SQLGraph: An Efficient Relational-Based Property Graph Store (2015) [pdf](research.google.com)
research.google.com
SQLGraph: An Efficient Relational-Based Property Graph Store (2015) [pdf]
http://research.google.com/pubs/archive/43287.pdf
5 comments
The paper details its model for the graph structure and attributes, and it shows results for the different approaches they evaluated -- in the end, they propose a novel approach of using rows/columns for adjacency lists and JSON blobs for properties/attributes.
The paper references 3 relational DBs: DB2, Oracle, and PostgreSQL...
The Berkeley DB reference is regarding the Titan backend datastore they used. Presumably they chose BDB over one of Titan's distributed backeds like Cassandra or HBase since BDB is single server so it's a more apt comparison to the other DBs.
The paper references 3 relational DBs: DB2, Oracle, and PostgreSQL...
Most modern relational databases such as DB2, Oracle or
Postgresql have features to support both relational and
nonrelational storage within the same database engine,
making it possible to perform an empirical comparison of
the utility of relational versus non-relational storage
structures for property graphs.
The Berkeley DB reference is regarding the Titan backend datastore they used. Presumably they chose BDB over one of Titan's distributed backeds like Cassandra or HBase since BDB is single server so it's a more apt comparison to the other DBs.
This is fairly interesting. Postgres has a lot of momentum going on. I guess there's not stopping someone from building a better neo4j on top of it.
Interesting paper indeed.
On the last page of the paper, Table 8 includes Gremlin->SQL CET translations, i.e. Common Table Expressions: http://www.postgresql.org/docs/current/static/queries-with.h... , https://en.wikipedia.org/wiki/Hierarchical_and_recursive_que... .
Note the paper uses TinkerPop/Gremlin 2 step names, and since then TinkerPop/Gremlin 3 has been released (http://tinkerpop.apache.org), which includes many enhancements so the current Gremlin 3 names may differ a little from the ones they reference in column one of the table.
P.S. If you want to see some of the new crazy shit you can do in Gremlin 3, check out Marko's talk and paper from GraphDay a few weeks ago:
Quantum Processes in Graph Computing (https://www.youtube.com/watch?v=qRoAInXxgtc)
"Quantum Walks with Gremlin" http://arxiv.org/pdf/1511.06278v1.pdf
On the last page of the paper, Table 8 includes Gremlin->SQL CET translations, i.e. Common Table Expressions: http://www.postgresql.org/docs/current/static/queries-with.h... , https://en.wikipedia.org/wiki/Hierarchical_and_recursive_que... .
Note the paper uses TinkerPop/Gremlin 2 step names, and since then TinkerPop/Gremlin 3 has been released (http://tinkerpop.apache.org), which includes many enhancements so the current Gremlin 3 names may differ a little from the ones they reference in column one of the table.
P.S. If you want to see some of the new crazy shit you can do in Gremlin 3, check out Marko's talk and paper from GraphDay a few weeks ago:
Quantum Processes in Graph Computing (https://www.youtube.com/watch?v=qRoAInXxgtc)
"Quantum Walks with Gremlin" http://arxiv.org/pdf/1511.06278v1.pdf
Common Table Expressions
In postgres they are "optimisation fences", something which there is significant resistance to changing[1][2], where-as elsewhere (at least in SQL Server) the query planner and engine can optimise across CTE boundaries (pushing search predicates back into them where possible). This can make a massive difference to the performance of some queries, potentially turning a full index scan (or worse, multiple scans) into a small number of seeks.
As always: test with realistic data sizes/patterns to make sure things work as you are expecting them to.
[1] see http://blog.2ndquadrant.com/postgresql-ctes-are-optimization...
[2] also https://news.ycombinator.com/item?id=7023907 for further discussion
How does a specific software offerer like Neo4j diversify it's service offering? Should it?
I always thought that the main advantage of graph databases is the O(1) lookup instead of the O(log n) lookup in a traditional foreign-key relationship. There is no need to search an index, since the pointer already records the location of the adjacent document. On disk, the seeking time would be roughly equivalent if the database is heavily used, since reads/writes would be batched as the disk head moves over the disk, or for other types of drives some similar technique would be used.
The problem with a "pointer" like this is that you then can't move stuff around as easily. SQL databases frequently reorganize the physical representation of rows to reclaim space used by deleted data, for example. Surely the same would apply to graph/object databases.
SQL databases _could_ conceivably optimize foreign key constraints behind the scenes by replacing/augmenting the referencing column with the physical location of the foreign row, but what do you do when the target row moves around in the physical store? You'd have to hunt down all the references and replace them. If anything can point to anything, that could have a cascading effect.
You could have conceivably have an indirection here: Instead of pointing to the physical location of another row, you pointed to the physical location of a special intermediate file that itself could contain the physical location. I don't know of any database that uses any tricks like these.
SQL databases _could_ conceivably optimize foreign key constraints behind the scenes by replacing/augmenting the referencing column with the physical location of the foreign row, but what do you do when the target row moves around in the physical store? You'd have to hunt down all the references and replace them. If anything can point to anything, that could have a cascading effect.
You could have conceivably have an indirection here: Instead of pointing to the physical location of another row, you pointed to the physical location of a special intermediate file that itself could contain the physical location. I don't know of any database that uses any tricks like these.
> The problem with a "pointer" like this is that you then can't move stuff around as easily
You can, it just requires one further level of pointer indirection. Rather than pointing to the physical location of the data, you store a pointer to an offset in an array of pointers.
Alternatively you can point directly if you're prepared to update those pointers when you move things around. Luckily a graph database is ideally suited for this kind of operation because it makes discovering those pointers exceptionally simple and efficient.
You can, it just requires one further level of pointer indirection. Rather than pointing to the physical location of the data, you store a pointer to an offset in an array of pointers.
Alternatively you can point directly if you're prepared to update those pointers when you move things around. Luckily a graph database is ideally suited for this kind of operation because it makes discovering those pointers exceptionally simple and efficient.
Yes, that was the solution I sketched out in my last paragraph. Do any databases actually do this?
The array of pointers thing is standard/common but I'm not aware of any which use the features of the graph itself to avoid pointer indirection. Typically the edges are stored in some kind of tree structure anyway so I'd guess that the savings aren't worth the added complexity when the thing does a lot of pointer lookups besides the initial one. But I'm probably going to experiment with it in my own db project.
It would be nice to compare it with AllegroGraph
This is over a year old, and using ancient versions of software. It would be nice if they showed their testing code not just some horrific and unreadable gremlin queries. Taking 1.6 seconds to create a new node in neo4j and 2.5 to update it? Yeah, I don't think that's anywhere close to legit.
Never met a vendor benchmark that wasn't faulty in some way... http://maxdemarzi.com/2015/10/16/benchmarks-and-supercharger...
Never met a vendor benchmark that wasn't faulty in some way... http://maxdemarzi.com/2015/10/16/benchmarks-and-supercharger...
To be fair, the SIGMOD'15 conference was in June 2015 so the paper is only
~6 mos old. While it is based on Gremlin 2 rather than Gremlin 3, most all the code in the paper is SQL CET statements -- there's very little Gremlin. In Table 8 on the last page they show their code for the Gremlin->SQL CET translations, and only a few tweaks are needed to update the step names in column one to Gremlin 3.
The conference was in June 2015.
The paper would have been submitted for peer review in Summer/Fall 2014.
Which means that it could have been written in Winter/Spring 2014.
SIGMOD is not like a dev conference where you can change the paper the day before your talk.
SIGMOD is not like a dev conference where you can change the paper the day before your talk.
If the paper is only 6 months old, why would the authors use an ancient version of Neo4j?
Titan 0.4 [1] and Neo4j 1.9.4 [2] were the versions released in the Fall of 2013 so maybe that's when they did the work.
[1] https://github.com/thinkaurelius/titan/releases/tag/0.4.0
[2] https://github.com/neo4j/neo4j/releases/tag/1.9.4
[1] https://github.com/thinkaurelius/titan/releases/tag/0.4.0
[2] https://github.com/neo4j/neo4j/releases/tag/1.9.4
Also which RDB did they use? I saw mention of Berkeley DB, but can we infer their schema would perform well with stock Postgres or MySQL?
Ideally, there should be code so we can reproduce independently.