Click here to close now.

Welcome!

Java Authors: XebiaLabs Blog, Irit Gillath, AppDynamics Blog, Cloud Best Practices Network, Elizabeth White

Related Topics: Java, SOA & WOA

Java: Book Excerpt

Book Excerpt | Good Relationships: The Spring Data Neo4j Guide Book

Part 1: Tutorial - the creation of cineasts.net, a complete web application

The Spring Data Neo4j Project
This project is part of the Spring Data project, which brings the convenient programming model of the Spring Framework to modern NOSQL databases. Spring Data Neo4j, as the name alludes to, aims to provide support for the graph database Neo4j.

Tutorial
The first part of the book provides a tutorial that walks through the creation of a complete web application called cineasts.net, built with Spring Data Neo4j. Cineasts are people who love movies, and the site is a gathering place for moviegoers. For cineasts.net we decided to add a social aspect to the rating of movies, allowing friends to share their scores and get recommendations for new friends and movies.

The tutorial takes the reader through the steps necessary to create the application. It provides the configuration and code examples that are needed to understand what's happening in Spring Data Neo4j. The complete source code for the app is available on Github.

Introducing Our Project
Once upon a time we wanted to build a social movie database. At first there was only the name: Cineasts, the movie enthusiasts who have a burning passion for movies. So we went ahead and bought the domain cineasts.net, and so we were off to a good start.

We had some ideas about the domain model too. There would obviously be actors playing roles in movies. We also needed someone to rate the movies - enter the cineast. And cineasts, being the social people they are, they wanted to make friends with other fellow cineasts. Imagine instantly finding someone to watch a movie with, or share movie preferences with. Even better, finding new friends and movies based on what you and your friends like.

When we looked for possible sources of data, IMDB was our first stop. But they're a bit expensive for our taste, charging $15k USD for data access. Fortunately, we found themoviedb.org which provides user-generated data for free. They also have liberal terms and conditions, and a nice API for retrieving the data.

We had many more ideas, but we wanted to get something out there quickly. Here is how we envisioned the final website:

The Spring Stack
Being Spring developers, we naturally chose components from the Spring stack to do all the heavy lifting. After all, we have the concept etched out, so we're already halfway there.

What database would fit both the complex network of cineasts, movies, actors, roles, ratings, and friends, while also being able to support the recommendation algorithms that we had in mind? We had no idea.

But hold your horses, there is this new Spring Data project, started in 2010, that brings the convenience of the Spring programming model to NOSQL databases. That should be in line with what we already know, providing us with a quick start. We had a look at the list of projects supporting the different NOSQL databases out there. Only one of them mentioned the kind of social network we were thinking of - Spring Data Neo4j for the Neo4j graph database. Neo4j's slogan of "value in relationships" plus "Enterprise NOSQL" and the accompanying docs looked like what we needed. We decided to give it a try.

Required Setup
To set up the project we created a public Github account and began setting up the infrastructure for a Spring web project using Maven as the build system. We added the dependencies for the Spring Framework libraries, added the web.xml for the DispatcherServlet, and the applicationContext.xml in the webapp directory.

Example 2.1. Project pom.xml
<properties>
<spring.version>3.0.7.RELEASE</spring.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework</groupId>
<!-- abbreviated for all the dependencies -->
<artifactId>spring-(core,context,aop,aspects,tx,webmvc)</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-test</artifactId>
<version>${spring.version}</version>
<scope>test</scope>
</dependency>
</dependencies>

Example 2.2. Project web.xml
<listener>
<listener-class>org.springframework.web.context.ContextLoaderListener</listener-class>
</listener>
<servlet>
<servlet-name>dispatcherServlet</servlet-name>
<servlet-class>org.springframework.web.servlet.DispatcherServlet</servlet-class>
<load-on-startup>1</load-on-startup>
</servlet>
<servlet-mapping>
<servlet-name>dispatcherServlet</servlet-name>
<url-pattern>/</url-pattern>
</servlet-mapping>

With this setup in place we were ready for the first spike: creating a simple MovieController showing a static view. See the Spring Framework documentation for information on doing this.

Example 2.3. applicationContext.xml
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<beans xmlns="http://www.springframework.org/schema/beans"
xmlns:context="http://www.springframework.org/schema/context"
xmlns:tx="http://www.springframework.org/schema/tx"
xsi:schemaLocation="
http://www.springframework.org/schema/beans
http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://www.springframework.org/schema/tx
http://www.springframework.org/schema/tx/spring-tx-3.0.xsd
http://www.springframework.org/schema/context
http://www.springframework.org/schema/context/spring-context-3.0.xsd">
<context:annotation-config/>
<context:spring-configured/>
<context:component-scan base-package="org.neo4j.cineasts">
<context:exclude-filter type="annotation"
expression="org.springframework.stereotype.Controller"/>
</context:component-scan>
<tx:annotation-driven mode="proxy"/>
</beans>

Example 2.4. dispatcherServlet-servlet.xml
<mvc:annotation-driven/>
<mvc:resources mapping="/images/**" location="/images/"/>
<mvc:resources mapping="/resources/**" location="/resources/"/>
<context:component-scan base-package="org.neo4j.cineasts.controller"/>
<bean id="viewResolver"
class="org.springframework.web.servlet.view.InternalResourceViewResolver"
p:prefix="/WEB-INF/views/" p:suffix=".jsp"/>

We spun up Tomcat in STS with the App and it worked fine. For completeness we also added Jetty to the maven-config and tested it by invoking mvn jetty:run to see if there were any obvious issues with the config. It all seemed to work just fine.

Setting the Stage
We wanted to outline the domain model before diving into library details. We also looked at the data model of the themoviedb.org data to confirm that it matched our expectations. In Java code this looks pretty straightforward: The domain model:

Example 3.1. Domain model
class
Movie {
String id;
String title;
int
year;
Set<Role> cast;
}
class
Actor {
String id;
String name;
Set<Movie> filmography;
Role playedIn(Movie movie, String role) { ... }
}
class
Role {
Movie movie;
Actor actor;
String role;
}
class
User {
String login;
String name;
String password;
Set<Rating> ratings;
Set<User> friends;
Rating rate(Movie movie, int stars, String comment) { ... }
void
befriend(User user) { ... }
}
class
Rating {
User user;
Movie movie;
int
stars;
String comment;
}

Then we wrote some simple tests to show that the basic design of the domain is good enough so far. Just creating a movie, populating it with actors, and allowing users to rate it.

Learning Neo4j
Graphs Ahead

Now we needed to figure out how to store our chosen domain model in the chosen database. First we read up on graph databases, in particular our chosen one, Neo4j. The Neo4j data model consists of nodes and relationships, both of which can have key/value-style properties. What does that mean, exactly? Nodes are the graph database name for records, with property keys instead of column names.

That's normal enough. Relationships are the special part. In Neo4j, relationships are first-class citizens, meaning they are more than a simple foreign-key reference to another record; relationships carry information. So we can link together nodes into semantically rich networks. This really appealed to us. Then we found that we were also able to index nodes and relationships by {key, value} pairs. We also found that we could traverse relationships both imperatively using the core API and declaratively using a query-like Traversal Description. Besides those programmatic traversals there was the powerful graph query language called Cypher and an interesting looking DSL named Gremlin. So there was lots of ways of working with the graph.

We also learned that Neo4j is fully transactional and therefore upholds ACID guarantees for our data. Durability is actually a good thing and we didn't have to scale to trillions of users and movies yet.

This is unusual for NOSQL databases, but easier for us to get our head around than non-transactional eventual consistency. It also made us feel safe, though it also meant that we had to manage transactions. Something to keep in mind later.

We started out by doing some prototyping with the Neo4j core API to get a feeling for how it works. And also to see what the domain might look like when it's saved in the graph database. After adding the Maven dependency for Neo4j, we were ready to go.

Example 4.1. Neo4j Maven dependency
<dependency>
<groupId>org.neo4j</groupId>
<artifactId>neo4j</artifactId>
<version>1.6.M02</version>
</dependency>
Learning Neo4j

Example 4.2. Neo4j core API (transaction code omitted)
enum RelationshipTypes implements RelationshipType { ACTS_IN };
GraphDatabaseService gds = new EmbeddedGraphDatabase("/path/to/store");
Node forrest=gds.createNode();
forrest.setProperty("title","Forrest Gump");
forrest.setProperty("year",1994);
gds.index().forNodes("movies").add(forrest,"id",1);
Node tom=gds.createNode();
tom.setProperty("name","Tom Hanks");
Relationship role=tom.createRelationshipTo(forrest,ACTS_IN);
role.setProperty("role","Forrest");
Node movie=gds.index().forNodes("movies").get("id",1).getSingle();
assertEquals("Forrest Gump", movie.getProperty("title"));
for
(Relationship role : movie.getRelationships(ACTS_IN,INCOMING)) {
Node actor=role.getOtherNode(movie);
assertEquals("Tom Hanks", actor.getProperty("name"));
assertEquals("Forrest", role.getProperty("role"));
}

Spring Data Neo4j
Conjuring magic

So far it had all been pure Spring Framework and Neo4j. However, using the Neo4j code in our domain classes polluted them with graph database details. For this application, we wanted to keep the domain classes clean. Spring Data Neo4j promised to do the heavy lifting for us, so we continued investigating it.

Spring Data Neo4j comes with two mapping modes. The more powerful one depends heavily on AspectJ, so we ignored it for the time being. The simple direct POJO-mapping copies the data out of the graph and into our entities. Good enough for a web-application like ours.

The first step was to configure Maven:

Example 5.1. Spring Data Neo4j Maven configuration
<dependency>
<groupId>org.springframework.data</groupId>
<artifactId>spring-data-neo4j</artifactId>
<version>2.0.0.RELEASE</version>
</dependency>

The Spring context configuration was even easier, thanks to a provided namespace:

Example 5.2. Spring Data Neo4j context configuration
<beans xmlns="http://www.springframework.org/schema/beans" ...
xmlns:neo4j="http://www.springframework.org/schema/data/neo4j"
xsi:schemaLocation="... http://www.springframework.org/schema/data/neo4j
http://www.springframework.org/schema/data/neo4j/spring-neo4j-2.0.xsd">

...
<neo4j:config storeDirectory="data/graph.db"/>

...
</beans>

Annotating the Domain
Decorations

Looking at the Spring Data Neo4j documentation, we found a simple Hello World example and tried to understand it. We also spotted a compact reference card that helped us a lot. The entity classes were annotated with @NodeEntity. That was simple, so we added the annotation to our domain classes too.

Entity classes representing relationships were instead annotated with @RelationshipEntity. Property fields were taken care of automatically. The only additional field we had to provide for all entities was an id-field to store the node- and relationship-ids.

Example 6.1. Movie class with annotation
@NodeEntity
class
Movie {
@GraphId Long nodeId;
String id;
String title;
int
year;
Set<Role> cast;
}

It was time to put our entities to the test. How could we now be assured that an attribute really was persisted to the graph store? We wanted to load the entity and check the attribute. Either we could have a Neo4jTemplate injected and use its findOne(id,type) method to load the entity. Or use a more versatile Repository. The same goes for persisting entities, both Neo4jTemplate or the Repository could be used. We decided to keep things simple for now. Here's what our test ended up looking like:

Example 6.2. First test case
@Autowired Neo4jTemplate template;
@Test @Transactional public void persistedMovieShouldBeRetrievableFromGraphDb() {
Movie forrestGump = template.save(new Movie("Forrest Gump", 1994));
Movie retrievedMovie = template.findOne(forrestGump.getNodeId(), Movie.class);
assertEqual("retrieved movie matches persisted one", forrestGump, retrievedMovie);
assertEqual("retrieved movie title matches", "Forrest Gump", retrievedMovie.getTitle());
}

As Neo4j is transactional, we have to provide the transactional boundaries for mutating operations.

Indexing
Do I Know You?

There is an @Indexed annotation for fields. We wanted to try this out, and use it to guide the next test. We added @Indexed to the id field of the Movie class. This field is intended to represent the external ID that will be used in URIs and will be stable across database imports and updates. This time we went with a simple GraphRepository to retrieve the indexed movie.

Example 7.1. Exact Indexing for Movie id
@NodeEntity class Movie {
@Indexed String id;
String title;
int
year;
}
@Autowired Neo4jTemplate template;
@Test @Transactional
public void
persistedMovieShouldBeRetrievableFromGraphDb() {
int
id = 1;
Movie forrestGump = template.save(new Movie(id, "Forrest Gump", 1994));
GraphRepository<Movie> movieRepository =
template.repositoryFor(Movie.class);
Movie retrievedMovie = movieRepository.findByPropertyValue("id", id);
assertEqual("retrieved movie matches persisted one", forrestGump, retrievedMovie);
assertEqual("retrieved movie title matches", "Forrest Gump", retrievedMovie.getTitle());
}

Repositories
Serving a Good Cause

We wanted to add repositories with domain-specific operations. Interestingly there was support for a very advanced repository infrastructure. Just declare an entity-specific repository interface and get all commonly used methods for free without implementing any of the boilerplate code. We started by creating a movie-related repository, simply by creating an empty interface.

Example 8.1. Movie repository
package
org.neo4j.cineasts.repository;
public interface
MovieRepository extends GraphRepository<Movie> {}

Then we enabled repository support in the Spring context configuration by simply adding:

Example 8.2. Repository context configuration
<neo4j:repositories base-package="org.neo4j.cineasts.repository"/>

Besides the existing repository operations (like CRUD, and many standard queries) it was possible to declare custom methods, which we explored later. Those methods' names could be more domain-centric and expressive than the generic operations. For simple use-cases like finding by ids this is good enough. We first let Spring autowire our MovieController with the MovieRepository. That way we could perform simple persistence operations.

Example 8.3. Usage of a repository
@Autowired MovieRepository repo;

...
Movie movie = repo.findByPropertyValue("id",movieId);

We went on exploring the repository infrastructure. A very cool feature was something that we so far only heard about from Grails developers. Deriving queries from method names. Impressive! We had a more explicit method for the id lookup.

Example 8.4. Derived movie-repository query method
public interface
MovieRepository extends GraphRepository<Movie> {
Movie getMovieById(String id);
}

In our wildest dreams we imagined the method names we would come up with, and what kinds of queries those could generate. But some more complex queries would be cumbersome to read and write. In those cases it is better to just annotate the finder method. We did this much later, and just wanted to give you a peek into the future. There is much more, you can do with repositories; it is worthwhile to explore.

Example 8.5. Annotated movie-repository query method
public interface
MovieRepository extends GraphRepository<Movie> {
@Query("start user=node:User({0}) match user-[r:RATED]->movie return movie order by r.stars desc limit Iterable<Movie> getTopRatedMovies(User uer);
}

•   •   •

Republished from the Good Relationships: The Spring Data Neo4j Guide Book.

More Stories By Mike Hunger

Mike Hunger has been passionate about software development for a long time. He is particularly interested in the people who develop software, software craftsmanship, programming languages, and improving code. For the last two years he has been working with Neo Technology on the Neo4j graph database. As the project lead of Spring Data Neo4j he helped developing the idea to become a convenient and complete solution for object graph mapping. He is also taking care of Neo4j cloud hosting efforts.

As a developer he loves to work with many aspects of programming languages, learning new things every day, participating in exciting and ambitious open source projects and contributing to different programming related books. Michael is also an active editor and interviewer at InfoQ.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@ThingsExpo Stories
Analytics is the foundation of smart data and now, with the ability to run Hadoop directly on smart storage systems like Cloudian HyperStore, enterprises will gain huge business advantages in terms of scalability, efficiency and cost savings as they move closer to realizing the potential of the Internet of Things. In his session at 16th Cloud Expo, Paul Turner, technology evangelist and CMO at Cloudian, Inc., will discuss the revolutionary notion that the storage world is transitioning from mere Big Data to smart data. He will argue that today’s hybrid cloud storage solutions, with commodity...
Cloud data governance was previously an avoided function when cloud deployments were relatively small. With the rapid adoption in public cloud – both rogue and sanctioned, it’s not uncommon to find regulated data dumped into public cloud and unprotected. This is why enterprises and cloud providers alike need to embrace a cloud data governance function and map policies, processes and technology controls accordingly. In her session at 15th Cloud Expo, Evelyn de Souza, Data Privacy and Compliance Strategy Leader at Cisco Systems, will focus on how to set up a cloud data governance program and s...
Roberto Medrano, Executive Vice President at SOA Software, had reached 30,000 page views on his home page - http://RobertoMedrano.SYS-CON.com/ - on the SYS-CON family of online magazines, which includes Cloud Computing Journal, Internet of Things Journal, Big Data Journal, and SOA World Magazine. He is a recognized executive in the information technology fields of SOA, internet security, governance, and compliance. He has extensive experience with both start-ups and large companies, having been involved at the beginning of four IT industries: EDA, Open Systems, Computer Security and now SOA.
The industrial software market has treated data with the mentality of “collect everything now, worry about how to use it later.” We now find ourselves buried in data, with the pervasive connectivity of the (Industrial) Internet of Things only piling on more numbers. There’s too much data and not enough information. In his session at @ThingsExpo, Bob Gates, Global Marketing Director, GE’s Intelligent Platforms business, to discuss how realizing the power of IoT, software developers are now focused on understanding how industrial data can create intelligence for industrial operations. Imagine ...
We certainly live in interesting technological times. And no more interesting than the current competing IoT standards for connectivity. Various standards bodies, approaches, and ecosystems are vying for mindshare and positioning for a competitive edge. It is clear that when the dust settles, we will have new protocols, evolved protocols, that will change the way we interact with devices and infrastructure. We will also have evolved web protocols, like HTTP/2, that will be changing the very core of our infrastructures. At the same time, we have old approaches made new again like micro-services...
Every innovation or invention was originally a daydream. You like to imagine a “what-if” scenario. And with all the attention being paid to the so-called Internet of Things (IoT) you don’t have to stretch the imagination too much to see how this may impact commercial and homeowners insurance. We’re beyond the point of accepting this as a leap of faith. The groundwork is laid. Now it’s just a matter of time. We can thank the inventors of smart thermostats for developing a practical business application that everyone can relate to. Gone are the salad days of smart home apps, the early chalkb...
Operational Hadoop and the Lambda Architecture for Streaming Data Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and Internet-of-Things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing and analyzing streaming data is the Lambda Architecture, representing a model of how to analyze rea...
Today’s enterprise is being driven by disruptive competitive and human capital requirements to provide enterprise application access through not only desktops, but also mobile devices. To retrofit existing programs across all these devices using traditional programming methods is very costly and time consuming – often prohibitively so. In his session at @ThingsExpo, Jesse Shiah, CEO, President, and Co-Founder of AgilePoint Inc., discussed how you can create applications that run on all mobile devices as well as laptops and desktops using a visual drag-and-drop application – and eForms-buildi...
SYS-CON Events announced today that Vitria Technology, Inc. will exhibit at SYS-CON’s @ThingsExpo, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Vitria will showcase the company’s new IoT Analytics Platform through live demonstrations at booth #330. Vitria’s IoT Analytics Platform, fully integrated and powered by an operational intelligence engine, enables customers to rapidly build and operationalize advanced analytics to deliver timely business outcomes for use cases across the industrial, enterprise, and consumer segments.
Containers and microservices have become topics of intense interest throughout the cloud developer and enterprise IT communities. Accordingly, attendees at the upcoming 16th Cloud Expo at the Javits Center in New York June 9-11 will find fresh new content in a new track called PaaS | Containers & Microservices Containers are not being considered for the first time by the cloud community, but a current era of re-consideration has pushed them to the top of the cloud agenda. With the launch of Docker's initial release in March of 2013, interest was revved up several notches. Then late last...
SYS-CON Events announced today that Dyn, the worldwide leader in Internet Performance, will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Dyn is a cloud-based Internet Performance company. Dyn helps companies monitor, control, and optimize online infrastructure for an exceptional end-user experience. Through a world-class network and unrivaled, objective intelligence into Internet conditions, Dyn ensures traffic gets delivered faster, safer, and more reliably than ever.
In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect at GE, and Ibrahim Gokcen, who leads GE's advanced IoT analytics, focused on the Internet of Things / Industrial Internet and how to make it operational for business end-users. Learn about the challenges posed by machine and sensor data and how to marry it with enterprise data. They also discussed the tips and tricks to provide the Industrial Internet as an end-user consumable service using Big Data Analytics and Industrial Cloud.
The explosion of connected devices / sensors is creating an ever-expanding set of new and valuable data. In parallel the emerging capability of Big Data technologies to store, access, analyze, and react to this data is producing changes in business models under the umbrella of the Internet of Things (IoT). In particular within the Insurance industry, IoT appears positioned to enable deep changes by altering relationships between insurers, distributors, and the insured. In his session at @ThingsExpo, Michael Sick, a Senior Manager and Big Data Architect within Ernst and Young's Financial Servi...
Performance is the intersection of power, agility, control, and choice. If you value performance, and more specifically consistent performance, you need to look beyond simple virtualized compute. Many factors need to be considered to create a truly performant environment. In his General Session at 15th Cloud Expo, Harold Hannon, Sr. Software Architect at SoftLayer, discussed how to take advantage of a multitude of compute options and platform features to make cloud the cornerstone of your online presence.
Even as cloud and managed services grow increasingly central to business strategy and performance, challenges remain. The biggest sticking point for companies seeking to capitalize on the cloud is data security. Keeping data safe is an issue in any computing environment, and it has been a focus since the earliest days of the cloud revolution. Understandably so: a lot can go wrong when you allow valuable information to live outside the firewall. Recent revelations about government snooping, along with a steady stream of well-publicized data breaches, only add to the uncertainty
The explosion of connected devices / sensors is creating an ever-expanding set of new and valuable data. In parallel the emerging capability of Big Data technologies to store, access, analyze, and react to this data is producing changes in business models under the umbrella of the Internet of Things (IoT). In particular within the Insurance industry, IoT appears positioned to enable deep changes by altering relationships between insurers, distributors, and the insured. In his session at @ThingsExpo, Michael Sick, a Senior Manager and Big Data Architect within Ernst and Young's Financial Servi...
Docker is an excellent platform for organizations interested in running microservices. It offers portability and consistency between development and production environments, quick provisioning times, and a simple way to isolate services. In his session at DevOps Summit at 16th Cloud Expo, Shannon Williams, co-founder of Rancher Labs, will walk through these and other benefits of using Docker to run microservices, and provide an overview of RancherOS, a minimalist distribution of Linux designed expressly to run Docker. He will also discuss Rancher, an orchestration and service discovery platf...
PubNub on Monday has announced that it is partnering with IBM to bring its sophisticated real-time data streaming and messaging capabilities to Bluemix, IBM’s cloud development platform. “Today’s app and connected devices require an always-on connection, but building a secure, scalable solution from the ground up is time consuming, resource intensive, and error-prone,” said Todd Greene, CEO of PubNub. “PubNub enables web, mobile and IoT developers building apps on IBM Bluemix to quickly add scalable realtime functionality with minimal effort and cost.”
Sensor-enabled things are becoming more commonplace, precursors to a larger and more complex framework that most consider the ultimate promise of the IoT: things connecting, interacting, sharing, storing, and over time perhaps learning and predicting based on habits, behaviors, location, preferences, purchases and more. In his session at @ThingsExpo, Tom Wesselman, Director of Communications Ecosystem Architecture at Plantronics, will examine the still nascent IoT as it is coalescing, including what it is today, what it might ultimately be, the role of wearable tech, and technology gaps stil...
CommVault has announced that top industry technology visionaries have joined its leadership team. The addition of leaders from companies such as Oracle, SAP, Microsoft, Cisco, PwC and EMC signals the continuation of CommVault Next, the company's business transformation for sales, go-to-market strategies, pricing and packaging and technology innovation. The company also announced that it had realigned its structure to create business units to more directly match how customers evaluate, deploy, operate, and purchase technology.