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Description
Don’t miss out! Join us at our upcoming hybrid event: KubeCon + CloudNativeCon North America 2022 from October 24-28 in Detroit (and online!). 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.
Operating Prometheus in a Serverless World - Colin Douch, Cloudflare
The traditional Prometheus configuration makes several assumptions about the architecture of the systems that it is monitoring that fail to be met in the world of Serverless Architectures. With the increasing adoption of Serverless computing in Distributed Systems architectures, the question then arises of how to achieve the same insight into them that we can achieve with more traditional architectures. In particular, with Timeseries Metrics, the choice is often to choose between substandard upstream offerings (such as the Prometheus Pushgateway), or capitulate to vendor lock-in and utilise a platform provided by your Cloud provider. So if we want to continue to use our existing Prometheus systems, then what choices do we have? This talk will cover the issues around existing solutions, Colin's solution to these issues that is currently in production at Cloudflare, and where we can go in upstream to make the experience better going forward.
A
Right
should
we
get
started
sound
good
all
right.
I
just
want
to
start
by
saying
how
tremendously
excited
I
am
to
be
back
speaking
at
in
person
conferences.
Again,
it's
great
to
see
all
of
your
smiling
faces,
hopefully
under
masks
please,
but
today
we're
gonna
have
a
bit
of
fun.
Talking
about
the
strange
scary
world
of
monitoring.
Serverless
applications
is
that
going
to.
Let
me
change
slide.
Oh
no
technology,
yeah
yeah!
A
It
works
for
a
bit
of
a
quick
introduction
for
those
of
you
who
haven't
met
me
yet
my
name
is
khan
douche.
I
have
been
doing
this
observability
thing
for
arguably
far
too
long.
I
started
out
in
like
physical
control
systems
before
realizing
that
spoiler
physics,
kind
of
sucks
and
the
less
you
have
to
deal
with
that
the
better.
But
I
now
tech
lead
our
amazing
observability
platform
team.
At
cloudflare.
A
We
build
the
tools
that
cloudflare
engineers
use
to
monitor
and
debug
cloudflare,
and
today,
I'm
going
to
take
you
on
a
bit
of
a
journey
through
cloudflare's
evolution
into
the
serverless
space,
because
it's
kind
of
interesting
right
over
the
last
10
years,
or
so
we've
seen
this
like
slow
transition
into
serverless
applications
and
at
least
from
an
open
source,
observability
standpoint.
It
really
feels
like
we
haven't
been
able
to
keep
up.
I
don't
know
whether
that's
the
vendor
lock-in,
that's
inherent
with
serverless
platforms
or
what
but
I'll
be
honest.
A
I'm
getting
old
part
of
me
just
wants
to
you
know
shake
my
fist
in
the
sky
and
say:
hey.
Let
the
young
folk
deal
with
it,
but
I
mean
the
more
I'm
forced
to
deal
with
serverless
functions,
the
more
I've
learned
to
love
them,
because
you
know
I
have
been
forced
to
use
them
as
I'd.
Imagine,
at
least
some
of
you
have
and
they're
great.
They
solve
quite
a
few
issues
that
our
standard
servered
applications
have
they
get
things
like
scaling
for
free.
They
get
things
like
sort
of
microservice,
architectures
really
really
easily.
A
And
that
brings
me
to
cloudflare
workers.
This
isn't
a
sales
pitch.
I
just
want
to
give
a
bit
of
flavor
to
our
experience,
but
back
in
2017
or
so
cloudflare
invented
its
own
serverless
platform.
You
know,
as
you
do,
and
the
annoying
thing
happened,
because
when
you
invent
something
the
absolute
worst
thing
that
can
possibly
happen
to
you
is
that
people
start
using
it
and
as
an
observability
team,
we
blinked-
and
you
know
in
that
blink.
A
We
had
a
bit
of
a
problem
because
we
had
this
weird
cambrian
explosion
of
serverless
applications
at
cloudflare
in
that
blink,
10,
20,
30,
different
serverless
applications,
we're
suddenly
built
on
top
of
cloudflare
workers
and
despite
the
circular
dependency,
this
was
a
bit
of
a
problem
and
I'll
be
the
first
to
admit.
We
fumbled
the
ball
on
that
one.
We
hadn't
really
sort
of
offered
any
sort
of
solution
to
these
teams,
and
this
led
to
a
whole
bunch
of
interesting
solutions
that
we'll
get
into
later.
A
Really
it
became
clear
very,
very
quickly
that
we
needed
to
offer
some
sort
of
solution
to
this,
some
sort
of
actual
offering
to
bring
serverless
functions.
Sort
of
back
into
the
fold
of
well-supported
architectures,
it
just
wasn't
very
clear
how
to
do
that
and
there's
a
lot
to
talk
about
there
from
metrics
to
logs
to
traces
and
everything
in
between.
A
But
let's
talk
about
time
series
today,
because
we
don't
have
that
much
time
and
I
think
we'll
all
agree
that
the
sort
of
de
facto
standard
for
collecting
and
storing
time
series
these
days
is
prometheus
right.
It's
sort
of
a
lingua
franca,
everything
uses
remote
right,
everything
uses
text
exposition
and
I
know
we
have
a
few
prometheus
maintainers
in
the
audience
today.
So
at
least
from
me.
Thank
you
because
you
make
my
life
a
hell
of
a
lot
easier.
A
But
if
we're
being
honest,
prometheus
makes
a
few
assumptions,
it
has
a
bit
of
a
downside
right
and
apologies
for
the
image
it's
really
hard
to
find
like
safe
for
work
old,
greek
art.
I
don't
know
why.
But
prometheus
has
a
bit
of
a
downside
right.
It
has
a
dark
side.
It
makes
a
few
assumptions
that
are
very
innovative,
at
least
for
the
time,
and,
if
we're
being
honest,
they
sort
of
force
you
into
a
particular
architecture.
A
Prometheus
at
a
high
level
assumes
a
few
things.
It
assumes
that
your
system
lives
long
enough
to
be
discovered
and
scraped.
It
assumes
that
your
service
is
sort
of
network
enabled
and
it
can
expose
things
over
the
network,
and
it
assumes
that
you
can
do
your
own
aggregation
and,
let's
break
those
down
a
bit
because
they're,
perhaps
not
obvious
prometheus,
firstly,
requires
that
your
system
lives
long
enough
right.
A
Prometheus,
rather,
inevitably,
I
think,
uses
a
pull-based
model
for
metrics
collection
right
for
those
who
haven't
seen
that
in
action,
the
general
idea
is
that,
instead
of
instead
of
your
service
sort
of
actively
pushing
metrics
out
to
some
sort
of
collector
prometheus
will
discover
where
your
service
is
reach
out
to
it
and
pull
metrics
back,
and
this
is
kind
of
cool
right,
because
it
gives
us
a
bunch
of
things
for
free.
We
get
things
like
health
monitoring,
because
if
we
can't
pull
your
metrics
well,
maybe
your
service
is
down.
A
We
get
things
like
capacity
control,
because
we
can
decide
how
often
to
pull
metrics
and
how
many
of
them
to
pull.
We
get
things
like
easy
testing,
because
we
don't
have
to
mock
out
collection,
endpoints,
but
prometheus
isn't
always
scraping
right.
It's
scraping
every
five
ten
fifteen
seconds,
whatever,
which
means
that,
in
reality,
your
service
has
to
live
for
at
least
five
15
seconds,
which
may
or
may
not
be
the
case
at
the
same
time,
and
this
sort
of
goes
hand
in
hand
with
the
last
one.
A
A
If
you
want
to
secure
the
communication,
you
need
to
have
firewall
rules,
you
need
to
have
tls
certificates,
maybe
client
certificates.
So
again
it
sort
of
gets
a
bit
messy
in
the
weeds
and,
finally,
prometheus
assumes
that
you
can
do
your
own
aggregation,
and
this
is
a
bit
opaque.
So
it's
easier
to
sort
of
represent
it
with
a
bit
of
a
counter
example,
if
we
imagine
say
a
service
that
only
ever
processes,
one
request
and
some
hypothetical
service,
we
can
ignore
the
previous
two
requirements.
A
For
now
we
say
somehow
prometheus
gets
our
metrics,
but
our
service
processes
run
requests.
So
what
would
a
request?
Count?
Metric,
look
like
in
that
case.
Well,
our
service
process
is
one
request
right.
The
request
count
is
a
one,
so
we
push
a
one
to
prometheus
and
if
we
get
a
second
request,
while
we
start
another
service
and
it
pushes
a
one
to
prometheus
and
well.
This
is
a
bit
of
a
problem
right
because
from
prometheus's
point
of
view,
well
that
metric
jumps
to
a
one
and
then
never
never
goes
anywhere.
A
So
we
can't
accurately
represent
things
like
request
counts
in
this
standard
setup,
we
could
maybe
do
some
magic
if
we
explode
our
cardinalities
and
do
recording
rules
or
something
like
that,
but
in
practice
we
probably
want
to
do
that.
We
need
our
service
to
be
able
to
do
some
level
of
aggregation.
A
So,
let's
summarize
those
a
bit
with
those
requirements.
Prometheus
is
very
well
suited
to
a
particular
model
of
architecture
right,
a
service
that
is
long
running
a
service
that
is
network
enabled
a
service
that
is
able
to
do
multiple
things
in
every
sense
of
the
word,
a
traditional
servered
application.
A
So
let's
consider
our
serverless
example.
Well,
our
serverless
functions
live
for
hundreds
of
milliseconds,
maybe
seconds
at
worst.
Our
serverless
functions
aren't
really
network
enabled
in
the
traditional
sense
they
can't
listen
on
a
port.
They
can't
spin
up
a
server
and
even
if
they
could,
that
would
sort
of
be
antithetical
to
the
whole
idea
of
serverless
in
the
first
place,
and
they
only
have
a
process,
one
request.
A
So
in
all
three
ways
our
serverless
functions
don't
hold
up
these
assumptions
and
when
we
were
first
sort
of
investigating
these
problems,
these
were
the
issues
we
we
encountered.
So
what
can
we
do
right?
We're
engineers
we
look
for
solutions,
and
so
we
can
look
at
prior
art.
Serverless
functions
have
been
around
for
what
10
years
nearly
at
this
point,
so
this
problem
must
be
solved
already
for
us
and
first
week.
First
thing
we
can
look
at
is
the
push
gateway?
A
A
Listening
on
the
network
living
long
enough
to
be
discovered
and
scraped,
so
we
only
need
to
be
able
to
push
something
and
it
works
and
how
the
push
gateway
sort
of
functions
and
what
makes
it
interesting
is
how
it
handles
sort
of
repeated
pushes
that
is
pushing
the
same
metric
twice,
for
example-
and
this
goes
back
to
our
request-
count
example:
if
we
push
that
request
count
with
a
one
and
then
we
push
it
with
another
one.
Well,
the
push
gateway
replaces
those
those
old
values.
A
So
we
still
get
this
repeating
one
problem
at
the
same
time,
in
the
same
upstream
documentation.
Well,
we
can
find
the
aggregation
gateway
by
our
friends
at
weaveworks
and
the
aggregation
gateway
is
very,
very
similar.
It
functions
as
a
push
gateway,
except
in
the
case
of
this
repeated
metrics
issue
right.
If
we
push
a
one
and
then
another
one
well,
the
aggregation
gateway,
perhaps
obviously
aggregates
them
right
and
secret.
A
A
Well,
maybe
maybe
not
because
at
this
point
we
decided
to
go
on
a
bit
of
a
survey
through
our
engineering
teams,
because
engineering
teams
at
cloudflare
they're
not
dumb,
right,
they're,
not
pushing
things
to
production
without
monitoring,
so
they
must
be
doing
it
somehow,
and
we
wanted
to
work
out
how
they
were
doing
it.
Whether
or
not
there
was
a
consensus
in
sort
of
what
was
the
best
solution
out
there.
A
At
the
same
time,
we
wanted
to
know
what
they
were
doing
like
what
sort
of
metrics,
because
each
of
these
solutions
has
a
different
set
of
trade-offs
and
I'll
be
honest.
What
we
found
was
a
bit
of
a
mess.
This
is
not
trying
to
disparage
any
cloud
flow
engineers.
They
were
doing
what
was
necessary,
but
there
was
really
no
consensus
around
what
was
the
solution
to
this
problem.
We
had
push
gateway,
setups
and
teams
weren't
using
counters.
We
had
aggregation
gateway
setups,
where
teams
were
weren't
using
gauges.
A
We
had
one
team
that
had
built
this
weird
frankensteinian
monster
of
a
binary
that
sort
of
combined
the
two
into
one
and
sort
of
had
weird
routing
logic,
to
route
different
metrics
to
different
gateways.
That
was
actually
very
interesting.
We
had
metrics
that
were
being
like
tromboned
back
through
internal
ui
pipelines.
A
We
had
teams
that
built
sort
of
custom
logic
on
top
of
sentry
to
pull
time
series
metrics.
There
was
really
no
consensus
about
where
we
could
go.
At
the
same
time.
We
wanted
to
know
what
sort
of
metrics
people
were
pushing
right.
How
were
they
dealing
with
these
trade-offs?
And
perhaps
most
obviously,
teams
needed
counters
counters
are
very
boring.
The
general
idea
is
right.
We
have
a
bunch
of
metrics
that
we
can
sum
up
together
to
produce
a
final
value.
Nothing
too
interesting.
Their
counters
are
counters.
A
At
the
same
time,
teams
need
gauges
and
gauges
are
more
interesting
because
gauges
well,
if
we're
say,
processing
a
thousand
requests
and
we're
just
replacing
gauge
values,
we're
dropping
999
of
those.
So
is
that
useful,
maybe
not-
and
this
is
where
I
want
to
revisit
that-
frankensteinian
binary
from
before,
because
that
team
was
actually
doing
some
really
interesting
stuff
like
taking
aggregates
over
different
gauge
values,
as
they
came
in
to
sort
of
capture.
These
changes
we'll
get
back
to
that
in
a
second,
but
that
was
actually
quite
interesting.
A
They
were
there
and,
I
suppose,
they're
useful,
but
histograms
ostensibly
are
just
counters
on
steroids
right,
we're
summing
up
all
the
individual
buckets
into
a
final
value,
but
beneath
the
surface
there
is
some
level
of
complication
there.
What
happens
if
we
get
a
push
with
different
sets
of
buckets?
How
do
we
sort
of
merge
those
together?
How
do
we
even
merge
a
summary
right?
Does
that
even
make
sense
to
do?
A
A
That's
not
too
interesting.
But
what
into?
What
is
interesting
is
how
those
behave
over
time,
because
when
we
deploy
a
new
version
with
a
new
static
label
set
well,
the
old
version
shuts
down
that
label
set
finishes
and
the
new
one
comes
up,
and
it's
interesting
because
that
is
not
implicit
in
the
fact
that
it's
a
gauge
that
is
purely
sort
of
borne
out
of
how
the
application
handles
these
metrics
over
time.
A
So
where
does
that
leave
us?
Let's
take
these
new
requirements
and
revisit
our
upstream
solutions.
Well,
the
push
gateway.
Maybe
that
works.
Maybe
it
doesn't,
in
fact,
if
you
read
the
documentation,
who
does
that
not
me?
Who
has
time
if
you
read
the
documentation?
Well,
the
push
gateway
is
sort
of
out
anyway.
It
very
clearly
states
that
hey
probably
don't
use
this
for
serverless
things
yeah,
oh
well,
we
can
sort
of
abandon
that
one.
That's
sad
promises,
so
many
things.
A
At
the
same
time
we
have
the
aggravation
gateway
and
the
aggregation
gateway
gets
us
sort
of
closer.
Maybe
it
gives
us
different
trade-offs
for
you.
We
can
represent
counters,
but
the
way
the
aggregation
gateway
handles
gauges
is
slightly
weird
and
we
we
still
can't
get
our
info
metrics
out
of
it,
which
is
what
we
really
wanted
to
do,
because
the
aggregation
gateway
works
only
on
types
right,
so
a
metric
type
has
a
behavior.
A
Maybe
that
works,
maybe
it
doesn't.
At
the
same
time,
we
were
investigating
event-based
solutions
right
as
an
industry
we're
moving
more
towards
these
sort
of
high
dimensionality
high
cardinality
events.
Maybe
we
could
do
something
there
and
we
decided
against
these
things
for
two
reasons,
mostly
a
at
least
at
the
time.
A
They
were
rather
immature
and
we
didn't
really
want
to
put
something
that
experimental
into
production
and
b.
We
didn't
really
want
to
fracture
our
environment
too
much
because,
as
an
internal
platform
team,
you
really
have
to
focus
on
your
developer
experience
and
having
two
separate
systems,
one
for
serverless
architectures
and
one
for
more
standard.
Architectures
didn't
really
sit
well
with
us.
We
wanted
to
sort
of
have
a
consistent
experience
throughout
the
whole
stack.
A
So
where
does
that
leave
us
right?
Because
all
of
these
upstream
solutions
sort
of
seem
out
of
the
window,
so
it
became
pretty
clear
pretty
quickly
that
we
were
gonna
need
to
build
something
ourselves
as
annoying
as
that
is
because
we
had
a
couple
of
requirements.
We
wanted
to
maintain
compatibility
with
all
of
the
existing
sort
of
client,
libraries
and
client
orchestration
and
at
the
same
time,
we
wanted
to
represent
all
of
these
different
semantics
that
we
had
summing
counters,
replacing
gauges,
but
also
infometrics,
and
again
these
these
statistical
functions
over
incoming
gauge
values.
A
So
it
was
pretty
clear
we
were
going
to
have
to
do
something
ourselves
and
that's
what
we
did.
We
called
it.
The
gravel
gateway
the
code
is
there
it's
in
rust.
So
buyer!
Beware
on
that
point.
The
main
thing
I
want
to
mention,
because
it'll
make
my
family
proud,
is
that
this
is
a
mining
pun.
I
come
from
a
mining
family.
I
think
this
is
funny
in
mining.
We
have
things
called
aggregates
like
sand,
gravel
and
things.
I
think
this
is
funny
literally
no
one
else
does
so.
If
I
could
have
a
pity
laugh.
A
A
At
the
same
time,
we
needed
to
aggregate
like
the
aggregation
gateway,
but
we
needed
to
support
way
more
different
types
of
aggregations.
All
of
these
things
we'd
identified
and
therein
lies
a
bit
of
a
problem
right,
because
here's
two
metrics
fancy
two
gauges,
ostensibly
right.
We
have
a
build
info
gauge
and
we
have
a
go
threads
gauge.
I
remember
this
slide
and
ostensibly
these
are
very
boring.
Metrics
they're
two
gauges,
but
there
is
some
sort
of
implied
behavior
in
how
our
applications
handle
them
over
time.
A
The
second
one
there
standard
gauge
goes
up
and
down
the
first
one
is
an
infometric
and
there's
nothing
inherent
in
that
text.
Exposition
that
says
it's
an
infometric
outside
of
the
metric
name,
but
we'll
ignore
that
this
is
the
problem
right,
because
how
the
application
handles
these
over
time
is
sort
of
the
defining
factor
in
these
metrics
and
in
our
serverless
example.
Well,
we
don't
have
an
overtime,
we
get
one
shot,
so
we
need
some
way
to
communicate
to
something
external
like
our
gateway.
A
How
these
metrics
are
supposed
to
behave
over
time
and
there's
really
only
a
few
controllable
fields
we
have.
If
we
want
to
sort
of
maintain
all
of
the
text
exposition
things
we
have
the
type,
but
that's
a
very
fixed
sort
of
set
of
things.
We
have
the
help
text
sort
of.
Maybe
we
have
the
metric
name
and
we
have
the
label
set
and
any
one
of
those
last
three
would
have
kind
of
worked.
But
ultimately
we
decided
on
smuggling
some
semantic
information
into
the
labels,
and
you
know
that's
what
we
did.
A
We
have
the
clear
mode
label
in
our
gateway.
Clear
mode
label
allows
communication
with
the
gateway
as
to
those
overtime,
semantics,
for
example,
we
have
a
clear
mode
info
that
represents
these
sort
of
info
semantics.
Every
type
has
a
default
clear
mode,
so
we
sum
counters.
We
replace
gauges
that
sort
of
thing,
but
being
able
to
specify
it
more
directly
allows
that
sort
of
direct
control.
It
allows
us
to
bring
serverless
functions
up
to
the
almost
level,
the
same
level
of
monitoring
as
as
our
more
traditional
servered
applications,
for
example.
A
A
A
When
that
new
one
comes
in
we've
gotten
rid
of
that
1.17,
we
replaced
it
with
a
1.18
again,
perhaps
simple,
the
more
interesting
one
is
statistical
functions
right,
and
this
is
where
I
wanted
to
revisit
that,
because
metrics
are
a
simple
number
they're,
a
single
number
that
changes
over
time
and
means
medians
percentiles
they're
a
bit
different,
so
we
can
sort
of
track
a
mean
as
a
single
number
over
time.
But
is
that
useful?
A
So
in
reality,
what
we
want
to
be
able
to
do
is
expire
values
over
time
and
to
do
that,
we
actually
stole
a
concept
from
another
internal
project.
We
have
called
cleodora,
go,
listen
to
my
ollyfest
talk.
If
you
want
to
talk
about
that,
but
the
general
idea
is
that
in
cleodora
we
have
a
concept
of
pebbles
which
works
well
with
the
gravel
gateway
idea.
A
Pebbles
are
effectively
a
sort
of
time-based
bucket
and
we
keep
a
circular
buffer
of
those
over
time
and
each
bucket
contains
a
sort
of
pre-aggregated
value
of
all
of
the
incoming
values
in
that
time
slot.
So
in
reality
we
get
this
aggregate
of
aggregates
out
the
other
end,
which
does
lose
us
some
level
of
precision,
but
in
practice
we
found
it
doesn't
really
matter
too
much.
There
are
some
edge
cases
there.
It's
not
perfect,
but
we
get
this
sort
of
idea
right.
A
We
have
this
rotating
buffer
and
we
can
get
rid
of
things
over
time,
and
this
is
where
we
get
more
complicated,
clear
modes.
Things
like
mean
five
minutes,
we're
taking
a
mean
over
the
last
five
minutes
of
data,
and
that
allows
us
to
get
better
insights.
I
think,
at
the
same
time,
because
we're
not
augmenting
the
text
exposition
format
in
any
way
we
get
compatibility
with
existing
client
libraries
for
free
right,
which
is
a
good
thing.
It
means
we
don't
have
to
train
engineers
to
do
anything
differently.
A
We
can
just
use
standard
client,
libraries
to
add
another
label
set
or
another
label
to
the
label
set
and
because
most
of
us
I'd
imagine
are
engineers
and
we
like
some
code
here,
you
go.
I
don't
know
why
we
like
code,
why
do
we
like
code?
Here's
some
code
excuse
the
javascript.
You
can
see
that
it's
basically
exactly
the
same
as
a
standard
as
a
standard
orchestration
right.
We
have
a
request
count.
A
A
A
So
that's
about
it.
We
have
the
gravel
gateway.
Let's
look
towards
the
future.
Shall
we?
Where
do
we
go
from
here?
Well,
in
the
gateway
itself,
we
have
a
few
things
coming
up,
we're
always
trying
to
evolve
it
because
we're
always
trying
to
improve
on
things
we're
adding
some
interesting
aggregations
things
like
deltas
of
counters
over
time
things
yeah
that
sort
of
stuff
more
statistical
functions.
We
want
to
rewrite
pebbles
a
bit
to
get
rid
of
some
of
those
edge
cases.
A
We're
also
doing
some
interesting
things
with
scaling
it
up,
because
it
turns
out
at
least
at
cloudflare.
We
get
some
rather
large
services,
which
you
know
kind
of
sucks
when
you're
doing
sort
of
six
seven
figure
requests
per
second
turns
out
a
little
go
binary
on
a
little
rust
binary
on
a
server.
I
can't
really
keep
up,
but
hey
we're
looking
at
sort
of
clustering.
A
The
main
interesting
thing
is
that
it
defines
a
whole
bunch
more
types,
and
I
know
at
least
some
of
you
out.
There
have
just
been
smugly
going
colin.
We've
already
solved
this
problem
and
you
are
right
because
in
openmetrics
we
get
things
like
infometrics.
We
get
things
like
state
set
metrics
all
of
these
different
fancy
types,
and
that
is
great,
because
maybe
we
can
get
rid
of
this
hacky
clear
mode
someday,
maybe
at
the
same
time
we're
moving
as
an
industry
towards
more
event-based
things,
because,
ostensibly
that's
what
we
have
right.
A
We
have
an
event
push
out
of
our
serverless
function,
we're
just
doing
some
level
of
aggregation
on
it.
So
again,
it's
a
really
interesting
area
of
research
to
sort
of
look
at
these
more
event-based
solutions
and
aggregating
time
series
out
of
them.
As
I
mentioned,
we
have
an
internal
thing
cleodora
and
go
see
my
ollyfest
talk.
If
you
want
to
know
more
about
that,
but
it's
definitely
an
interesting
area
of
research
and
I'm
excited
to
see
where
we
go
from
there.