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From YouTube: (OCB) Fireside Chat and Demo with Hazelcast
Description
The OpenShift Container Platform (OCP) is emerging as the most favored platform for deploying and managing applications in the cloud using containers. OpenShift is an ideal platform for hosting scalable applications, especially in a microservices architecture.
The Hazelcast In-Memory Computing Platform can support microservices in multiple ways in OpenShift, by providing distributed in-memory data, computation, and/or messaging capabilities. These can be implemented either as an embedded microservice component or as a client app to a Hazelcast cluster. Hazelcast increases the performance, resilience, and capacity of containerized applications running in OpenShift.
Along with our guest Scott McMahon, Technical Director of Americas and Partners, we will demonstrate how Hazelcast can be easily set up in OCP in minutes with just a few commands using the Hazelcast Enterprise PaaS image for OpenShift and we will also cover how to provide custom configuration, scale, and monitor the Hazelcast
A
A
A
A
A
Excellent
now
you're
at
hazelcast,
tell
me
about
tell
me
about
tell
me
about
you,
let's,
let's
find
out
who,
who
you
are
so
you're
the
technical
director
for
for
the
americas.
What
does
that
mean?
What
do
you?
What
do
you
do
with
the
company?
Why
are
you
so
so
interesting?
Why
have
I
read
so
much
terrific
material
about
you?
I
just
we
need
to
know.
A
B
I
am
the
technical
liaison
that
works
with
our
partners,
such
as
ibm
and
red
hat
and
others
to
make
sure
that
our
technology
is
implemented
correctly.
We
work
with
our
customers
and
and
use
things
to
get
the
most
value
for
you
know
for
for
the
software
that
people
buy
from
us,
so
I
came
over
so
a
little
bit
about
me.
B
I
started
my
career
as
a
software
engineer
in
silicon
valley
a
long
long
time
ago,
but
I
I
guess
I've
always
been
involved
in
analytics
and
I've
always
saw
the
the
capability
or
the
the
possibilities
around
what
these
computer
systems
that
we
were
building
over
time.
You
know
going
way
back
to
doing
websites
and
things
what
we
could
actually
do
with
them.
So
I
kind
of
followed
my
career
through
from
esbs
and
and
message
based.
B
You
know
that
kind
of
thing
that
we
did
through
bpm
and
the
analytics
space
and
and
now
I've
kind
of
come
full
circle
and
we're
back
to
actually
doing
what
I
think
of
as
business
processes,
but
doing
it
at
a
at
a
real-time
sort
of
speed
and
using
all
the
in-memory
stuff,
but
also
applying
the
the
technology
for
doing
the
analytics
and
being
able
to
understand,
what's
actually
happening
out
there.
So
that's.
A
B
A
little
bit
like
back
when
the
internet
was
something
for
academic
people
and
nobody
ever
heard
of
it
and
we
were
all
using
links
in
this
tim
berners-lee
came
up
with
this
thing
called
world
wide
web
that
he
called
it,
and
you
know
just
following
that
from
news
groups
on
on.
You
know:
unix
systems
all
the
way
up
to
now
we're
doing
e-commerce,
and
you
know
shopping
carts
online.
So
from
the
beginning
really.
A
That's
interesting.
You
brought
that
up.
I
remember
sitting
in
some
college
class
at
some
point
and
the
instructor
was
like
okay.
Well,
you
know
so
here's
your
assignment
tonight
and
then
he
wrote
something
on
the
blackboard
with
chalk
and
it
was
like
you
know:
seven,
nine,
three,
two
zero
blah
blah
blah
at
something,
something
and
he's
like
for
any
of
you
who
know
what
that
is,
you
can
send
your
homework
assignments
there
and
I
was
looking
at
it.
I'm
like.
A
A
B
B
We,
along
with
everybody
else,
really
started
seeing
the
shifting
of
of
how
that
the
data
that
our
systems
were
ingesting
like
how
are
we
getting
this
data
and
how
are
we
processing
it
and
the
things
that
we
do
with
it
and
it
was
changing.
You
couldn't
just
load
it
into
a
storage
and
then
come
back
and
and
do
some
analytics
later.
You
had
to
really
react
in
real
time
when
you
start
talking
about
you
know
mobile
devices
like
we
were
talking
about
multiple.
B
So
that's
where
we
started
looking
at
stream,
processing
and
and
going
back
talking
about
saying
you
know
going
way
back.
We
tried
to
do
this
10
years
ago
or
15
years
ago,
with
the
esbs
and
the
bpm
solutions,
the
business
process,
management
and
monitoring
whatever
they
wanted
to
call
it,
but
the
systems
just
couldn't
keep
up
now
with
the
in-memory
technology.
We
realized
we
actually
did
have
a
system
that
could
do
this,
so
we
can
do
a
million
events
a
second.
We
can
actually
look
at
all
these
individual
events
and
process
them.
B
So
that's
where
we
started
thinking.
We
have
the
perfect
base
in
memory
storage
solution,
but
we
need
to
build
an
engine
on
top
of
it
that
can
actually
handle
this
kind
of
workload
that
we're
seeing
and
that's
that's
where
we
started:
building
the
streaming
engine
that
leverages
the
scalability
and
all
of
the
underlying
in-memory
technology,
but
can
handle
these
new
workloads
these
new
things.
B
A
B
We
yeah
just
a
couple
years
ago.
It
was
80
we're
growing,
I
think,
as
as
our
customers
or
our
user
base
really
starts
to
see,
they
start
to
experience
these
things
that
they
need
to
do
like
you
know.
How
am
I
going
to
handle
this
we're
growing
fast
and
and
there's
a
lot?
It's
it's
really
fun.
I
I'll
just
say
that,
because,
like
we're
doing
things
where
you
know
some
of
the
top
companies
in
the
world,
we
get
to
go
work
with
them
and
enable
them
to
do
what
they're
doing.
B
A
They
would
go
into
like
a
big
bank
or
something-
and
you
know
you
know
they're
like
well,
you
know
you
know
it's.
You
gotta
explain
to
me
this
open
source
thing
and
you
know
how
come
you
guys?
Don't
have
blue
hair
and
and
skateboards
right?
Don't
you
all
buzzing
around
the
office
on
skateboards
and
it
was
like
impossible
to
sell
our
product
which
at
the
time
was
red,
hat
linux
nine?
A
This
was
before
we
came
out
with
our
enterprise
product,
and
you
know
we
just
spent
more
time
trying
to
defend
open
source
to
you
know.
Well.
Why
should
I
pay
you
for
something?
That's
free,
I
I
remember
you
know
being
in
in
goa,
not
goldman
morgan
stanley,
we're
up
in
the
you
know
the
99th
floor
or
whatever
in
new
york
city.
A
You
know
we're
in
a
board
room
and
everyone's
sitting
around
you
know
the
table
with
like
bottles
of
poland,
spring
water,
right
and
and
burn
bomb
goes
to
me
he's
like
well,
you
know,
okay,
great,
so
you
guys
have
presented
your
technology
blah
blah
blah.
Why
should
I
pay
for
something?
That's
free
and
I
looked
at
him
and
I
was
like
what
do
you
get
in
your
hand
like
you
got
poland
spring
bottled
water
and
everyone
in
the
room
was
like
yeah,
I'm
like
well?
Why
didn't
you
just
grab?
You
know
a
couple.
A
Five
gallon
buckets
and
you
know,
go
downstairs
and
go
to
the
hudson
river
and
fill
up
your
five
gallon
buckets
with
water
from
the
river,
bring
it
on
up
here
and
serve
it
to
people,
and
that
was
our
first
multi-million
dollar
deal
because
they
got
it
that,
like
you,
know,
yeah
your
technology
is
open
source,
but
it's
just
like
water.
You
know
you,
it's,
it
needs
to
be
refined,
it
needs
to
be
packaged,
distributed
marketed.
And,
frankly,
if
someone
gets
sick
drinking
your
water,
you
know
you
know
that
you
can
get.
A
You
know,
get
taken
care
of
so
that
that's
my
my
long
diatribe
of
what
it
was
like
way
back
when
what
kind
of
challenges
do
your
people
see
out
there,
given
that
you're
a
fairly
fairly
young
company,
you
know
and
we're
going
to
get
into
the
whole
ai
ml?
Is
ai
real
or
is
it
a
foul?
You
know
what
what
does
it
really
mean,
but
but
like
what?
A
A
A
A
But
red
hat
sent
a
couple
engineers
up
to
our
office
here
in
mass
to
make
red
hat
linux
5
work
on
the
alpha
platform,
so
they
ported
red
hat
linux
5
to
run
on
alpha
and
it
you
know
it
did,
and
souza
did
the
same
thing
and
debian
did
the
same
thing
as
well,
but
that
but
red
hat
linux
6
was
like
the
big
one.
That
was
the
one
that
like
really
started
to
really
kind
of
become
mainstream.
B
A
No,
it's
not,
and
I'll
and
I'll.
I
know
exactly
why.
So
when
red
hat
came
to
get
red
hat
linux
5
running
on
the
alpha
about
a
month
later,
the
the
guys
from
souza
came
from
nuremberg
and
it
was
jurgen
geck.
You
can
look
him
up,
he
was
the
cto
of
souza
and
it
was
jurgen
geck
and
he
was
like
a
rock
star
man.
A
I
was
like
there
in
a
lab,
you
know
with
machines
everywhere,
and
you
know
bricks
of
stores
and
and
like
jurgen
walks
in
with
like
four
or
five
engineers-
and
it
was
like
this-
there
was
like
this
electricity
in
the
room.
It
was
like.
You
know,
I
don't
know
like
john
travolta
just
showed
up,
you
know,
but
I
spent
four
or
five
days
with
them
when
they
were
porting
it
to
to
alpha.
B
There
you
go,
I
have
the
definition,
then
straight
from
the
horse's
mouth.
So
now
going
back
to
hazelcats
what
you
were
asking
me,
I
think
it's
it's
funny,
because
the
in-memory
data
grid
part,
you
know
that
was
all
about
performance
and-
and
that
was
in
my
mind,
a
tactical
solution.
You
were
dealing
with
people
that
were
already
building
their
applications,
they're
building
microservices,
whatever
they're
doing,
and
they
just
needed
to
make
it
faster.
B
This
is
the
benefits
of
doing
this.
This
is
this.
Is
the
business
value.
This
is
what
you're
going
to
get
you're
going
to
get
new
ways
to
do
things
that
you
don't
know
about
yet
and,
and
it's
going
to
give
you
advantages
or
or
even
just
handle
the
problems
that
you're.
Having
that
you
don't
know
yet
that
you
even
have
so
the
the
conversation
moves
strictly
from
tactical.
How
do
I
make
this
thing
faster
to
more
strategic?
B
What
is
the
value,
and
what
am
I
going
to
be
doing
in
the
future
as
these
things
grow
and
we're
only
going
to
see
it
grow
more
as
5g
rolls
out?
What
are
they
saying?
That's
40
gigabits
gigabytes
per
second,
like
it's,
it's
orders
of
magnitude
more
so
that
just
gives
developers
of
apps
and
sensors
and
all
these
different
things
more
bandwidth
to
work
with,
and
ultimately
all
of
that
comes
back
to
somewhere
that
you
have
to
handle
it.
You
have
to
process
it.
You
have
to
be
able
to
do
something
with
it.
A
You
know
their
products
tested
and
certified
on
the
red
hat
portfolio,
whether
it's
you
know
linux
or
ansible
or
openstack,
or
you
know
our
storage
platforms
or
you
know
openshift
or
whatever.
It
is
but
like
like
what
what
it
is
hazelcast
the
company
name
and
is
your
product
also
called
hazelcast,
so
they
I
see,
I
see
a
lot
of
companies
these
days
where
the
company
name
is
the
product
and
like,
unlike
you,
know,
red
hat,
we're
a
company,
and
then
we
have
products,
it's
like
red
hat
enterprise,
linux
or
openshift.
A
B
Of
that,
when
you
said
hazelcast,
you
could
either
be
talking
about
the
company
or
the
product,
it
was
just
hazelcast
when
we
started
building
the
streaming
engine
on
top
of
it.
We
originally
really
saw
this
as
two
different
things.
So,
even
though
it's
the
same
application,
the
stream
processing
engine
is
leveraging.
A
B
Of
the
the
underlying
in-memory
data
grid
technology,
we
sort
of
divided
that
and
we
called
it
hazelcast
jet,
and
then
we
renamed
in
a
very
creative
fashion,
the
in-memory
data
grid
to
in-memory
data
grid
imdg.
So
we
had
hazelcast
jet
and
hazelcast
imdg
they're,
very
different
capabilities,
but
they're
the
same
application,
but
it
was.
It
was
a
little
confusing
and
it
was
a
little
sort
of
just
inelegant.
I
guess
is
the
best
way
to
put
it
so
we're
actually
pretty
excited
now,
with
our
version.
B
Five
we're
having
a
major
version
coming
out
in
the
fall,
we're
pushing
that
all
back
together,
we're
putting
it
all
into
one
application,
even
though
it
really
kind
of
technically
was
and
we're
going
back
to
just
saying
the
hazelcast
application,
and
it
will
have
the
stream
processing
capabilities.
It
will
have
the
in
memory,
the
storage.
B
So
now
it's
just
capabilities
same
platform,
same
thing,
one
application,
seeing
where
java
application.
So
it's
a
single
jar,
no
dependencies,
you
just
run
it
start.
If
you
start
10
of
them,
they'll
all
join
together
and
create
a
big
one.
If
you
start
one,
it
can
run
on
a
raspberry
pi.
Actually,
it's
a
15
meg.
So.
A
Yeah,
so
let
me
ask
you
this:
if,
if
I
had
subscribed,
you
know
red
hat's
products,
I
would
say
you
know:
red
hat
makes
linux.
Linux
is
an
operating
system
that
allows
hardware
to
work.
So
people
can,
you
know,
do
computing.
You
know,
openshift
is
a
container
platform.
You
know,
red
hat
storage
is
a
storage
subsystem.
A
You
know
red
hat's
kvm
is
a
virtualization
technology.
What
it?
What
is,
what
do
your
products
do
without
like
the
the
suit
like
we're
gonna
get
there
but
like
without
the
super
deep
detailed
one
just
give
me
like
the
give
me
the
the
one
sentence
thing
about
what
it
is
because,
like
you
can't
find
that
on
most
companies
website,
you
know
they.
B
Hazelcast
is
an
all-in-memory
platform
to
for
high
performance
processing
of
data.
I
mean
in
a
nutshell,
we're
all
about
high
performance,
we're
all
in
memory.
We
process
events,
we
store
event,
you
know
store
data
and
all
that,
but
in
a
nutshell,
we
are
an
all
in
memory
platform
for
hype,
extremely
high
performance
data
processing.
If
you
want
to
put
it
that
way.
A
B
Yeah,
so
from
the
beginning,
that
was,
that
was
an
original
sort
of
requirement
for
the
design.
Is
it
had
to
be
scalable
so
hazelcast,
you
start
up
one
instance
of
the
application
it
starts
up.
It
runs
okay
on
the
hazelcast
node,
now
there's
discovery
mechanism.
If
you
start
another
one
and
it's
it's
flexible,
you
just
configure
it
whether
you're
on
cloud
or
on-premise
or
in
containers
and
kubernetes
and
the
different
things,
but
you
start
up
another
one.
They
will
discover
each
other
and
then
they
pool
up
their
resources,
both
ram
and
cpu
resources.
B
So
the
memory
is
handled
in
a
partitioning
scheme
so
that,
as
you
add
more
instances
of
the
application
more
nodes,
if
you
want
to
call
it
that
from
a
cluster
point
of
view,
you
grow
your
your
available
storage
and
you
grow
your
distributed
processing.
So
we
have
built
over
the
years
the
engine
of
that
handles
all
of
the
hard
part
around
the
distributed
processing.
How
do
I
you
know?
How
do
I
run
in
parallel?
How
do
I
coordinate?
How
do
I
do
my
locking
and
synchronization
all
of
those
things
as
a
developer?
B
You
don't
have
to
worry
about
it.
You
just
write
it
as
if
it's
a
single
application
and
then
the
system
handles
all
of
the
complex
part
about
how
to
do
that.
So
the
idea
from
the
beginning
was
that
it
had
to
scale
you
had
to
be
able
to
add
more
resources,
gives
you
more
storage,
gives
you
more
cpu
and
there's
theoretically
no
limit
now,
ultimately
you're
going
to
run
into
like
a
network
bottleneck
or
you
know,
saturation
type
of
issues,
but
we've
got
hundreds
of
nodes
running
with
terabytes
of
data
and
and
massively
distributed.
B
You
know
when
you
write
an
executor
or
a
processing
service.
The
system
runs
that
out
in
parallel
understands
how
the
data
is
distributed,
and
then
it's
it's
similar
almost
to
mapreduce
we're
not
really
like
mapreduce,
but
it'll
run
and
do
various
things
in
parallel
and
then
kind
of
shrink
back
and
then
ultimately
give
you
your
answer
and
the
streaming.
The
stream
processing
engine
does
the
same
thing
it
it
figures
out.
B
You
define
your
process
of
what
you
want
to
happen
or
how
you
want
to
handle
these
events,
but
the
system
takes
it
and
distributes
it
out
across
all
of
the
available
resources
and
uses
all
those
resources,
because
we
want
to
get
the
extreme
high
performance
as
much
as
we
can
get
so
we're
going
to
use
all
the
physical
resources
to
to
be
as
low
latency
as
high
throughput
as
possible,
but
also
the
engine
that
we
built
handles
the
complexities
of
that.
So.
A
By
the
way
we
are
live
on
youtube
and
twitch
and
facebook
and
a
bunch
of
other
places
right
now.
So
if
anyone
has
questions-
and
they
want
to
basically
what
I
like
to
call
stump
scott
wednesday
drop
them
into
the
chat
down
below
and
then
our
our
bots
will
magically
make
them
show
up
here
in
our
little
bridge.
But
it's
it's.
It's
officially
called
stump
scott
wednesday.
B
A
That
I
like
that,
yeah,
but
how
do
you?
How
do
you
maintain
state
like
before
red
hat?
I
worked
for
this
company
called
mission,
critical
linux,
no
one's
ever
heard
of
it.
We
ended
up
getting
bought
by
red
hat,
but
we
were.
We
were
building
something
called
convolo
data
guard,
which
was
high
availability,
high
availability,
clustering
for
linux,
and
we
required
shared
storage
and
in
the
shared
storage
you
could
have
multiple
nodes.
There
was
like
a
quorum
partition
where
the
different
nodes
would
vote
about.
A
Who
has
state,
and
so
you
had
to
have
at
least
three,
so
you
didn't
get
a
split
brain
like
you
couldn't
have
four
or
two
or
something
like
this.
That's
probably
like
super
old
technology.
Anyone
interested
you
could
go,
probably
go.
Google
convolo
data
guard.
You
probably
wouldn't
be
able
to
find
mission
mission
critical
linux
anymore.
We
became
red
hat,
but
whatever?
How
do
you?
How
does
it?
How
do
you
do
that
in
in
multi-cloud,
like
what
like,
where
is
state
maintained
if
you've
got
nodes
all
over
the
place
and
you're
doing
shared
memory?
B
That
that
is
a
very
interesting
question
and
it's
something
that
we
definitely
have
to
deal
with.
There
is,
if
you've
probably
heard
of
cap
theorem
cap
and
that's
where
you
get
into
the
differences
between
availability
and
consistency.
So,
yes,
we
have
to
deal
with
that.
We
have
simple
quorum
if
we
wanted,
if,
depending
on
what
the
sla
you
know,
what
the
use
case
is,
so
we
can
do
simple
forums
that,
for
most
common
use,
cases
is
sufficient
because
it
may
it
prioritizes
availability
over
consistency.
B
Now,
if
you
get
into
use
cases
where
you
do
have
to
require
a
a
iron
clad
level
of
consistency,
we've
actually
implemented
the
raft
protocol.
If
I
want
to
get
into
you,
know
those
kind
of
details
so
that
we
can
have
structures
that
are
shared,
that
guarantee
consistency
may
get
make
consistency
guarantee
so
that
isn't.
That
is
an
issue,
but
we
have
given
choices,
and
you
can
pick
you.
Consistency
does
come
with
a
performance
impact,
so,
as
a
user
you
can
pick
which
one
whatever
your
use
case
is
now.
B
If
you
will
that's
that's
the
new
kind
of
way
to
do
it,
where
you
wrap
things
together
and
then
you
have
automatically
compensating
for
failures
and
they'll
roll
back
and
do
these
things.
But
what
that's
going
to
give
us
is
a
way
to
handle
the
micro
services
type
of
architecture
where
you
can
put
things
together
and
wrap
them
in
a
saga
and
then,
if
something
goes
wrong,
you
roll
the
whole
thing
back.
It
compensates
for
itself,
but
it
gives
you
sort
of
a
transactional
nature
for
multiple.
B
You
know
for
what
is
the
word
when
you,
when
you
put
a
bunch
of
things
together,
I'm
forgetting
the
word
and
be
able
to
handle
that
so
it
is
then
stateful
microservices
type
of
architecture
and-
and
that
is
because,
from
the
beginning,
we've
had
that
storage
layer.
So
we've
had
the
ability
to
do
this,
whereas
street
pure
stream,
processing
engine
probably
doesn't
have
the
storage
layer
and
a
pure
storage
layer
probably
doesn't
have
the
stream
processing
engine.
B
B
B
But
we
only
stopped
there
that
was
45
nodes
and
that
was
running
up.
I
believe
it
was
on
aws
or
one
of
the
clouds.
I
don't
remember
which
one
it
was,
but
we
stopped
there
and
there
was
no
limit.
We
could
go
further,
but
it
just
seemed
like
a
good
round
number.
So
we
stopped
there
and
said
all
right.
A
Though,
like
what
do
you
get
you're
doing
a
billion
a
second
of
like
what
does
that
mean
like
if
I'm
a,
if
I'm
a
consumer
and
like
how
does
that?
How
does
that
benefit
me?
A
customer
like
a
billion?
What
like
what?
What
like?
Okay
great,
I
mean
a
billion,
you
know,
requests
a
billion
messages
like
what
like
what
is
it
so.
B
I
I
was
kind
of
thinking
the
same
thing
when
we,
when
we
got
that
out,
I
thought
well,
nobody
has
a
billion
events
a
second.
We
do
a
lot
of
credit
card
transaction
processing
and
we
do
fraud,
detection
and
we
apply
all
the
stuff
and
I
think
really,
even
even
with
the
largest
we
pretty
much
most
credit
card
transactions
touch
hazelcast
in
some
way.
I
don't
know
who
I
can
name
or
not
name,
but
you
know
even
the
largest
one
like
I
know
I
can
say
cap
capital,
one,
you
know
they.
B
They
use
us
for
their
fraud
detection.
They
might
do
30
000
a
second
something
like
that.
So
these
use
cases
I
thought.
Well,
you
know
it's
it's
just
a
fun
kind
of
academic
thing
really,
but
now,
actually,
now
that
it's
come
out
there
are,
I
again
so
there's
some
some
streaming,
video
processing
sites
that
you
go
to
to
watch
movies
and
things
like
that
they
do
have
billions
of
events
a
second
in
in
their
events.
These
are
requests
replies.
B
You
know,
searches
for
movies.
You
know
whatever
the
the
command
to
start
the
streaming
sort
of
thing.
They
really
are
they're
worldwide
and
they
are
using
us
now
and
testing
for
use
cases.
We've
never
seen
so
what
it
gets
you
as
a
consumer
is
that
you,
whether
it's
a
mobile,
app
or
a
web,
you
know
browser
or
an
application.
B
It's
going
to
perform
in
what
I
think
of
as
more
like
human
scale,
where
you
click
on
something
you
want
an
answer.
You
you
send
a
query,
you
don't
want
to
wait,
it
shouldn't
take
10
seconds
and
then
finally
come
back
where
you're
you're
you
know,
should
I
did.
I
click
it
right
or
maybe
I
need
to
click
it
again,
and
then
you
open
two
things
you
want
to
have
real-time
interaction
or
the
ability
to
process
these
events
in
the
time
frame.
That
is
important.
B
It's
kind
of
you
know
it's
across
the
board.
It
depends
on,
if
you
mean
your
user
satisfaction,
or
do
you
mean
the
ability
to
react
in
a
time
frame
where
I
can,
I
can
make
money
and
take
advantage
of
the
situation.
You
know
provide
a
service
that
I
wasn't
able
to
provide
before
those
types
of
things.
A
B
We
truly
are
a
global
company.
The
original
founders
were
actually
in
turkey,
but
the
company
they
came
over
to
the
us,
incorporated
in
2010
in
silicon
valley,
so
we're
you
know:
we're
palo
alto
san
mateo.
Now
we've
moved
from
palo
alto
to
symptom
because
our
office,
our
office,
was
too
small.
So
we
had
we
outgrew
that,
but
we're
global.
We
have
still
a
large
engineering
team
in
turkey.
We
have
some
in
london,
we
have
some.
A
B
A
Off
again,
it's
103
degrees.
Here
it's
100
excuse
me
106
here
just
north
of
boston
and
it
and
I
went
to
turn
on
my
air
conditioner
for
the
first
time
in
two
years,
because
usually
I
don't
need
it
and
it
was
out
of
freon
because
there's
been
there
was
a
link,
a
leak
in
the
line,
so
thankfully
they
were
able
to
come
and
fix
it.
But.
B
A
B
A
A
You
know
if
you
talk
to
any
company
that
makes
like
a
service
mesh,
you
know
tool
or
offering
like
that.
It's
like
microservices,
microservices,
microservices
and
everyone's
all.
Like
you
know.
This
is
where
it's
going
to
go.
We've
got
to
have
a
service
mesh
to
manage
all
these
things,
because
the
containers
are
getting
so
tiny
and
you
know
50
90
000
of
these
things,
but
you
know
when
I
talk
to
some
people,
they're
actually
saying
the
containers
are
getting
bigger
yeah.
A
B
That's
actually
a
very
common
use
case
for
us
and-
and
the
reason
is
that,
especially
if
you're
a
java
technology
since
we're
java,
you
can
actually
embed
hazelcats
directly
in
your
service
and
and
if
not,
you
can
use
a
client
and
we
say
that
we're
polyglots.
So
we
have
clients
that
talk
to
the
cluster
and
all
the
major
languages,
so
so
either
way.
B
What
we
typically
are
used
for
is
that
we
provide
a
very
easy
common
data
storage
access
layer,
so
in
all
of
that
is
shared
in
the
microservices.
So
if
one
microservice
does
something
that
inserts
data
or
modifies
data,
that's
immediately
available
to
any
other
microservice
on
a
different
container
that
might
call
into
that
and
fetch
that
data
or
request
that
data.
The
way
we
do,
that
is,
that
memory
partitioning
scheme,
because
the
what
we
say
that
we
are
our
nodes
or
our
clients
are
smart
and
what
that
really
means
is
each
one
of
them.
B
B
The
data
access
time
and
latency
will
be
linear,
because
it's
one
network
hop.
So
you
don't
have
to
go
from
one
to
another
to
another
and
go
find
something.
Everyone
knows
exactly
where
that
data
is
so
when
a
request
comes
in,
even
if
it's,
if
it's
three
nodes
or
300
nodes,
it's
still
one
network
hop
across
the
switch,
gets
the
data
fetches
it
back,
so
you
can
theoretically
grow
as
big
as
you
want
and
still
have
that
linear
consistent
access
time
now,
as
far
as
the
containers
getting
bigger.
Yes,
they
are.
B
I
agree
with
that,
but
that's
just
bloat.
That's
just
people
not
really
building
those
containers,
as
lean
as
they
want
I've.
Seen
now,
I've
seen
some
of
them
get
to
be.
You
know
a
gigabytes
in
size
because
they're
not
getting
like
terabytes
in
size
because
right,
not
terabytes.
Well,
you
know
just
big
because
everybody.
B
Yeah
and
it's
too
big,
that's
not
what
they're
made
for
and
that's
just
you
know
we
can
do
our
part
and
give
fast
access,
but
ultimately,
a
lot
of
that
container.
Bloat
is
just
around
discipline
of
of
your
development
processes,
but
but
we
do
give
you
the
easier
way
to
handle
all
of
that
back
data
plane
and
you
don't
have
to
do
it
yourself,
because
that's
the
hard
part
consistency.
B
How
do
I
access
the
data?
How
do
I
guarantee
you
know?
The
changes
are
reflected
immediately.
All
those
types
of
things
with
with
you
know
parallel
and
concurrent
systems.
That's
what
we
built,
so
we
built
that
engine
to
handle
all
that.
So
all
you
really
have
to
do
is
say,
get
put,
and-
and
that's
it
you
don't
have
to
worry
about
the
details.
A
So
what
about
what
about
public
cloud?
There's
a
lot
of
companies
out
there.
I
forget
everyone's
doing
studies
on
resource
allocation
and
in
public
cloud,
and
you
know
a
lot
of
the
a
lot
of
the
reports
that
I've
seen
from
companies.
I
don't
know
if
I
can
name
their
names
or
are
that,
basically,
when,
when
customers
are
running
workloads
in
public
cloud
they're
over
allocating
resources
like.
A
B
Well,
I
hope
we
don't
exacerbate
the
problem.
I
think
I
think
we
do.
We
would
help
because
we
offer
that
shared
data
plane
and
you
can
be
much
more
concise
in
the
data
that
you
store
and
you
don't
have
to
have
copies
and
make
distributed
types
of
things
now.
On
the
other
hand,
we
do
enable
easily,
you
know,
add
more
containers,
because
we
make
it
very
easy
to
do
so
again.
B
Again.
I
think
you
just
go
back
to
kind
of
your
engineering
discipline
in
that
you
need
to
have
a
good
plan
and
make
sure
you
control
your
resources,
but
I
think
hazelcast
would
give
you
actually
now
that
I
think
about
it.
It
would
probably
give
you
help
you
with
using
less
resources,
because
you
wouldn't
need
to
add
a
storage
layer.
You
would
have
hazel
cast
directly
in
your
applications.
B
It's
shared,
it
can
grow
and
shrink
independently
of
what
your
applications
are
depending
on
your
storage
needs
and
your
processing
needs,
but
it
could
be
more
concise
and
and
tighter
on
the
storage.
But
again
you
know
you
can
have
the
best
tools.
It
really
comes
down
to
to
adhering
to
your
discipline
and
engineering
best
practices,
but
I
think
we
could
help.
A
Okay,
well,
that
was
as
close
as
I
can
get
to
stump
scott
wednesday
when,
when
we
were
talking
before
we
went,
live
and
by
the
way
we
are
live,
hello,
everyone.
You
said
something
about
a
you
know
like
the
terms:
ai
and
ml,
ai
and
ml
ai
and
ml.
Oh,
we
do
ai
and
ml.
This
is
like
been
like
the
buzzword
bingo
for
at
least
three
years,
and
you
said
something
when
we
were
talking
before
you
know.
The
camera
went
live
that
like
ai.
I
thought
what
word,
what
did
you?
A
B
And-
and
I
probably
maybe
that's
not
the
right
way
to
say
it-
it's
not
science
fiction,
it's
actually
really
there.
The
question
is:
how
do
you
put
it
into
production?
How
do
you
actually
use
it?
So
when
you
start
talking
about
ai
machine
learning,
these
different
algorithms,
you
go
all
the
way
back
to
the
60s.
You
know
in
the
labs
at
the
at
the
colleges.
They
were
developing
these
these
algorithms
and
these
methods
for
for
doing
intelligent
type
of
decision
making,
but
it
was
stuck
in
the
lab.
B
You
couldn't
get
it
out
of
there
and-
and
I
would
say
in
like
the
last
10
15
years
now
you
have
companies
that
are
coming
out
with
tools
that
mean
you
can
you
can
build
models?
You
can
build
these
machine
learning
models
and
you
don't
need
a
phd
in
data
science
to
do
it.
You
can,
you
know
they're
automated,
I
don't
know
who
I
can
mention,
but
like
h2o
and
data
robot,
and
all
these
guys,
you
know
they're
giving
the
tooling
for
the
average
person.
B
B
So
we
that's
where
we
are
focusing
we're
building
with
our
streaming
engine.
If,
if
I
can
go
a
little
bit
into
the
technical
details,
we
built
it
on
the
concept
of
a
dag,
a
directed
acyclic
graph,
that's
what
spark
uses,
and
so
it's
a
pretty
you
know
easily
proven
type
of
technology
to
control
and
manage
the
process
that
you're
building.
But
we
want
to
enable
the
injection
and
usage
of
these
models,
we're
not
trying
to
be
a
data
science
tool,
we're
not
trying
to
be
a
training
tool
or
anything
like
that.
B
We
just
want
to
say
all
right:
you
go
build
your
models
and
whatever
you
want,
whether
it's
python
c,
plus,
plus
or
java,
you
know,
but
those
are
the
kind
of
the
three
main
you
export
this
model
and
we
will
ingest
it
and
then
we
will
call
it
and
we
will
run
it
and
that's
where
I
was
saying
the
science
fiction
ideas
like
and
that
you
know
that
is
pretty
provocative.
But
it's
really
like
you
can
talk
about
ai
everybody
talks
about
ai.
They
just
want
it
on
their
web
servers
like
blockchain.
B
You
know
just
get
it
on
your
website
so
that
you
know
you
can
get
robuckets
or
something.
But
how
do
you
actually
put
that
in
production
it's
there?
But
how
do
you
use
it
and
that's
where
we
really
saw
ourselves
as
being
able
to
to
implement
this
and
be
able
to
run
these
things
in
production?
You
know
like
fraud,
detection
on
credit
card
scores
we're
doing
that
we're.
Actually
we
have
a
most.
You
know,
like
I've
said
most
credit
card
transactions
touch
hazelcast
in
some
way,
so
transaction
might
come
in.
B
You
know
the
user
swipes
the
card.
Generally
speaking,
you've
got
a
one
second
sla
from
card
swipe
to
either
approval
or
decline,
and
that's
going
through
all
the
network
goes
through.
The
processor
goes
all
the
way
to
the
issuing
bank,
the
bank
issues
the
money
comes
back.
That's
that
whole
thing,
so
your
fraud
detection
generally
you've
got
about
50
milliseconds
to
pull
that
off
and
what
we've
seen
that
we
could
do
with
our
models
and
not
just
data
science
models.
B
I
think
I
can
use
the
capital
one
example
in
that
they
saved
something
like
100
million
dollars
a
year
in
in
not
only
just
detecting
better
fraud
and
declining
fraud
transactions,
but
also
false
positives,
not
getting
false
positives,
because
if
you
score
something
as
fraud
and
it's
not-
and
you
deny
it-
then
the
user
is
just
going
to
pull
out
another
card
and
use
it.
So
you've
lost
that
revenue,
so
you
know,
you've
got
to
you've,
got
to
have
a
better
way
to
to
detect
fraud
and
also
not
make
mistakes
in
in
declining.
B
A
Okay,
now
you
guys
have
been
working
with
us.
The
reason
why
you're
on
the
show
today
is
not
because
we,
we
think
you
know
we
think
you're
nice
and
we
like
your
flower
arrangement
there
on
your
bureau
in
the
background.
But
you
know
our
engineers
have
been
working
with
the
hazelcast
team
for
quite
some
time
to
test
and
certify
your
solution
on
red,
hat,
openshift
and
and
other
products.
But
let's
be
realistic.
Openshift
is
you
know,
certainly
you
know
the
one
of
the
future
technologies
for
us.
A
Your
software
you've
built
an
operator
which
is
sort
of
like
an
artificial
intelligence
agent
for
managing
and
and
and
orchestrating
your
technology
along
with
openshift.
You
folks
are
in
the
red
hat
marketplace.
People
can
go
to
the
marketplace
and
buy
you
can
download
your
operator
from
our
registry,
and
you
know
I
just
wanted
to
throw
a
plug
in
there
that
this
is
all
about.
A
You
know
improving
the
the
supportability
of
the
day,
two
supportability
of
of
for
customers
when
when
they
want
to
use,
you
know
your
your
products
along
with
ours,
that
was
just
a
gratuitous
plug
there.
For
us,
we
have
11
minutes
left
and
I
know
that
you
know
people
probably
are
like.
What's
the
architecture?
Look
like
scott
like
how
does
this
work?
Look
like
it's
great,
you
know
you
guys
haven't
been.
B
B
Yeah,
well
maybe
one
day,
but
as
it
stands,
I
like
competition,
some
are
better
than
others,
so
you
know
we
have
these
applications
competing.
They
had
features
zoom
added
the
virtual
background.
Now
everybody
has
virtual
backgrounds
and
it's
it's
been
pretty
fun
so
to
really
to
talk
about
the
architecture
a
little
bit.
B
This
is
a
sort
of
a
conceptual
idea
of
what
it
is
we
are
in
in
essence,
though,
a
cluster
or
grid,
as
we
say,
and
the
reason
that's
important,
is
because
we
engineered
that
from
the
beginning
for
the
for
the
storage,
so
all
they
do,
they
have
to
just
have
a
way
to
to
find
each
other.
So
when
say,
you
have
a
three
node
cluster
running
and
you
want
to
add
a
fourth
one
when
that
fourth
one
comes
up,
it
just
needs
to
have
a
way
to
find
it
and
find
the
other
one.
B
We
can
run
you
know
directly
ip
or
we
interact
with
all
the
orchestration
engines,
kubernetes
and
openshift,
and
you
know
all
the
things
that
that
you
would
expect,
but
once
they
once
they
find
each
other
once
the
new
one
finds
the
cluster
it
just
opens,
a
direct
socket
connects,
they
share
information,
the
topology
is
sort
of
distributed,
and
then
then
that's
it
that's
how
it
joins
and
as
a
as
a
client
or
or
you
know,
however,
you
want
to
access
the
cluster.
You
do
the
same
way.
You
just
you
just
open
a
you
know.
B
The
client
opens.
The
socket
has
direct
access.
What
this,
since
this
is
the
way
we
engineered
it
from
the
beginning.
It
really
fits
all
of
the
containerization
ideas
that
have
come
along
later.
We
didn't
actually
build
this
thinking,
kubernetes,
because
kubernetes
didn't
exist
or
openshift
because
it
didn't
exist,
but
it
fits
the
way
that
those
scalable
resources
work,
because
that's
what
we
wanted
to
build
our
platform
at.
B
So
what
you
see
here
on
the
screen-
this
is
sort
of
euphemistically
referred
to
as
the
the
north
south
west
east
diagram,
and
what
it
really
is
is
that
we
have
that
distributed
data
storage
layer,
which
is
what
we
call
memory
or
store
data
at
rest.
If
you
will
that's
that's
where
we're
actually
putting
it
into
memory,
it's
high,
it's
highly
available,
easily
accessed
and
high
speed,
but
it's
stored,
and
then
on
top
of
that.
A
B
A
B
I
would
say
it's
it's
mostly
nosql
and
by
the
way
people
think
nosql
means
no
sql.
It
actually
stands
for
not
only
sql,
so
we
we
are
an
object
store
at
our
core,
so
we've
always
been
that
and
that's
because
we're
about
performance.
So
if
you
have
a
normalized
schema,
then
every
object
access
has
to
be
reassembled
and
that's
why
schema
database
databases
are
better
for
reporting
and
analytics
and
then
object
stores
are
better
for
performance.
So
you
know
there's
trade-offs
between
the
two.
B
Now
we
have
actually
added
a
sql
layer
engine
on
not
only
on
the
storage
layer
where
you
can
use
sql,
ansi,
sql
and
and
interact
with
the
objects,
and
we
we've
built
in
a
parser
and
all
the
stuff
that
you
need
for
for
that
kind
of
access.
But
that
is
an
object,
store.
We've
also
built
what
we
call
streaming
sql
to
attach
at
any
one
of
the
points
of
a
what
you
see
there
that
graph,
that's
basically
a
representation
of
the
dag.
B
I
was
talking
about
the
directed
acyclic
you're,
going
to
attach
a
sql
query
at
any
point
on
there
and
what
we
call
streaming
sql
and
then
get
a
continuous
update
of
the
events.
You
know
from
a
sql
language,
but
and
with
a
sql
type
output
of
events
streaming
through.
But
you
know
to
go
back
to
it
that
that
entire
middle
box
is
the
hazelcast
application
and
it
brings
that
storage
layer.
B
You
know
what
we
used
to
call
in
memory
data
grid,
but
now
it's
you
know
just
distributed
memory
database
and
then
the
streaming
engine.
On
top
of
that,
that's
that's
the
hazelcast
application,
and
that
brings
all
of
those
capabilities
to
to
be
available
for
your
for
your
storage
and
your
and
your
processing.
A
Okay,
well,
thank
you.
Your
marketing
folks,
you
know,
were
sending
me
emails.
They're,
like
hey,
you
gotta,
make
sure
you
can
drop
your
slide
down
now.
If
you
wanted
to
okay,
you
know
your
marketing
folks
were
like
hey.
You
gotta,
make
sure
you
talk
about
now.
I'm
going
to
read
this
because
I
was
trying
to
you
know
be
the
the
good
person
here.
The.
A
B
I
think
we
have
what
what
they're
really
referring
to
is
just
that
the
the
landscape
is
changing
the
landscape
of
how
we're
processing
data,
how
we're
doing
things
is,
is
going
to
change
it's
not.
You
can't
store
something
in
a
in
a
persistent
storage
and
then
come
back
overnight
and
do
do
queries
or
analytics,
or
you
know
whatever
we
the
way
we
used
to
do
it.
You
have
to
do
it
in
situ.
You
have
to
do
it
as
you're
as
you're
running.
B
If,
if
you
don't
have
the
performance-
and
you
can't
keep
up
with
it
in
real
time,
storing
it
and
trying
to
do
it
later,
you're
never
going
to
keep
up
with
it
at
that
point,
so
I
think
really
for
us
the
way
that
we're
seeing
things
work
has
fundamentally
shifted
over
the
last
four
or
five
years,
and
it's
just
it's
just
going
to
continue
to
grow
and
continue
to
move
in
that
direction.
Where
you
just
have
to
have
systems
that
can
interact
and
and
work
in
a
real-time
manner.
B
You
can't
wait,
you
can't
you
can't
store
and
wait.
It's
just
you
just
can't
do
that
anymore.
So
that's
really
where
we
see
the
whole
fundamental
shift
of
of
how
things
operate
and
it's
got
to
be
distributed.
There
was
so
if
you
go
way
back,
there
was
a
woman
named
grace
hopper
and
she
was
a
data
scientist
in
the
mer.
B
A
A
Had
a
I
had
a
trio,
you
remember
the
trio,
oh
yeah,
it
was
it
kind
of
had
like
a
keyboard
on
it.
It
was
a
phone,
but
it
had
a
full
keyboard.
Like
kind
of
like
the
blackberries.
Do
I
guess
or
something
or
I
don't
know,
anyways
you're
right,
the
phones
certainly
have
I
just.
I
just
hope
they
don't
keep
getting
any
bigger.
I
don't
want.
A
I
don't
want
to
have
to
wear
a
hip
holster
to
carry
a
phone
and
like
yeah
I
mean
I
work
with
hundreds
of
you
know,
propeller
heads
and
they're
all
walking
around
the
building
and
they're
like.
Oh,
my
god.
I
can't
believe
you
haven't
rooted
your
phone,
I'm
like
dude.
I
don't
want
to
root
my
phone,
I
want
it.
I
want
to
be
able
to
text
and
make
phone
calls,
and
you
know
I
don't
care
about
rooting,
my
phone
anyways.
We
we
are
out
of
time,
so
we'd
love
to
have
you
back
again.