Re: What makes 'graph traversals' and 'relational joins' the same?

[Stephen Mallette]

Trying to catch up on threads a bit...enjoying the discussion and I hope
I'm following along fully because it's sounding really nice. Letting the
type system be so open in previous versions of TinkerPop has created so
many inconsistencies and inelegant solutions which have only be exaggerated
by Gremlin Language Variants. Anyway, regarding:

>> 5. ComplexTypes don’t go over the wire — a ComplexTypeProxy with
>> appropriately provided toString() is all that leaves the TP4 VM.
>>

> As a tuple, ComplexTypes / ADTs go over the wire. The values of their
> primitive fields should probably go with them. However, the values of
their
> element / entity fields are just references; the attached element doesn't
> go with them.

I think I'd agree with Josh that we'd send these back over the wire,
especially if there is agreement that they are just a tuple form which
means that providers won't need to get into low-level serializer
development for custom types. TinkerPop would just know how to deal with
them for network transport. I guess providers would just have to provide
libraries with the ComplexType/ADT implementations in the programming
languages they wanted to support. In cases where they didn't, a user could
be left to work with a raw TinkerPop ComplexType/ADT instance which could
arguably be a better state than where they are left now which would be
serialization errors.



On Thu, Apr 25, 2019 at 2:07 PM Joshua Shinavier  wrote:

> Hi Marko. Responses inline.
>
> On Wed, Apr 24, 2019 at 10:30 AM Marko Rodriguez 
> wrote:
>
> > Hi,
> >
> > I think I understand you now. The concept of local and non-local data is
> > what made me go “ah!”
> >
>
> Nice. I also brought this up yesterday in the Property Graph Schema Working
> Group, where there is a discussion going on about whether/how graph
> databases can contain multiple graphs. Can an element belong to multiple
> graphs, can it have different properties in different graphs, etc. If each
> graph element is atomic, referencing other graph elements but not
> containing them, then it is very straightforward to think of a property
> graph as a simple set of elements. Graph relations are just set relations,
> making it easy to pull graphs apart and put graphs together (e.g. when
> building a stream, merging streams, etc.). If you are willing to make the
> open world assumption (e.g. "I know e[7] is a 'knows' edge, but I don't
> know what its out- and in-vertices are"), then you can't even partition a
> graph in such a way that the partitions are not valid graphs.
>
>
> So let me reiterate what I think you are saying.
> >
> > v[1] is guaranteed to have its id data local to it. All other information
> > could be derived via id-based "equi-joins.” Thus, we can’t assume that a
> > vertex will always have its properties and edges co-located with it.
>
>
> Yes indeed. A particular graph vendor may choose to co-locate properties
> with a vertex and edges with out- or in-vertex (or both, e.g. as JanusGraph
> does), but this is an optimization. At a logical level, you can think of an
> element and its dependents as belonging to separate relations.
>
>
>
> > However, we can assume that it knows where to get its property and edge
> > data when requested.
>
>
> Yes; you need to be able to select().
>
>
>
> > Assume the following RDBMS-style data structure that is referenced by
> > com.example.MyGraph.
> >
> > vertex_table
> > id label
> > 1  person
> > 2  person
> > …
>
>
> That is one way to go. I believe this scheme is what Ryan and David would
> call the Grothendieck construction; all relations of a given arity are
> marked with their type and concatenated into a single relation. I am still
> a little sketchy on the Grothendieck construction, so I hope that is a
> correct statement.
>
> However, you can also think of distinct element types (edge labels, vertex
> labels, property keys, hyperedge signatures) as distinct relations. So you
> instead of vertex_table, you would have
>
> person_table
> id
> 1
> 2
>
> Vertices are such trivial relations that they don't need to be stored as a
> tables. Edges are more interesting:
>
> knows_table
> out in
> 1 2
> 1 4
>
> Properties are similar:
>
> name_table
> out out_label out in
> 1 person marko
> 2 person vadas
> 3 project lop
> 4 person josh
> 5 project ripple
> 6 person peter
>
> The property table has a bit of a twist, because its out-label is a
> disjoint union of "person" and "project"; both persons and projects can
> have names, so you tag the out-element with its label/type. This is not
> necessary for "knows" because the out-label is always "person".
>
>
>
> > properties_table
> > id  name   age
> > 1   marko  29
> > 2   josh   35
> > …
> >
> > edge_table
> > id outV  label  inV
> > 0  1knows   2
> > …
> >
>
> Yes, this also works, and is equivalent to what I wrote above, with one
> tweak: if tagged unions are supported (which IMO they should be, so we have
> both a "times" and a "plus" in our type algebra), 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Joshua Shinavier]

Hi Marko. Responses inline.

On Wed, Apr 24, 2019 at 10:30 AM Marko Rodriguez 
wrote:

> Hi,
>
> I think I understand you now. The concept of local and non-local data is
> what made me go “ah!”
>

Nice. I also brought this up yesterday in the Property Graph Schema Working
Group, where there is a discussion going on about whether/how graph
databases can contain multiple graphs. Can an element belong to multiple
graphs, can it have different properties in different graphs, etc. If each
graph element is atomic, referencing other graph elements but not
containing them, then it is very straightforward to think of a property
graph as a simple set of elements. Graph relations are just set relations,
making it easy to pull graphs apart and put graphs together (e.g. when
building a stream, merging streams, etc.). If you are willing to make the
open world assumption (e.g. "I know e[7] is a 'knows' edge, but I don't
know what its out- and in-vertices are"), then you can't even partition a
graph in such a way that the partitions are not valid graphs.


So let me reiterate what I think you are saying.
>
> v[1] is guaranteed to have its id data local to it. All other information
> could be derived via id-based "equi-joins.” Thus, we can’t assume that a
> vertex will always have its properties and edges co-located with it.


Yes indeed. A particular graph vendor may choose to co-locate properties
with a vertex and edges with out- or in-vertex (or both, e.g. as JanusGraph
does), but this is an optimization. At a logical level, you can think of an
element and its dependents as belonging to separate relations.



> However, we can assume that it knows where to get its property and edge
> data when requested.


Yes; you need to be able to select().



> Assume the following RDBMS-style data structure that is referenced by
> com.example.MyGraph.
>
> vertex_table
> id label
> 1  person
> 2  person
> …


That is one way to go. I believe this scheme is what Ryan and David would
call the Grothendieck construction; all relations of a given arity are
marked with their type and concatenated into a single relation. I am still
a little sketchy on the Grothendieck construction, so I hope that is a
correct statement.

However, you can also think of distinct element types (edge labels, vertex
labels, property keys, hyperedge signatures) as distinct relations. So you
instead of vertex_table, you would have

person_table
id
1
2

Vertices are such trivial relations that they don't need to be stored as a
tables. Edges are more interesting:

knows_table
out in
1 2
1 4

Properties are similar:

name_table
out out_label out in
1 person marko
2 person vadas
3 project lop
4 person josh
5 project ripple
6 person peter

The property table has a bit of a twist, because its out-label is a
disjoint union of "person" and "project"; both persons and projects can
have names, so you tag the out-element with its label/type. This is not
necessary for "knows" because the out-label is always "person".



> properties_table
> id  name   age
> 1   marko  29
> 2   josh   35
> …
>
> edge_table
> id outV  label  inV
> 0  1knows   2
> …
>

Yes, this also works, and is equivalent to what I wrote above, with one
tweak: if tagged unions are supported (which IMO they should be, so we have
both a "times" and a "plus" in our type algebra), then property_table
should also include a "label" column, and edge_table should include
"outLabel" and "inLabel", i.e.:

properties_table
id  label name   age
1   person marko  29
2   person vadas   27
…

and

edge_table
id label outV  outLabel  inV inLabel
0  knows 1 person  2 person
…

In a tagged union, you mark the type of a field along with the value or
reference, for the sake of type checking and pattern matching.

Hard to say whether the table-per-relation or the table-per-arity approach
is better. FWIW, at Uber, we use a table per relation for the sake of
better data isolation. If you want to take advantage of the physical types
of the database, you may want multiple properties_tables, one per datatype
(so you're not storing every property value as a string).


If we want to say that the above data structure is a graph, what is
> required of “ComplexType” such that we can satisfy both Neo4j-style and
> RDBMS-style graph encodings? Assume ComplexType is defined as:
>
> interface ComplexType
>   Iterator adjacents(String label, Object... identifiers)
>

You can think of a ComplexType as a row in a database. It just has the
local fields specific to the type. In order to access attached elements,
you need a select(), and your adjacents() looks pretty close to that. I
would write:

Iterator adjacents(String label, String field)

So for example, adjacents("knows", "out") from v[1] gives you an iterator
of "knows" edges for which v[1] is the out-vertex. Btw. adjacents() here is
the same as from() / comeFrom() in previous emails.



> Take this basic Gremlin traversal:
>
> g.V(1).out(‘knows’).values(‘name’)
>
> I now believe this 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hey,

Thinking through things more and re-reading your emails.

Its like this:

From an object you want to be able to go to the relations in which that 
object is a particular entry.
From that relation you want to go to another object referenced in 
another entry.

For instance assume this set of 3-tuple relations:

talk_table
speaker  listener  statement
markojosh  “sup bro"
markokuppitz   “dude man"

Lets say I’m at josh and I want to know what marko said to him:

josh.adjacents(‘talk’,’listener’, …) // and this is why you have 
from().restrict().to()

Using your from()/restrict()/to() notation:

josh.from(‘talk’,’listener’).restrict(‘speaker’,marko).to(‘statement’) 
=> “sup bro”

I want to get some terminology down:

Relation: a tuple with key/value entries. (basically a map)
Key: A relation column name.
Value: A relation column value.

So there are three operations:

1. Get the relations in which the current object is a value for the 
specified key. [select] // like a back()
2. Filter out those relations that don’t have a particular value for a 
particular key. [filter]
3. Get those objects in the remaining relations associated with a 
particular key. [project] // like a forward()

What did Kuppitz hear from Marko?


kuppitz.select(‘talk’,’listener’).filter(‘speaker’,marko).project(‘statement’) 
=> “dude man”

So, how do we do this with just goto pointer chasing?

kuppitz.goto(‘listener’).filter(goto(‘speaker’).is(marko)).goto(‘statement’)

That is, I went from Kuppitz to all those relations in which he is a listener. 
I then filtered out those relations that don’t have marko as the speaker. I 
then went to the statements associated with those remaining relations. However, 
with this model, I’m assuming that “listener” is unique to the talk_table and 
this is not smart…

Anywho, is this more in line with what you are getting at?

Thanks for your patience,
Marko.

http://rredux.com 




> On Apr 24, 2019, at 11:30 AM, Marko Rodriguez  wrote:
> 
> Hi,
> 
> I think I understand you now. The concept of local and non-local data is what 
> made me go “ah!”
> 
> So let me reiterate what I think you are saying.
> 
> v[1] is guaranteed to have its id data local to it. All other information 
> could be derived via id-based "equi-joins.” Thus, we can’t assume that a 
> vertex will always have its properties and edges co-located with it. However, 
> we can assume that it knows where to get its property and edge data when 
> requested. Assume the following RDBMS-style data structure that is referenced 
> by com.example.MyGraph.
> 
> vertex_table
> id label
> 1  person
> 2  person
> …
> 
> properties_table
> id  name   age
> 1   marko  29
> 2   josh   35
> …
> 
> edge_table
> id outV  label  inV
> 0  1knows   2
> …
> 
> If we want to say that the above data structure is a graph, what is required 
> of “ComplexType” such that we can satisfy both Neo4j-style and RDBMS-style 
> graph encodings? Assume ComplexType is defined as:
> 
> interface ComplexType
>   Iterator adjacents(String label, Object... identifiers)
> 
> Take this basic Gremlin traversal:
> 
> g.V(1).out(‘knows’).values(‘name’)
> 
> I now believe this should compile to the following:
> 
> [goto,V,1] [goto,outE,knows] [goto,inV] [goto,properties,name]
> 
> Given MyGraph/MyVertex/MyEdge all implement ComplexType and there is no local 
> caching of data on these respective objects, then the bytecode isn’t 
> rewritten and the following cascade of events occurs:
> 
> mygraph
> [goto,V,1] => 
>   mygraph.adjacents(“V”,1) => 
> SELECT * FROM vertex_table WHERE id=1
> myvertex1
> [goto,outE,knows] => 
>   myvertex1.adjacents(“outE”,”knows”) => 
> SELECT id FROM edge_table WHERE outV=1 AND label=knows
> myedge0
> [goto,inV,knows] => 
>   myedge1.adjacents(“inV”) => 
> SELECT vertex_table.id FROM vertex_table, edge_table WHERE 
> vertex_table.id=edge_table.inV AND edge_table.id=0
> myvertex2
> [goto,properties,name] => 
>   myvertex2.adjacents(“properties”,”name”) => 
> SELECT name FROM properties_table WHERE id=2
> “josh"
> 
> Lets review the ComplexType adjacents()-method:
> 
> complexType.adjacents(label,identifiers...)
> 
> complexType must have sufficient information to represent the tail of the 
> relation.
> label specifies the relation type (we will always assume that a single String 
> is sufficient)
> identifiers... must contain sufficient information to identify the head of 
> the relation.
> 
> The return of the the method adjacents() is then the object(s) on the other 
> side of the relation(s).
> 
> Now, given the way I have my data structure organized, I could beef up the 
> MyXXX implementation such that MyStrategy rewrites the base bytecode to:
> 
> [goto,V,1] [goto,out,knows][goto,properties,name]
> 
> The following cascade of events occurs:
> 
> mygraph
> [goto,V,1] => 
>   mygraph.adjacents(“V”,1) => 
>

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hi,

I think I understand you now. The concept of local and non-local data is what 
made me go “ah!”

So let me reiterate what I think you are saying.

v[1] is guaranteed to have its id data local to it. All other information could 
be derived via id-based "equi-joins.” Thus, we can’t assume that a vertex will 
always have its properties and edges co-located with it. However, we can assume 
that it knows where to get its property and edge data when requested. Assume 
the following RDBMS-style data structure that is referenced by 
com.example.MyGraph.

vertex_table
id label
1  person
2  person
…

properties_table
id  name   age
1   marko  29
2   josh   35
…

edge_table
id outV  label  inV
0  1knows   2
…

If we want to say that the above data structure is a graph, what is required of 
“ComplexType” such that we can satisfy both Neo4j-style and RDBMS-style graph 
encodings? Assume ComplexType is defined as:

interface ComplexType
  Iterator adjacents(String label, Object... identifiers)

Take this basic Gremlin traversal:

g.V(1).out(‘knows’).values(‘name’)

I now believe this should compile to the following:

[goto,V,1] [goto,outE,knows] [goto,inV] [goto,properties,name]

Given MyGraph/MyVertex/MyEdge all implement ComplexType and there is no local 
caching of data on these respective objects, then the bytecode isn’t rewritten 
and the following cascade of events occurs:

mygraph
[goto,V,1] => 
  mygraph.adjacents(“V”,1) => 
SELECT * FROM vertex_table WHERE id=1
myvertex1
[goto,outE,knows] => 
  myvertex1.adjacents(“outE”,”knows”) => 
SELECT id FROM edge_table WHERE outV=1 AND label=knows
myedge0
[goto,inV,knows] => 
  myedge1.adjacents(“inV”) => 
SELECT vertex_table.id FROM vertex_table, edge_table WHERE 
vertex_table.id=edge_table.inV AND edge_table.id=0
myvertex2
[goto,properties,name] => 
  myvertex2.adjacents(“properties”,”name”) => 
SELECT name FROM properties_table WHERE id=2
“josh"

Lets review the ComplexType adjacents()-method:

complexType.adjacents(label,identifiers...)

complexType must have sufficient information to represent the tail of the 
relation.
label specifies the relation type (we will always assume that a single String 
is sufficient)
identifiers... must contain sufficient information to identify the head of the 
relation.

The return of the the method adjacents() is then the object(s) on the other 
side of the relation(s).

Now, given the way I have my data structure organized, I could beef up the 
MyXXX implementation such that MyStrategy rewrites the base bytecode to:

[goto,V,1] [goto,out,knows][goto,properties,name]

The following cascade of events occurs:

mygraph
[goto,V,1] => 
  mygraph.adjacents(“V”,1) => 
SELECT * FROM vertex_table WHERE id=1
myvertex1
[goto,out,knows] => 
  myvertex1.adjacents(“outE”,”knows”) => 
SELECT vertex_table.id FROM vertex_table,edge_table WHERE outV=1 AND 
label=knows AND inV=vertex_table.id
myvertex2
[goto,properties,name] => 
  myvertex2.adjacents(“properties”,”name”) => 
SELECT name FROM properties_table WHERE id=2
“josh"

Now, I could really beef up MyStrategy when I realize that no path information 
is used in the traversal. Thus, the base bytecode compiles to:

[my:sql,SELECT name FROM properties_table,vertex_table,edge_table WHERE … lots 
of join equalities]

This would then just emit “josh” given the mygraph object.

——

To recap.

1. There are primitives.
2. There are Maps and Lists.
3. There are ComplexTypes.
4. ComplexTypes are adjacent to other objects via relations.
- These adjacent objects may be cached locally with the 
ComplexType instance.
- These adjacent objects may require some database lookup.
- Regardless, TP4 doesn’t care — its up to the provider’s 
ComplexType instance to decide how to resolve the adjacency.
5. ComplexTypes don’t go over the wire — a ComplexTypeProxy with 
appropriately provided toString() is all that leaves the TP4 VM.

Finally, to solve the asMap()/asList() problem, we simply have:

asMap(’name’,’age’) => complexType.adjacents(‘asMap’,’name’,’age')
asList() => complexType.adjacents(‘asList’)

It is up to the complexType to manifest a Map or List accordingly.

I see this as basically a big flatmap system. ComplexTypes just map from self 
to any number of logical neighbors as specified by the relation.

Am I getting it?,
Marko.

http://rredux.com 




> On Apr 24, 2019, at 9:56 AM, Joshua Shinavier  wrote:
> 
> On Tue, Apr 23, 2019 at 10:28 AM Marko Rodriguez 
> wrote:
> 
>> Hi,
>> 
>> I think we are very close to something useable for TP4 structure/. Solving
>> this problem elegantly will open the flood gates on tp4/ development.
>> 
> 
> Yes, and formality often brings elegance. I don't think we can do much
> better than relational algebra and relational calculus in terms of
> formality, so to the extent we can reduce the fundamental TP4 traversal
> steps to basic 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Joshua Shinavier]

On Tue, Apr 23, 2019 at 10:28 AM Marko Rodriguez 
wrote:

> Hi,
>
> I think we are very close to something useable for TP4 structure/. Solving
> this problem elegantly will open the flood gates on tp4/ development.
>

Yes, and formality often brings elegance. I don't think we can do much
better than relational algebra and relational calculus in terms of
formality, so to the extent we can reduce the fundamental TP4 traversal
steps to basic relational operations, the floodgates will also be open to
applications of query validation and query optimization from the last 40+
years of research.



> I still don’t grock your comeFrom().goto() stuff. I don’t get the benefit
> of having two instructions for “pointer chasing” instead of one.
>

There are just a handful of basic operations in relational algebra.
Projection, selection, union, complement, Cartesian product. Joins, as well
as all other operations, can be derived from these. A lot of graph
traversal can be accomplished using only projection and selection, which is
why we were able to get away with only to/goto and from/comeFrom in the
examples above. However, I believe you do need both operations. You can
kind of get away without from() if you assume that each vertex has local
inE and outE references to incoming and outgoing edges, but I see that as a
kind of pre-materialized from()/select(). If you think of edges strictly as
relations, and represent them in a straightforward way with tables, you
don't need the local inE and outE; whether you have them depends on the
graph back-end.



> Lets put that aside for now and lets turn to modeling a Vertex. Go back to
> my original representation:
>
> vertex.goto(‘label’)
> vertex.goto(‘id’)
>

Local (in my view). All good.



> vertex.goto(‘outE’)
> vertex.goto(‘inE’)
> vertex.goto(‘properties’)
>

Non-local (in my view). You can use goto(), but if the goal is to bring the
relational model into the fold, at a lower level you do have a select()
operation. Unless you make projections local to vertices instead of edges,
but then you just have the same problem in reverse. Am I making sense?


Any object can be converted into a Map. In TinkerPop3 we convert vertices
> into maps via:
>
> g.V().has(‘name’,’marko’).valueMap() => {name:marko,age:29}
> g.V().has(‘name’,’marko’).valueMap(true) =>
> {id:1,label:person,name:marko,age:29}
>

Maps are A-OK. In the case of properties, I think where we differ is that
you see a property like "name" as a key/value pair in a map local to the
vertex. I see the property as an element of type "name", with the vertex as
a value in its local map, logically if not physically. This allows maximum
flexibility in terms of meta-properties -- exotic beasts which seem to be
in a kind of limbo state in TP3, but if we're trying to be as general as
possible, some data models we might want to pull in, like GRAKN.AI, do
allow this kind of flexibility.



> In the spirit of instruction reuse, we should have an asMap() instruction
> that works for ANY object. (As a side: this gets back to ONLY sending
> primitives over the wire, no
> Vertex/Edge/Document/Table/Row/XML/ColumnFamily/etc.). Thus, the above is:
>
> g.V().has(‘name’,’marko’).properties().asMap() =>
> {name:marko,age:29}
> g.V().has(‘name’,’marko’).asMap() =>
> {id:1,label:person,properties:{name:marko,age:29}}
>

Again, no argument here, although I would think of a map as an
optimization. IMO, the fundamental projections from v[1] are id:1 and
label:Person. You could make a map out of these, or just use an offset,
since the keys are always the same. However, you can also build a map
including any key you can turn into a function. properties() is such a key.


You might ask, why didn’t it go to outE and inE and map-ify that data?
> Because those are "sibling” references, not “children” references.
>
> goto(‘outE’) is a “sibling” reference. (a vertex does not contain
> an edge)
> goto(‘id’) is a “child” reference. (a vertex contains the id)
>

I agree with both of those statements. A vertex does not contain the edges
incident on it. Again, I am thinking of properties a bit more like edges
for maximum generality.



> Where do we find sibling references?
> Graphs: vertices don’t contain each other.
> OO heaps: many objects don’t contain each other.
> RDBMS: rows are linked by joins, but don’t contain each other.
>

Yep.


So, the way in which we structure our references (pointers) determines the
> shape of the data and ultimately how different instructions will behave. We
> can’t assume that asMap() knows anything about
> vertices/edges/documents/rows/tables/etc. It will simply walk all
> child-references and create a map.
>

Just to play devil's advocate, you *could* include "inE" and "outE" as keys
in the local map of a vertex; it's just a matter of what you choose to do.
inE and outE are perfectly good functions from a vertex to a set of edges.


We don’t want TP to 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hi,

I think we are very close to something useable for TP4 structure/. Solving this 
problem elegantly will open the flood gates on tp4/ development.

——

I still don’t grock your comeFrom().goto() stuff. I don’t get the benefit of 
having two instructions for “pointer chasing” instead of one.

Lets put that aside for now and lets turn to modeling a Vertex. Go back to my 
original representation:

vertex.goto(‘label’)
vertex.goto(‘id’)
vertex.goto(‘outE’)
vertex.goto(‘inE’)
vertex.goto(‘properties’)

Any object can be converted into a Map. In TinkerPop3 we convert vertices into 
maps via:

g.V().has(‘name’,’marko’).valueMap() => {name:marko,age:29}
g.V().has(‘name’,’marko’).valueMap(true) => 
{id:1,label:person,name:marko,age:29}

In the spirit of instruction reuse, we should have an asMap() instruction that 
works for ANY object. (As a side: this gets back to ONLY sending primitives 
over the wire, no Vertex/Edge/Document/Table/Row/XML/ColumnFamily/etc.). Thus, 
the above is:

g.V().has(‘name’,’marko’).properties().asMap() => {name:marko,age:29}
g.V().has(‘name’,’marko’).asMap() => 
{id:1,label:person,properties:{name:marko,age:29}}

You might ask, why didn’t it go to outE and inE and map-ify that data? Because 
those are "sibling” references, not “children” references. 

goto(‘outE’) is a “sibling” reference. (a vertex does not contain an 
edge)
goto(‘id’) is a “child” reference. (a vertex contains the id)

Where do we find sibling references?
Graphs: vertices don’t contain each other.
OO heaps: many objects don’t contain each other.
RDBMS: rows are linked by joins, but don’t contain each other.

So, the way in which we structure our references (pointers) determines the 
shape of the data and ultimately how different instructions will behave. We 
can’t assume that asMap() knows anything about 
vertices/edges/documents/rows/tables/etc. It will simply walk all 
child-references and create a map.

We don’t want TP to get involved in “complex data types.” We don’t care. You 
can propagate MyDatabaseObject through the TP4 VM pipeline and load your object 
up with methods for optimizations with your DB and all that, but for TP4, your 
object is just needs to implement:

ComplexType
- Iterator children(String label)
- Iterator siblings(String label)
- default Iterator references(String label) { 
IteratorUtils.concat(children(label), siblings(label)) }
- String toString()

When a ComplexType goes over the wire to the user, it just represented as a 
ComplexTypeProxy with a toString() like v[1], 
tinkergraph[vertices:10,edges:34], etc. All references are disconnected. Yes, 
even children references. We do not want language drivers having to know about 
random object types and have to deal with implementing serializers and all that 
non-sense. The TP4 serialization protocol is primitives, maps, lists, bytecode, 
and traversers. Thats it!

*** Only Maps and Lists (that don’t contain complex data types) maintain their 
child references “over the wire.”

——

I don’t get your hypergraph example, so let me try another example:

tp ==member==> marko, josh

TP is a vertex and there is a directed hyperedge with label “member” connecting 
to marko and josh vertices.

tp.goto(“outE”).filter(goto(“label”).is(“member”)).goto(“inV”)

Looks exactly like a property graph query? However, its not because goto(“inV”) 
returns 2 vertices, not 1. EdgeVertexFlatmapFunction works for property graphs 
and hypergraphs. It doesn’t care — it just follows goto() pointers! That is, it 
follows the ComplexType.references(“inV”). Multi-properties are the same as 
well. Likewise for meta-properties. These data model variations are not 
“special” to the TP4 VM. It just walks references whether there are 0,1,2, or N 
of them.

Thus, what is crucial to all this is the “shape of the data.” Using your 
pointers wisely so instructions produce useful results.

Does any of what I wrote update your comeFrom().goto() stuff? If not, can you 
please explain to me why comeFrom() is cool — sorry for being dense (aka “being 
Kuppitz" — thats right, I said it. boom!).

Thanks,
Marko.

http://rredux.com 




> On Apr 23, 2019, at 10:25 AM, Joshua Shinavier  wrote:
> 
> On Tue, Apr 23, 2019 at 5:14 AM Marko Rodriguez 
> wrote:
> 
>> Hey Josh,
>> 
>> This gets to the notion I presented in “The Fabled GMachine.”
>>http://rredux.com/the-fabled-gmachine.html <
>> http://rredux.com/the-fabled-gmachine.html> (first paragraph of
>> “Structures, Processes, and Languages” section)
>> 
>> All that exists are memory addresses that contain either:
>> 
>>1. A primitive
>>2. A set of labeled references to other references or primitives.
>> 
>> Using your work and the above, here is a super low-level ‘bytecode' for
>> property graphs.
>> 
>> v.goto("id") => 1
>> 
> 
> LGTM. An id is 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Joshua Shinavier]

On Tue, Apr 23, 2019 at 5:14 AM Marko Rodriguez 
wrote:

> Hey Josh,
>
> This gets to the notion I presented in “The Fabled GMachine.”
> http://rredux.com/the-fabled-gmachine.html <
> http://rredux.com/the-fabled-gmachine.html> (first paragraph of
> “Structures, Processes, and Languages” section)
>
>  All that exists are memory addresses that contain either:
>
> 1. A primitive
> 2. A set of labeled references to other references or primitives.
>
> Using your work and the above, here is a super low-level ‘bytecode' for
> property graphs.
>
> v.goto("id") => 1
>

LGTM. An id is special because it is uniquely identifying / is a primary
key for the element. However, it is also just a field of the element, like
"in"/"inV" and "out"/"outV" are fields of an edge. As an aside, an id would
only really need to be unique among other elements of the same type. To the
above, I would add:

v.type() => Person

...a special operation which takes you from an element to its type. This is
important if unions are supported; e.g. "name" in my example can apply
either to a Person or a Project.


v.goto("label") => person
>

Or that. Like "id", "type"/"label" is special. You can think of it as a
field; it's just a different sort of field which will have the same value
for all elements of any given type.



> v.goto("properties").goto("name") => "marko"
>

OK, properties. Are properties built-in as a separate kind of thing from
edges, or can we treat them the same as vertices and edges here? I think we
can treat them the same. A property, in the algebraic model I described
above, is just an element with two fields, the second of which is a
primitive value. As I said, I think we need two distinct traversal
operations -- projection and selection -- and here is where we can use the
latter. Here, I will call it "comeFrom".

v.comeFrom("name", "out").goto("in") => {"marko"}

You can think of this comeFrom as a special case of a select() function
which takes a type -- "name" -- and a set of key/value pairs {("out", v)}.
It returns all matching elements of the given type. You then project to the
"in" value using your goto. I wrote {"marko"} as a set, because comeFrom
can give you multiple properties, depending on whether multi-properties are
supported.

Note how similar this is to an edge traversal:

v.comeFrom("knows", "out").goto("in") => {v[2], v[4]}

Of course, you could define "properties" in such a way that a
goto("properties") does exactly this under the hood, but in terms of low
level instructions, you need something like comeFrom.


v.goto("properties").goto("name").goto(0) => "m"
>

This is where the notion of optionals becomes handy. You can make
array/list indices into fields like this, but IMO you should also make them
safe. E.g. borrowing Haskell syntax for a moment:

v.goto("properties").goto("name").goto(0) => Just 'm'

v.goto("properties").goto("name").goto(5) => Nothing


v.goto("outE").goto("inV") => v[2], v[4]
>

I am not a big fan of untyped "outE", but you can think of this as a union
of all v.comeFrom(x, "out").goto("in"), where x is any edge type. Only
"knows" and "created" are edge types which are applicable to "Person", so
you will only get {v[2], v[4]}. If you want to get really crazy, you can
allow x to be any type. Then you get {v[2], v[4], 29, "marko"}.



> g.goto("V").goto(1) => v[1]
>

That, or you give every element a virtual field called "graph". So:

v.goto("graph") => g

g.comeFrom("Person", "graph") => {v[1], v[2], v[4], v[6]}

g.comeFrom("Person", "graph").restrict("id", 1)

...where restrict() is the relational "sigma" operation as above, not to be
confused with TinkerPop's select(), filter(), or has() steps. Again, I
prefer to specify a type in comeFrom (i.e. we're looking specifically for a
Person with id of 1), but you could also do a comprehension g.comeFrom(x,
"graph"), letting x range over all types.



> The goto() instruction moves the “memory reference” (traverser) from the
> current “memory address” to the “memory address” referenced by the goto()
> argument.
>

Agreed, if we also think of primitive values as memory references.



> The Gremlin expression:
>
> g.V().has(‘name’,’marko’).out(‘knows’).drop()
>
> ..would compile to:
>
>
> g.goto(“V”).filter(goto(“properties”).goto(“name”).is(“marko”)).goto(“outE”).filter(goto(“label”).is(“knows”)).goto(“inV”).free()
>


In the alternate universe:

g.comeFrom("Person", "graph").comeFrom("name", "out").restrict("in",
"marko").goto("out").comeFrom("knows", "out").goto("in").free()

I have wimped out on free() and just left it as you had it, but I think it
would be worthwhile to explore a monadic syntax for traversals with
side-effects. Different topic.

Now, all of this "out", "in" business is getting pretty repetitive, right?
Well, the field names become more diverse if we allow hyper-edges and
generalized ADTs. E.g. in my Trip example, say I want to know all drop-off
locations for a given rider:

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hey Josh,

This gets to the notion I presented in “The Fabled GMachine.”
http://rredux.com/the-fabled-gmachine.html 
 (first paragraph of “Structures, 
Processes, and Languages” section)

 All that exists are memory addresses that contain either:

1. A primitive
2. A set of labeled references to other references or primitives.

Using your work and the above, here is a super low-level ‘bytecode' for 
property graphs.

v.goto("id") => 1
v.goto("label") => person
v.goto("properties").goto("name") => "marko"
v.goto("properties").goto("name").goto(0) => "m"
v.goto("outE").goto("inV") => v[2], v[4]
g.goto("V").goto(1) => v[1]

The goto() instruction moves the “memory reference” (traverser) from the 
current “memory address” to the “memory address” referenced by the goto() 
argument.

The Gremlin expression:

g.V().has(‘name’,’marko’).out(‘knows’).drop()

..would compile to:


g.goto(“V”).filter(goto(“properties”).goto(“name”).is(“marko”)).goto(“outE”).filter(goto(“label”).is(“knows”)).goto(“inV”).free()

…where free() is the opposite of malloc().

If we can get things that “low-level” and still efficient to compile, then we 
can model every data structure. All you are doing is pointer chasing through a 
withStructure() data structure. .

No one would ever want to write strategies for goto()-based Bytecode. Thus, 
perhaps there could be a PropertyGraphDecorationStrategy that does:

g = Gremlin.traversal(machine).withStructure(JanusGraph.class)  // this will 
register the strategy
g.V().has(‘name’,’marko’).out(‘knows’).drop() // this generates goto()-based 
bytecode underneath the covers
==submit==>
[goto,V][filter,[goto…]][goto][goto][free]] // Bytecode from the “fundamental 
instruction set” 
[V][has,name,marko][out,knows][drop] // PropertyGraphDecorationStrategy 
converts goto() instructions into a property graph-specific instruction set.
[V-idx,name,marko][out,knows][drop] // JanusGraphProviderStrategy converts 
V().has() into an index lookup instruction.

[I AM NOW GOING OFF THE RAILS]

Like fluent-style Gremlin, we could have an AssemblyLanguage that only has 
goto(), free(), malloc(), filter(), map(), reduce(), flatmap(), barrier(), 
branch(), repeat(), sideEffect() instructions. For instance, if you wanted to 
create an array list (not a linked list! :):

[“marko”,29,true]

you would do:

malloc(childrefs(0,1,2)).sideEffect(goto(0).malloc(“marko”)).sideEffect(goto(1).malloc(29)).sideEffect(goto(2).malloc(true))

This tells the underlying data structure (e.g. database) that you want to 
create a set of “children references" labeled 0, 1, and 2. And then you goto() 
each reference and add primitives. Now, if JanusGraph got this batch of 
instructions, it would do the following:

Vertex refs = graph.addVertex()
refs.addEdge(“childref", graph.addVertex(“value”,”marko”)).property(“ref”,0)
refs.addEdge(“childref", graph.addVertex(“value”,29)).property(“ref”,1)
refs.addEdge(“childref", graph.addVertex(“value”,true)).property(“ref”,2)

The reason for childref, is that if you delete the list, you should delete all 
the children referenced data! In other words, refs-vertex has cascading deletes.

list.drop()
==>
list.sideEffect(goto(0,1,2).free()).free()

JanusGraph would then do:

refs.out(“childref").drop()
refs.drop()

Or, more generally:

refs.emit().repeat(out(“childref”)).drop()

Trippy.

[I AM NOW BACK ON THE RAILS]

Its as if “properties”, “outE”, “label”, “inV”, etc. references mean something 
to property graph providers and they can do more intelligent stuff than what 
MongoDB would do with such information. However, someone, of course, can create 
a MongoDBPropertyGraphStrategy that would make documents look like vertices and 
edges and then use O(log(n)) lookups on ids to walk the graph. However, if that 
didn’t exist, it would still do something that works even if its horribly 
inefficient as every database can make primitives with references between them!

Anywho @Josh, I believe goto() is what you are doing with multi-references off 
an object. How do we make it all clean, easy, and universal?

Marko.

http://rredux.com 




> On Apr 22, 2019, at 6:42 PM, Joshua Shinavier  wrote:
> 
> Ah, glad you asked. It's all in the pictures. I have nowhere to put them 
> online at the moment... maybe this attachment will go through to the list?
> 
> Btw. David Spivak gave his talk today at Uber; it was great. Juan Sequeda 
> (relational <--> RDF mapping guy) was also here, and Ryan joined remotely. 
> Really interesting discussion about databases vs. graphs, and what category 
> theory brings to the table.
> 
> 
> On Mon, Apr 22, 2019 at 1:45 PM Marko Rodriguez  > wrote:
> Hey Josh,
> 
> I’m digging what you are saying, but the pictures didn’t come through for me 
> ? … Can you provide them again (or if dev@ is filtering them, can you give me 
> URLs to them)?
> 
> Thanks,

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hey Josh,

I’m digging what you are saying, but the pictures didn’t come through for me ? 
… Can you provide them again (or if dev@ is filtering them, can you give me 
URLs to them)?

Thanks,
Marko.


> On Apr 21, 2019, at 12:58 PM, Joshua Shinavier  wrote:
> 
> On the subject of "reified joins", maybe be a picture will be worth a few 
> words. As I said in the thread 
>  on 
> property graph standardization, if you think of vertex labels, edge labels, 
> and property keys as types, each with projections to two other types, there 
> is a nice analogy with relations of two columns, and this analogy can be 
> easily extended to hyper-edges. Here is what the schema of the TinkerPop 
> classic graph looks like if you make each type (e.g. Person, Project, knows, 
> name) into a relation:
> 
> 
> 
> I have made the vertex types salmon-colored, the edge types yellow, the 
> property types green, and the data types blue. The "o" and "I" columns 
> represent the out-type (e.g. out-vertex type of Person) and in-type (e.g. 
> property value type of String) of each relation. More than two arrows from a 
> column represent a coproduct, e.g. the out-type of "name" is Person OR 
> Project. Now you can think of out() and in() as joins of two tables on a 
> primary and foreign key.
> 
> We are not limited to "out" and "in", however. Here is the ternary 
> relationship (hyper-edge) from hyper-edge slide 
> 
>  of my Graph Day preso, which has three columns/roles/projections:
> 
> 
> 
> I have drawn Says in light blue to indicate that it is a generalized element; 
> it has projections other than "out" and "in". Now the line between relations 
> and edges begins to blur. E.g. in the following, is PlaceEvent a vertex or a 
> property?
> 
> 
> 
> With the right type system, we can just speak of graph elements, and use 
> "vertex", "edge", "property" when it is convenient. In the relational model, 
> they are relations. If you materialize them in a relational database, they 
> are rows. In any case, you need two basic graph traversal operations:
> project() -- forward traversal of the arrows in the above diagrams. Takes you 
> from an element to a component like in-vertex.
> select() -- reverse traversal of the arrows. Allows you to answer questions 
> like "in which Trips is John Doe the rider?"
> 
> Josh
> 
> 
> On Fri, Apr 19, 2019 at 10:03 AM Marko Rodriguez  > wrote:
> Hello,
> 
> I agree with everything you say. Here is my question:
> 
> Relational database — join: Table x Table x equality function -> Table
> Graph database — traverser: Vertex x edge label -> Vertex
> 
> I want a single function that does both. The only think was to represent 
> traverser() in terms of join():
> 
> Graph database — traverser: Vertices x Vertex x equality function -> 
> Vertices
> 
> For example, 
> 
> V().out(‘address’)
> 
> ==>
> 
> g.join(V().hasLabel(‘person’).as(‘a’)
>V().hasLabel(‘addresses’).as(‘b’)).
>  by(‘name’).select(?address vertex?)
> 
> That is, join the vertices with themselves based on some predicate to go from 
> vertices to vertices.
> 
> However, I would like instead to transform the relational database join() 
> concept into a traverser() concept. Kuppitz and I were talking the other day 
> about a link() type operator that says: “try and link to this thing in some 
> specified way.” .. ?? The problem we ran into is again, “link it to what?”
> 
> - in graph, the ‘to what’ is hardcoded so you don’t need to specify 
> anything.
> - in rdbms, the ’to what’ is some other specified table.
> 
> So what does the link() operator look like?
> 
> ——
> 
> Some other random thoughts….
> 
> Relational databases join on the table (the whole collection)
> Graph databases traverser on the vertex (an element of the whole collection)
> 
> We can make a relational database join on single row (by providing a filter 
> to a particular primary key). This is the same as a table with one row. 
> Likewise, for graph in the join() context above:
> 
> V(1).out(‘address’) 
> 
> ==>
> 
> g.join(V(1).as(‘a’)
>V().hasLabel(‘addresses’).as(‘b’)).
>  by(‘name’).select(?address vertex?)
> 
> More thoughts please….
> 
> Marko.
> 
> http://rredux.com   >
> 
> 
> 
> 
> > On Apr 19, 2019, at 4:20 AM, pieter martin  > > wrote:
> > 
> > Hi,
> > The way I saw it is that the big difference is that graph's have
> > reified joins. This is both a blessing and a curse.
> > A blessing because its much easier (less text to type, less mistakes,
> > clearer semantics...) to traverse an edge than to construct a manual
> > join.A curse because there are 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Joshua Shinavier]

On the subject of "reified joins", maybe be a picture will be worth a few
words. As I said in the thread
 on
property graph standardization, if you think of vertex labels, edge labels,
and property keys as types, each with projections to two other types, there
is a nice analogy with relations of two columns, and this analogy can be
easily extended to hyper-edges. Here is what the schema of the TinkerPop
classic graph looks like if you make each type (e.g. Person, Project,
knows, name) into a relation:

[image: image.png]


I have made the vertex types salmon-colored, the edge types yellow, the
property types green, and the data types blue. The "o" and "I" columns
represent the out-type (e.g. out-vertex type of Person) and in-type (e.g.
property value type of String) of each relation. More than two arrows from
a column represent a coproduct, e.g. the out-type of "name" is Person OR
Project. Now you can think of out() and in() as joins of two tables on a
primary and foreign key.

We are not limited to "out" and "in", however. Here is the ternary
relationship (hyper-edge) from hyper-edge slide

of
my Graph Day preso, which has three columns/roles/projections:

[image: image.png]


I have drawn Says in light blue to indicate that it is a generalized
element; it has projections other than "out" and "in". Now the line between
relations and edges begins to blur. E.g. in the following, is PlaceEvent a
vertex or a property?

[image: image.png]


With the right type system, we can just speak of graph elements, and use
"vertex", "edge", "property" when it is convenient. In the relational
model, they are relations. If you materialize them in a relational
database, they are rows. In any case, you need two basic graph traversal
operations:

   - project() -- forward traversal of the arrows in the above diagrams.
   Takes you from an element to a component like in-vertex.
   - select() -- reverse traversal of the arrows. Allows you to answer
   questions like "in which Trips is John Doe the rider?"


Josh


On Fri, Apr 19, 2019 at 10:03 AM Marko Rodriguez 
wrote:

> Hello,
>
> I agree with everything you say. Here is my question:
>
> Relational database — join: Table x Table x equality function ->
> Table
> Graph database — traverser: Vertex x edge label -> Vertex
>
> I want a single function that does both. The only think was to represent
> traverser() in terms of join():
>
> Graph database — traverser: Vertices x Vertex x equality function
> -> Vertices
>
> For example,
>
> V().out(‘address’)
>
> ==>
>
> g.join(V().hasLabel(‘person’).as(‘a’)
>V().hasLabel(‘addresses’).as(‘b’)).
>  by(‘name’).select(?address vertex?)
>
> That is, join the vertices with themselves based on some predicate to go
> from vertices to vertices.
>
> However, I would like instead to transform the relational database join()
> concept into a traverser() concept. Kuppitz and I were talking the other
> day about a link() type operator that says: “try and link to this thing in
> some specified way.” .. ?? The problem we ran into is again, “link it to
> what?”
>
> - in graph, the ‘to what’ is hardcoded so you don’t need to
> specify anything.
> - in rdbms, the ’to what’ is some other specified table.
>
> So what does the link() operator look like?
>
> ——
>
> Some other random thoughts….
>
> Relational databases join on the table (the whole collection)
> Graph databases traverser on the vertex (an element of the whole
> collection)
>
> We can make a relational database join on single row (by providing a
> filter to a particular primary key). This is the same as a table with one
> row. Likewise, for graph in the join() context above:
>
> V(1).out(‘address’)
>
> ==>
>
> g.join(V(1).as(‘a’)
>V().hasLabel(‘addresses’).as(‘b’)).
>  by(‘name’).select(?address vertex?)
>
> More thoughts please….
>
> Marko.
>
> http://rredux.com 
>
>
>
>
> > On Apr 19, 2019, at 4:20 AM, pieter martin 
> wrote:
> >
> > Hi,
> > The way I saw it is that the big difference is that graph's have
> > reified joins. This is both a blessing and a curse.
> > A blessing because its much easier (less text to type, less mistakes,
> > clearer semantics...) to traverse an edge than to construct a manual
> > join.A curse because there are almost always far more ways to traverse
> > a data set than just by the edges some architect might have considered
> > when creating the data set. Often the architect is not the domain
> > expert and the edges are a hardcoded layout of the dataset, which
> > almost certainly won't survive the real world's demands. In graphs, if
> > their are no edges then the data is not reachable, except via indexed
> > lookups. This is the standard 

Re: What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hello,

I agree with everything you say. Here is my question:

Relational database — join: Table x Table x equality function -> Table
Graph database — traverser: Vertex x edge label -> Vertex

I want a single function that does both. The only think was to represent 
traverser() in terms of join():

Graph database — traverser: Vertices x Vertex x equality function -> 
Vertices

For example, 

V().out(‘address’)

==>

g.join(V().hasLabel(‘person’).as(‘a’)
   V().hasLabel(‘addresses’).as(‘b’)).
 by(‘name’).select(?address vertex?)

That is, join the vertices with themselves based on some predicate to go from 
vertices to vertices.

However, I would like instead to transform the relational database join() 
concept into a traverser() concept. Kuppitz and I were talking the other day 
about a link() type operator that says: “try and link to this thing in some 
specified way.” .. ?? The problem we ran into is again, “link it to what?”

- in graph, the ‘to what’ is hardcoded so you don’t need to specify 
anything.
- in rdbms, the ’to what’ is some other specified table.

So what does the link() operator look like?

——

Some other random thoughts….

Relational databases join on the table (the whole collection)
Graph databases traverser on the vertex (an element of the whole collection)

We can make a relational database join on single row (by providing a filter to 
a particular primary key). This is the same as a table with one row. Likewise, 
for graph in the join() context above:

V(1).out(‘address’) 

==>

g.join(V(1).as(‘a’)
   V().hasLabel(‘addresses’).as(‘b’)).
 by(‘name’).select(?address vertex?)

More thoughts please….

Marko.

http://rredux.com 




> On Apr 19, 2019, at 4:20 AM, pieter martin  wrote:
> 
> Hi,
> The way I saw it is that the big difference is that graph's have
> reified joins. This is both a blessing and a curse.
> A blessing because its much easier (less text to type, less mistakes,
> clearer semantics...) to traverse an edge than to construct a manual
> join.A curse because there are almost always far more ways to traverse
> a data set than just by the edges some architect might have considered
> when creating the data set. Often the architect is not the domain
> expert and the edges are a hardcoded layout of the dataset, which
> almost certainly won't survive the real world's demands. In graphs, if
> their are no edges then the data is not reachable, except via indexed
> lookups. This is the standard engineering problem of database design,
> but it is important and useful that data can be traversed, joined,
> without having reified edges.
> In Sqlg at least, but I suspect it generalizes, I want to create the
> notion of a "virtual edge". Which in meta data describes the join and
> then the standard to(direction, "virtualEdgeName") will work.
> In a way this is precisely to keep the graphy nature of gremlin, i.e.
> traversing edges, and avoid using the manual join syntax you described.
> CheersPieter
> 
> On Thu, 2019-04-18 at 14:15 -0600, Marko Rodriguez wrote:
>> Hi,
>> *** This is mainly for Kuppitz, but if others care. 
>> Was thinking last night about relational data and Gremlin. The T()
>> step returns all the tables in the withStructure() RDBMS database.
>> Tables are ‘complex values’ so they can't leave the VM (only a simple
>> ‘toString’).
>> Below is a fake Gremlin session. (and these are just ideas…) tables
>> -> a ListLike of rowsrows -> a MapLike of primitives
>> gremlin> g.T()==>t[people]==>t[addresses]gremlin>
>> g.T(‘people’)==>t[people]gremlin>
>> g.T(‘people’).values()==>r[people:1]==>r[people:2]==>r[people:3]greml
>> in>
>> g.T(‘people’).values().asMap()==>{name:marko,age:29}==>{name:kuppitz,
>> age:10}==>{name:josh,age:35}gremlin>
>> g.T(‘people’).values().has(‘age’,gt(20))==>r[people:1]==>r[people:3]g
>> remlin>
>> g.T(‘people’).values().has(‘age’,gt(20)).values(‘name’)==>marko==>jos
>> h
>> Makes sense. Nice that values() and has() generally apply to all
>> ListLike and MapLike structures. Also, note how asMap() is the
>> valueMap() of TP4, but generalizes to anything that is MapLike so it
>> can be turned into a primitive form as a data-rich result from the
>> VM.
>> gremlin> g.T()==>t[people]==>t[addresses]gremlin>
>> g.T(‘addresses’).values().asMap()==>{name:marko,city:santafe}==>{name
>> :kuppitz,city:tucson}==>{name:josh,city:desertisland}gremlin>
>> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)). by(se
>> lect(‘a’).value(’name’).is(eq(select(‘b’).value(’name’))).   
>> values().asMap()==>{a.name:marko,a.age:29,b.name:marko,b.city:santafe
>> }==>{a.name:kuppitz,a.age:10,b.name:kuppitz,b.city:tucson}==>{a.name:
>> josh,a.age:35,b.name:josh,b.city:desertisland}gremlin>
>> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)). by(’n
>> ame’). // shorthand for equijoin on name
>> column/key   

Re: What makes 'graph traversals' and 'relational joins' the same?

[pieter martin]

Hi,
The way I saw it is that the big difference is that graph's have
reified joins. This is both a blessing and a curse.
A blessing because its much easier (less text to type, less mistakes,
clearer semantics...) to traverse an edge than to construct a manual
join.A curse because there are almost always far more ways to traverse
a data set than just by the edges some architect might have considered
when creating the data set. Often the architect is not the domain
expert and the edges are a hardcoded layout of the dataset, which
almost certainly won't survive the real world's demands. In graphs, if
their are no edges then the data is not reachable, except via indexed
lookups. This is the standard engineering problem of database design,
but it is important and useful that data can be traversed, joined,
without having reified edges.
In Sqlg at least, but I suspect it generalizes, I want to create the
notion of a "virtual edge". Which in meta data describes the join and
then the standard to(direction, "virtualEdgeName") will work.
In a way this is precisely to keep the graphy nature of gremlin, i.e.
traversing edges, and avoid using the manual join syntax you described.
CheersPieter

On Thu, 2019-04-18 at 14:15 -0600, Marko Rodriguez wrote:
> Hi,
> *** This is mainly for Kuppitz, but if others care. 
> Was thinking last night about relational data and Gremlin. The T()
> step returns all the tables in the withStructure() RDBMS database.
> Tables are ‘complex values’ so they can't leave the VM (only a simple
> ‘toString’).
> Below is a fake Gremlin session. (and these are just ideas…)  tables
> -> a ListLike of rows rows -> a MapLike of primitives
> gremlin> g.T()==>t[people]==>t[addresses]gremlin>
> g.T(‘people’)==>t[people]gremlin>
> g.T(‘people’).values()==>r[people:1]==>r[people:2]==>r[people:3]greml
> in>
> g.T(‘people’).values().asMap()==>{name:marko,age:29}==>{name:kuppitz,
> age:10}==>{name:josh,age:35}gremlin>
> g.T(‘people’).values().has(‘age’,gt(20))==>r[people:1]==>r[people:3]g
> remlin>
> g.T(‘people’).values().has(‘age’,gt(20)).values(‘name’)==>marko==>jos
> h
> Makes sense. Nice that values() and has() generally apply to all
> ListLike and MapLike structures. Also, note how asMap() is the
> valueMap() of TP4, but generalizes to anything that is MapLike so it
> can be turned into a primitive form as a data-rich result from the
> VM.
> gremlin> g.T()==>t[people]==>t[addresses]gremlin>
> g.T(‘addresses’).values().asMap()==>{name:marko,city:santafe}==>{name
> :kuppitz,city:tucson}==>{name:josh,city:desertisland}gremlin>
> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)). by(se
> lect(‘a’).value(’name’).is(eq(select(‘b’).value(’name’))).   
> values().asMap()==>{a.name:marko,a.age:29,b.name:marko,b.city:santafe
> }==>{a.name:kuppitz,a.age:10,b.name:kuppitz,b.city:tucson}==>{a.name:
> josh,a.age:35,b.name:josh,b.city:desertisland}gremlin>
> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)). by(’n
> ame’). // shorthand for equijoin on name
> column/key   values().asMap()==>{a.name:marko,a.age:29,b.name
> :marko,b.city:santafe}==>{a.name:kuppitz,a.age:10,b.name:kuppitz,b.ci
> ty:tucson}==>{a.name:josh,a.age:35,b.name:josh,b.city:desertisland}gr
> emlin>
> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)). by(’n
> ame’)==>t[people<-name->addresses]  // without asMap(), just the
> complex value ‘toString'gremlin>
> And of course, all of this is strategized into a SQL call so its
> joins aren’t necessarily computed using TP4-VM resources.
> Anywho — what I hope to realize is the relationship between “links”
> (graph) and “joins” (tables). How can we make (bytecode-wise at
> least) RDBMS join operations and graph traversal operations ‘the
> same.’?
>   Singleton: Integer, String, Float, Double, etc. Collection:
> List, Map (Vertex, Table, Document)   Linkable: Vertex, Table
> Vertices and Tables can be “linked.” Unlike Collections, they don’t
> maintain a “parent/child” relationship with the objects they
> reference. What does this mean……….?
> Take care,Marko.
> http://rredux.com 
> 
> 
> 

What makes 'graph traversals' and 'relational joins' the same?

[Marko Rodriguez]

Hi,

*** This is mainly for Kuppitz, but if others care. 

Was thinking last night about relational data and Gremlin. The T() step returns 
all the tables in the withStructure() RDBMS database. Tables are ‘complex 
values’ so they can't leave the VM (only a simple ‘toString’).

Below is a fake Gremlin session. (and these are just ideas…)
tables -> a ListLike of rows
rows -> a MapLike of primitives

gremlin> g.T()
==>t[people]
==>t[addresses]
gremlin> g.T(‘people’)
==>t[people]
gremlin> g.T(‘people’).values()
==>r[people:1]
==>r[people:2]
==>r[people:3]
gremlin> g.T(‘people’).values().asMap()
==>{name:marko,age:29}
==>{name:kuppitz,age:10}
==>{name:josh,age:35}
gremlin> g.T(‘people’).values().has(‘age’,gt(20))
==>r[people:1]
==>r[people:3]
gremlin> g.T(‘people’).values().has(‘age’,gt(20)).values(‘name’)
==>marko
==>josh

Makes sense. Nice that values() and has() generally apply to all ListLike and 
MapLike structures. Also, note how asMap() is the valueMap() of TP4, but 
generalizes to anything that is MapLike so it can be turned into a primitive 
form as a data-rich result from the VM.

gremlin> g.T()
==>t[people]
==>t[addresses]
gremlin> g.T(‘addresses’).values().asMap()
==>{name:marko,city:santafe}
==>{name:kuppitz,city:tucson}
==>{name:josh,city:desertisland}
gremlin> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)).
 by(select(‘a’).value(’name’).is(eq(select(‘b’).value(’name’))).
   values().asMap()
==>{a.name:marko,a.age:29,b.name:marko,b.city:santafe}
==>{a.name:kuppitz,a.age:10,b.name:kuppitz,b.city:tucson}
==>{a.name:josh,a.age:35,b.name:josh,b.city:desertisland}
gremlin> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)).
 by(’name’). // shorthand for equijoin on name column/key
   values().asMap()
==>{a.name:marko,a.age:29,b.name:marko,b.city:santafe}
==>{a.name:kuppitz,a.age:10,b.name:kuppitz,b.city:tucson}
==>{a.name:josh,a.age:35,b.name:josh,b.city:desertisland}
gremlin> g.join(T(‘people’).as(‘a’),T(‘addresses’).as(‘b’)).
 by(’name’)
==>t[people<-name->addresses]  // without asMap(), just the complex value 
‘toString'
gremlin>

And of course, all of this is strategized into a SQL call so its joins aren’t 
necessarily computed using TP4-VM resources.

Anywho — what I hope to realize is the relationship between “links” (graph) and 
“joins” (tables). How can we make (bytecode-wise at least) RDBMS join 
operations and graph traversal operations ‘the same.’?

Singleton: Integer, String, Float, Double, etc.
Collection: List, Map (Vertex, Table, Document)
Linkable: Vertex, Table

Vertices and Tables can be “linked.” Unlike Collections, they don’t maintain a 
“parent/child” relationship with the objects they reference. What does this 
mean……….?

Take care,
Marko.

http://rredux.com