Graph Databases Oracle Spatial and Graph One Graph

Graph Databases Oracle Spatial and Graph One Graph
Vol. 28, No. 1 · FEBRUARY 2014$15
Knowledge Happens
One Graph to Rule
Them All
We interview Neo4j CEO
Emil Eifrem.
See page 4.
Graph Databases
Relational warhorse Joe Celko
expounds.
See page 8.
Oracle Spatial and
Graph
Oracle Database 12c has it all.
See page 15.
Much more inside . . .
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3
I ntervie w
One Graph to
Rule Them All
with Emil Eifrem
Emil Eifrem
Emil Eifrem is co-founder and CEO of Neo Technology and
the Neo4j open-source graph database project.
From the relational fortress in which NoCOUG members live,
NoSQL doesn’t seem very threatening or even compelling. Are
we living in a dream world? These walls seem pretty solid
though.
That’s a great question. Exponential growth looks very flat in
the beginning. The NoSQL numbers are still small compared to
the relational world, much in the same way that the rela­
tional numbers were very small compared to IMS and
CODASYL when relational first came into the arena. One
thing that makes today’s trend different of course is that now
we’re really talking about multiple database models being used
in tandem with existing RDBMSs, to address particular types of
data problems.
We rarely see people moving entirely off of one system and
onto another just because they have a new requirement. Nor­
mally that doesn’t make sense. On the other hand, if you look
at unconstrained greenfield projects where technology choices
are made based purely on the technical and business require­
ments—and where you have no technology inhibitions—I will
bet that the numbers will lean very favorably towards solutions
that include some alternatives to the relational model. Of course
in those cases as well, relational databases are often a part of the
solution.
So, should developers feel threatened? No, I don’t think
there’s any sense of threat. Rather, I think there’s an opportunity
to leverage new technologies alongside the familiar ones, to
create more value for the business. There is a huge wealth
of options available today when you look at data and data­
base management. By understanding the strengths and weak­
nesses of each tool, you will see what makes the most sense for
a given and use that knowledge to your advantage.
Some of the criticisms of NoSQL databases seem quite valid.
Lack of management and monitoring tools, limited feature sets,
limited security, and traps for the unwary. I just discovered that
MongoDB uses a database-wide lock for every write . . .
Many of the NoSQL databases are really new to the scene,
and it’s taken relational databases many years, if not decades, to
get to where they are today. The RDBMS is a really mature, wellunderstood, battle-proven technology. So I generally agree with
4
that statement. We have been working on Neo4j since 2000,
which is why we have a completely ACID transactional core,
unusual in NoSQL land. This is one reason we don’t have a
global write lock! We have the equivalent of row-level locking,
which means that if you write to two different nodes in the
graph, you can run those write operations in parallel. We do this
through a variant of MVCC, allowing you to read an old version
of a property on a node while another write transaction
is modifying it.
Our database kernel, granted, is more mature in such areas
than most other NoSQL databases (including other graph data­
bases) because unlike most of those others, we’ve been at this for
the past 13 to 14 years. That said, the ecosystem is still very
young, and there are certain areas that haven’t yet been as much
of an R&D focus, such as matching the elaborate and finegrained security models you get with RDBMSs. The main take­
away here is that while they’re different and maybe not as mature
in all areas, they are already far enough along to have a solid
production track record in some of the most mission-critical
applications you can imagine (such as routing trains and pack­
ages in real time, for example). I actually think one of the bigger
challenges is that the industry as a whole is largely still getting its
head around how to use graph databases, and so sometimes
there is confusion when people assume graph databases behave
like others in the NoSQL categories. We are quite different in
fact. Slowly, though, people are learning, and word is getting out.
Graph databases are quickly becoming recognized as being their
own battle-proven technology. I think that’s just the nature of a
new technology.
Graph databases seem very different from other NoSQL categories. There is no sharding, replication, eventual consistency, or
schemaless design. Or is there?
Great question. I agree with the overall statement that graph
databases are highly differentiated. There is some overlap be­
tween key value stores and document databases, and there is
some overlap between document databases and column family,
as well as key value store and column family, etc. They all solve
broadly similar problems. But graph databases by and large
solve a very different problem from all of the other categories
in NoSQL: they’re built to help applications quickly and easily
navigate complex and evolving systems of densely connected
data, as opposed to simply managing chunks of discrete data.
February 2014
Having said that, Neo4j does include some of the things that
you mentioned up front. It has a high-availability clustering
mode that is based on replication. It has a schema-free model,
which means that you can add an arbitrary amount of proper­
ties to one node. And you can also add and remove relationships
arbitrarily between nodes, to reflect whatever data is coming
in—without having to predefine or guess ahead of time how
your data is really going to connect. Therefore graph databases
as a category differentiate themselves from the other NoSQL
categories first and foremost by their data model.
However Neo4j specifically has also been a bit of a NoSQL
contrarian in terms of our architecture choices. The fact that
Neo4j is built on an ACID transactional core is really impor­
tant—and contrarian. In Neo4j 2.0, we started adding optional
schema, which is again contrarian. We play a lot of tricks to
ensure that queries never get bogged down by network hops—
culminating in a scale-out approach that lets you shard data in
cache without sharding it on disk. This again is quite contrarian
in the NoSQL world. The last area in which we’re contrarian
actually makes us closer in a sense to relational databases. It’s
the fact that Neo4j not only acknowledges relatedness between
data items—which by the way is an anti-pattern in the other
NoSQL databases—but also goes a step beyond even relational
databases by lifting up the connections to be first-class citizens.
It does this first by allowing the relationships (not just the nodes)
to have an arbitrary number of properties. This lets you ascribe
weights, for example, to friendships as well as start and end
dates, etc. This is quite powerful. The other thing that’s different
is that relationships are database objects that each connect two
actual pieces of data. So rather than defining how types of data
relate, you create a relationship between the actual bits of data,
with pointers on either end linking the nodes, and a database
engine that’s capable of traversing it in either direction. In this
way, every piece of data that has a link to another piece of data
has a relationship between them. The query-time benefits are
astounding. This lets queries move very quickly across the
graph, following connections through in-memory pointer chas­
ing and entirely bypassing join operations. There’s no need for
the database to traverse down the index’s b-tree or sideways via
a range scan with each hop to determine whether data is related.
The result is queries that are 1000x faster in many cases com­
pared to their SQL equivalent, with much more compact queries.
What was the specific situation or interesting customer story
that led you to create Neo4j?
I was working at an enterprise content management startup
in Sweden. I was the CTO and had 20 or so engineers working
on the product, half of which spent the vast majority of their
time just fighting with the relational database. It took us a while
to figure out why. But when we did the root-cause analysis to
determine why we were struggling so much, we saw that the real
reason things were going so slowly was that there was a mis­
match between the shape of the data that we were dealing with
in the application and the abstraction that the relational data­
base exposed. The relational database exposed a tabular view of
the world. But our enterprise content management system was
filled with hierarchical and connected data. Content was stored
in a big tree, with symbolic links or shortcuts pointing from item
to item, all of which led to a big connected messy data structure,
which was very difficult to squeeze down in a relational data­
The NoCOUG Journal
base. The mismatch was not unlike the object-relational mis­
match that we all know about, only worse, because all of the
objects were connected in a highly unpredictable way. After a
while we realized that we could represent all of it as a network or
a graph. At the time we thought that there must be a database
that persists data in the same way as a relational database but just
exposes a different data model. We wanted a database that ex­
posed a graph data model rather than a tabular data model. So
we started looking around for that, but couldn’t find anything.
Ultimately we decided to build it. How hard could it be, we
thought! Now, 14 years later, we’re still working to evolve the
graph database, which has many of the properties of a relational
database, married with a graph data model.
How does Neo4j work under the covers? Is “j” for Java?
The “j” is for JVM. Neo4j written on top of the Java Virtual
Machine, which has a bad rep in some camps but in my book is
one of the finest pieces of engineering that the software industry
has ever produced. It’s deployed virtually everywhere in the
world. Wherever we run software, the Java Virtual Machine is
running. Neo4j is not Java-exclusive in any way: you can use it
from any language. We have a declarative query language called
Cypher, which you, of course, can execute from .NET, Ruby,
PHP, Java, or the Unix console, as well as from our browserbased query tool. However, internally it’s written primarily in
Java with a bit of Scala. Neo4j has native drivers for all of the
popular languages and an HTTP endpoint that can be used to
run queries in Cypher.
What are some of the ways that your customers have been using
Neo4j?
Let me count the ways! Certainly, the original use case is one
that we see a good deal of. In that example you have the graph
of identities: users, groups, groups of groups, and so on. Col­
loquially we call this a “hierarchy.” (Actually a hierarchy is just a
specialized type of graph where the relationships all go up and
down.) But often in the real world a hierarchy is not just a hier­
archy. Data can often relate through sideways connections, and
nodes can sometimes connect upward to multiple parents. Any
such line breaks the strict hierarchy, turning it into a generalized
graph. Going back to the use case, content looks very much the
same. You have the individual piece of content and then collec­
tions of content and then collections of collections (which can
often also contain content directly), and so on. Now you inter­
leave those two mostly hierarchical graphs with relationships
between the identity graph and the content graph, which is how
you express permissions. This gives you a tangled web, which is
hard to model and even harder to query. This pattern is the
perfect example of where graph databases are a great fit, and it
comes up in a lot of different applications.
For example, let’s now take network and data center manage­
ment. The nodes in the network are the physical and virtual
machines, the software assets, and the processes running on
those machines. These are each different graphs, which relate to
each other . . . and at the lowest level they are tied together by
physical and virtual networks. Moving on, there’s the original
graph use case, which is geo. Leonhard Euler, who invented
graph theory back in the early 1700s, first used his new theory
to solve a geo-routing problem. You would therefore expect
there to be (and you would be right) a number of logistics com­
panies that have discovered graph databases and are using them
5
to complement their existing relational infrastructure to handle
routing, from packages to trains and so on. Facebook of course
popularized the social graph, and that’s quite a common use as
well: both for declared relationships, and inferred ones, that you
arrive at by hopping through, say, common interests. Master
data management (of “hierarchical” data) comes up a lot: orga­
nizational hierarchy, product master, etc. I can show you a typi­
cal HR query that we got from a consultant that takes up more
than one page in SQL and just four lines in Neo4j’s Cypher
query language.
“It is very likely that there are
vastly more graph problems out
there than most people think.
We now see valid, actual uses
across most verticals.”
Generally speaking, if you’re finding yourself doing lots of
recursive joins or joining across lots of join tables or running
queries of unknown/arbitrary path-length, like ShortestPath
(aka the “Kevin Bacon” query) or chasing up or down a graph to
find all of the dependencies, then graph databases can help re­
lieve some pain. Besides the performance advantages, many
developers just find that graphs are an elegant way to frame
their problems when dealing with connected data. So to sum­
marize: adoption has been in a wide range of verticals, mostly
alongside relational databases and sometimes alongside other
NoSQL databases, to tackle the various challenges one bumps
up against with connected queries and connected data.
You’re surely not suggesting that graph databases be considered
for non-graph problems? In Normalized Data Base Structure:
A Brief Tutorial (1971), Dr. Codd carved out a special exemption for what he called “network problems,” but for the general
case, he said “What is less understandable is the trend toward
more and more complexity in the data structures with which
application programmers and terminal users directly interact.
Surely, in the choice of logical data structures that a system is to
support, there is one consideration of absolutely paramount
importance—and that is the convenience of the majority of
users. . . . To make formatted data bases readily accessible to
users (especially casual users) who have little or no training in
programming we must provide the simplest possible data structures and almost natural language. . . . What could be a simpler,
more universally needed, and more universally understood data
structure than a table?”
Correct, that would certainly be ridiculous. However, it is
very likely that there are vastly more graph problems out there
than most people think. We now see valid, actual uses across
most verticals where people use software: anywhere from finan­
cial services to telecom to media and publishing to health care
to automotive—horizontally across the board. Businesses are
faced with the need to understand intricately connected realworld systems across a variety of domains: social networks, bio­
logical networks, logistics networks, and so on. The ability to
leverage connected data is a horizontal concern, and I believe
that by the end of this decade most systems that use software
will touch a graph database in some way.
6
NoCOUG users already have it pretty good with Oracle Spatial
and Graph. How is the Neo4j approach different from the
Oracle approach?
I’m afraid I can’t comment, as I don’t know the inner workings
of Oracle Spatial and Graph. I have to say however, it did put a
smile on my face when I read Oracle’s blog post stating that
Oracle Spatial was being renamed to Oracle Spatial and Graph
because of the growing popularity of graph databases! What I
can tell you about Neo4j is that it’s written from the ground up to
be optimized for storing and querying graphs, and it is a native
graph database. This means that every layer in the stack was de­
signed to make storing and retrieving graph data highly perfor­
mant and scalable.
What new in Neo4j? What’s on the roadmap?
We started working on Neo4j in 2000, and it took us a decade
to release Neo4j 1.0 in 2010. We’re Swedes and have some pretty
high standards when it comes to building a system that you can
trust with your data. The majority of our focus in the Neo4j 1.x
series was to continue to improve performance, scalability, and
robustness. Clustering, for example, was added very early on in
the 1.x series. Fast-forward to last year: all of 2013 was spent
working on Neo4j 2.0, which was focused almost entirely on
ease of use. We released Neo4j 2.0 at the end of last year, adding
several key features to make graph databases easier to use. Prior
to 2.x, we did a pretty good job of building a scalable and robust
database that solves some very important technical and business
problems, but we hadn’t yet made it very easy to use. All of that
changed with Neo4j 2.x. We believe the 2.x series is going to
catapult graphs into the mainstream.
Going forward, one thing I am particularly excited about is
something we informally call a SQL gateway. It’s an automated
way of replicating data between Neo4j and a relational database.
What we have seen out in the field is that many times graph-y
data is trapped in a two- or three-column table in a relational
database (e.g., person ID, related person ID). It’s actually fairly
easy to replicate data like this into Neo4j bidirectionally, and
people do this all the time. It’s extra work to do it yourself though,
and because it’s such a common pattern, we want to make it
easier. It will allow people who have an existing Oracle installa­
tion, where some aspects of their data set is a graph, to more
easily take that portion of the data model and replicate it into
Neo4j. The result is that you keep all of your data in Oracle but
add the power of a graph by replicating relevant parts of the data
set into Neo4j. This pattern of “polyglot persistence” is one I
think we’ll be seeing more and more of in the years to come—not
just with graphs but with databases in general. As far as the rest
of the roadmap, it’s continuing to improve what Neo4j already
does well, with a special focus on scalability and ease of use. s
Member IDs 6322478, 6510532, 6527185,
7082228, 9094728, and 9224454 have
won a prize: The 7 Habits of Highly Effective People
by Stephen Covey. Must attend the winter
conference to collect your prize.
February 2014
CONFERENCE
R E PO RT
How to Become an Expert
C
onference #108 at TechMart in Santa Clara had 181
attendees, more than any board member can remem­
ber except at Conference #100 at Computer History
Museum.
Only at NoCOUG will the Director of Managed Services of
Database Specialists become your personal IT trainer!
If you want to become an expert, you must learn from one!
Look, Ma, I won a toaster! Kamran Rassouli photo-bombs the
candid moment.
The Winner’s Circle with NoCOUG president Naren Nagtode
and vice-president Hanan Hit.
The NoCOUG Journal
From this angle, it looks like Confio’s “Looney Tuner” Janis
Griffin is rummaging through keynote speaker Tom Kyte’s backpack while board member Randy Samberg distracts him at the
wine-and-cheese reception sponsored by Fusion-io. Funny camera
angles are a terrible thing to waste!
Even the photographer won a prize. High five, Ahbaid!
7
B oo k
E x cerpt
Graph Databases
from Joe Celko’s Complete
Guide to NoSQL
Joe Celko
Reprinted with permission from Joe Celko’s Complete Guide to
NoSQL. DOI: 10.1016/B978-0-12-407192-6.00003-0. Copyright
© 2014 Elsevier Inc. All rights reserved. Journal readers can use the
code PBTY14 to purchase the book at a discounted price at the
Elsevier Store (http://goo.gl/d61hl3).
Introduction
This chapter discusses graph databases, which are used to
model relationships rather than traditional structured data.
Graph databases have nothing to do with presentation graphics.
Just as FORTRAN is based on algebra, and relational databases
are based on sets, graph databases are based on graph theory, a
branch of discrete mathematics. Here is another way we have
turned a mind tool into a class of programming tools!
Graph databases are not network databases. Those were the
prerelational databases that linked records with pointer chains
that could be traversed record by record. IBM’s Information
Man­agement System (IMS) is one such tool still in wide use; it is
a hierarchical access model. Integrated Database Management
System (IDMS), TOTAL, and other products use more complex
pointer structures (e.g., single-link lists, double-linked lists, junc­
tions, etc.) to allow more general graphs than just a tree. These
pointer structures are “hardwired” so that they can be navigated;
they are the structure in which the data sits.
In a graph database, we are not trying to do arithmetic or
statistics. We want to see relationships in the data. Curt
Monash the database expert and blogger (http://www.dbms2.
com/, http://www.monash.com) coined the term for this kind
of analysis: relationship analytics.
Programmers have had algebra in high school, and they may
have had some exposure to naive set theory in high school or
college. You can program FORTRAN and other procedural lan­
guages with high school–level algebra and only a math major
needs advanced set theory, which deals with infinite sets (com­
puters do not have infinite storage no matter what your boss
thinks). But you cannot really understand RDBMS and SQL
without naive set theory.
But only math majors seem to take a whole semester of graph
theory. This is really too bad; naive graph theory has simple con­
cepts and lots of everyday examples that anyone can understand.
Oh, did I mention that it is also full of sudden surprises where
simple things become nonpolynomial (NP)-complete problems?
Let’s try to make up that gap in your education.
8
In computer science, we use the “big O” notation, O(n), to
express how much effort it takes to run an algorithm as the size of
the input, (n). For example, if we have a simple process that han­
dles one record at a time, the O(n) is linear; add one more record
and it takes one more unit of execution time. But some algorithms
increase in more complex ways. For example, sorting a file can be
done in O(n log2(n)) time. Other algorithms can have a complex­
ity that is a polynomial usually with squares and cubes.
Then we get to the NP algorithms. They usually involve hav­
ing to try all possible combinations to find a solution, so they
have a factorial in their complexity, O(n!).
NP complexity shows up in many of the classic graph theory
problems. Each new edge or node added to the graph can result
in more and more combinations. We often find that we look for
near-optimal solutions instead of practical reasons.
3.1 Graph Theory Basics
A graph has two things in it. There are edges (or arcs) and
nodes (or vertices); the edges are drawn as lines that connect
nodes, which are drawn as dots or circles. That is it! Two parts!
Do not be fooled; binary numbers only have two parts and we
build computers with them.
3.1.1 Nodes
Nodes are abstractions. They usually (not always) model what
would be an entity in RDBMS. In fact, some of the power of
graph theory is that a node can model a subgraph. A node may
or may not have “something inside it” (electrical components) in
the model; it can just sit there (bus stop) or simply be (transition
state). A node is not an object. Objects have methods and local
data inside them. In a complex graph query, we might be looking
for an unknown or even nonexistent node. For example, a bus
stop with a Bulgarian barbeque stand might not exist. But a bus
stop with a barbeque in a Bulgarian neighborhood might exist,
and we not do know it until we connect many factors together
(e.g., riders getting off at the Bulgarian Culture Center bus stop,
restaurants or Bulgarian churches within n blocks of the bus stop,
etc.). Other examples of graphs you might have seen are:
➤ Schematic maps: the nodes are the bus stops, towns, and so
forth.
➤ Circuit diagrams: the nodes are electrical components.
➤ State transitions: the nodes are the states (yes, this can be
modeled in SQL).
February 2014
3.1.2 Edges
Edges or arcs connect nodes. We draw them as lines that may
or may not have an arrow head on them to show a direction of
flow. In schematic maps, the edges are the roads. They can have
a distance or time on them. In the circuit diagrams, the edges are
the wires that have resistance, voltage, etc. Likewise, the abstract
state transitions are connected by edges that model the legal
transition paths.
In one way, edges are more fun than nodes in graph theory. In
RDBMS models of data, we have an unspecified single relation­
ship between tables in the form of a REFERENCES clause. In a
graph database, we can have multiple edges of different kinds
between nodes. These can be of various strengths that we know
(e.g., “I am your father, Luke,” if you are a Star Wars fan; “is a pen
pal of ”) and ones that we establish from other sources (e.g., “sub­
scribes to the Wall Street Journal”; “friend of a friend of a friend”;
“son of a son of a sailor,” if you are a Jimmy Buffett fan).
At the highest level of abstraction an edge can be directed or
undirected. In terms of maps, these are one-way streets; for state
transitions, this is prior state–current state pairs and so forth. We
tend to like undirected graphs since the math is easier and there
is often an inverse relationship of some sort (e.g., “father–son” in
the case of Luke Skywalker and Darth Vader).
Colored edges are literally colored lines on a display of a
graph database. One color is used to show the same kind of edge,
the classic “friend of a friend of a friend,.” or a Bacon(n) relation­
ship (I will explain this shortly) used in social networks when
they send you a “you might also know . . .” message and ask you
to send an invitation to that person to make a direct connection.
Weighted edges have a measurement that can accumulate. In
the case of a map—distances—the accumulation rule is additive;
in the case of the Bacon(n) function it diminishes over distance
(you may ask, “Who? Oh, I forgot about him!”).
3.1.3 Graph Structures
The bad news is that since graph theory is fairly new by math­
ematical standards, which means less than 500 years old, there
are still lots of open problems and different authors will use dif­
ferent terminology. Let me introduce some basic terms that
people generally agree on:
null graph is a set of nodes without any edges. A complete graph has an edge between every pair of nodes. Both
of these extremes are pretty rare in graph databases.
➤A
walk is a sequence of edges that connect a set of nodes
without repeating an edge.
➤A
➤A connected graph is a set of nodes in which any two nodes
can be reached by a walk.
path is a walk that goes through each node only once. If
you have n nodes, you will have (n−1) edges in the path.
➤A
cycle or circuit is a path that returns to where it started.
In RDBMS, we do not like circular references because
referential actions and data retrieval can hang in an end­
less loop. A Hamiltonian circuit is one that contains all
nodes in a graph.
➤A
➤A tree is a connected graph that has no cycles. I have a book
on how to model trees and hierarchies in SQL (Celko,
2012). Thanks to hierarchical databases, we tend to think of
The NoCOUG Journal
a directed tree in which subordination starts at a root node
and flows down to leaf nodes. But in graph databases, trees
are not as obvious as an organizational chart, and finding
them is a complicated problem. In particular, we can start
at a node as the root and look for the minimal spanning
tree. This is a subset of edges that give us the shortest path
from the root we picked to each node in the graph.
Very often we are missing the edges we need to find an an­
swer. For example, we might see that two people got traffic tick­
ets in front of a particular restaurant, but this means nothing
until we look at their credit card bills and see that these two
people had a meal together.
3.2 RDBMS Versus Graph Database
As a generalization, graph databases worry about relation­
ships, while RDBMSs worry about data. RDBMSs have difficul­
ties with complex graph theoretical analysis. It’s easy to manage
a graph where every path has length one; that is just a threecolumn table (node, edge, node). By doing self-joins, you can
construct paths of length two, and so forth, called a breadth-first
search. If you need a mental picture, think of an expanding
search radius. You quickly get into Cartesian explosions for lon­
ger paths, and can get lost in an endless loop if there are cycles.
Furthermore, it is extremely hard to write SQL for graph analysis
if the path lengths are long, variable, or not known in advance.
3.3 Six Degrees of Kevin Bacon Problem
The game “Six Degrees of Kevin Bacon” was invented in 1994
by three Albright College students: Craig Fass, Brian Turtle, and
Mike Ginelli. They were watching television movies when the
film Footloose was followed by The Air Up There, which lead to
the speculation that everyone in the movie industry was con­
nected in some way to Kevin Bacon. Kevin Bacon himself was
assigned the Bacon number 0; anyone who had been in a film
with him has a Bacon number of 1; anyone who worked with that
second person has a Bacon number of 2; and so forth. The goal
is to look for the shortest path. As of mid-2011, the highest finite
Bacon number reported by the Oracle of Bacon is 8.
This became a fad that finally resulted in the website “Oracle
of Bacon” (http://oracleofbacon.org), which allows you to do
online searches between any two actors in the Internet Movie
Database (www.imdb.com). For example, Jack Nicholson was in
A Few Good Men with Kevin Bacon, and Michelle Pfeiffer was in
Wolf with Jack Nicholson. I wrote a whitepaper for Cogito, Inc.
of Draper, UT, in which I wrote SQL queries to the Kevin Bacon
problem as a benchmark against their graph database. I want to
talk about that in more detail.
3.3.1 Adjacency List Model for General Graphs
Following is a typical adjacency list model of a general graph
with one kind of edge that is understood from context. Structure
goes in one table and the nodes in a separate table, because they
are separate kinds of things (i.e., entities and relationships). The
SAG card number refers to the Screen Actors Guild membership
identifier, but I am going to pretend that they are single letters in
the following examples.
CREATE TABLE Actors
(sag_card CHAR(9) NOT NULL PRIMARY KEY
actor_name VARCHAR(30) NOT NULL);
9
CREATE TABLE MovieCasts
(begin_sag_card CHAR(9) NOT NULL
REFERENCES Nodes (sag_card)
ON UPDATE CASCADE
ON DELETE CASCADE,
end_sag_card CHAR(9) NOT NULL
REFERENCES Nodes (sag_card)
ON UPDATE CASCADE
ON DELETE CASCADE,
PRIMARY KEY (begin_sag_card, end_sag_card),
CHECK (begin_sag_card <> end_sag_card));
I am looking for a path from Kevin Bacon, who is 's' for “start”
in the example data, to some other actor who has a length less
than six. Actually, what I would really like is the shortest path
within the set of paths between actors.
The advantage of SQL is that it is a declarative, set-oriented
language. When you specify a rule for a path, you get all the
paths in the set. That is a good thing—usually. However, it also
means that you have to compute and reject or accept all possible
candidate paths. This means the number of combinations you
have to look at increases so fast that the time required to process
them is beyond the computing capacity in the universe. It would
be nice if there were some heuristics to remove dead-end searches, but there are not.
I made one decision that will be important later; I added selftraversal edges (i.e., an actor is always in a movie with himself)
with zero length. I am going to use letters instead of actor names.
There are a mere five actors called {'s', 'u', 'v', 'x', 'y'}:
INSERT INTO Movies - 15 edges
VALUES ('s', 's'), ('s', 'u'), ('s', 'x'), ('u', 'u'), ('u', 'v'), ('u', 'x'), ('v', 'v'), ('v', 'y'), ('x',
'u'), ('x', 'v'), ('x', 'x'), ('x', 'y'), ('y', 's'), ('y', 'v'), ('y', 'y');
I am not happy about this approach, because I have to decide
the maximum number of edges in the path before I start looking
for an answer. But this will work, and I know that a path will have
no more than the total number of nodes in the graph. Let’s create
a query of the paths:
CREATE TABLE Movies
(in_node CHAR(1) NOT NULL,
out_node CHAR(1) NOT NULL)
INSERT INTO Movies
VALUES ('s', 's'), ('s', 'u'), ('s', 'x'), ('u', 'u'), ('u', 'v'), ('u', 'x'), ('v', 'v'), ('v', 'y'),
('x', 'u'), ('x', 'v'), ('x', 'x'), ('x', 'y'), ('y', 's'), ('y', 'v', ('y', 'y');
CREATE TABLE Paths
(step1 CHAR(2) NOT NULL,
step2 CHAR(2) NOT NULL,
step3 CHAR(2) NOT NULL,
step4 CHAR(2) NOT NULL,
step5 CHAR(2) NOT NULL,
path_length INTEGER NOT NULL,
PRIMARY KEY (step1, step2, step3, step4, step5));
Let’s go to the query and load the table with all the possible
paths of length five or less:
DELETE FROM Paths;
INSERT INTO Paths
SELECT DISTINCT M1.out_node AS s1, -- it is 's' in this example
M2.out_node AS s2,
M3.out_node AS s3,
M4.out_node AS s4,
M5.out_node AS s5,
(CASE WHEN M1.out_node NOT IN (M2.out_node, M3.out_node, M4.out_
10
node, M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M2.out_node NOT IN (M3.out_node, M4.out_node,
M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M3.out_node NOT IN (M2.out_node, M4.out_node,
M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M4.out_node NOT IN (M2.out_node, M3.out_node,
M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M5.out_node NOT IN (M2.out_node, M3.out_node,
M4.out_node) THEN 1 ELSE 0 END) AS path_length
FROM Movies AS M1, Movies AS M2, Movies AS M3, Movies AS M4,
Movies AS M5
WHERE M1.in_node = M2.out_node
AND M2.in_node = M3.out_node
AND M3.in_node = M4.out_node
AND M4.in_node = M5.out_node
AND 0 < (CASE WHEN M1.out_node NOT IN (M2.out_node, M3.out_node,
M4.out_node, M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M2.out_node NOT IN (M1.out_node, M3.out_node,
M4.out_node, M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M3.out_node NOT IN (M1.out_node, M2.out_node,
M4.out_node, M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M4.out_node NOT IN (M1.out_node, M2.out_node,
M3.out_node, M5.out_node) THEN 1 ELSE 0 END
+ CASE WHEN M5.out_node NOT IN (M1.out_node, M2.out_node,
M3.out_node, M4.out_node) THEN 1 ELSE 0 END);
SELECT * FROM Paths ORDER BY step1, step5, path_length;
The Paths.step1 column is where the path begins. The other
columns of Paths are the second step, third step, fourth step, and
so forth. The last step column is the end of the journey. The
SELECT DISTINCT is a safety thing and the “greater than zero” is
to clean out the zero-length start-to-start paths. This is a complex query, even by my standards.
The path length calculation is a bit harder. This sum of CASE
expressions looks at each node in the path. If it is unique within
the row, it is assigned a value of 1; if it is not unique within the
row, it is assigned a value of 0.
There are 306 rows in the path table. But how many of these
rows are actually the same path? SQL has to have a fixed number
of columns in a table, but paths can be of different lengths. That
is to say that (s, y, y, y, y)=(s, s, y, y, y)=(s, s, s, y, y)=(s, s, s, s, y). A
path is not supposed to have cycles in it, so you need to filter the
answers. The only places for this are in the WHERE clause or
outside of SQL in a procedural language.
Frankly, I found it was easier to do the filtering in a procedural language instead of SQL. Load each row into a linked list
structure and use recursive code to find cycles. If you do it in
SQL, you need a predicate for all possible cycles of size 1, 2, and
so forth, up to the number of nodes in the graph.
Bacon Number
SQL
Cogito
1
00:00:240.172ms
2
00:02:0600:00:13
3
00:12:5200:00:01
4
00:14:0300:00:13
5
00:14:5500:00:16
6
00:14:4700:00:43
Table 3.1 Query Times for Bacon Numbers.
Internally, graph databases will also use a simple (node, edge,
node) storage model, but they will additionally add pointers to
link nearby nodes or subgraphs. I did a benchmark against a
“Kevin Bacon” database. One test was to find the degrees with
Kevin Bacon as “the center of the universe,” and then a second
test was to find a relationship between any two actors. I used
2,674,732 rows of data. Ignoring the time to set up the data, the
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query times for the simple Bacon numbers are given in Table 3.1.
The timings are raw clock times starting with an empty cache
running on the same hardware. The SQL was Microsoft SQL
Server, but similar results were later obtained with DB2 and
Oracle.
The figures became much worse for SQL as I generalized the
search (e.g., change the focus actor, use only actress links, use
one common movie, and add directors). For example, changing
the focus actor could be up to 9,000 times slower, most by sev­
eral hours versus less than one minute.
3.3.2 Covering Paths Model for General Graphs
What if we attempt to store all the paths in a directed graph in
a single table in an RDBMS? The table for this would look like
the following:
CREATE TABLE Paths
(path_nbr INTEGER NOT NULL,
step_nbr INTEGER NOT NULL
CHECK (path_nbr >= 0),
node_id CHAR(1) NOT NULL,
PRIMARY KEY (path_nbr, step_nbr));
Each path is assigned an ID number and the steps are num­
bered from 0 (the start of the path) to k, the final step. Using the
simple six-node graph, the one-edge paths are:
1
1
2
2
3
3
4
4
5
5
0A
1B
0B
1F
0C
1D
0B
1D
0D
1E
Now we can add the two-edge paths:
6
6
6
7
7
7
8
8
8
9
9
9
0A
1B
2F
0A
1B
2D
0A
1C
2D
0B
1D
2E
And finally the three-edge paths:
10 0 A
10 1 B
10 2 D
10 3 E
11 0 A
11 1 B
11 2 D
11 3 E
These rows can be generated from the single-edge paths using
a common table expression (CTE) or with a loop in a procedural
language, such as SQL/ PSM. Obviously, there are fewer longer
paths, but as the number of edges increases, so does the number
of paths. By the time you get to a realistic-size graph, the number
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of rows is huge. However, it is easy to find a path between two
nodes, as follows:
SELECT DISTINCT :in_start_node, :in_end_node, (P2.step_nbr- P1.step_
nbr) AS distance
FROM Paths AS P1, Paths AS P2
WHERE P1.path_nbr=P2.path_nbr
AND P1.step_nbr <= P2.step_nbr
AND P1.node_id = :in_start_node
AND P2.node_id = :in_end_node;
Notice the use of SELECT DISTINCT because most paths will
be a subpath of one or more longer paths. Without it, the search
for all paths from A to D in this simple graph would return:
70A
71B
72D
80A
81C
82D
10 0 A
10 1 B
10 2 D
11 0 A
11 1 B
11 2 D
However, there are only two distinct paths, namely (A, B, D)
and (A, C, D). In a realistic graph with lots of connections, there
is a good chance that a large percentage of the table will be re­
turned.
Can we do anything to avoid the size problems? Yes and no.
In this graph, most of the paths are redundant and can be re­
moved. Look for a set of subpaths that cover all of the paths in
the original graph. This is easy enough to do by hand for this
simple graph:
10A
11B
12F
2
2
2
2
0A
1B
2D
3E
3
3
3
3
0A
1C
2D
3E
The problem of finding the longest path in a general graph is
known to be NP-complete, and finding the longest path is the
first step of finding a minimal covering path set. For those of you
without a computer science degree, NP-complete problems are
those that require drastically more resources (storage space and/
or time) as the number of elements in the problem increases.
There is usually an exhaustive search or combinatory explosion
in these problems.
While search queries are easier in this model, dropping,
changing, or adding a single edge can alter the entire structure,
forcing us to rebuild the entire table. The combinatory explo­
sion problem shows up again, so loading and validating the
table takes too long for even a medium number of nodes. In
another example, MyFamily.com (owner of Ancestry.com)
wanted to let visitors find relationships between famous people
and themselves. This involves looking for paths 10 to 20+ edges
long, on a graph with over 200 million nodes and 1 billion
11
edges. Query rates are on the order of 20 per second, or 2 million
per day.
3.3.3 Real-World Data Has Mixed Relationships
Now consider another kind of data. You are a cop on a crime
scene investigator show. All you have is a collection of odd facts
that do not fall into nice, neat relational tables. These facts tie
various data elements together in various ways. You now have 60
minutes to find a network of associations to connect the bad guys
to the crime in some as-of-yet-unknown manner.
Ideally, you would do a join between a table of “bad guys”
and a table of “suspicious activities” on a known relationship.
You have to know that such a join is possible before you can
write the code. You have to insert the data into those tables as
you collect it. You cannot whip up another relationship on-thefly.
Let’s consider an actual example. The police collect surveil­
lance data in the form of notes and police reports. There is no
fixed structure in which to fit this data. For example, U-Haul
reports that a truck has not been returned and they file a police
report. That same week, a farm-supply company reports some­
one purchased a large amount of ammonium nitrate fertilizer. If
the same person did both actions, and used his own name (or
with a known alias) in both cases, then you could join them into
a relationship based on the “bad guys” table. This would be fairly
easy; you would have this kind of query in a view for simple
weekly reports. This is basically a shortest-path problem and it
means that you are trying to find the dumbest terrorist in the
United States.
In the real world, conspirator A rents the truck and con­
spirator B buys the fertilizer. Or one guy rents a truck and can­
not return it on time while another totally unrelated person
buys fertilizer paying in cash rather than using an account that
is known to belong to a farmer. Who knows? To find if you have
a coincidence or a conspiracy, you need a relationship between
the people involved. That relationship can be weak (both people
live in New York state) or strong (they were cellmates in pris­
on).
Figure 3.1 is a screenshot of this query and the subgraph that
answers it. Look at the graph that was generated from the sample
data when it was actually given a missing rental truck and a fer­
tilizer purchase. The result is a network that joins the truck to the
fertilizer via two ex-cons, who shared jail time and a visit to a
dam. Hey, that is a red flag for anyone! This kind of graph net­
work is called a causal diagram in statistics and fuzzy logic. You
will also see the same approach as a fishbone diagram (also
known as cause-and-effect diagram and Ishikawa diagram after
their inventor) when you are looking for patterns in data. Before
now, this method has been a “scratch-paper” technique. This is
fine when you are working on one very dramatic case in a
60-minute police show and have a scriptwriter.
In the real world, a major police department has a few hun­
dred cases a week. The super-genius Sherlock Holmes charac­
ters are few and far between. But even if you could find such
geniuses, you simply do not have enough whiteboards to do this
kind of analysis one case at a time in the real world. Intelligence
must be computerized in the 21st century if it is going to work.
Most crime is committed by repeat offenders. Repeat offend­
ers tend to follow patterns—some of which are pretty horrible,
if you look at serial killers. What a police department wants to
12
do is describe a case, then look through all the open cases to
see if there are three or more cases that have the same pattern.
One major advantage is that data goes directly into the graph,
while SQL requires that each new fact has to be checked against
the existing data. Then the SQL data has to be encoded on some
scale to fit into a column with a particular data type.
Figure 3.1 Possible Terrorist Attack Graph.
3.4 Vertex Covering
Social network marketing depends on finding “the cool kids,”
the trendsetters who know everybody in a community. In some
cases, it might be one person. The Pope is fairly well known
among Catholics, for example, and his opinions carry weight.
Formally, a vertex cover of an undirecterd graph G is a set C
of vertices such that each edge of G is incident to at least one
vertex in C. The set C is said to cover the edges of G. Informally,
you have a weird street map and want to put up security cameras
at intersections (nodes) in such a way that no street (edge) is not
under surveillance. We also talk about coloring the nodes to
mark them as members of the set of desired vertices. Figure 3.2
shows two vertex coverings taken from a Wikipedia article on
this topic.
However, neither of these coverings is minimal. The threenode solution can be reduced to two nodes, and the four-node
solution can be reduced to three nodes, as follows:
CREATE TABLE Grid
(v1 SMALLINT NOT NULL CHECK (v1>0),
v2 SMALLINT NOT NULL CHECK (v2>0),
PRIMARY KEY (v1, v2),
CHECK (v1<v2),
color SMALLINT DEFAULT 0 NOT NULL CHECK (color>= 0));
INSERT INTO Grid (v1, v2)
VALUES (1, 2), (1, 4), (2, 3), (2, 5), (2, 6);
{1, 2, 6} and {2, 4} are vertex covers. The second is the minimal
cover. Can you prove it? In this example, you can use brute force
and try all possible coverings. Finding a vertex covering is
known to be an NP-complete problem, so brute force is the only
sure way. In practice, this is not a good solution because the
February 2014
combinatorial explosion catches up with you sooner than you
think.
One approach is to estimate the size of the cover, then pick
random sets of that size. You then keep the best candidates, look
for common subgraphs, and modify the candidates by adding or
removing nodes. Obviously, when you have covered 100% of the
edges, you have a winner; it might not be optimal, but it works.
Figure 3.2 Vertex coverings from Wikipedia:
(a) three-node solution, and (b) a four-node solution.
Another consideration is that the problem might start with a
known number of nodes. For example, you want to give n sample
items to bloggers to publicize your product. You want to gift the
bloggers with the largest readership for the gifts, with the least
overlap.
3.5 Graph Programming Tools
As of 2012, there are no ANSI or ISO standard graph query
languages. We have to depend on proprietary languages or opensource projects. They depend on underlying graph databases,
which are also proprietary or open-source projects. Support for
some of the open-source projects is available from commercial
providers; Neo4j is the most popular product and it went com­
mercial in 2009 after a decade in the open-source world. This has
happened in the relational world with PostgreSQL, MySQL, and
other products, so we should not be surprised.
There is an ISO standard known as resource description
framework (RDF), which is a standard model for data inter­
change on the Web. It is based on the RDF that extends the link­
ing structure of the Web to use URIs (uniform resource iden­ti­fiers)
to name the relationship between things as well as the two ends
of the link (a triple). URIs can be classified as locators (URLs),
names (URNs), or both. A uniform resource name (URN) func­
tions like a person’s name, while a uniform resource locator
(URL) resembles that person’s street address. In other words, the
URN defines an item’s identity, while the URL provides a method
for finding it.
The differences are easier to explain with an example. The
ISBN uniquely identifies a specific edition of a book, its identity.
But to read the book, you need its location: a URL address. A
typical URL would be a file path for the electronic book saved on
a local hard disk.
Since the Web is a huge graph database, many graph data­
bases build on RDF standards. This also makes it easier to have
a distributed graph database that can use existing tools.
3.5.1 Graph Databases
Some graph databases were built on an existing data storage
system to get started, but then were moved to custom storage
engines. The reason for this is simple: performance. Assume you
want to model a simple one-to-many relationship, such as the
Kevin Bacon problem. In RDBMS and SQL, there will be a table
for the relationship, which will contain a reference to the table
with the unique entity and a reference for each row matching to
that entity in the many side of the relationship. As the relational
tables grow, the time to perform the join increases because you
are working with entire sets.
The NoCOUG Journal
In a graph model, you start at the Kevin Bacon node and tra­
verse the graph looking at the edges with whatever property you
want to use (e.g., “was in a movie with”). If there is a node prop­
erty, you then filter on it (e.g., “this actor is French”). The edges
act like very small, local relationship tables, but they give you a
traversal and not a join.
A graph database can have ACID transactions. The simplest
possible graph is a single node. This would be a record with
named values, called properties. In theory, there is no upper limit
on the number of properties in a node, but for practical pur­
poses, you will want to distribute the data into multiple nodes,
organized with explicit relationships.
3.5.2 Graph Languages
There is no equivalent to SQL in the graph database world.
Graph theory was an established branch of mathematics, so the
basic terminology and algorithms were well-known were the
first products came along. That was an advantage. But graph
database had no equivalent to IBM's System R, the research proj­
ect that defined SEQUEL, which became SQL. Nor has anyone
tried to make one language into the ANSI, ISO or other industry
standard.
SPARQL
SPARQL (pronounced “sparkle,” a recursive acronym for
SPARQL Protocol and RDF Query Language) is a query lan­
guage for the RDF format. It tries to look a little like SQL by
using common keywords and a bit like C with special ASCII
characters and lambda calculus. For example:
PREFIX abc: <http://example.com/exampleOntology#>
SELECT ?capital ?country
WHERE {
?x abc:cityname ?capital;
abc:isCapitalOf ?y.
?y abc:countryname ?country;
abc:isInContinent abc:Africa.}
where the ? prefix is a free variable, and : names a source.
SPASQL
SPASQL (pronounced “spackle”) is an extension of the SQL
standard, allowing execution of SPARQL queries within SQL
statements, typically by treating them as subqueries or function
clauses. This also allows SPARQL queries to be issued through
“traditional” data access APIs (ODBC, JDBC, OLE DB, ADO.
NET, etc.).
Gremlin
Gremlin is an open-source language that is based on travers­
als of a property graph with a syntax taken from OO and the C
programming language family (https://github.com/tinkerpop/
gremlin/wiki). There is syntax for directed edges and more com­
plex queries that looks more mathematical than SQL-like.
Following is a sample program. Vertexes are numbered and a
traversal starts at one of them. The path is then constructed by
in–out paths on the 'likes' property:
g = new Neo4jGraph('/tmp/neo4j')
// calculate basic collaborative filtering for vertex 1
m = [:]
g.v(1).out('likes').in('likes').out('likes').groupCount(m) m.sort{-it.value}
13
// calculate the primary eigenvector (eigenvector centrality) of a graph
m = [:]; c = 0;
g.V.out.groupCount(m).loop(2){c++<1000}
m.sort{-it.value}
Eigenvector centrality is a measure of the influence of a node
in a network. It assigns relative scores to all nodes in the network
based on the concept that connections to high-scoring nodes
contribute more to the score of the node in question than equal
connections to low-scoring nodes. It measures the effect of the
“cool kids” in your friends list. Google’s PageRank is a variant of
the eigenvector centrality measure.
Cypher (NEO4j)
Cypher is a declarative graph query language that is still grow­
ing and maturing, which will make SQL programmers comfort­
able. It is not a weird mix of odd ASCII charterers, but
human-readable keywords in the major clauses. Most of the
keywords like WHERE and ORDER BY are inspired by SQL.
Pattern matching borrows expression approaches from SPARQL.
The query language is comprised of several distinct clauses:
➤
START: starting points in the graph, obtained via index
lookups or by element IDs.
➤
MATCH: the graph pattern to match, bound to the starting
points in START.
➤
WHERE: filtering criteria.
➤
RETURN: what to return.
➤
CREATE: creates nodes and relationships.
➤
DELETE: removes nodes, relationships, and properties.
➤
SET: sets values to properties.
➤
➤
and NOT; comparison operators; simple math; regular expres­
sions; and so forth.
Trends
Go to http://www.graph-database.org/ for PowerPoint shows
on various graph language projects. It will be in flux for the next
several years, but you will see several trends. The proposed lan­
guages are declarative, and are borrowing ideas from SQL and
the RDBMS model. For example, GQL (Graph Query Language)
has syntax for SUBGRAPH as a graph venison of a derived table.
Much like SQL, the graph languages have to send data to external
users, but they lack a standard way of handing off the informa­
tion.
It is probably worth the effort to get an open-source down­
load of a declarative graph query language and get ready to up­
date your resume.
Concluding Thoughts
Graph databases require a change in the mindset from com­
putational data to relationships. If you are going to work with
one of these products, then you ought to get math books on
graph theory. A short list of good introductory books are listed
in the Reference section. s
References
➤
Celko, J. (2012). Trees and hierarchies in SQL for smarties. Burlington, MA: Morgan-Kaufmann. ISBN: 9780123877338.
➤
Chartrand, G. (1984). Introductory graph theory. Mineola,
NY: Dover Publications. ISBN: 978-0486247755.
➤
Chartrand, G., & Zhang, P. (2004). A first course in graph
theory. New York: McGraw-Hill. ISBN: 978-0072948622.
FOREACH: performs updating actions once per element in
a list.
➤
Gould, R. (2004). Graph theory. Mineola, NY: Dover
Publications. ISBN: 978-0486498065.
WITH: divides a query into multiple, distinct parts.
➤
Trudeau, R. J. (1994). Introduction to graph theory. Mine­
ola, NY: Dover Publications. ISBN: 978-0486678702.
➤
Maier, D. (1983). Theory of relational databases. Rockville.
MD: Computer Science Press. ISBN: 978-0914894421.
➤
Wald, A. (1973). Sequential analysis. Mineola, NY: Dover
Publications. ISBN: 978-0486615790.
For example, following is a query that finds a user called John
in an index and then traverses the graph looking for friends of
John’s friends (though not his direct friends) before returning
both John and any friends-of-friends who are found:
START john=node:node_auto_index(name = 'John')
MATCH john-[:friend]->()-[:friend]->fof
RETURN john, fof
We start the traversal at the john node. The MATCH clause uses
arrows to show the edges that build the friend-of-friend edges
into a path. The final clause tells the query what to return.
In the next example, we take a list of users (by node ID) and
traverse the graph looking for those other users who have an
outgoing friend relationship, returning only those followed users
who have a name property starting with S:
Joe Celko served 10 years on the ANSI/ISO SQL Standards Com­
mittee and contributed to the SQL-89 and SQL-92 Standards. Mr.
Celko is author of a series of books on SQL and RDBMS for
Elsevier/Morgan Kaufmann. He is an independent consultant
based in Austin, Texas. He has written over 1200 columns in the
computer trade and academic press, mostly dealing with data and
databases.
START user=node(5,4,1,2,3)
MATCH user-[:friend]->follower
WHERE follower.name =~ 'S.*'
RETURN user, follower.name
The WHERE clause is familiar from SQL and other program­
ming languages. It has the usual logical operators of AND, OR,
14
February 2014
SPECIAL
F E AT U R E
Oracle Spatial
and Graph
by Bill Beauregard
Bill Beauregard
F
or the last ten years, Oracle Spatial and Graph has de­
livered the industry’s most advanced enterprise spatial
and graph data management solution. Oracle Spatial
and Graph is completely integrated with Oracle Data­
base 12c, thereby providing developers with the enterprise-class
performance, scalability, reliability, and security necessary for
today’s graph-based applications. It provides support for two
graph data models, along with the industry’s leading spatial data
management.
RDF Semantic Graph supports the World Wide Web Con­
sortium (W3C) Resource Description Framework (RDF) graph
standard. This model supports the unique data management,
querying, and inferencing commonly used in a variety of social
network analysis and linked open data solutions.
Network Data Model (NDM) Graph is a property graph data
model used to model and analyze physical and logical networks
used in industries such as transportation, logistics, and utilities.
NDM persists the network connectivity metadata in the da­
tabase, while a Java API provides fast in-memory graph analyt­
ics, including shortest path, nearest neighbors, within cost, and
reachability.
Spatial data management supports the complex requirements
found in Geographic Information Systems (GISs), enterprise ap­
plications, and location-enabled business and web mapping ap­
plications. Capabilities include native support for spatial data
models and types such as georaster (for geo-referenced imagery
and gridded data); topology; 3D, including triangulated irregular
networks (TINs) and point clouds (supporting LIDAR data); and
linear referencing. In addition, a geocoding engine, a routing
engine, and spatial web services conformant with Open Geospatial
Consortium (OGC) and ISO standards are also included. These
geospatial features provide a complete platform to deploy a range
of enterprise GIS and web mapping solutions used in the public
sector, utilities, telecommunications, retail, and logistics.
The following sections provide detailed overview of Oracle’s
two native graph capabilities: RDF Semantic Graph and the Net­
work Data Model.
RDF Semantic Graph Features Overview
Graphs are becoming central to a new category of social net­
work and linked data applications common in public sector,
health sciences, finance, media and intelligence communities.
The graph data structure is an ideal metadata layer to support the
integration of various data sources.
The NoCOUG Journal
Figure 1: Representing relational table as an RDF graph.
Figure 2: RDF creates a flexible common enterprise vocabulary.
The RDF graph model can represent relational database
schema as a graph. It can align the entities and semantics in the
graph (schema) with the semantics of an enterprise vocabulary
or ontology. This ensures that application developers code to a
common, semantically consistent vocabulary when performing
federated queries. The RDF model can also federate diverse data
types, such as relational, text, spatial, images, and spreadsheets.
15
tion operations on RDF/OWL models. Space-efficient storage
saves up to 60% disk space for scalable and performant loading,
querying, and inferencing. It has proven scalability beyond tens
of billions of triples (LUBM 200K benchmark) and can readily
scale into the tens of petabytes of triples.
Figure 3: Modeling diverse federated data types with RDF.
Storing, Loading, and Data Manipulation
RDF is the W3C standard for semantic data. An RDF data
element is a “resource.” A unique benefit of RDF graphs is that
resources enable data integration between different and even
disparate data sets. Integration is possible because each resource
has a globally unique universal resource identifier (URI), like a
Social Security number. Resources form simple “subject-predi­
cate-object” statements. The predicate is a property of the subject
and the type of relationship between the subject and the object.
The object is either a value for the property (a literal) or the URI
of another subject that connects triples to form a graph. Ulti­
mately, RDF graphs comprise nodes (subjects and objects) con­
nected by directed, labeled edges (predicates) as illustrated by the
labeled arrow in the figure below.
Figure 4: An RDF triple has a directed, labeled edge.
RDF graphs are flexible. It is easy to expand and combine
RDF graphs because business information about the data (meta­
data) is stored in the graph as relationships rather than as col­
umn headers in a table.
Relational data can easily be converted or mapped to an RDF
graph.
Querying RDF Graphs in Oracle Database 12c
RDF has its own graph query language, SPARQL. It is a sim­
pler way to write query patterns that are joined together. For
example, the following SPARQL query finds pairs of siblings,
people with the same parents.
SELECT ?x ?y
FROM <rdf_graph>
WHERE
{
?x hasFather ?f .
?x hasMother ?m .
?y hasFather ?f .
?y hasMother ?m .
FILTER( ?x != ?y)
}
The same query in SQL is more complex:
SELECT g1.subject x, g3.subject y
FROM rdf_graph g1, rdf_graph g2, rdf_graph g3, rdf_graph g4
WHERE g1.predicate = 'hasFather'
AND g2.predicate = 'hasMother'
AND g3.predicate = 'hasFather'
AND g4.predicate = 'hasMother'
AND g1.subject = g2.subject
AND g3.subject = g4.subject
AND g1.object = g3.object
AND g2.object = g4.object
AND g1.subject != g3.subject
RDF Semantic Graph supports SPARQL 1.1, the latest W3C
standard and a range of deployment approaches, including a web
service endpoint and Java APIs. Unique to Oracle, SQL can
query RDF graphs by embedding SPARQL queries in SQL using
the SEM_MATCH table function.
Here is an example of a SPARQL query:
PREFIX foaf: <http://...>
SELECT ?n1 ?n2
FROM <http://g1>
WHERE
{
?p foaf:name ?n1
OPTIONAL
{
?p foaf:knows ?f .
?f foaf:name ?n2
}
FILTER (REGEX(?n1, "^A"))
}
Here is an example of a SQL query with embedded SPARQL:
Figure 5: Mapping relational data to RDF triples.
The example above illustrates another benefit of RDF graphs;
sparse data is stored flexibly and efficiently. Only existing rela­
tionships for a given subject are stored. Add new relationships
by simply inserting a new triple for a particular subject.
The unit of storage in RDF Semantic Graph is a model.
Models are user-defined and application-specific. In addition,
virtual model capability provides a view-like feature to combine
models for querying. RDF Semantic Graph leverages the stan­
dard Oracle Database 12c loading, storing, and data manipula­
16
SELECT n1 n2
FROM TABLE(SEM_MATCH (
'{
?p foaf:name ?n1
OPTIONAL
{
?p foaf:knows ?f .
?f foaf:name ?n2
}
FILTER (REGEX (?n1, "^A"))
} ',
SEM_MODELS('g1') ,…,
SEM_ALIASES (SEM_ALIAS ('foaf','http://…')), …
))
February 2014
The SEM_MATCH table function in Oracle Spatial and
Graph supports SPARQL 1.1. Additionally, RDF Semantic Graph
supports the OGC GeoSPARQL standard that allows spatial que­
rying and filtering on location data stored in the graph.
Viewing Relational Data as RDF Triples
It is possible to apply RDF views on relational tables using
Oracle Spatial and Graph. RDF views present relational data
in RDF triple format. SPARQL queries on these views enable
powerful data integration across federated data sources, a key
requirement for Linked Data and enterprise integration appli­
cations. Development is simplified; graph pattern-matching re­
lationship queries on relational tables do not require converting
the tables to RDF triples.
RDF Semantic Graph supports three types of view definitions:
a) automatic mapping (also referred to as Direct Mapping); b)
custom mapping, using the W3C R2RML language; and c) materialized views. The simplest way to create a mapping of relational
data to RDF data is by calling the SEM_APIS.CREATE_
RDFVIEW_MODEL procedure to create an RDF view. It takes as
input the list of tables or views you would like to view as RDF.
This provides a direct mapping of those relational tables or views.
The following example illustrates Direct Mapping:
-- Use the following relational tables.
CREATE TABLE dept (
deptno NUMBER CONSTRAINT pk_DeptTab_deptno PRIMARY KEY,
dname VARCHAR2(30),
loc VARCHAR2(30)
);
CREATE TABLE emp (
empno NUMBER PRIMARY KEY,
ename VARCHAR2(30),
job VARCHAR2(20),
deptno NUMBER REFERENCES dept (deptno)
);
-- The next statament is a query against an RDF view named empdb_model
-- to find the employees who work for any department located in Boston.
SELECT emp
FROM TABLE(SEM_MATCH(
'{?emp emp:ref-DEPTNO ?dept . ?dept dept:LOC "Boston"}',
SEM_Models('empdb_model'),
NULL,
SEM_ALIASES(
SEM_ALIAS('dept','http://empdb/TESTUSER.DEPT#'),
SEM_ALIAS('emp','http://empdb/TESTUSER.EMP#')
),
null));
This produces the following output:
EMP
-------------------------------------------------------------------------------http://empdb/TESTUSER.EMP/EMPNO=1
http://empdb/TESTUSER.EMP/EMPNO=3
The preceding query is functionally comparable to this:
SELECT e.empno FROM emp e, dept d
WHERE e.deptno = d.deptno AND d.loc = 'Boston';
Alternatively, an R2RML mapping document could custom­
ize the mapping between the relational tables and the RDF graph
in the example above.
Native Inferencing in Oracle Database 12c
Inferencing allows machine-driven discovery of implicit facts
from explicit RDF data. It gives application developers the ad­
vantage of deriving more application knowledge with no coding
by using standards-defined rules and semantics. Inferencing
adds new facts to the graph.
-- Insert some data.
INSERT INTO dept (deptno, dname, loc)
VALUES (1, 'Sales', 'Boston');
INSERT INTO dept (deptno, dname, loc)
VALUES (2, 'Manufacturing', 'Chicago');
INSERT INTO dept (deptno, dname, loc)
VALUES (3, 'Marketing', 'Boston');
INSERT INTO emp (empno, ename, job,
VALUES (1, 'Alvarez', 'SalesRep', 1);
INSERT INTO emp (empno, ename, job,
VALUES (2, 'Baxter', 'Supervisor', 2);
INSERT INTO emp (empno, ename, job,
VALUES (3, 'Chen', 'Writer', 3);
INSERT INTO emp (empno, ename, job,
VALUES (4, 'Davis', 'Technician', 2);
deptno)
deptno)
deptno)
deptno)
-- Create an RDF view model using direct mapping of two tables, EMP and
DEPT,
-- with a base prefix of http://empdb/.
-- Specify KEY_BASED_REF_PROPERTY=T for the options parameter.
BEGIN
sem_apis.create_rdfview_model(
model_name => 'empdb_model',
tables => SYS.ODCIVarchar2List('EMP', 'DEPT'),
prefix => 'http://empdb/',
options => 'KEY_BASED_REF_PROPERTY=T'
);
END;
/
-- Query an RDF view using SPARQL in a SEM_MATCH-based SQL query.
The NoCOUG Journal
Figure 6: Inferencing adds the fact that California
is part of North America.
RDF Semantic Graph provides native, forward-chaining, per­
sistent inferencing using any combination of the built-in RDF,
RDFS, and OWL 2 RL and EL profiles, as well as user-defined
rules for specialized inference capabilities. Because inferencing is
persistent and performed in advance of queries, it enhances
query performance. Recent performance enhancements include
optimized large owl:sameAs sets, incremental inference to up­
date entailments after triple inserts, and parallel inference on
multi-core or multi-CPU architectures.
17
For high-security applications, Oracle Spatial and Graph sup­
ports ladder-based inferencing. This feature applies appropriate
security labels to newly inferred triples. RDF Semantic Graph
supports additional reasoning requirements with a plug-in frame­
work to integrate third-party special-purpose reasoners.
XML, JSON and Relational Interoperability
Oracle uniquely allows a SQL query to consult an ontology to
provide more complete SQL results. Ontology-assisted SQL que­
rying allows SQL queries to extract more semantically complete
results from relational tables. By aligning relational data with a
pre-defined ontology (or enterprise vocabulary) developers can
better organize their instance data with a rich domain context.
For example, the following SQL query to find patients with
upper extremity fractures on the table below returns no results:
SELECT p_id, diagnosis
FROM Patients
WHERE diagnosis ='Upper_Extremity_Fracture';
Figure 7: SQL query on patient diagnosis table.
However, the National Cancer Institute (NCI) medical ontol­
ogy indicates that a hand fracture is a type of upper extremity
fracture.
To support integration with external business intelligence (BI)
and mid-tier tools, Oracle Spatial and Graph also provides a
SPARQL Gateway feature that presents SPARQL query results in
XML format. This is essential for supporting enterprise software
tools that ingest XML data sources, such as Oracle Business In­
telligence (OBIEE). RDF Semantic Graph can return RDF query
results in JSON interoperability format as well.
Fine-Grained Security
As mentioned earlier, certain applications require finegrained security. Model-level access control is the default for
RDF graph data. Oracle Label Security provides triple-level secu­
rity for the most stringent security levels. It defines sensitivity
labels on individual triples and users that conditionally restrict a
user’s access to individual triples stored in an RDF model. Oracle
Spatial and Graph is unique in providing such a fine-grained
security model.
Graph Analytics
To support social network analysis (SNA), Oracle Spatial and
Graph supports SPARQL 1.1 property path expressions that find
relationships within a large social graph. Oracle Advanced
Analytics data mining and R statistical analysis can operate on
graph query results. The Network Data Model provides inmemory graph analytics for shortest path, reachability, within
cost, and nearest neighbor analysis on RDF data.
SPARQL Property Path Expressions
Support for SPARQL property path expressions is included.
An expression determines whether there is any connectivity in
the graph between two nodes in the graph, such as between Tim
and Sam in the example below.
Figure 9: Modeling social connectivity with RDF.
Oracle Advanced Analytics provides data mining and R sta­
tistical analysis capabilities for Oracle Database 12c. These fea­
tures can also analyze the results of SPARQL pattern-matching
queries on graphs in Oracle Database 12c.
Figure 8: Portion of the NCI medical ontology.
When the Oracle SEM_RELATED operator is added to the
SQL query, it consults the NCI ontology and finds patient 1 has
an upper extremity fracture.
-- Ontology assisted SQL query
SELECT p_id, diagnosis
FROM Patients
WHERE SEM_RELATED (
diagnosis,
'rdfs:subClassOf',
'Upper_Extremity_Fracture';
'Medical_ontology' = 1)
AND SEM_DISTANCE() <= 2;
18
Figure 10: Oracle Data Mining.
February 2014
Oracle R Enterprise
➤
Open source language and environment
➤
Split, merge, and exchange partitions.
Parallel Support
Support for parallelism is an important differentiating feature
of Oracle Spatial and Graph. Parallelism enhances the perfor­
mance of graph data loading, queries, and inferencing opera­
tions. Parallelism also speeds index creation; it subdivides graph
B-tree index creation into multiple tasks performed in parallel.
Unique Enterprise Manageability
RDF Semantic Graph supports Oracle Database 12c utilities
and tools, including:
➤
Figure 11: Oracle R Enterprise.
Statistical computing and graphics
➤
Easily produces publication-quality plots
➤ Highly extensible with open-source R packages
➤
Bulk loading, including the Jena Adapter bulk loader,
Oracle external tables, and SQL*Loader Direct Path load­
ing
➤
Replication and recovery, including Data Guard: physical
standby, Data Pump staging tables, and Recovery Manager
➤
Tuning with parallelism, Btree indexing of triples and
quads, typed literals indexing, SPARQL query hints, statis­
tics gathering, and Dynamic Sampling
Semantic Indexing for Documents
Semantic indexing for documents goes beyond key word
searching to find documents that have semantically related con­
tent. SPARQL graph pattern queries interrogate the semantic
index to find relevant documents. For instance, referring to our
earlier SPARQL example, one could find all documents that
mention pairs of siblings. The results could include the names of
the siblings and the document(s) that mention them, as well as
other attributes.
Indexing occurs automatically by inserting documents, text,
and URLs into a table that has a semantic index defined on it. An
associated third-party natural language processing (NLP) engine
or annotator analyzes the text and generates RDF triples or XML
from the concepts and terms found in the text for the semantic
index. Inferencing on the semantic index can discover new re­
lationships and expand search results.
Enterprise Performance and Scalability
RDF Semantic Graph supports parallelism, compression, par­
titioning, Oracle Real Applications Clusters, and the Oracle
Exadata Database Machine for enterprise-level performance,
scalability, and reliability.
Partitioning Support for Spatial Indexes
Models subdivide a graph in Oracle Database 12c and reside
in separate partitions. Partitioning offers significant perfor­
mance, scalability, and manageability benefits, including the fol­
lowing:
➤
Reduce response times for long-running queries; parti­
tioning can reduce disk I/O operations.
➤
Reduce response times for concurrent queries; I/O opera­
tions run concurrently on each partition.
➤
Maintain indexes more easily with partition-level opera­
tions to create and rebuild an index.
➤
Rebuild indexes on partitions without affecting the queries
on other partitions.
➤
Change storage parameters for each local index indepen­
dent of other partitions.
The NoCOUG Journal
Figure 12: Oracle Enterprise Manager supports
RDF Semantic Graph.
➤
Query management and execution controls in Jena
Adapter, including timeout, query abort framework, query
hints, and parallel execution
➤
Performance Analysis with Enterprise Manager
Open Standards
Oracle is a World Wide Web Consortium (W3C) member
and active contributor and/or editor in various technical work­
ing groups.
RDF Semantic Graph supports the latest W3C specifications,
including RDF, RDF Schema (RDFS), SPARQL 1.1 query lan­
guage, OWL 2 (RL and EL profiles) knowledge representation
languages for authoring ontologies, the Simple Knowledge
Organization System (SKOS), and the RDB2RDF specifications
for creating RDF views on relational tables—Direct Mapping
(DM) and Mapping Language (R2RML). In addition, Oracle
was instrumental in defining and supporting the Open
Geospatial Consortium’s (OGC’s) GeoSPARQL 1.0 query spec­
ification.
Rich Ecosystem of Tools
Leading third-party commercial graph tools and applications
19
support RDF Semantic Graph. Products include ontology mod­
eling and engineering, vocabulary alignment, and graph visual­
Figure 13: Business Intelligence (OBIEE) integration with RDF.
ization tools. In addition, Oracle Business Intelligence and
Oracle Advanced Analytics products can analyze RDF data. Fi­
nal­ly, Oracle’s RDF graph database supports the leading opensource application development frameworks, including Apache
Jena, Sesame, Protégé, and associated open-source tools as well
as linked open data ontologies.
Big Data and NoSQL Interoperability
As graph data management becomes increasingly important
to big data and NoSQL platforms, Oracle now supports RDF
graphs in Oracle NoSQL Database. By incorporating W3C stan­
dards–based RDF support and open-source Apache Jena support
(a Jena Adapter) into Oracle NoSQL Database, users benefit
from high performance, low latency, and a no schema design
ideal for low-latency retrieval and high-volume capture on sim­
ple data. This key-value store is ideally suited for exploiting the
cost/performance benefits of horizontally scaled cloud-based
and Linked Open Data architectures.
Network Data Model (NDM) Graph Features Overview
The NDM graph is a feature of Oracle Spatial and Graph that
supports the network modeling and analysis requirements of
leading telecommunications, electrical utilities, transportation,
and logistics companies worldwide.
NDM stores general network (or property graph) data struc­
tures persistently in Oracle Database 12c. It explicitly stores and
maintains network connectivity and provides network analysis
capability, including shortest path, nearest neighbors, within
cost, and reachability.
NDM has a PL/SQL API for managing network data in the
database and a Java API for performing network analysis in midtier memory. The Java API is also instrumental in creating and
applying network constraints.
Figure 14: NDM Architecture.
20
To support analysis of very large graph models that exceed the
platform’s memory limits, the NDM supports partitioning of
large networks into manageable sub-networks that automatically
load graph partitions into memory as needed for efficient inmemory analysis. This enables application developers to benefit
from the speed of in-memory analytics on networks that are
larger than available memory.
Geocoding and Route Analysis
NDM is central to support a range of web mapping, point-topoint routing, and geocoding services common in most geospa­
tial applications. It uses the features of geocoding and the routing
engine in Oracle Spatial and Graph. NDM supports commercial
street network data from data providers Here (formerly Navteq)
and Tom Tom in their respective Oracle formats.
Modeling Capabilities
➤
Model and represent any point along a link for all analysis
functions, such as specific addresses in street networks
with any number of properties on the nodes and links.
Figure 15: San Francisco Park: Finding Parking
Availability Analytical Capabilities.
➤ Model partial-link paths (sub-paths).
➤
Customize link and node properties (e.g., costs).
➤
Perform path analysis with multiple link and node proper­
ties (e.g., distance/time/hops costs).
➤
Perform partitioning of logical networks (e.g., social and
biochemical pathway networks) based on metrics appro­
priate to the application.
➤
Compute the shortest route connecting a given set of nodes
(the traveling salesperson problem).
➤
Generate a polygon representing the region that is reach­
able from a given node with a specific cost. A typical ap­
plication is the generation of drive time and drive distance
polygons.
Figure 16: Calculating Optimal Paths on Roadways.
February 2014
➤
Generate the shortest path on a hierarchical network,
where links are prioritized by property (e.g., highways,
local roads), to support queries such as finding the route
between two addresses that favors highways over local
roads as much as possible.
➤
Compute a buffer based on network cost; the buffer repre­
sentation contains coverage and cost information.
➤
Compute K shortest paths between two nodes.
Real-World Feature Modeling and Analysis
Oracle Spatial and Graph NDM simplifies feature editing and
analysis by providing a feature analysis function that associates
Figure 17: Mapping Features to a Network.
feature representations with network elements.
Feature modeling simplifies application development by as­
sociating real-world objects with network elements. For example,
if a utility network application needs to find affected households
when a substation experiences a power failure, it is necessary to
associate the application features (substations, power lines, and
transformers) with network elements (links and nodes). Feature
modeling maintains these relationships through feature meta­
data, simplifying application development and maintenance.
as finding the fastest travel route for a specified time of day.
NDM supports modeling and analysis of multimodal transporta­
tion networks, and computing the fastest paths on multimodal
transportation networks.
Summary
Graphs are central to a new category of social media and
linked data applications emerging in the life sciences, finance,
media, and public sectors. In addition, graphs have been a fun­
damental data model to support a range of enterprise applica­
tions that require the modeling and analysis of network models
common in telecommunications, utilities, and transportation.
The availability of two graph models in Oracle Spatial and Graph
ensures that applications developers can choose the appropriate
model for their application. In doing so, they can leverage the
industry’s most scalable and secure platform for enterprise-scale
graph applications.
More information about Oracle Spatial and Graph is available
on oracle.com: http://www.oracle.com/technetwork/database/
options/spatialandgraph/overview/index.html. s
Bill Beauregard is a senior principal product manager with Oracle
Corporation.
Network Modeling with Time; Multimodal Transportation
Routing
Oracle Professional
Consulting and
Training Services
Certified training and professional
consulting when you need it,
where you need it.
Figure 18: Developing Routing, Geocoding, Mapping Solutions.
Most real-world networks have a time element. Travel times
on road segments vary with the time of day. Utility networks
experience different demand loads based on seasonal demand
and the time of day. To support temporal analysis of such net­
works, Oracle Spatial and Graph supports the modeling of net­
works containing a time dimension. NDM supports queries such
The NoCOUG Journal
www.quilogyservices.com
[email protected]
866.784.5649
21
S Q L C orner
Disruptive Innovations?
by Iggy Fernandez
Iggy Fernandez
Excerpted from The Twelve Days of NoSQL
(http://iggyfernandez.wordpress.com/)
would ruin our margins. What should we do? And that really is the
dilemma.”
oSQL and Big Data are potentially “disruptive in­
novations” in the sense used by Harvard professor
Clayton M. Christensen. In The Innovator’s Dilem­
ma: When New Technologies Cause Great Firms to
Fail, Chris­tensen defines disruptive innovations and explains
why it is dangerous to ignore them:
Exactly in the manner that Christensen described, the ecommerce pioneer Amazon.com created an in-house product
called Dynamo in 2007 to meet the performance, scalability, and
availability needs of its own e-commerce platform after it con­
cluded that mainstream database management systems were not
capable of satisfying those needs. The most notable aspect of
Dynamo was the apparent break with the relational model; there
was no mention of relations, relational algebra, or SQL.
N
“Generally, disruptive innovations were technologically straightforward, consisting of off-the-shelf components put together in a
product architecture that was often simpler than prior approaches.
They offered less of what customers in established markets wanted
and so could rarely be initially employed there. They offered a different package of attributes valued only in emerging markets remote from, and unimportant to, the mainstream.”
Established players usually ignore disruptive innovations be­
cause they do not see them as a threat to their bottom lines. In
fact, they are more than happy to abandon the low-margin seg­
ments of the market, and their profitability actually increases
when they do so. The disruptive technologies eventually take
over most of the market.
An example of a disruptive innovation is the personal com­
puter. The personal computer was initially targeted only at the
home computing segment of the market. Established manufac­
turers of mainframe computers and minicomputers did not see
PC technology as a threat to their bottom lines. Eventually, how­
ever, PC technology came to dominate the market, and estab­
lished computer manufacturers such as Digital Equipment
Cor­poration, Prime, Wang, Nixdorf, Apollo, and Silicon Graphics
went out of business.
So where lies the dilemma? Christensen explains:
“In every company, every day, every year, people are going into
senior management, knocking on the door saying: ‘I got a new
product for us.’ And some of those entail making better products
that you can sell for higher prices to your best customers. A disruptive innovation generally has to cause you to go after new markets,
people who aren’t your customers. And the product that you want
to sell them is something that is just so much more affordable and
simple that your current customers can’t buy it. And so the choice
that you have to make is: Should we make better products that we
can sell for better profits to our best customers. Or maybe we ought
to make worse products that none of our customers would buy that
22
Schemaless Design
One of the innovations of the NoSQL camp is “schemaless
design.” Data is stored in “blobs” and documents and the NoSQL
database management system does not police their structure.
Let’s do a thought experiment. Suppose that we don’t have a
schema and let’s suppose that the following facts are known.
➤
Iggy Fernandez is an employee with EMPLOYEE_ID = 1
and SALARY = $1000.
➤
Mogens Norgaard is an employee with EMPLOYEE_ID =
2, SALARY = €1000, and COMMISSION_PCT = 25.
➤ Morten
Egan is an employee with EMPLOYEE_ID = 3,
SALARY = €1000, and unknown COMMISSION_PCT.
Could we ask the following questions and expect to receive
correct answers?
➤
Question: What is the salary of Iggy Fernandez?
➤
Correct answer: $1000.
➤
Question: What is the commission percentage of Iggy
Fernandez?
➤
Correct answer: Invalid question.
➤
Question: What is the commission percentage of
Mogens Norgaard?
➤
Correct answer: 25%
➤
Question: What is the commission percentage of Morten
Egan?
➤
Correct answer: Unknown.
If we humans can process the above data and correctly answer
the above questions, then surely we can program computers to
do so.
The above data could be modeled with the following three
relations. It is certainly disruptive to suggest that this be done on
February 2014
the fly by the database management system but not outside the
realm of possibility.
EMPLOYEES
EMPLOYEE_ID NOT NULL NUMBER(6)
EMPLOYEE_NAME VARCHAR2(128)
UNCOMMISSIONED_EMPLOYEES
EMPLOYEE_ID NOT NULL NUMBER(6)
SALARY NUMBER(8,2)
COMMISSIONED_EMPLOYEES
EMPLOYEE_ID NOT NULL NUMBER(6)
SALARY NUMBER(8,2)
COMMISSION_PCT NUMBER(2,2)
A NoSQL company called Hadapt has already stepped for­
ward with such a feature:
“While it is true that SQL requires a schema, it is entirely untrue that the user has to define this schema in advance before query
processing. There are many data sets out there, including JSON,
XML, and generic key-value data sets that are self-describing—
each value is associated with some key that describes what entity attribute this value is associated with [emphasis added]. If
these data sets are stored in Hadoop, there is no reason why
Hadoop cannot automatically generate a virtual schema against
which SQL queries can be issued. And if this is true, users should
not be forced to define a schema before using a SQL-on-Hadoop
solution—they should be able to effortlessly issue SQL against a
schema that was automatically generated for them when data was
loaded into Hadoop.
Thanks to the hard work of many people at Hadapt from several different groups, including the science team who developed an
initial design of the feature, the engineering team who continued
to refine the design and integrate it into Hadapt’s SQL-on-Hadoop
solution, and the customer solutions team who worked with early
customers to test and collect feedback on the functionality of this
feature, this feature is now available in Hadapt.” (http://hadapt.
com/blog/2013/10/28/all-sql-on-hadoop-solutions-are-missing-the-point-of-hadoop/)
This is not really new ground. Oracle Database provides the
ability to convert XML documents into relational tables (http://
docs.oracle.com/cd/E11882_01/appdev.112/e23094/xdb01int.
htm#ADXDB0120), though it ought to be possible to view XML
data as tables while physically storing it in XML format in order
to benefit certain use cases. It should also be possible to redun­
dantly store data in both XML and relational formats in order to
benefit other use cases.
In Extending the Database Relational Model to Capture More
Meaning, Dr. Codd explains how a “formatted database” arises
from an unorganized collection of facts:
“Suppose we think of a database initially as a set of formulas in
first-order predicate logic. Further, each formula has no free variables and is in as atomic a form as possible (e.g, A & B would be
replaced by the component formulas A, B). Now suppose that most
of the formulas are simple assertions of the form Pab…z (where P
is a predicate and a, b, … , z are constants), and that the number
of distinct predicates in the database is few compared with the
number of simple assertions. Such a database is usually called formatted, because the major part of it lends itself to rather regular
structuring. One obvious way is to factor out the predicate common to a set of simple assertions and then treat the set as an instance of an n-ary relation and the predicate as the name of the
relation.”
In other words, a collection of facts can always be organized
into relations if necessary.
Big Data in a Nutshell
In 1998, Sergey Brin and Larry Page invented the PageRank
algorithm for ranking web pages (The Anatomy of a Large-Scale
Hypertextual Web Search Engine by Brin and Page) and founded
Google.
The PageRank algorithm required very large matrix-vector
multiplications (Mining of Massive Datasets Ch. 5 by Rajaraman
and Ullman) so the MapReduce technique was invented to han­
dle such large computations (MapReduce: Simplified Data
Processing on Large Clusters).
Smart people then realized that the MapReduce technique
could be used for other classes of problems, and an open-source
project called Hadoop was created to popularize the MapReduce
technique (http://gigaom.com/2013/03/04/the-history-ofhadoop-from-4-nodes-to-the-future-of-data/).
Other smart people realized that MapReduce could handle
the operations of relational algebra such as join, anti-join, semijoin, union, difference, and intersection (Mining of Massive
Datasets Ch. 2) and began looking at the possibility of processing
large volumes of business data (a.k.a. “Big Data”) better and more
cheaply than mainstream database management systems.
Initially programmers had to write Java code for the “map­
pers” and “reducers” used by MapReduce. However, smart peo­
ple soon realized that SQL queries could be automatically
translated into the necessary Java code, and “SQL-on-Hadoop”
was born. Big Data thus became about processing large volumes
of business data with SQL but better and more cheaply than
mainstream database management systems.
However, the smart people have now realized that MapReduce
is not the best solution for low-latency queries (http://gigaom.
com/2013/11/06/facebook-open-sources-its-sql-on-hadoopengine-and-the-web-rejoices/). Big Data has finally become
about processing large volumes of business data with SQL but
better and more cheaply than mainstream database management
systems and with or without MapReduce.
That’s the fast-moving story of Big Data in a nutshell. s
The statements and opinions expressed here are the author’s and
do not necessarily represent those of Oracle Corporation.
Copyright © 2014, Iggy Fernandez
“And so the choice that you have to make is: Should we make better products
that we can sell for better profits to our best customers. Or maybe we ought to
make worse products that none of our customers would buy that would ruin
our margins. What should we do? And that really is the dilemma.”
The NoCOUG Journal
23
S ession
D escription S
NoCOUG Winter Conference
Session Descriptions
For the most up-to-date information, please visit http://www.nocoug.org.
–Keynote–
Oracle Database In-Memory Option—The Next Big Thing
—Juan Loaiza, Oracle Corporation. . . . . . . . . . . . . . . . . 9:30–10:30
The Oracle Database In-Memory option dramatically acceler­
ates the performance of analytic queries by storing data in a
highly optimized columnar in-memory format. Analytic opera­
tions run in real-time and return completely current and consis­
tent data. A unique dual-format approach ensures outstanding
performance and complete data consistency for all workloads.
Oracle Database In-Memory automatically maintains data in
both the existing Oracle row format for OLTP operations, and a
new purely in-memory column format optimized for analytical
processing. Both formats are simultaneously active and transac­
tionally consistent. Unlike other in-memory approaches that
represent data exclusively in column format thus delivering poor
OLTP performance, Oracle Database In-Memory eliminates the
need for expensive overhead to maintain analytic indexes, and
therefore greatly accelerates OLTP operations. “Virtually every
existing application that runs on top of the Oracle database will
run dramatically faster by simply turning on the new In-Memory
feature. Customers don’t have to make any changes to their applications whatsoever; they simply flip on the in-memory switch, and
the Oracle database immediately starts scanning data at a rate of
billions or tens of billions of rows per second.” New applications
that were previously impractical due to performance limitations
can be developed with existing tools in use today. All of Oracle’s
industry-leading availability, security, and management features
continue to work unchanged.
As Senior Vice President of Systems Technology at Oracle, Juan
Loaiza is in charge of developing the mission-critical capabilities
of Oracle Database, including data and transaction management,
high availability, performance, backup and recovery, enterprise
replication, and Oracle Exadata. Juan joined the Oracle Database
development organization in 1988 and has contributed to every
Oracle Database release since Oracle Version 6.
–Auditorium–
Editor’s Pick
Oracle Graph: Graph Features in Oracle
Database 12c—Zhe Wu, Oracle Corporation. . . . . . . 11:00–12:00
With every release since its introduction over ten years ago,
Oracle Spatial and Graph has delivered the most advanced spatial
and graph data management capabilities to database management
systems. Formerly known as Oracle Spatial option, Oracle Spatial
and Graph underlines its existing graph capabilities, which com­
prise the most robust, mature database graph technologies avail­
able in the industry. Oracle Spatial and Graph provides two graph
data models: Network Data Model graph (NDM), and RDF
Semantic Graph. NDM is a property graph model used to model
and analyze physical and logical networks used in industries such
as transportation, logistics, and utilities. NDM persistently man­
24
ages the network connectivity in the database, while a Java API
provides fast in-memory graph analytics, including: shortest path,
nearest neighbors, within cost, and reachability. RDF Semantic
Graph supports the World Wide Web Consortium (W3C)
Resource Description Framework (RDF) standards. It provides
RDF data management, querying and inferencing that are com­
monly used in a variety of applications ranging from semantic
data integration to social network analysis and linked open data
applications. It includes RDF views on relational data, more ex­
tensive inferencing capabilities, the latest SPARQL support, spa­
tial RDF data support, and graph analytics and statistics support.
Oracle Spatial and Graph RDF support has become the industry’s
leading open, scalable, and secure RDF database.
Oracle Spatial: Spatial Features in Oracle Database 12c
—Jean Ihm, Oracle Corporation. . . . . . . . . . . . . . . . . . . . . 1:00–2:00
With every release since its introduction over ten years ago,
Oracle Spatial and Graph has delivered the most advanced spa­
tial and graph data management capabilities for database man­
agement systems. The geospatial data features are designed to
support the most complex requirements found in geographic
information systems (GISs), enterprise applications, and loca­
tion-enabled business and web applications. It extends the
Oracle Locator spatial query and analysis features in Oracle
Database with more advanced spatial analysis and processing
capabilities. These geospatial data features include native support
for advanced models and types such as GeoRaster (for geo-refer­
enced imagery and gridded data); topology; 3D, including trian­
gulated irregular networks (TINs) and point clouds (supporting
LIDAR data); and linear referencing. Geocoding, a routing en­
gine, and spatial web services conformant with Open Geospatial
Consortium (OGC) and ISO standards are also included. These
advanced features provide a complete platform for geospatial ap­
plications in many domains, including defense, land manage­
ment, retail, insurance, and finance.
SQL: The Best Development Language for Big Data
—Hermann Baer, Oracle Corporation. . . . . . . . . . . . . . . . 2:30–3:30
Large-scale data processing is undergoing tremendous trans­
formations: new data sources are more readily available, and
businesses are focusing more heavily on analytic solutions.
Hadoop and other non-relational data sources are becoming
more common, but working with and analyzing this data is hard.
SQL, on the other hand, is the most commonly used language for
data analysis. So how can we combine these two? This session
discusses Oracle’s analytical SQL capabilities and how complex
analytical queries running on large to extremely large data sets
are becoming a reality, even on data sources outside the rela­
tional world. You’ll also learn how Oracle envisions the future of
a unified analytical world.
(continued on page 26)
February 2014
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NoCOUG’s president, Hanan Hit. 
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For information about our Gold
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Total Expenses
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25
(continued from page 24)
Real-World Performance: Why Is My SQL Slow?
—Bob Carlin, Oracle Corporation. . . . . . . . . . . . . . . . . . . 4:00–5:00
This session focuses on identifying problems with SQL state­
ments and shows how to deliver true real-world performance
gains without resorting to random hacking and guesses. Hidden
within the Oracle Database Development team, the Real World
Performance team focuses on your database performance. With
its unique insight into how the database is designed to be used
and having studied how it is actually used, the team can bring a
new perspective on how to get the best performance from your
database. The Real World Performance team uses healthy amounts
of humor, irreverence, pragmatism, and passion for database
performance to identify what Oracle users fail to appreciate and
where the big gains are.
–Room 102–
DBA Boot Camp—Kyle Hailey, Delphix. . . . . . . . . . . . 11:00–5:00
Spend the whole day with ACE Director Kyle Hailey and dive
deep into DBA topics such as I/O performance, SQL tuning, wait
events, and database cloning.
Kyle Hailey was a principal designer for the Oracle Enterprise
Manager performance pages. He is a member of Oracle Oak Table
and the co-author of Oracle Insights: Tales of the Oak Table, and
he was a technical editor of Oracle Wait Interface. He holds a patent in the area of database performance diagnosis and has been a
speaker at Hotsos, NOCOUG, RMOUG, NYCOUG, and Oracle
World; he also organizes Oaktable World. Kyle teaches classes
around the world on Oracle performance tuning. Currently Kyle
works as a performance architect at Delphix, along with industryleading software, kernel, and filesystem designers, to take corporate
data management to a new level of agility.
Lies, Damned Lies, and I/O Statistics. . . . . . . . . . . . 11:00–12:00
Given a description of gas dynamics and the atmosphere, you
would be hard pressed to forecast tornadoes. The term “emer­
gence” refers to the phenomena of surprising behaviors arising in
complex systems. Modern storage systems are complex and full
of emergent behavior that makes forecasting application I/O
performance fiendishly difficult. In collaboration with Matt
Hayward, Adam Leventhal, and others, Kyle Hailey has learned
some rules of thumb for how to make accurate I/O performance
forecasts. You’ll learn about forecasting, benchmarking, and ana­
lyzing I/O performance in this talk.
Visual SQL Tuning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1:00–2:00
The load on the database is caused by SQL; thus it makes sense
that performance bottlenecks are caused by poorly performing
SQL statements. We will follow a solid step-by-step method for
analyzing, understanding, and tuning these problem SQL state­
ments though Visual SQL Tuning (VST) diagrams. VST is a
method of laying out the tables and joins of a query graphically to
indicate key features of the query in the graphics. Through the
VST, you’ll learn how to quickly visualize any coding errors in the
query; discover flaws in the underlying database schema; and
most important, find the best execution path through the query.
ASH Masters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2:30–3:30
Learn about ASH Masters, a GitHub repository of queries
against Average Active History (ASH). Learn about ASH math,
26
average active sessions, and the power and pitfalls of ASH que­
ries. ASH queries will be shown that display over database per­
formance from on high to diving deep down into the nitty-gritty
of a performance bottleneck’s internal workings. ASH is the most
powerful source of performance analytic data in Oracle. Querying
ASH can be difficult, with a number of pitfalls, but the rewards
of correct data analysis of ASH make it the most powerful tool a
DBA has for performance analysis. Learn how to query ASH cor­
rectly, and learn where to get powerful prewritten ASH queries.
Agile Data: Revolutionizing Database Cloning . . . . . 4:00–5:00
Database virtualization allows the same datafiles to be shared
by multiple clones, allowing almost instantaneous creation of new
copies of databases with almost no disk footprint. Along with stor­
age efficiency, database virtualization allows agile management of
database copies. The data agility eliminates bottlenecks in devel­
opment by removing wait time for creating database environ­
ments, allows developers to have their own full copy of the
database, and provides QA and UAT with immediate copies of the
development environments for testing. This presentation will
compare and contrast different types of database virtualization
from Oracle 11 CloneDB, Oracle 12c Snap Clones, 12c Snapshot
Manager Utility, Oracle ZFS Appliance, Delphix Appliance,
VMware Data Director, NetApp Snap Manager for Oracle, and
EMC. We’ll explain how database virtualization works and discuss
the advantages and disadvantages of different approaches.
–Room 103–
ADF in a Nutshell—Peter Koletzke, Quovera. . . . . . . . 11:00–5:00
Learn how to build an application using the same tools Oracle
uses to build Fusion Applications: Oracle Application Develop­
ment Framework (ADF). This course focuses on introducing
and demonstrating the core technologies needed to build an
ADF application: ADF Business Components, ADF Faces, and
ADF Controller. Half of this all-day session consists of hands-on
labs where you can experience and practice development the
ADF way.
Peter Koletzke is a technical director and principal instructor for
Quovera in Palo Alto, California. He has over 30 years of industry
experience and has presented at various Oracle users group conferences more than 310 times. Additionally, he has won awards such as
Pinnacle Publishing’s Technical Achievement, Oracle Development
Tools User Group Editor’s Choice (three times), Oracle Development
Tools User Group (ODTUG) Best Speaker, NY Oracle Users Group
Editor’s Choice (three times), East Coast Oracle/Southeastern Oracle
Users Conference Oracle Designer Award, and the ODTUG Volunteer
of the Year. Peter is an Oracle ACE Director, an Oracle Certified
Master, and coauthor—variously with Duncan Mills, Avrom RoyFaderman, and Dr. Paul Dorsey—of the Oracle Press (McGraw-Hill
Professional) books Oracle JDeveloper 11g Handbook, Oracle
JDeveloper 10g for Forms & PL/SQL Developers, Oracle
JDeveloper 10g Handbook, Oracle9i JDeveloper Handbook,
Oracle JDeveloper 3 Handbook, Oracle Developer Advanced
Forms and Reports, Oracle Designer Handbook, 2nd Edition, and
The Oracle Designer/2000 Handbook.
ADF in a Nutshell: Part I. . . . . . . . . . . . . . . . . . . . . . . 11:00–12:00
ADF in a Nutshell: Part II . . . . . . . . . . . . . . . . . . . . . . . . 1:00–2:00
ADF in a Nutshell: Part III.. . . . . . . . . . . . . . . . . . . . . . . 2:30–3:30
ADF in a Nutshell: Part IV. . . . . . . . . . . . . . . . . . . . . . . . 4:00–5:00
February 2014
Database Specialists: DBA Pro Service
Database Specialists: DBA Pro Service
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familiar with your databases
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NoCOUG Winter Conference Schedule
Thursday, February 20, 2014—Oracle Conference Center, Redwood City, CA
The NoCOUG Journal design and production: Giraffex, Inc., S.F.
Front cover photo: Little Kid Using Laptop By Night by djedzura, Photos.com
Please visit http://www.nocoug.org for updates and directions, and to submit your RSVP.
Cost: $50 admission fee for non-members. Members free. Includes lunch voucher.
8:00–9:00 a.m.
Registration and Continental Breakfast—Refreshments served
9:00–9:30
Welcome: Hanan Hit, NoCOUG president
9:30–10:30
Keynote: Oracle Database In-Memory Option—The Next Big Thing—Juan Loaiza, Oracle Corporation
10:30–11:00
Break
11:00–12:00
Parallel Sessions #1
Auditorium: Oracle Graph: Graph Features in Oracle Database 12c—Zhe Wu, Oracle Corp. Journal Editor’s Pick
Room 102: Lies, Damned Lies, and I/O Statistics—Kyle Hailey, Delphix
Room 103: ADF in a Nutshell: Part I—Peter Koletzke, Quovera
12:00–1:00 p.m. Lunch
1:00–2:00
Parallel Sessions #2
Auditorium: Oracle Spatial: Spatial Features in Oracle Database 12c—Jean Ihm, Oracle Corp.
Room 102: Visual SQL Tuning—Kyle Hailey, Delphix
Room 103: ADF in a Nutshell: Part II—Peter Koletzke, Quovera
2:00–2:30
Break and Refreshments
2:30–3:30
Parallel Sessions #3
Auditorium: SQL: The Best Development Language for Big Data—Hermann Baer, Oracle Corp.
Room 102: ASH Masters—Kyle Hailey, Delphix
Room 103: ADF in a Nutshell: Part III—Peter Koletzke, Quovera
3:30–4:00
Raffle
4:00–5:00
Parallel Sessions #4
Auditorium: Real-World Performance: Why Is My SQL Slow?—Bob Carlin, Oracle Corp.
Room 102: Agile Data: Revolutionizing Database Cloning—Kyle Hailey, Delphix
Room 103: ADF in a Nutshell: Part IV—Peter Koletzke, Quovera
5:00–
NoCOUG Networking and No-Host Happy Hour at BJ’s Restaurant & Brewhouse, San Mateo
RSVP required at http://www.nocoug.org
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