latest
version,
I
think
so
at
least
we
select
our
head
cast
project
and
what
we
do
is
we
just
say
we
want
to
add
something.
We
select
our.
B
If
everything
goes
right,
we
get
free
Potts,
but
actually,
hopefully,
connect
together,
as
I
said,
I'm,
not
higher
potential
because
of
the
of
the
namespace,
but
I
think
it
still
works,
we'll
figure
it
out.
So
it
holds
down
the
image
over
my
tethering
over
a
Europe
based
data
plan,
so
that
will
be
interesting.
B
B
Yeah,
well,
you
see
that
the
image
is
also
a
bit
older,
its
486
early
access
to
there's
free
six,
three,
six,
something
out
there.
We
go
so
here's!
Actually.
We
have
now
a
cluster
of
three
members
and
there
is
1705,
1706
and
1707.
If
we
look
back
here,
we
see
it's
five,
six
seven,
so
it
actually
kind
of
worked.
That's,
unfortunately,
not
very
readable
in
the
lock
file.
B
So
we
actually
got
a
highs
across
cluster
running
just
pretty
much
out
of
the
box
in
the
open
shift.
Now,
I
think
that's
interesting,
and
it's
it's
really!
Nice
I,
unfortunately,
don't
know
the
the
command
out
of
my
hat
right
now
to
start
another
one
to
to
scale
up
and
I.
Think
it's
not
in
this
bill
WI.
Yet,
unfortunately,
if.
A
B
B
Much
yeah
right,
it
will
also
be
view.
I
will
look
like
a
slightly
different
as
far
as
I
know,
right
now.
Well,
I,
unfortunately,
don't
remember
how
I
made
this
nice
version
of
the
lock
file,
but
we
saw
it
actually
works.
So,
let's
go
to
the
last
couple
of
slides
and
I.
Think
I
was
pretty
much.
It
I
think
it
was
easy
enough.
At
least
it's
not
as
easy
as
using
hey
Zika
stand
alone,
but
what
what
I
really
like
here
is.
It
makes
deployment
easy,
and
that
is
so
for
developers.
B
They
often
don't
think
about
the
employment
part.
That
is
where
you
we,
we
figure
out
that
DevOps
really
makes
sense.
So
you
have
to
you
in
the
past
we're
developers
and
we
had
infrastructure
team
or
operations
team.
Whatever
you
call
it
and
operations
doesn't
want
it
to
to
think
about
any
kind
of
exception,
so
you
get
a
Java
stack
trace
and
they
just
restore
it,
and
engineering
doesn't
want
to
think
about
deployment.
I
worked
fine
for
some
time
until
somebody
figured
out.
Well,
maybe
something
like
a
def
up
is
the
coolest
thing
to
do
so.
B
I
think
that
there's
this
combination
of
Hayes
cast
no
blue
shift
is
exactly
what
that
what
is
made
for
de
box
DevOps,
it's
easy
to
deployment
or
easy
to
deploy
and
hazel
cast,
is
also
easy
to
understand
for
people
that
are
not
working
everyday
with
it.
So
we
haven't
seen
any
Hozuki
source
code
yet,
but
you
can
really
believe
me.
It
looks
like
pure
Java
code,
so
for
me,
I
think.
That
is
what
what
we
have
to
be
happy
about.
B
I
think
we're
we're
in
a
good
in
a
good
situation
in
a
good
time
these
days
and
at
Jack's
a
year
in
Germany
or
here
in
Germany
I'm
in
Spain.
Right
now,
but
Jack's
in
Germany,
one
guy
from
from
get
up
gave
a
keynote
and
he
said
we're
going
forward
to
a
complete
new
millennium
off
deployment
and
his
daughter
is
writing
her
own
Minecraft
plugins
at
plugins
at
the
moment,
she's
running
her
own
server
and
he
said
when
she's
going
to
be
a
full-time
engineer,
a
full-time
DevOps
at
in
the
end.
B
At
some
point
she
doesn't
expect
to
have
those
long
hard,
rule-based
things
for
deployments
she's.
She
will
expect
it
to
just
click
and
run
pretty
much.
What
openshift
gives
us
and
what
is
amazing
for
haze'
cast.
One
more
thing.
Sorry
go
right
ahead:
okay,
one
more
thing
about
hazel
cast
the
main
version:
all
of
the
cool
features
are
apache
license.
You
can
go
to
github
and
just
download
it
send
pull
requests
and
issues,
or
hopefully
you
can't
file
issues.
B
You
probably
file
feature
requests
and
there's
also
an
enterprise
version,
but
I'm
working
on
the
community
side,
so
I
don't
care
about
that
one.
Besides
the
fact
that
it
pays
for
my
salary,
but
we're
not
talking
about
that.
So
that's
the
reason
why
I
normally
consider
Hayes
caster
or
to
be
the
first
place
for
developers
as
a
developer.
You
want
to
get
started
easy,
just
look
for
the
examples.
B
It's
really
bad
easy
as
it
as
it
seems
on
the
first
glance,
and
if
you
want
to
pay
us
some
money
and
work
from
my
salary
hate
castle,
comets,
it's
already
in
the
site's
down
there
and
I
think
everybody
should
be
able
to
just
go
for
the
whole
presentation
today
and
just
do
it
out
of
almost
out
of
a
box
at
least
so.
Do
we
have
any
more
questions
we'll
probably
have.
A
Wonderful
thing
to
share
that
with,
along
with
the
video
that
we'll
put
up
there'll,
be
a
blog
post
on
openshift,
blog
OpenShift,
calm
with
the
video
links
and
the
podcasts
slide
deck
rich
or
anyone
else.
If
you
have
questions,
please
feel
free
to
ask.
I
think.
We've
we've
really
covered
quite
a
lot
of
territory
here
at
this
morning,
and
I'm
really
grateful
for
you
taking
the
time
to
do
this.