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https://issues.apache.org/jira/browse/TINKERPOP-1564?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Marko A. Rodriguez updated TINKERPOP-1564:
------------------------------------------
Description:
This proposal unifies OLTP and OLAP into a single framework that removes the
need for OLAP {{GraphComputer}} by introducing distributed, data local
processing to OLTP. In essence, this is a proposal for a step-by-step query
routing framework within {{Traversal}}. This proposal can work across machines
in a cluster, threads on a machine, or in a hierarchical fashion
machines&threads. The example presented will discuss distribution across
machines in a cluster as its the most complicated scenario.
Currently, an OLTP traversal executes at a particular machine (or thread) and
pulls vertex/edge/etc. data to it accordingly in order to solve the traversal.
In OLAP, the traversal is cloned and distributed to all machines in the cluster
and traversals communicate with one another by sending {{Traversers}} (i.e.
messages) between themselves ensuring data local processing. Given recent
advancements in GremlinServer and {{RemoteTraversal}}, it is possible to add
traverser routing to OLTP and thus, effect the computational paradigm of
Gremlin OLAP in Gremlin OLTP with some added benefits not possible in Gremlin
OLAP.
Assume a 4 machine cluster and the following traversal:
{code}
g.V(1).out(‘knows’).has(‘age’,gt(20)).out(‘likes’).values(‘name’)
{code}
Every time there is a "walk" (adjacency), it is possible that the {{Traverser}}
is no longer accessing data local to the current machine. In order to do data
local query routing, every adjacency would feed into a {{PartitionStep}}. The
traversal above would be cloned (via {{Bytecode}} distribution) across the
cluster where "sibling" {{PartitionSteps}} would have network access to one
another using the same protocol of {{RemoteConnection}} though called
{{PartitionConnection}}. Thus, given the 4 node cluster example, the above
traversal would be overlaid as below. Note that {{partition()}} would not be a
new step in the language, but simply provided here to show where
{{PartitionStrategy}} would insert {{PartitionSteps}} into the traversal.
{code}
g.V(1).out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
| |
^
__.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
| |
|
__.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
| |
|
__.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
{code}
The top traversal is called the "master traversal" and the other three "worker
traversals." Note that this is identical to current Gremlin OLAP. Now, the
master traversal would be the traversal that is {{.next()}}'d for results. So,
when the "master traversal" is {{next()}}'d, {{g.V(1)}} will fetch {{v[1]}} and
then its outgoing knows-adjacencies. These adjacent "reference vertices" would
be fed into the first {{remote()}} and a "routing algorithm" would determine
where in the cluster the particular vertex's data is. Thus, {{partition()}}
({{PartitionStep}}) serves as a router, pushing {{Traversers}} local to the
data. Finally, note that the final {{PartitionSteps}} can only feed back to the
"master traversal" for ultimate aggregation and return to the user.
TinkerPop currently has all the structures in place to make this possible:
1. Encapsulation of computational metadata via {{Traverser}}.
2. The ability to detach {{Traversers}} and migrate/serialize them via
{{Traverser.detach()}} and {{Traverser.attach()}}.
3. The concept of {{ReferenceElement}} so the traverser only carries
with it enough information to re-attach at the remote site.
4. {{Bytecode}} and the ability to send {{Traversals}} across the
cluster.
5. GremlinServer and {{Client}}/{{Cluster}} messaging protocol.
What does {{PartitionStep}} look like? *Please see comments below*
Here are the benefits of this model:
* Gremlin OLTP is Gremlin OLAP. The semantics of Gremlin OLAP are exactly what
is proposed here but with the added benefit that message passing happens at the
partition/subgraph level, not the star vertex level.
* There is no need for {{SparkGraphComputer}} as GremlinServer now plays the
role of SparkServer. The added benefit, no pulling data from the graph database
and re-representing it in an RDD or {{SequenceFile}}.
* No longer are "local children traversals" the boundary for "OLAP." Local
children can be processed beyond the star graph, but would require pulling data
from a remote machine is necessary. However, given a good graph partitioning
algorithm, local children will most likely NOT leave the subgraph partition and
thus, will remain a local computation.
* Failover is already built into the architecture. If a {{PartitionStep}} can
not be accessed, but the machine's data is still available (perhaps via
replication), then data will simply be pulled over the wire instead of
traversers routed to the "dead node."
* The infrastructure for side-effects and reducing barrier steps already
implemented for Gremlin OLAP would automatically work for distributed Gremlin
OLTP.
* If the entire graph is hot in-memory across the cluster, then distributed
in-memory graph computing is possible. Again, no more linear-scans over
partitions like with Giraph/Spark/etc. ({{GraphComputer}}).
* If transactions are worked out, then distributed OLTP Gremlin provides
mutation capabilities (something currently not implemented for
{{GraphComputer}}). That is {{addV}}, {{addE}}, {{drop}}, etc. just works.
**Caveate, transactions in this environment across GremlinServer seems
difficult.**
So thats that. This could very well be the future of Gremlin OLAP. The disjoint
between OLAP and OLTP would go away, the codebase would be simplified, and the
computational gains in terms of performance and expressivity would be great.
This is a big deal idea.
was:
This proposal unifies OLTP and OLAP into a single framework that removes the
need for OLAP {{GraphComputer}} by introducing distributed, data local
processing to OLTP. In essence, this is a proposal for a step-by-step query
routing framework within {{Traversal}}. This proposal can work across machines
in a cluster, threads on a machine, or in a hierarchical fashion
machines&threads. The example presented will discuss distribution across
machines in a cluster as its the most complicated scenario.
Currently, an OLTP traversal executes at a particular machine (or thread) and
pulls vertex/edge/etc. data to it accordingly in order to solve the traversal.
In OLAP, the traversal is cloned and distributed to all machines in the cluster
and traversals communicate with one another by sending {{Traversers}} (i.e.
messages) between themselves ensuring data local processing. Given recent
advancements in GremlinServer and {{RemoteTraversal}}, it is possible to add
traverser routing to OLTP and thus, effect the computational paradigm of
Gremlin OLAP in Gremlin OLTP with some added benefits not possible in Gremlin
OLAP.
Assume a 4 machine cluster and the following traversal:
{code}
g.V(1).out(‘knows’).has(‘age’,gt(20)).out(‘likes’).values(‘name’)
{code}
Every time there is a "walk" (adjacency), it is possible that the {{Traverser}}
is no longer accessing data local to the current machine. In order to do data
local query routing, every adjacency would feed into a {{RemoteStep}}. The
traversal above would be cloned (via {{Bytecode}} distribution) across the
cluster where "sibling" {{RemoteSteps}} would have network access to one
another using the same protocol of {{RemoteConnection}}. Thus, given the 4 node
cluster example, the above traversal would be overlaid as below. Note that
{{remote()}} would not be a new step in the language, but simply provided here
to show where {{RemoteStrategy}} would insert {{RemoteSteps}} into the
traversal.
{code}
g.V(1).out(‘knows’).remote().has(‘age’,gt(20)).out(‘likes’).remote().values(‘name’).remote()
| |
^
__.out(‘knows’).remote().has(‘age’,gt(20)).out(‘likes’).remote().values(‘name’).remote()
| |
|
__.out(‘knows’).remote().has(‘age’,gt(20)).out(‘likes’).remote().values(‘name’).remote()
| |
|
__.out(‘knows’).remote().has(‘age’,gt(20)).out(‘likes’).remote().values(‘name’).remote()
{code}
The top traversal is called the "master traversal" and the other three "worker
traversals." Note that this is identical to current Gremlin OLAP. Now, the
master traversal would be the traversal that is {{.next()}}'d for results. So,
when the "master traversal" is {{next()}}'d, {{g.V(1)}} will fetch {{v[1]}} and
then its outgoing knows-adjacencies. These adjacent "reference vertices" would
be fed into the first {{remote()}} and a "routing algorithm" would determine
where in the cluster the particular vertex's data is. Thus, {{remote()}}
({{RemoteStep}}) serves as a router, pushing {{Traversers}} local to the data.
Finally, note that the final {{RemoteSteps}} can only feed back to the "master
traversal" for ultimate aggregation and return to the user.
TinkerPop currently has all the structures in place to make this possible:
1. Encapsulation of computational metadata via {{Traverser}}.
2. The ability to detach {{Traversers}} and migrate/serialize them via
{{Traverser.detach()}} and {{Traverser.attach()}}.
3. The concept of {{ReferenceElement}} so the traverser only carries
with it enough information to re-attach at the remote site.
4. {{Bytecode}} and the ability to send {{Traversals}} across the
cluster.
5. GremlinServer and {{RemoteConnection}} with the concept of
{{RemoteTraversal}} and proposed {{RemoteStep}} extension.
What does {{RemoteStep}} look like? *Please see comments below*
Here are the benefits of this model:
* Gremlin OLTP is Gremlin OLAP. The semantics of Gremlin OLAP are exactly what
is proposed here but with the added benefit that message passing happens at the
partition/subgraph level, not the star vertex level.
* There is no need for {{SparkGraphComputer}} as GremlinServer now plays the
role of SparkServer. The added benefit, no pulling data from the graph database
and re-representing it in an RDD or {{SequenceFile}}.
* No longer are "local children traversals" the boundary for "OLAP." Local
children can be processed beyond the star graph, but would require pulling data
from a remote machine is necessary. However, given a good graph partitioning
algorithm, local children will most likely NOT leave the subgraph partition and
thus, will remain a local computation.
* Failover is already built into the architecture. If a {{RemoteStep}} can not
be accessed, but the machine's data is still available (perhaps via
replication), then data will simply be pulled over the wire instead of
traversers routed to the "dead node."
* The infrastructure for side-effects and reducing barrier steps already
implemented for Gremlin OLAP would automatically work for distributed Gremlin
OLTP.
* If the entire graph is hot in-memory across the cluster, then distributed
in-memory graph computing is possible. Again, no more linear-scans over
partitions like with Giraph/Spark/etc. ({{GraphComputer}}).
* If transactions are worked out, then distributed OLTP Gremlin provides
mutation capabilities (something currently not implemented for
{{GraphComputer}}). That is {{addV}}, {{addE}}, {{drop}}, etc. just works.
**Caveate, transactions in this environment across GremlinServer seems
difficult.**
So thats that. This could very well be the future of Gremlin OLAP. The disjoint
between OLAP and OLTP would go away, the codebase would be simplified, and the
computational gains in terms of performance and expressivity would be great.
This is a big deal idea.
> Distributed OLTP Traversals via RemoteStep
> ------------------------------------------
>
> Key: TINKERPOP-1564
> URL: https://issues.apache.org/jira/browse/TINKERPOP-1564
> Project: TinkerPop
> Issue Type: Improvement
> Components: driver, process, server
> Affects Versions: 3.2.3
> Reporter: Marko A. Rodriguez
>
> This proposal unifies OLTP and OLAP into a single framework that removes the
> need for OLAP {{GraphComputer}} by introducing distributed, data local
> processing to OLTP. In essence, this is a proposal for a step-by-step query
> routing framework within {{Traversal}}. This proposal can work across
> machines in a cluster, threads on a machine, or in a hierarchical fashion
> machines&threads. The example presented will discuss distribution across
> machines in a cluster as its the most complicated scenario.
> Currently, an OLTP traversal executes at a particular machine (or thread) and
> pulls vertex/edge/etc. data to it accordingly in order to solve the
> traversal. In OLAP, the traversal is cloned and distributed to all machines
> in the cluster and traversals communicate with one another by sending
> {{Traversers}} (i.e. messages) between themselves ensuring data local
> processing. Given recent advancements in GremlinServer and
> {{RemoteTraversal}}, it is possible to add traverser routing to OLTP and
> thus, effect the computational paradigm of Gremlin OLAP in Gremlin OLTP with
> some added benefits not possible in Gremlin OLAP.
> Assume a 4 machine cluster and the following traversal:
> {code}
> g.V(1).out(‘knows’).has(‘age’,gt(20)).out(‘likes’).values(‘name’)
> {code}
> Every time there is a "walk" (adjacency), it is possible that the
> {{Traverser}} is no longer accessing data local to the current machine. In
> order to do data local query routing, every adjacency would feed into a
> {{PartitionStep}}. The traversal above would be cloned (via {{Bytecode}}
> distribution) across the cluster where "sibling" {{PartitionSteps}} would
> have network access to one another using the same protocol of
> {{RemoteConnection}} though called {{PartitionConnection}}. Thus, given the 4
> node cluster example, the above traversal would be overlaid as below. Note
> that {{partition()}} would not be a new step in the language, but simply
> provided here to show where {{PartitionStrategy}} would insert
> {{PartitionSteps}} into the traversal.
> {code}
> g.V(1).out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
> | |
> ^
>
> __.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
> | |
> |
>
> __.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
> | |
> |
>
> __.out(‘knows’).partition().has(‘age’,gt(20)).out(‘likes’).partition().values(‘name’).partition()
> {code}
> The top traversal is called the "master traversal" and the other three
> "worker traversals." Note that this is identical to current Gremlin OLAP.
> Now, the master traversal would be the traversal that is {{.next()}}'d for
> results. So, when the "master traversal" is {{next()}}'d, {{g.V(1)}} will
> fetch {{v[1]}} and then its outgoing knows-adjacencies. These adjacent
> "reference vertices" would be fed into the first {{remote()}} and a "routing
> algorithm" would determine where in the cluster the particular vertex's data
> is. Thus, {{partition()}} ({{PartitionStep}}) serves as a router, pushing
> {{Traversers}} local to the data. Finally, note that the final
> {{PartitionSteps}} can only feed back to the "master traversal" for ultimate
> aggregation and return to the user.
> TinkerPop currently has all the structures in place to make this possible:
> 1. Encapsulation of computational metadata via {{Traverser}}.
> 2. The ability to detach {{Traversers}} and migrate/serialize them via
> {{Traverser.detach()}} and {{Traverser.attach()}}.
> 3. The concept of {{ReferenceElement}} so the traverser only carries
> with it enough information to re-attach at the remote site.
> 4. {{Bytecode}} and the ability to send {{Traversals}} across the
> cluster.
> 5. GremlinServer and {{Client}}/{{Cluster}} messaging protocol.
> What does {{PartitionStep}} look like? *Please see comments below*
> Here are the benefits of this model:
> * Gremlin OLTP is Gremlin OLAP. The semantics of Gremlin OLAP are exactly
> what is proposed here but with the added benefit that message passing happens
> at the partition/subgraph level, not the star vertex level.
> * There is no need for {{SparkGraphComputer}} as GremlinServer now plays the
> role of SparkServer. The added benefit, no pulling data from the graph
> database and re-representing it in an RDD or {{SequenceFile}}.
> * No longer are "local children traversals" the boundary for "OLAP." Local
> children can be processed beyond the star graph, but would require pulling
> data from a remote machine is necessary. However, given a good graph
> partitioning algorithm, local children will most likely NOT leave the
> subgraph partition and thus, will remain a local computation.
> * Failover is already built into the architecture. If a {{PartitionStep}} can
> not be accessed, but the machine's data is still available (perhaps via
> replication), then data will simply be pulled over the wire instead of
> traversers routed to the "dead node."
> * The infrastructure for side-effects and reducing barrier steps already
> implemented for Gremlin OLAP would automatically work for distributed Gremlin
> OLTP.
> * If the entire graph is hot in-memory across the cluster, then distributed
> in-memory graph computing is possible. Again, no more linear-scans over
> partitions like with Giraph/Spark/etc. ({{GraphComputer}}).
> * If transactions are worked out, then distributed OLTP Gremlin provides
> mutation capabilities (something currently not implemented for
> {{GraphComputer}}). That is {{addV}}, {{addE}}, {{drop}}, etc. just works.
> **Caveate, transactions in this environment across GremlinServer seems
> difficult.**
> So thats that. This could very well be the future of Gremlin OLAP. The
> disjoint between OLAP and OLTP would go away, the codebase would be
> simplified, and the computational gains in terms of performance and
> expressivity would be great. This is a big deal idea.
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