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Description
Weekly demo issue link - https://gitlab.com/gitlab-org/incubation-engineering/apm/apm/-/issues/9
A
A
A
But
I
will
document
all
those
queries
in
here
as
well,
once
I've
done
the
analysis.
So
if
this
sort
of
matrix
I've
set
up
here,
the
what
we're
looking
for
is
a
database.
That's
is
multi-modal.
So
it's
not
just
time
series,
because
we
want
to
use
this
data
store
for
other
observability
data
as
well.
So
we
want
it
to
be
able
to
handle
things
like
traces
and
complex
data
structures
like
that,
and
we
really
need
replication,
probably
partitioning
as
well
as
as
things
scale
up.
We
want
at
least
medium
to
high
query
flexibility.
A
So
what
I'm
talking
about
there
is,
you
know,
sort
of
how
complicated
the
queries
you
can
write.
You
do
complex
groups
and
joins,
and
things
like
that
and
a
level
of
maturity
and
I've
really
based
that
on
how
long
the
the
project
has
been
running
the
amount
of
traction
things
like
likes
on
the
repository
code
and
and
discussions
on
stack
overflow
and
things
like
that
as
well.
A
Create
db
is
one
I'm
less
familiar
with,
but
it
does
claim
to
be
multimodal
so
we'll
I'll,
I'm
just
going
to
keep
that
in
there
for
the
time
being,
the
other
ones.
I've
excluded.
For
that
reason
and
levels
of
maturity
and
stuff,
like
that,
so
I'm
just
working
through
a
checklist
now
and
we're
using
the
time
series
benchmark,
benchmark
suite
here
that
we
forked
from
the
original
one
from
timescale
and
things
that
we've
changed
in
there
we've
got
our
own
evaluation
script,
which
just
utilizes
all
the
built-in
scripts.
A
I've
had
to
make
some
tweaks
and
fixes,
and
some
slight
modifications
to
get
it
working
the
way
I
want
that
orchestrates
for
the
number
of
databases,
the
load,
the
setup
number
of
workers,
things
like
that
compose
there
that
sets
everything
up
so
yeah,
we're
running
it
in
docker.
I
think
you're
behind
this.
It
just
keeps
it
simple,
but
also
it's
containerized,
which
is
how
we
would
be
running
it
in
production
anyway.
A
So
we,
you
know,
there's
a
few
databases,
I'm
setting
up
there
and
it
will
bring
these
up
one.
At
a
time.
I
have
a
prometheus
and
c
advisor
running
to
collect
general
background
and
container
metrics
if
you're
not
familiar
with
the
advisor
it's.
What
is
used
in
the
cubelet
in
kubernetes
and
to
create
to
collect
pod
metrics
in
there
and
the
runner
itself
is
containerized
as
well
and
orchestrates
the
rest,
so
that
just
makes
it
simple
to
run.
A
If
we
just
have
a
look
in
one
of
these,
this
is
a
click
house
gives
you
a
brief,
very
simple
description
of
the
create
double
group
by
all.
I
haven't
looked
at
that
query
yet
so
I'm
not
sure
what
it's
doing.
Some
information
about
the
run,
duration
and
you've
got
percentiles
in
there,
which
is
very
useful,
so
we'll
probably
look
at
sort
of
95th
percentile
for
evaluating
for
comparing,
which
is
fairly
typical
and
a
query
rate
throughput
there
as
well.
A
So
what
I
want
to
do
with
this
is
just
write:
a
quick
script,
maybe
python
notebook,
something
like
that
that
will
get
all
these
and
make
some
graphs,
so
we
can
do
a
quick
comparison
with
them
all
and
then,
after
that,
what
we're
wanting
to
do
back
on
here.
Let
me
find
the
issue:
do
run
that
comparison
initially
just
between
time
scale
and
click
house.