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Description
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A
Bargain
for
Android
the
motivation
behind
the
app
always
wanted
to
build
a
crowdsource
deal
finding
app,
because
every
time
I
walked
past
forever
21
my
girlfriend
wanted
to
go
in
when
I'm
in
front
of
a
store.
Why
can't
I
have
an
appt
to
see
if
there
are
any
deals
inside
Oh
jeans,
ten
bucks,
let's
get
it
and
bounce
in
30
seconds,
oh
well,
I
won
what
one
can
only
hope.
A
This
is
the
demo
before
I
begin.
I
would
like
to
describe
my
setup.
I
have
my
galaxy
nexus
connected
to
my
computer
via
a
USB,
so
that
is
a
bit
of
a
lag
I'll
go
slow
to
make
sure
you
understand
everything
in
the
demo,
so
you
click
the
icon
to
load
the
app
I
manual
input
it
the
launcher
and
latitude
at
downtown
Montreal.
So
we
get
interesting
stores
like
the
apple
store
or
urban
outfitters.
A
So
let
me
start
by
describing
a
post
that
is
the
an
image
of
the
description,
the
price,
the
store
and
the
user.
They
posted
it
and
that's
the
like
button.
Just
wanna
like
like
a
post,
so
you
click
the
camera
icon
to
make
a
post.
So,
let's
do
that
now
and
that
loads,
a
form
that
you
fill.
You
fill
the
description.
A
A
A
A
Avva
get
the
users
favorite
deals,
so
you
see
the
headphones
that
I
just
favored
it
swipe
again
and
we'll
see
the
Buddha
that
I
just
posted
and
that's
the
end
of
the
demo,
the
data
model.
So,
let's
start
with
explaining
how
it
works.
So
the
co-signer
database
is
wrapped
around
by
a
virtual
rest
api
and
that
has
a
template
of
key
space
column,
family
and
roki.
I
have
three
column:
families
in
my
database
posts
post
by
user
and
post
like
by
user.
A
That
is
what
my
data
looks
like
they're
ordered
by
timestamp
and
those
are
relevant.
Data
store,
ID
from
Google
Places
image,
a
base64
encoding
up,
base64,
encoding
of
the
image
and
other
relevant
information,
and
when
the
user
makes
a
post
I
package,
all
that
and
send
it
to
posts,
/,
google,
a
google
store,
ID
and
post
by
user
/
facebook,
user
ID,
and
when
the
user
likes
a
post,
I
assign
it
a
post
like
by
user
/,
the
user
ID
when
getting
the
post
from
the
database.
A
I
get
the
user's
launcher
and
latitude
and
curry
Google,
Places
and
Google
Places
returns
me
the
20
most
closest
stores
in
a
500-meter
radius.
I
gather
the
relevant
information.
More
specifically
get
the
the
store
ID
right
there
and
I
go
to
Cassandra
and
ask
Cassandra
posts,
/
store,
ID,
add
the
desert
store,
have
any
posts,
and
if
there
are
any
post,
I
lower
it
to
the
view
and
I
go
post
by
user.
A
Does
that
facebook
user
have
any
posts
loaded
loaded,
the
view
did
the
user,
like
any
posts
post
like
by
user
loaded
to
the
view
there
is
no
computation
required
when
you
after
retrieving
the
post,
because
it
was
already
organized
when
the
user
made
the
post.
The
problem
with
this
design
is
that
deals.
Never
expire.
I
need
to
put
some
sort
of
filtering
system
in
the
back
end
so
that
I
can
filter
out
older
posts
that
physically
still
have
them
in
the
Cassandra
database.
A
I
had
two
challenges
with
Cassandra
setting
it
up
and
the
data
model
once
I
set
up
Virgil
around
Cassandra
and
was
able
to
get
it
working
on
my
remote
linux
machine
everything
went
smoothly
and
I
didn't
have
to
worry
about
the
back
end
on
the
data
model.
With
the
help
of
Patrick's
webinar,
the
data
model
is
dead.
Long
live
data
model.
I
figured
out
how
to
organize
my
deals
and
I
had
an
aha
moment
where
everything
made
sense
and
for
the
pros
once
I
figured
out
how
the
data
model
worked.