►
From YouTube: Introduction to JanusGraph Database - Jason Plurad, IBM
Description
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Introduction to JanusGraph Database - Jason Plurad, IBM
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JanusGraph is a scalable graph database that is optimized to store and query graphs with billions of vertices and edges distributed across a multi-machine cluster. We'll share several uses cases from IBM products that take advantage of a graph database to highlight the strengths that graph provides over relational and other NoSQL alternatives. We'll discuss the JanusGraph architecture which gives developers the flexibility to leverage their knowledge and operational skills from other storage backends. We'll cover how open source developers from the community united to establish JanusGraph at the Linux Foundation, and then discuss JanusGraph project directions and opportunities to build out the open source graph ecosystem.
https://sched.co/NrsI
A
I
think
we're
about
ready
to
get
started.
Thank
you
everybody
for
coming.
This
is
the
introduction
to
the
Jan's
graph
database.
My
name
is
Jason
Florida
I'm
a
developer
and
open
source
I
work
in
the
IBM
cognitive
applications
group.
What
we
do
is
we
focus
on
open
source
and
specifically
around
data
and
AI,
and
what
we
try
to
do
is
identify
open
source
projects
that
we
think
could
be
strategic
to
help
improve
our
products
and
our
services,
and
we
reach
out
to
those
communities
and
try
to
continue
to
build
them
and
grow
them.
A
There's
anyone
here
familiar
with
Janus
crack,
okay,
great
so
Janice
graph.
Is
it's
a
open
source
scalable
graph
database?
So
it's
a
dental
index
foundation.
So
this
is
an
introductory
talk
and
we're
just
gonna
start
the
beginning.
We'll
talk
about
typical
graph
use
cases.
What
brought
us
in
IBM
to
the
graph,
some
of
the
open
source
graph
evolution
we've
seen
over
the
years,
I've
been
involved
in
this
space,
probably
since
2013
or
so,
and
where
we
see
Jan's
graph
progressing.
A
We've
been
at
the
Linux
Foundation
since
2016
and
continuing
to
grow,
even
though
it's
still
a
relatively
small
project,
so
we're
gonna
be
talking
about
graphs.
Today,
we're
not
talking
about
graphs
in
the
sense
of
a
bar
graph
or
a
pie
graph,
we're
talking
about
if
more
of
the
mathematical
through
mathematical
graph,
specifically
a
property
graph
model,
it
contains
vertices,
edges
and
properties.
We're
gonna
go
into
these
more
in
depth
on
the
next
slides.
This
here
is
the
typical
example
that
we
show
in
Janice
graph.
A
This
is
called
our
graph
of
the
Gods
and
it's
a
rich
model.
It
connects
different
types
of
objects
or
vertices.
Those
are
your
entities
in
the
graph
through
edges,
which
would
be
the
relationships
so
specifically
the
vertex
it's
the
entity
in
graph.
It
always
has
an
identifier
and
a
label,
and
it
can't
be
connected.
It
doesn't
have
to
be
connected
to
other
edges
in
the
graph.
Obviously,
the
most
value
is,
if
it
is
connected
to
other
vertices
by
in
an
edge,
you
can
also
have
additional
properties
on
it.
A
These
are
what
we'll
see
on
the
next
slide
with
with
edges
so
edges.
These
connects
your
entities,
so
it's
a
directional
relationship.
Now
this
is
key.
You
have
the
the
out
vertex.
This
is
your
source.
It
connects
to
the
in
vertex,
which
would
be
your
target.
So
in
this
example,
here
we
have
the
father
edge.
It
comes
from
the
father
and
it
points
towards
it
starts
at
the
son
and
would
be
pointing
towards
the
father
or
the
child
mourning
towards
its
father.
A
A
The
way
that
you
work
through
a
graph
is,
through
these
edges,
so
being
able
to
jump
from
one
vertex
to
another
that
that's
what
we
call
it
traversal
then
again,
properties
again.
These
are
on
both
vertices
and
edges
it.
These
are
typically
how
you
store
additional
data,
so
more
specific
attributes
that
would
help
differentiate
one
vertex
from
another
or
one
edge
from
another.
A
Typically,
these
are
simple
key
value
pairs.
You
can
have
a
collection
of
items
as
your
value,
whether
it's
a
set
or
a
list.
So
what
we
like
about
graph
is
specifically
the
property
graph
model.
It's
very
rich
compared
to
what
you
have
in
a
relational
model,
because
it's
you
don't
have
to
predefined
this
game.
Upfront
like
you,
would
with
a
relational
database.
A
It's
very
easy
to
connect
data,
add
additional
later,
as
it
comes
available,
and
the
flexibility
is
what
attracted
us
to
it
being
able
to
connect
data
to
each
other
as
more
relationships
or
more
properties
are
become
available.
That
gives
you
a
lot
of
flexibility
to
get
your
data
together
and
run
out
analysis
on
it.
A
One
of
the
first
use
cases
that
we
had
was
around
engagement
analytics
in
IBM.
We
use
a
product
called
IBM
connections,
it's
our
social
product,
it
has
forum
posts,
it
has
blogs,
it
has
file
sharing
those
sorts
of
activities
and
we've
been
using
this
product
for
years
internally,
and
we
we
just
wanted
to
be
able
to
drive
more
value
out
of
it.
A
The
interesting
thing
about
the
IBM
Connection
product
is
that
each
of
those
applications
that
I
had
mentioned
forums
and
blogs,
etc.
They
were
all
developed
independently
using
a
relational
database,
so
their
data
was
siloed.
So
there
wasn't
that
opportunity
to
really
understand
how
people
were
interacting
with
each
other
across
the
whole
suite
of
applications.
By
putting
that
data
into
a
graph,
we
were
able
to
easily
connect
that
kind
of
a
data.
A
Now
how
this
can
be
interesting
is
that
you
can
better
identify
different
clusters
of
expertise,
whether
it's
regional
or
by
technology
you
can
identify,
who
are
the
information
brokers,
who
the
people
that
actually
connect
those
pockets
of
information.
That
would
be
strategic,
that
to
be
working
together
better
where
we
found
that
to
be
interesting,
those
connector
people,
those
that
they're
very
valuable
to
the
company.
A
So
the
idea
retention
of
your
resources
was
very
interesting,
Martin
individual-level.
We
thought
it
would
be
valuable
also
to
give
some
information
back
to
the
people
who
are
using
the
social
tools
to
really
understand
what
kind
of
value
that
they're
driving
both
for
themselves
and
for
the
company
by
using
the
the
connections
collaboration
tool.
So
here
this
is
a
personal
social
dashboard.
Using
the
same
exact
data
but
more
geared
towards
the
individual,
so
that
they
can
better
understand
how
they
were
contributing
through
the
social
tooling
and
how
that
connected
them
to
other
people.
A
So
here
along
the
bottom,
we
have
scores
based
on
how
they
were
able
to
share
with
other
people.
What
kinds
of
people
reacted
to
the
things
that
they
were
posted
eminence
is
more
of
a
measure
of
how
how
more
broadly
you,
your
their
posts,
were
shared
by
your
peers
and,
obviously
your
network.
This
is
you
know
pretty
common
these
days,
and
you
know
one
of
the
the
big
aspects
that
we
had
to
consider
with
building.
This
was
the
idea
of
privacy
as
well.
People
were
very
concerned
up
front.
A
What,
if
my
manager
sees
this,
they
were
afraid
that
is
going
to
be
possibly
a
performance
indicator,
but
really
the
the
idea
was
to
give
a
personal
feedback
loop.
So
you
can
understand.
Well,
you
know
what
are
your
gaps
where
you
can?
Where
should
you
look
for
mentoring,
where
she
look
for
training
and
because,
in
the
spirit
of
openness
having
that
feedback
loop
really
would
help
next
use
case
we
ran
into
was
around
airline
routing?
A
We
worked
with
a
major
airline
in
the
United
States
on
this
IBM
is,
you
know
they
are
currently
running
a
lot
of
the
back-end
systems
for
the
Airlines
routing
right
now
on
the
mainframe,
but
but
the
what
we
wanted
to
see
was
you
know
how
how
easy
would
it
be
to
get
that
data
into
a
graph
database?
It's
that
way
when
the
airline
needs
to
worry
about
routing
a
passenger
say
when
a
their
flight
gets
delayed
or
it
gets
cancelled.
You.
B
A
A
It
was
generated
by
one
of
my
colleagues,
and
we
actually
use
this
ultimately
as
a
learning
tool
inside
the
company
to
help
teach
people
how
to
use
a
graph
and
how
to
understand,
since
it's
pretty
simple,
to
see
how
how
well
connected
everything
is
the
the
and
that's
it's
relatable
from
a
personal
level
and
the
travel
aspect
of
being
delayed
at
an
airport
and
trying
to
understand
how
to
jump
from
one
to
another.
You
know
what
other
rules
do
you
have
to
put
on
top
of
that.
A
A
Next
cloud,
databases:
this
is
a
photo
from
our
cloud
offering
in
2015
we
acquire
a
couple
cloud-based
database
companies.
One
was
cloud
and
one
of
those
compose
compose
is
our
portfolio
of
managed
open-source
databases
and
we
added
Janice
craft
into
that
as
another
open
source
offering
because
I
think
what
we
were
finding
with
our
customers
is
that
open-source
databases
are
great
running
them
in
the
cloud.
Is
another
story
and
being
able
to
have
a
24
by
7
managed
cloud
offering
lowered
the
bar
in
terms
of
them
being
able
to
it
open-source
databases?
A
Many
of
our
customers
are
big
companies
that
are
well
entrenched
in
older
technologies,
so
having
a
lightweight
model
for
them
to
play
with
a
open-source
database
on
the
cloud
was
great.
We
had
some
additional
features
on
top,
such
as
the
visualization
browser
below
which
allowed
them
to
better
understand
how
their
data
was
connected
once
they
put
it
into
the
graph.
A
That's
out
there
right
now,
if
you
go
out
there
to
compose
calm,
but
typically
what
we
found
is
that
once
we
started
seeing
a
graph,
it
was
easy
to
find
a
graph
in
many
different
areas,
just
because
in
the
graph
database
base
we
like
to
say
that
the
graph
itself
is
it's
a
whiteboard
friendly
type
of
model.
It's
easy
to
understand.
A
Once
you
get
up
to
the
whiteboard
to
draw
the
your
vertices,
your
nodes
or
entities,
and
then
just
connect
them,
so
it
gets
pretty
addictive
and
you
know
that's
typically
one
of
the
first
things
that
people
want
to
see.
They
say
well,
where's,
my
graph.
Let
me
see
it
cuz
once
you
can
visualize
it,
you
can
better
understand
your
data.
A
So
moving
on
to
how
we
progressed
in
the
open
source,
one
of
the
first
projects
that
we
got
involved
with
was
tinker
pop.
When
we
were
thinking
about
doing
graph.
You
know
the
first
thing
we
did
obviously
didn't
go
out
to
the
open
source
to
see
what
was
going
on
and
what
we
found
at
the
time
was
that
all
of
the
open
source
or
o:v
graph
databases
were
actually
implementing.
This
open
source
stack
called
tinker
pop.
A
It
was
a
vendor-neutral
open
source
project
was
created
by
Marco
Rodriguez
and
they
were
all
implementing
this
deck,
and
that
was
great.
That
gave
us
the
flexibility
to
use
the
same
model
and
compared
the
performance
characteristics
of
different
graph
databases
at
the
time
and
what
was
going
on
was
pretty
amazing
because
the
the
implementation
of
this
open
source
project
it
was
being
done
by
both
the
private
area
and
by
open
source
databases,
and
that
was
great,
so
tinker
pop
itself.
It
provides
the
graph
model
definition.
It
provides
a
graph
server.
A
A
Many
of
the
major
graph
vendors
graph
database
vendors
are
still
implementing
the
tikar
part
stock.
Today,
neo4j
is
probably
the
biggest
graph
database
right
now
that
people
may
know
about,
but
other
big
entries
are
the
data
sex
and
price
graph.
Microsoft
has
a
cloud
managed,
Azure,
a
cosmos
database.
It
does
graph
and
other
much
different
styles
in
those
sequel,
databases
and
Amazon
Neptune,
which
notably
does
property,
graphs
and
RDF
graphs.
Rdf
graphs
are
the
it's
the
Semantic
Web
type
of
graph.
A
So
we
talked
a
little
bit
about
the
graph
structure
before
and
now
with
gremlin
gremlin
is
a
domain-specific
language
for
graphs.
It
defines
how
you
would
walk
from
one
vertex
to
another
along
the
edge.
So
that's
what
a
traversal
is
and
gremlin
you've
seen
his
character.
He's
part
of
the
documentation
actually
from
tinker
papa
I
didn't
mention
before,
but
I'm
a
PMC
member
on
Pachi
tinker
pop,
so
we
use
gremlin
the
characters
alter
our
documentation.
It
helps
to
make
concepts
more
approachable,
the
Marco,
the
our
founder.
A
He
he
actually
worked
with
a
graphic
designer
up
front
to
use
the
characters
so
that
way
to
make
it
more
relatable
and
pretty
much
just
tell
the
story
of
graph
and
I
think
it's
been
pretty
pretty
effective,
so
we're
gonna
go
through
a
example
of
a
graph
traversal.
So
the
graph
here
is
a
pretty
simple
graph.
I,
don't
know
if
you
can
read
all
the
details
on
it,
but
this
is
a
tinkerer
pop,
modern
graph.
It's
a
very
simple
graph,
six
vertices
six
edges.
It
contains
people
and
software.
A
A
What
projects
did
Margo's
colleagues
create
with
others,
and
this
is
what
the
query
looks
like
in
gremlin
right
here
and
we're
gonna.
Take
that
step
by
step
and
kind
of
break
down.
What
each
step
does
gremlin
itself
contains,
probably
about
sixty
different
steps,
but
they
break
down
to
five
different
categories,
but
it's
a
it's
a
Turing
complete
language,
it's
pretty
flexible
and
a
lot
of
interesting
things
are
happening
in
the
tinker
pop
space.
A
So
we'll
start
at
the
top
G
V,
so
G
that
stands
for
the
graph
traversal
source.
Basically,
that's
your
graph
davi
would
be
all
the
vertices,
so
I've
designated
here
with
the
little
green
guy's,
the
little
gremlins
in
each
node.
So
when
you
call
Gav
now
we
have
a
traverser.
You
know
a
little
gremlin
who's
gonna
walk
at
each
vertex.
A
The
next
step
is
has
named
Marco,
so
this
is
a
filter
step
and
what
it
will
do
is
it
will
select
only
the
vertices
which
have
the
name
of
Marco.
So
here
the
only
green
guy
left
is
over
here,
the
Marco
node,
all
the
other
traverser
they
have
been
killed.
So
you
can
see
what
the
problem
is
here
is
that
with
the
G
dot
V
in
the
previous
step,
we
selected
all
the
vertices,
and
now
we
only
have
one
left.
If
you
have
a
large
graph,
that
is
not
very
efficient.
A
It
works
fine
in
a
six
node
graph,
but
if
you
have
millions
of
nodes
that
that's
a
bit
of
a
problem,
so
typically
what
you'll
see
in
a
graph
database
implementation
is
that
you'll
have
an
index.
So
here
we
have
utilized
index
on
property
name
to
do
this,
so
it'll
go
directly
to
that.
One
node,
so
indexes
will
increase
that
performance
being
able
to
zoom
in
on
a
single
node.
Instead
of
doing
a
full
scan
beautiful
all
right,
so
next
we're
moving
out
on
the
nose
edge.
A
So,
starting
from
here
from
the
Marco
node,
what
happens
is
the
traverser
splits
he
splits
across
both
of
these
edges?
So
he
knows
two
edges,
and
now
we
have
an
alive
gremlin
here
in
the
live
gremlin
here
now
we
need
to
see
who
he
created
what
projects
were
created
so
following
along
the
created
edge
here
so
out
e
we're
only
looking
at
the
edge
cuz
we're
gonna
in
the
next
step,
filter
on
properties
on
that
edge,
where
the
weight
is
less
than
one.
So
this
has
a
weight
of
one.
A
Only
this
only
this
gremlin
is
still
alive
following
inward
towards
the
target
vertex.
So
the
gremlin
moves
from
the
edge
to
this
vertex
admit
the
values
of
the
name.
So
here
we
can
see
that
the
name
of
this
software
project
is
locked.
So
that's
just
one
way
to
walk
through
this
graph.
We
could
have
gone
through
a
different
direction.
We
could
have
done
this
in
many
different
ways,
just
like
any
other.
A
You
know
like
programming
language
for
that
matter,
but
that
just
gives
you
a
flavor
of
how
you
would
do
a
walk,
so
that
covers
tinker
pop,
but
going
deeper.
You
still
tinker
pop
doesn't
provide
a
full
implementation
of
craft
databases
and
that's
where
we
turn
to
something
called
Titan
DB.
This
fills
in
the
gaps
that
Tinker
Papa
leaves
behind.
So
this
was
actually
a
project
that
created
by
Marco
Rodriguez
and
his
partner
Matias
broke
ler.
It
was
the
full
implementation
of
a
graph
database.
A
Again
it
was
an
open
source
project,
Apache
licensed
by
this
one,
consulting
firm
Aurelius.
But
what
happened
was
you
know?
They
were
getting
good
adoption
from
the
community,
but
they
got
acquired
by
data
stacks
in
2015
when
they
got
acquired,
they
they
kind
of
left
the
community
hanging.
They
didn't
say
what
the
future
of
the
project
was
gonna
be,
so
even
though,
is
still
open,
source
development
had
slowed
down
significantly
and
meanwhile
they
released
Davis
stacks
graph
one.
So
a
community
was
left
hanging
out
they're,
not
sure
what
to
do.
A
Oh
actually
mentioned
that
data,
the
Stax
Enterprise
graph
it
what
it
was
a
Enterprise
commercial
license
only
so
we
got
together
with
some
partners
and
we
we
made
a
fork
of
the
Titan
graph
database
and
turned
into
jams
graph
at
the
Linux
Foundation,
as
I
mentioned
2016
and
really
the
the
goal
here
was
to
reconnect
the
open
source
community
and
embrace
open
governance.
Since
we
were
hosted
at
the
Linux
Foundation,
no
single
company
who
controlled
it
anymore
and
we
can
just
move
together
as
we
were
before.
So
we
have
our
founders.
A
You
know
Google
IBM,
Hortonworks,
now,
clitter
are
a
good
set
of
companies,
but
I
mean
since
then,
we've
continued
to
add
new
companies
and
individuals
as
well
they're
all
listed
there,
and
these
are
big
companies
pretty
much
anyone
who's.
Looking
for
a
scalable,
open,
store,
scrap
database,
Janus
graph
is
probably
one
the
top
options
that
come
to
mind
right
now
in
the
graph
database
base.
Most
of
the
options
are
our
proprietary.
A
We're
seeing
some
good
adoption
from
other
open
source
projects
as
well
I've
lifted
some
of
them
here
in
particular,
I'd
point
out
the
ones
that
are
actually
hosted
at
the
Linux
Foundation
first
is
the
project
called
Egeria.
It
falls
under
the
ODP
AI
umbrella.
It's
for
open
data,
open
metadata
and
governance.
A
So
what
you're
able
to
do
with
that
project
is
keep
track
of
your
data
sources
at
governance
around
it
at
access
control.
Things
like
that
it
can
use
Janis
graph
as
a
primary
data
store
as
an
example
or
because
it
has
a
pluggable
model
own
app.
If
you
went
to
some
of
the
keynotes
earlier
today,
they
talked
a
lot
about
how
this
is
very
much
becoming
the
standards
around
networking.
A
A
This
is
a
graph
slide
of
the
Janus
graph
architecture,
so,
in
addition
to
being
an
open
source
project
with
a
good
open
source
license
one
of
the
key
things
that
we
liked
about
Titan
and
now
Janus
graph
was
the
flexibility
of
it.
So
here
in
this
diagram,
the
light
green
items,
those
actually
come
out
of
the
tinkerer
prop
project,
the
the
tinkerer
pop
API
is
the
structure
and
the
language,
but
this
these
darker
green
boxes.
This
is
what
Janus
graph
provides.
It
provides
a
management
layer
for
managing
your
schema.
A
It
provides
the
database
layer
for
doing
transactions
and
data
management,
but
probably
the
best
part
for
us
was
the
flexible
storage
and
indexing
layer.
So
this
is
an
abstraction
that
allowed
us
to
experiment
again
with
different
storage
backends.
So
on
the
storage
backends,
we
have
Patrick
Cassandra,
HBase,
Berkeley
DB.
Since
then,
we've
added
backends
for
I
believe
there's
any
others.
There's
Foundation
DB
Silla
DB,
but
it
goes
on
and
on
what's
great
about.
That
is
that
you
can
take
advantage
of
the
skills
that
you
have
already.
A
So
if
you
already
have
skills-
or
you
already
have
a
data
center
of
HBase,
you
can
use
that
and
you
can
do
graph
on
top
of
it
or,
if
you
wanted
to
say,
compare
the
performance
characteristics
of
Cassandra
versus
HBase.
You
can
do
that
Google
or
partner.
They.
They
have
Google
Cloud
BigTable,
which
is
based
on
the
HBase
spec.
So
you
can
move
easily
using
chance
graph
from
on-prem
to
the
cloud.
A
Similarly
for
the
indexing
backends,
so
the
reason
that
we
have
different
backends
here
for
the
storage
and
the
indexing,
your
primary
storage.
This
is
for
storing
your
vertices
in
your
edges.
If
you
wanted
to
do
things
like
full-text
search,
numerical
search,
geospatial
search,
that's
where
we
have
additional
data
stored
in
the
these
other
backends,
whether
it's
elasticsearch,
Apache,
Solr,
leasing.
A
So
again
the
key
benefits
here
again:
it's
the
licensing
that
that's
number
one
having
a
free
software
license.
That's
permissive
and
lets
you
do
what
you
want
with
it
right
now
in
the
database
space.
There
are
a
lot
of
vendors
like
MongoDB
and
elasticsearch
that
are
starting
to
move
more
towards
a
restrictive
license
to
protect.
A
You
know
how
they
want
to
operate,
but
with
the
apache
license,
you
can
do
whatever
you
want
with
this
software,
and
you
know
not
having
one
company
leading
it
just
means
that
it
always
be
open
for
you
to
use
and
collaborate
with
I
already
talked
about
the
pluggable
storage
options.
We
found
that
really
to
be
the
most
interesting
because,
like
I
said,
you
have
the
choice
of
using
either
open
source
options.
A
You
can
write,
use
your
own
proprietary
backends,
whatever
you
need
to
suit
your
your
use
cases
so
again,
I'm
from
the
open
source
group
of
IBM
and
our
commitment
to
open
source.
It
comes
down
to
code,
content
and
community.
So
we
are
I
talked
about.
You
know
some
of
the
some
of
the
code.
You
know
with
Janus
crap,
but
we
built
many
different
assets
around
helping
people
getting
on
boarded
with
Janus
graph.
So
I
have
some
links
down
here.
A
B
A
Exposure
to
Janus
grow
so
from
a
project
perspective,
like
I
said,
we
started
in
2016
we're
continuing
and
grow
we've
added,
both
committers
and
technical
steering
committee
members,
since,
with
the
onset
many
different
companies
have
on
board,
but
it
really
is
just
going
going
continuing
on
with
the
idea
of
diversity.
One
of
the
key
gaps
that
we
had
from
the
start
was
around
client
driver
support.
A
The
stack
is
built
in
Java,
but
many
of
our
programmers
these
days
are
working
with
other
languages,
so
over
the
past
year
or
so,
there's
been
a
big
push
to
get
client
drivers
out
right
now,
we've
already
released
the
dotnet
driver
I
think
Python
is
very
close
to
being
being
done
in
JavaScript.
Those
are
the
top
three
languages
that
people
have
been
asking
for
different
platform
support.
Most
people
are
using
Linux
I'm
in
a
Linux
conference,
but
you
know
there
are
still
plenty
people
who
use
Windows
and
those
people
have
been
looking
for
support.
A
We
recently
released
docker
images
for
the
first
time,
which
is
fantastic,
getting
on
board
with
that
Apache
on
barri.
This
is
a
more
for
the
operation
side
of
the
house.
Being
able
to
have
an
Operations
console
to
easily
be
able
to
deploy,
this
is
good
if
you're
using
Apache,
HBase
and
solar,
so
you
can
easily
have
Janice
crack
in
that
environment.
I
talked
a
little
bit
before
about
the
the
back-end
support.
A
These
are
mostly
coming
from
the
community,
so
I
mean
foundation,
DB
apple,
recently
open
source.
This
I
think
last
year
and
one
of
our
TSC
members
started
flushing
out
a
back-end
support
for
that
database.
It's
interesting
about
foundation
DB
is
that
they
they
used
to
be
open
source
as
well
and
they
got
bought
by
apple
and
then
they
went
closed.
Then
apple
opened
them
up
again,
but
when
they
were
originally
open
source,
they
had
a
back-end
adapter
for
titan.
So
it's
coming
full
circle
on
them.
A
Everyone
wants
to
be,
but
really
what
I
mean
I
it's
a
database.
What
we
want
to
continue
focusing
on
is
performance
so
being
able
to
benchmark
now.
This
isn't
necessarily
benchmarking.
Against
other
databases,
I
mean
with
the
flexibility
that
you
have
different
storage,
backends
I
mean
I.
Think
that's
really
the
key
right.
Now.
People
always
ask
well
which
back-end
should
I
use.
So
I
mean
being
able
to
benchmark
there,
that's
something
we
want
to
focus
on
that
developer
experience
around
bulk
loading
and
ETL
that
that
could
all
use
help
from
an
industry
perspective.
A
This
property
graph
schema
working
group
they're
there,
several
vendors
right
now,
who
are
they
get
together
and
get
some
standards
around
graph
databases,
I,
think
the
outcomes
from
the
property
graph
schema
working
group
there's
going
to
be
a
w3c.
The
World
Wide
Web
Consortium
they're,
going
to
have
a
a
graph
standard
that
that's
where
we're
headed
with
a
standardized
graph
language
and
from
a
Janis
graph
perspective.
You
know
it's
been
our
mission
to
be
a
open
source.
A
A
Don't
have
any
benchmarks
with
me:
they
I
believe
you
can
probably
find
some,
but
I
mean
from
from
a
performance
benchmarks.
I
mean
that's
something
I
think
our
community
can
work
better
to
publicize
and
produce,
but
right
now
we
don't
have
any
benchmarking
in
the
product
at
all.
Right.
It's
been
done
by
other
parties,
so
III
don't
have
that.
A
Well,
I
mean
they're
in
dirt,
so
neo4j
came
about,
I
mean
it's
probably
the
oldest
graph
database
and
there's
it
has
I'm
the
key
difference
that
people
like
to
point
to
with
neo4j
is
that
it
it
uses
cipher
they
originally
they
were
implementing,
tinker
pop
and
they
have
gremlin
support,
but
over
time
they
decided
to
create
their
own
graph.
Language
called
cipher.
A
So
that's
one
of
the
main
differences
it.
It
has
a
GPL
license
and
once
you
get
beyond
a
certain
scale,
your
card
to
get
a
commercial
license,
but
I
mean
it's
a
good
product.
It's
I
mean
as
far
as
differences
I
mean
it's
single
vendor
control.
So
there
are
good
things
that
come
with
that,
so
they
have
more
tooling
available.
A
They
have
dedicated
people
to
do
marketing.
So
those
are
some
of
the
differences
same
with
tiger
graph.
I
mean
again,
that's
another
proprietary
database,
again
singer
single
bender
commercial
license,
I
think
that
they
were
founded
by
former
Twitter
engineers,
so
I
think
you
know
when
I've
spoken
with
them,
what
they're
trying
to
do
is
go
after
neo4j
more
in
the
large
graph
arena.
A
And
they
have
a
different
language
tiger
graph
does
not
implement,
tinker
pop
or
open
cipher;
they
have
their
own
language,
I
think
they
call
graph
QL,
but
again
like
I
mentioned
before,
and
this
is
why
the
graph
database
space
is
still
emerging.
It's
still
I
mean
after
so
many
years.
It's
solids
and
standardized
like
the
relational
database
with
sequel
is
but
I
think
moving
forward
with
the
w3c
and
getting
onto
a
graph
standard.
It'll
help
settle
things
in
the
industry
around
graph
databases.