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Description
Don’t miss out! Join us at our next event: KubeCon + CloudNativeCon Europe 2022 in Valencia, Spain from May 17-20. Learn more at https://kubecon.io The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
A New Generation of NATS - Matthias Hanel & Jean-Noël Moyne, Synadia
NATS is a high performance cloud native distributed communications utility that allows you to build globally available and secure applications based on streams and services that are both fast and simple to operate. In this talk you will learn about all the numerous new features that have been introduced NATS version 2 and more specifically in the last few months and with the introduction of JetStream a new distributed persistence layer that enables NATS to offer new higher qualities of services and functionalities such as streaming. You will also see a demo showcasing the flexibility of the NATS Adaptive Edge Architecture including how it can be combined with the new functionalities enabled by JetStream to offer continuous and guaranteed publication of data on streams even during network failures.
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Now,
if
you
were
to
look
at
other
messaging
solutions
out
there,
you
would
find
that
they
only
cover
parts
of
all
these
features,
and
some
of
these
features
are
also
not
covered
by
any
now
in
practice.
What
this
means
is
that
you
end
up
with
a
patchwork
of
various
messaging
systems,
covering
different,
somewhat
overlapping
sets
of
features
that
are
completely
independent
from
each
other
and
therefore
not
integrated,
whether
it
is
for
security
ability
to
exchange
data
between
them
ability
to
apply
policies,
etc.
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One
thing
to
understand
about
security
in
the
context
of
nuts
is
that,
although
you
could
manage
individual
end
users
with
it
most
of
the
time,
because
nuts
is
a
middleware,
users
are
actually
applications.
So
you
want
to
assign
users
to
adapter
instances
or
single
tenant
instances
of
services
and
message
streams.
Again.
Applications
only
need
to
present
their
jwt
to
the
servers
to
be
authenticated.
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It
uses
a
nuts
optimized
rough
quorum
algorithm,
which
is
much
faster
and
requires
no
configuration
compared
to,
for
example,
a
praxis
based
quorum
algorithm.
You
can
create
gesture
clusters
of
one
three
or
five
servers,
depending
on
whether
you
do
not
want
any
kind
of
falterers
or
wanna
be
able
to
survive
one
or
two
servers
going
down.
At
the
same
time.
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This
means,
for
example,
you
can
easily
control
the
import,
export
and
copying
of
streams
between
accounts
streams
can
have
multiple
sources,
meaning
multiple
subjects,
including
using
wildcards
or
other
strings.
That
stream
has
three
retention
policies
available,
either
limits,
meaning
that
messages
are
stored
into
the
stream.
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Only
up
until
some
limit
is
reached,
at
which
point
you
could
decide
to
discard
either
the
oldest
or
the
newest
message
in
the
stream
in
order
to
make
room
for
the
new
message
interests,
meaning
that
the
data
is
only
kept
in
a
string
for
as
long
as
they
are
either
durable
or
ephemeral.
Subscribers
on
that
string
and
finally
working
queue
which
allows
you
to
use
a
stream
as
a
cue,
you
can
impose
per
stream
and
per
subject
message
limits
like
size,
for
example.
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That
stream
gives
you
many
options
when
it
comes
to
replay
policies,
you
can
decide
to
replay
all
of
the
message
in
a
string,
the
last
message
in
a
string,
the
last
message
for
each
subject
in
a
stream
plus
new
messages
or
starting
at
a
sequence
number
or
by
starting
at
a
point
in
time.
Finally,
you
can
decide
of
the
speed
at
which
you
want
the
replay
to
happen.
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And
finally,
one
of
the
greatest
features
of
jetstream
is
its
speed
with
jet
stream.
You
can
achieve
some
very
high
throughput.
Putting
messages
inside
trends
and
especially
replaying
messages
from
streams
those
rates
can
be
orders
of
magnitudes
faster
than
what
you
get
out
of
other
streaming
solutions.
A
Moving
on
from
jet
stream.
I
would
now
like
to
spend
a
little
bit
of
time
going
over
some
of
the
unique
functionalities
of
nets.
One
of
them
is
subject.
Mapping.
Subject.
Mapping
is
relatively
simple
to
understand.
It's
the
ability
to
say
that
any
message
published
on
subject
through
will
be
written
to
be
on
subject
bar
the
obvious
interest
of
doing
this
is
that
it
gives
you
administrative
control
over
the
subject.
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You
can
also
use
it
to
perform
on-the-fly
administration
and
monitoring
of
streams
and
stream
consumers.
You
can
not
only
create
delete
streams
and
consumers,
but
you
can
view
information
and
reports
on
streams.
You
can
also
view
messages
and
size
streams,
monitor
streams
and
even
administratively
remove
messages
from
streams.
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The
knight's
command
line
interface
tool
also
allows
you
to
do
key
value
operations
and
run
all
kinds
of
benchmarks
to
measure
the
performance
of
nets
and
jet
stream.
In
your
target
environment,
you
can
create
benchmarks
of
coordinates,
publish
and
subscribe
request
reply
and
for
judge
stream
using
sync
or
asynchronous
publishers
and
consumers.
A
And
finally,
as
we
transition
to
matthias's
part
of
the
presentation,
I
want
to
spend
a
little
bit
of
time
introducing
the
last
important
feature
that
was
introduced
in
nuts
too,
which
is
the
flexible
deployments
architecture.
The
nuts
deployment
architectures
allow
you
to
deploy
on
multiple
data,
centers,
multiple
regions,
multiple
clouds
on-prem
on
the
edge,
even
if
that
edge,
is
only
partially
connected
or
any
combinations
thereof.
A
The
simplest,
not
deployment
architecture
is
a
single
net
server
and
clients
connecting
directly
to
it
very
quickly.
You
will
want
either
fault
tolerance
and
or
scalability
and
want
to
create
nuts
clusters.
That's
cluster
are
made
up
of
any
number
of
nats
server
that
communicate
with
each
other
and
service
any
number
of
client
applications.
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This
allows
you
to
have
both
disaster
recovery
and
the
ability
to
leverage
data
locality
when
needed.
Finally,
nuts
also
has
the
concept
of
a
leaf.
Node
lyft
node
is
a
sort
of
an
extension
of
a
cluster
that
you
can
deploy
wherever
you
need
one,
and
now
I'm
going
to,
let
matias
tell
you
more
about
lyft
nodes
and
what
you
can
do
with
them.
B
Hello,
my
name
is
matthias.
I'm
a
software
engineer
at
senedia.
I
want
to
give
you
a
brief
demo
of
our
adaptive
edge
architecture.
Let's
begin,
while
you
can
connect
your
applications
directly
to
a
net's
cluster
or
supercluster
in
the
cloud,
some
applications
might
have
additional
requirements,
such
as
the
ability
to
communicate
locally,
while
internet
connectivity
is
down.
B
Our
solution
for
this
is
to
connect
net
server
as
leaf
nodes
to
your
cluster
or
supercluster
in
the
cloud.
Applications
will
connect
to
leaf
nodes,
thus
retaining
the
ability
to
communicate
among
each
other
once
the
leaf.
Node
connection
is
down
leaf.
Nodes
themselves
are
lightweight
and
can
run
on
a
raspberry
pi
to
server
in
the
cloud
they
appear
essentially
as
a
client
connection
that
proxies
non-local
traffic
of
their
respective
clients.
B
I
could
set
up
a
net
supercluster
as
done
in
other
longer
running
demos,
but
in
the
interest
of
time
I'll
use
ngs,
which
is
a
net
supercluster
hosted
by
zunadia
nats,
is
built
to
be
multi-tenant
operated
as
shared
utility.
So
instead
I
focus
on
what
a
developer
for
the
adaptive
edge
would
need
to
do.
B
B
B
Another
requirement
is
to
bridge
internet
downtimes
by
persisting
important
data
and
automatically
upload
it
once
the
connectivity
is
back.
Our
solution
for
this
is
to
have
jet
stream
our
persistence
layer
enabled
in
the
cloud
as
well
as
in
leaf
nodes
inside
a
stream
jet
stream,
then
records
whatever
local
traffic
you're
interested
in
a
stream
residing
in
the
cloud
extends
it
up
to
source
from
the
wands
in
each
leaf
node.
This
causes
the
automatic
download
of
messages
as
they
are
stored
locally
or
in
the
event
of
an
outage.
B
Once
connectivity
is
back,
the
important
part
about
this
is
to
specify
domain
inside
the
jet
stream
config
block.
This
is
what
keeps
a
jet
stream
in
this
leaf,
node
independent
from
the
one
in
the
cloud
and
makes
it
available
from
anywhere
within
your
net's
network,
under
this
name
now,
I'm
creating
a
stream
in
each
domain
using
a
pre-created
config
file
for
a
stream
without
limits.
B
B
You
will
see
that
the
message
count
for
the
stream
just
created
is
roughly
the
sum
of
the
other
two
roughly
because
of
like
timing
when
the
requests
hit
the
server
so
217
109,
109
so
218,
but
this
request
was
made.
First,
I'm
also
creating
a
durable
push
consumer
that
will
give
us
everything
in
the
stream
just
created.