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A
A
A
A
A
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A
A
This
is
so
that
any
filtering
that
happens
will
resort
result
in
less
network
traffic
and
more
savings,
for
example,
stream
inputs,
source
connectors
and
producers
will
apply
smart
modules
before
sending
data
to
the
fluvio
cluster
and
stream.
Outputs
such
as
sync,
connectors
and
consumers,
or
the
fluvio
cli,
will
upload
smart
modules
to
the
fluvia
cluster
and
process
data
before
the
stream
is
sent
over
the
network.
A
We're
going
to
take
a
second
and
give
provide
sort
of
a
reference
architecture
for
the
before
and
after
kafka
versus
fluvio.
Kafka
is
a
one
of
the
most
popular
data
streaming
open
source
platforms
in
the
world,
and
we
wanted
to
just
compare
what
a
reference
architecture
looks
like.
So
in
this
kafka
versus
fluvio
diagram,
you
will
see
the
complexity
of
managing
the
various
clusters
to
build
an
end-to-end
pipe
pipeline
with
kafka.
A
A
A
They've
estimated
the
traffic
volume
would
require
22
000
partitions.
Excuse
me
to
run
their
network
of
users
for
an
average
traffic
volume.
Kafka
needs
one
gigabit
of
ram
per
partition
with
fluvio.
A
partition
requires
50
megabits
of
ram
by
moving
to
fluvio
they're
able
to
achieve
a
cost
savings
of
over
forty
one
thousand
dollars
per
month.
A
B
B
Okay,
in
this
scenario,
I'm
gonna
store
some
user
accounts
and
they're
gonna
have
phone
numbers
and
social
security
numbers,
and
we
wanna
omit
the
social
security
number
and
mask
the
phone
number,
and
for
this
I'm
going
to
be
using
our
infineon
cloud
and
you're
able
to
perform
these
same
steps
by
signing
up
for
a
free
account.
But
for
the
demo
I'm
going
to
use
my
demo
account.
B
B
Okay,
I
understood
fluvio
produce
to
the
user
accounts
topic
and
the
sample
data
file.
You
see
above
okay,
I'm
going
to
switch
back
over
to
the
web
ui
and
in
the
records
tab
for
the
user
accounts
topic
you're,
going
to
see
that
what
I
just
produced
and
I'm
going
to
write
my
smart
module
in
the
editor.
Smart
module,
editor
tab
right
here
in
the
web
ui
and
the
web
ui
currently
only
supports
writing
in
wrestling,
but
other
language
support
is
planned.
B
And
here
I
need
to
write
my
map
function.
That's
going
to
do
the
transformation,
I'm
going
to
be
pasting
code
snippets
into
the
editor,
to
save
some
time
during
this
demo
and
that's
why
I'll
point
out
that
flovio
records
or
just
arbitrary
bytes
allows
you
to
store
any
kind
of
data
that
you
need
in
this
case
we're
using
json
and
we
need
to
deserialize
it.
So
first
thing
I'm
going
to
do
is
define
a
struct
here,
user
account.
B
That
represents
the
data
that
I
just
produced
earlier
and
we
need
to
bring
in
a
serialization
library
in
wrestling.
We
have
sergey,
which
is
the
most
popular
one,
and
then
we
need
to
decorate
the
struct
with
the
deserialized
procedural
macro,
and
then
we
also
need
some
modules
from
and
types
from,
the
fluvia,
smart
module
library
and
then
we're
just
going
to
define
our
function
and.
B
Okay-
and
the
first
thing
we
need
to
do
is
actually
do
the
deserialization
in
this
function,
so
we're
gonna
do
serialize
and
store
the
record.
The
account
deserialized
account
in
this
account
variable
of
type
user
account.
This
is
the
struct
we've
defined
above
we're
just
using
certain
json
or
passing
in
a
reference
to
the
record
value.
B
Okay,
and
now
we
want
to
create
here,
is
a
redacted
version
of
the
account
and
we're
just
going
to
define
this
as
a
directly
like
a
json
object.
It's
going
to
have
full
name,
email
phone
and
we're
going
to
omit
ssn,
because
we
don't
want
that
to
be
in
the
output,
and
we
also
want
to
mask
the
phone
number.
So
we
need
to
define
another
variable.
That's
going
to
hold
the
last
four
of
your
phone
number
and
we're
going
to
do
this.
B
The
quick
way
we're
going
to
take
the
phone
number
from
the
account
reverse
split
on
the
hyphen
and
then
just
get
the
first
item,
which
will
be
the
last
four
of
the
phone,
and
we
need
to
update
the
the
json
output
to
instead
use
format
it
like
this,
where
all
the
stuff.
That's
been
masked
out
is
replaced
with
asterisks
and
we
and
we're
going
to
inject
the
last
four.
B
So
look
how
you
expect
okay
and
then
all
we
need
to
do
is
serialize
it
back
into
a
json
string
and
we're
using
certainty
again,
and
then
we
just
need
to
return
bytes
from
this
function
right,
so
we're
going
to
convert
that
string.
If,
since
the
output
of
this
function
is
record
data,
it's
go
and
by
calling
dot
into
on
the
json
string,
it's
going
to
automatically
convert
converted
to
bytes
and
that's
what's
going
to
be
sent
out
from
this
function,
and
I
think
that
should
be
it.
B
And
so
let's
apply
this,
and
what
that's
going
to
do.
Is
it's
going
to
compile
this
code
using
the
rust
compiler,
but
in
our
cloud
back-end,
so
that
usually
takes
a
few
seconds
at
least
and
yeah,
and
here
you
see
that
the
the
data
has
been
transformed.
So
let's
look
at
it
again,
so
this
is
what
went
in.
I
guess
I
can
actually
show
you
that
in
the
records
tab.
B
B
B
You
can
see
as
a
producer
the
consume
was
active
and
with
the
smart
module
automatically
transformed
everything
I
just
produced,
but
to
kind
of
give
you
a
better
feel
of
this
happening
that
this
actually
happens
real
time.
I'm
going
to
do
this
in
slow
motion,
so
I'm
going
to
use
a
no
jazz
script
that
I
wrote
that's
going
to
produce
records
slowly,
so
you
can
kind
of
just
better
visualize
what's
happening.
B
A
B
A
B
B
I
think
you
went
over
it
earlier,
so
we
also
support
filter
and
arraymap
and
aggregate
and
filter,
and
I
can
just
quickly
explain.
Those
like
filter
is
just
where
you
have
right.
Logic
to
make
a
decision
is
whether
to
consume
this
record
or
not,
and
aggregate
is
a
little
more
powerful
allows
you
to
look
at
multiple
records
in
the
stream
and
then
aggregate
some
sort
of
value
based
on
it.
Much
like
in
relational
database
systems
with
aggregate
functions.
A
Awesome,
I
don't
see
any
other
questions,
so
I
would
just
like
to
let
the
audience
know.
You're
welcome
to
go
to
www.infinion.com.
There's
a
try
now
button.
If
you'd
like
to
start
your
own
infineon
cloud
account
and
test
out
the
real-time
data
streaming.
This
is
you
know
this
is
the
most
modern
platform
available
written
on
rust.
As
you
can
see,
the
the
performance
metrics,
the
memory
metrics
are
are
far
better
than
any
other
data
streaming
platform
out
there
and
we're
really
excited
to
to
show
this.
A
So,
if
anybody's
available
on
march
24th,
we
have
our
kafka
versus
fluvio
event
streaming
for
high
performance
data
pipelines,
webinar
feel
free
to
register.
For
that
we
also
have
some
great
content
in
the
handout
section,
and
we
appreciate
you
joining
us
for
the
webinar
today
and
this
concludes
our
session.
Thank
you.
So
much
bye.