Graphs are everywhere

Making sense of the graph revolution


Objectivity’s Nick Quinn shows us just how popular graph databases have become, as well as showing us the ins and outs.

this article originally published in JAX
, Objectivity’s Nick Quinn shows us just how popular
graph databases have become, as well as showing us the ins and

Job references, hyperlinks, phone calls, friend
requests, emails, pings, meetups, family, financial transactions
and the list goes on and on — data is not only getting bigger, it
is becoming more connected. Not all connections are made equal.
Many of your connections say something interesting about you.
Consider the simple example, in which a Person can be connected to
a Building by various edge types, and then think about what those
edge types mean.


Edge Type



Might Indicate about the person



Might indicate about the Building






They have a home



It is an apartment building






They like to eat out



It is a restaurant






They have a job



It is a company






They have money to spend



It is a store


Table 1: Graph inferencing

This is the value that relationships have. They
might seem to represent mundane activities, but can reveal patterns
of behaviour that give significant value to the owners of the data.
What if the person eats at a fast food restaurant frequently? They
may be interested in coupons for other fast food restaurants.
Conversely, what if the person is very well connected and they shop
at a store? It is possible that their friends may be interested in
that store. In the same way, if that person is a known bank thief
and they frequent a deli next to a bank, this might be a valuable
lead for the police. These three scenarios highlight very popular
use cases for graph databases: advertising/marketing, social
networks or network management, and fraud/criminal

But just what is a graph database? A graph
database is a native storage engine that enables efficient storage
and retrieval of graph structured data. Graph databases are
typically used:

  1. When the data source is highly
  2. Where the connections are
    important (add value to the data), and
  3. When the user access pattern
    requires traversals of those connections.

Graph databases are different than other types
of databases because they have a unique data model (vertices and
edges) and they are used in unique applications built around graph
use cases. Unlike graph analysis engines or visualization
frameworks, graph databases are optimized around concurrent access
of persisted data, so users can navigate the data as it is being
added or updated. Consider a recommendation engine based on a
social graph (like Figure 1) where the purchasing patterns of your
“friends” or “neighbours” are used to suggest merchandise that you
might be interested in buying.

Figure 1: Social Graph

In order to identify popular items to recommend,
the recommendation engine could traverse the connections in the
data to find interesting patterns of purchases. You might only be
interested in data pertaining to certain types of People: students,
perhaps, or women over the age of 40 who own a home. This
navigational query can be quite complex, but it is possible to
execute it using an API designed for navigational

Reading Distributed Graph Data

Distributing data is the practice of storing
data in multiple physical locations. There are many reasons why
someone would want to distribute their data using a distributed
database technology, but the most notable are scalability and data
localization. Data localization represents the idea of storing data
where it most makes sense, which is usually local to the
applications that are accessing it most frequently. Also, the
ability of a database to distribute data into partitions is
critical to their ability to horizontally scale.

Any distributed graph database engine faces a
difficult problem though. Accessing highly connected data in a
distributed environment commonly means going back and forth between
partitions of data to retrieve neighbouring data. This can
significantly reduce the performance of a navigational

Figure 2: Distributed Graph Data

To curtail this issue, there are two common
strategies. The first is to partition your graph to isolate highly
connected subgraphs to minimize the number of calls across the
network and utilize local cache. In other words, place the vertex
objects in a way that reduces the number of edges that cross
between hosts. This subgraph isolation can be achieved with custom
placement strategies.

The second strategy involves using a distributed
navigator. A distributed navigation engine will distribute the
processing load for traversing subgraphs to the partition on which
they are located.

Custom Placement

InfiniteGraph™ is a distributed graph database
that is designed to handle large data sets and get the highest
performance for accessing your data. Using your own data model,
InfiniteGraph lets you build a custom placement model that can
achieve the isolated graph partitioning that is so important for
achieving high-performance navigational queries in a distributed
environment. Consider the case where you are placing medical data
for hospitals and patients. Knowing that doctor data is related to
and accessible through hospitals, it makes sense to physically
co-locate instances of hospitals and doctors. Likewise, you would
want to physically co-locate the patients and their visits. This
would then give both a logical and natural type of placement
strategy and would achieve a high degree of isolation for graph
partitioning. This sort of custom placement is easy to implement in

Figure 3: Custom Placement

Distributed Navigation

Google’s Pregel is a standard for large scale
graph processing in a distributed environment that was originally
used for the Google cluster. For a number of years, performing
large-scale graph analysis in distributed systems was done mostly
by pulling in all remote data and processing it locally. The
problem with this approach is poor performance and a lack of
scalability. To tackle this problem, Pregel enabled the execution
of graph algorithms (like PageRank and ShortestPath) in a
map-reduce fashion by:

  1. Enabling each vertex object to
    send/receive messages,
  2. Executing an algorithm in a
    sequence of super-steps where a function would run at

    each vertex in parallel,
  3. Possibly synchronizing the results
    at each step, and
  4. Passing a message to the next
    vertex in the path until the algorithm
    terminates and
    returns the results.

This system of graph analysis in a series of
steps, where each vertex sends a message to the next vertex in the
path, is scalable and optimized in a distributed system. If the
vertex is located on a remote host, the processing will be done on
that remote host. At the same time though, this sort of processing
can be limited because it relies on the distributed data
architecture. Even if the algorithm is only required to process
locally, it uses the same logic of sending messages from vertex to
vertex, which is only optimal for use in a distributed environment.
The IG navigation engine utilizes the Pregel logic for graph
processing in distributed environments while at the same time
sidestepping the limitations of processing locally.

Figure 4: Example of InfiniteGraph’s Distributed

Similar to Pregel, when the path continues on
another host, the distributed navigation engine can send the
partial path to a remote query server to continue the navigation on
that host and aggregate the results at the end. But unlike Pregel,
if the navigation is localized to the host that the process is
currently running on, the traversal will continue to be executed in
memory instead of sending messages. Also, in an effort to maximize
the performance of the navigation, the query engine will
intelligently cache remote objects when they are accessed


Another unique and very powerful feature of IG’s
navigation API is the ability to execute a complex query using a
GraphView. A GraphView enables you to build filtered views of the
data on top of which you can execute a traversal. The GraphView can
be constructed simply by excluding/including vertex and edge
objects based on their type. The following shows how to include a
base type (Person), but exclude a derived type

Listing 1

GraphView myView = new GraphView();



Navigator myNavigator = myStartVertex.navigate(myView, myGuide,

pathQualifier, resultQualifier, myPolicies, resultHandler);


Alternatively, using a GraphView, you can
exclude/include vertex and edge types based on their type and a
predicate. The following shows how to include a type (Person) based
on a predicate. Note: The fields “gender”, “age” and “ownHome” are
all members of the Person class.

Listing 2

GraphView myView = new GraphView();

long personTypeId = myGraphDB.getTypeId(Person.class.getName());

myView.includeClass(personTypeId,“gender==’F’ && age>40 && ownHome==true”);

Navigator myNavigator = myStartVertex.navigate(myView, myGuide,

pathQualifier, resultQualifier, myPolicies, resultHandler);


GraphViews effectively reduces the size of the data set by
allowing the navigator to ignore paths that don’t lead to desired
results. When a GraphView is used inside of InfiniteGraph’s
accelerated and distributed graph traversal engine, it allows users
to get real-time results over large and highly connected data

In these examples above, “myGuide” is a Guide
which defines ordering behaviour like Breadth-First or Depth-First
traversal and “pathQualifier” and “resultQualifier” are instances
of a Qualifier which defines behaviour around qualifying paths and
results, respectively. Also, “myPolicies” is a PolicyChain, set of
navigational policies, that can be used to define the global
behaviour of the navigator. For example, the
MaximumResultCountPolicy sets the maximum number of paths returned
and the EdgeTraversalPolicy defines which edge types can be
traversed. Finally, the “resultHandler” is an instance of the
NavigationResultHandler which processes the paths that are returned
by the navigator. It will outline actions around the results that
are returned which may include things like writing to a file,
aggregating and analysis, or kicking off another


In Graph Theory, a “supernode” is a vertex with
a disproportionally high number of connected edges. Supernodes make
it difficult to do a navigational query in real-time due to the
amount of effort it may be to pursue paths through it that may be
unfruitful. Consider a celebrity in the social graph dataset who
has a higher than normal number of followers. Avoiding the
celebrity node or limiting the number or types of edges that you
traverse when going through celebrity nodes can significantly
increase the performance of the navigation.

Figure 5: Supernode

With InfiniteGraph, we offer two strategies to addressing
the supernode problem. First, you can use GraphViews to exclude
vertex types to avoid the supernodes in the graph or exclude edge
types to limit the number of paths traversed through supernodes.
Using the celebrity supernode problem described above, you can
exclude the celebrity objects themselves or you exclude vertex
objects that have a certain amount of followers. Secondly, we offer
a policy called a FanoutLimitPolicy which will limit the number of
edges that you traverse for any given vertex during a navigational
query. These two strategies, especially the GraphView, will
increase the performance of the navigation by orders of

Writing Graph Data

Consider the problem of writing highly
interconnected data to a graph database. When an edge is added,
both sides of the edge (source and target vertices) must be updated
in a single transaction. This can lead to lots of lock collision
around vertices with high connectivity (such as V2 below) because
edges that share this common vertex (E12 & E23) might be added
in different write transactions and need to update the shared
vertex (V2) at the same time.

Figure 6: Graph Data

InfiniteGraph offers a high-performance indirect
ingest option called Accelerated Ingest in addition to the
traditional direct ingest option. Using the indirect method, you
can achieve a much higher rate of ingest, but with relaxed
consistency, temporarily sacrificing access to the “full picture”.
In the case above, all of the graph components will be placed
immediately, but the edge updates to the vertex objects (V1, V2,
and V3) will be processed asynchronously in batches which are still
atomic, isolated, and durable, but are phased. This will allow for
a contention free, high performance ingest which is perfect for
applications or systems that are not as concerned with getting an
immediately consistent state.

Visualization Tools and Toolkits

Graph data sets are more easily understood when
viewed and browsed with some kind of visualization framework and
visualization tools or toolkits are usually distributed with a
graph database. There are also many third party vendors that
support visualization with graph data sets including Tom Sawyer,
KeyLines, Graphviz and D3js. InfiniteGraph offers a visualization
tool called the IG Visualizer which is a standalone tool that can
connect to any instance of an IG database and offers a rich
development environment to view/traverse the data, use indexes, and
customize views and layouts. Many of the features discussed in this
article including configuring graph views and executing a
distributed navigation, can be done through the IG Visualizer

Figure 7: The IG Visualizer Tool

If you have a high connected data set and use cases that
seem to fit the need for a graph database, consider InfiniteGraph.
InfiniteGraph is a leading distributed graph database with a simple
Java API. It is built on top of Objectivity/DB™, a proven
object-oriented data storage engine that is known for specializing
in connections between data. InfiniteGraph supports direct and
high-performance writes with custom placement models, a complex
distributed, parallel query engine that allows traversals through a
native navigation API, and fully ACID transactions that allow
concurrent access to the data. To get more information or
resources, visit the Objectivity website [1] or the InfiniteGraph
wiki page (

Nick Quinn is the Principal Engineer for
InfiniteGraph, a distributed graph-oriented data management
solution from Objectivity Inc. Since joining Objectivity, Nick has
been work exclusively on the InfiniteGraph project and has played a
key role on the design and architecture of InfiniteGraph 3.0 and
3.1. Prior to joining Objectivity in 2010, Nick worked as a lead
Java Developer at Savi Networks, a Lockheed Martin Company. Nick
holds a Master’s Degree in the College of Engineering from Santa
Clara University in Santa Clara, California.





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