How do Graph Databases work

why are graph databases faster how are graph databases implemented and how do graph databases store data and how to use graph databases
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Published Date:01-08-2017
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The World’s Leading Graph Database Ebook The Definitive Guide to Graph Databases for the RDBMS Developer by Michael Hunger, Ryan Boyd & William Lyon neo4j.comThe World’s Leading Graph Database The Definitive Guide to Graph Databases for the RDBMS Developer TABLE OF CONTENTS The Definitive Guide Introduction 1 to Graph Databases Why Relational Databases Aren’t Always Enough 2 for the RDBMS Developer Why Graph Databases? 5 Michael Hunger, Ryan Boyd & William Lyon Data Modeling: Relational vs. Graph Models 8 Introduction: Query Languages: SQL vs. Cypher 18 Why We Wrote This Ebook Deployment Paradigms: When two database technologies share space in the same book title, there’s bound to be Bringing in Graphs 26 confusion as to the motives of its writing. First things first: We didn’t write this book to bash relational databases or to criticize a still- Drivers: Connecting to valuable technology. Without relational databases, many of today’s most mission-critical a Graph Database 30 applications wouldn’t run, and without the early innovation of RDBMS pioneers, we would have never gotten to where we are with today’s database technology. Conclusion 33 Rather, we wrote this book to introduce you – a developer with RDBMS experience – to a database technology that changed not only how we see the world, but how we build the Other Resources 34 future. Today’s business and user requirements demand applications that connect more and more of the world’s data, yet still expect high-levels of performance and data reliability. We believe those applications of the future will be built using graph databases, and we don’t want you to be left behind. In fact, we’re here to help you through every step of the learning For those cases when process. you need a different While other NoSQL (Not only SQL) databases advertise themselves with in-your-face defiance solution, we hope of RDBMS technology, we prefer to be a helpful resource in helping you add graph databases this book helps you to your professional skillset. recognize when – and Relational databases still have their perfect use cases. But for those cases when you need a different solution, we hope this book helps you recognize when – and how – to use a graph how – to use a graph database to tackle those new challenges. database to tackle As you read and peruse these pages, feel free to reach out to us with your questions. You can those new challenges. most commonly find us on Stack Overflow , our Google Group or our public Slack channel. Happy graphing, –Michael, Ryan & Will 1 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Chapter 1: Why Relational Databases Aren’t Always Enough Relational databases are powerful tools. Since the 80s, they have been the power-horse of most software applications and continue to be so today. Relational databases (RDBMSs) were initially designed to codify paper forms and tabular structures, and they do that exceedingly well. For the right use case and the right architecture, they are one of the best tools for storing and organizing data. Because relational databases store highly structured data in tables with predetermined columns and many rows of the same type of information, they require developers and applications to strictly structure the data used in their applications. But today’s user requirements and applications are asking for more. More features, more data, more agility, more speed and – most importantly – more connections. The Mismatch between Relational Databases & Data Relationships Despite their name, relational databases Despite their name, relational databases are not well-suited for today’s highly connected are not well-suited data, because they don’t robustly store relationships between data elements. for today’s highly (It’s worth noting that relational databases take their name from the highly specific connected data, mathematical notion of a “relation” – a.k.a. a table – as part of E.F. Codd’s relational algebra. because they don’t The name does not derive from describing relationships between data.) robustly store Traditionally, developers have been taught to store data in the columns and rows of a relationships between relational model. Yet, columns and rows aren’t really how data exists in the real world. Rather, data exists as objects and the relationships between those different objects. data elements. These types of complex, real-world data are increasing in volume, velocity and variety. As a result, data relationships – which are often more valuable than the data itself – are growing at an even faster rate. The problem: Relational databases aren’t designed to capture this rich relationship information. The bottom line: Applications (and the enterprises that create them) are missing out on critical connections essential for today’s high-stakes, data-driven decisions. The Agile Realities of Today’s Software Applications Every development team faces the reality of ever-changing business and user requirements that call for frequent modifications and pivots to a given data architecture. Database administrators (DBAs) and developers face a steady stream of business requests to add elements or attributes to meet new requirements – such as storing information about the latest social platform – but such regular schema changes are problematic for RDBMS developers and come with a high maintenance cost. That’s because relational databases don’t adapt well to change. Rather, their fixed schema works best for problems that are well-defined at the outset. 2 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Slow and expensive schema redesigns also hurt the agile software development process by hindering your team’s ability to innovate quickly – a significant opportunity cost no matter the size of your bottom line. The verdict: Relational databases aren’t engineered for the speed of business agility. How Connected Data Queries Cripple RDBMS Performance Despite advances in computing, faster processors and high-speed networks, the perfor- mance of some relational database applications continues to slow. This performance slump has several known symptoms (see “SQL Strain” section below), but the root cause usually boils down to one factor: queries about data relationships. Since relational databases aren’t built or optimized to handle connected data, any attempt to answer data relationship queries – such as a recommendation engine, a fraud detection pattern or a social graph – involves numerous JOINs between database tables. In relational databases, references to other rows and tables are indicated by referring to their primary-key attributes via foreign-key columns. (See Figure 1 below for an example.) Applications (and the enterprises that create them) are missing out on critical connections essential for today’s high- stakes, data-driven decisions. Figure 1: A JOIN table between the Persons and Departments tables in a relational database using foreign key constraints. These references are enforceable with constraints, but only when the reference is never op- tional. JOINs are then computed at query time by matching primary- and foreign-keys of the many (potentially indexed) rows of the to-be-JOINed tables. These operations are compute- and memory-intensive and have an exponential cost as queries grow. Consequently, modeling and storing connected data becomes impossible without extreme complexity. That complexity surfaces in cases like SQL statements that require dozens of lines of code just to accomplish simple operations. Overall performance also degrades from query complexity, the number and levels of data relationships and the overall size of the database. With today’s real-time, always-on expectations of software applications, traditional relational databases are simply inappropriate whenever data relationships are key to success. 3 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer 5 Signs Your RDBMS Application Suffers from SQL Strain Many relational database applications are working fine within their limits. Some, however, may be showing significant signs of strain induced by the database, especially when an RDBMS is being used to handle highly connected data. Here are five of the most common signs you may be trying to solve a connected data problem with a relational database: 1. A Large Number of JOINs When you utilize queries that JOIN many different tables, there’s an explosion of complexity and computing resource consumption. This results in a corresponding increase in query response times. 2. Numerous Self-JOINs (or Recursive JOINs) Self-JOIN statements are common for hierarchy and tree representations of data, but traversing relationships by repeatedly JOINing tables to themselves is inefficient. In fact, some of the longest SQL queries in the world involve recursive JOINs. 3. Frequent Schema Changes At a time when business agility is at a premium, requests for changes are more often than not put off by DBAs because the schema of relational databases isn’t designed for frequent modifications and pivots. Common schema changes indicate that the data or require - ments are rapidly evolving, calling for a more flexible data model. 4. Slow-Running Queries (Despite Extensive Tuning & Hardware) Your DBA might use every trick in the book to speed up query times, but many SQL queries still aren’t fast enough to support your application’s needs. In addition, denormalizing data models for performance can negatively impact data quality and update behavior. Or in some cases, you might have a handle on query performance only because of excessive hardware. Throwing more hardware at the problem might temporarily fix a problem, but queries shouldn’t require over 100 cores in order to perform well. As your data grows, even more hardware will be required. 5. Pre-Computing Your Results Because queries run so slowly, many applications pre-compute their results using a snapshot of the past data. However, this is effectively using yesterday’s data for queries that should be handled in real time today. Furthermore, your system usually must pre-compute 100% of your data, even if only 1-2% of it will be accessed at any given time, wasting your computational resources. An Alternative (or Addition) to Relational Databases As previously mentioned, relational databases have their appropriate use cases. For highly structured, predetermined schemas, an RDBMS is the perfect tool. But as we’ve seen, relational databases aren’t always enough. Applications that require connected data insights can’t rely on the rela- tional model. While relational databases sometimes need to be replaced entirely, often the RDBMS solution can’t (or doesn’t need to) be shut down. In these cases, developers and architects can use a polyglot persistence approach – using different databases for their best-of-breed strengths. So whether you’re replacing your RDBMS or just complementing it with another data store, the volume, velocity and variety of today’s data – and data relationships – require a solution that’s engineered from the ground up to store and organize connected data. It’s time to meet graph databases. 4 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Chapter 2: Why Graph Databases? We already know that relational databases aren’t enough (by themselves) for handling the volume, velocity and variety of today’s data, but what’s the clear alternative? There are a lot of other database options out there – including a number of NoSQL data stores – but none of them are explicitly designed to handle and store data relationships. None, that is, except graph databases. The biggest value that graphs bring to the development stack is their ability to store relationships and connections as first-class entities. For instance, the early adopters of graph technology reimagined their businesses around the value of data relationships. These companies have now become industry leaders: LinkedIn, Google, Facebook and PayPal. As pioneers in graph technology, each of these enterprises had to build their own graph database from scratch. Fortunately for today’s developers, that’s no longer the case, as graph database technology is now available off the shelf. Let’s take a further look into why you should consider a graph database for your next If you’re already connected-data application. We’ll start with some basic definitions. familiar with relational databases, you’ll find What Is a Graph? graphs to be a breeze. You don’t need to understand the arcane mathematical wizardry of graph theory in order to understand graph databases. On the contrary, if you’re already familiar with relational databases, you’ll find graphs to be a breeze. First thing: A graph – in mathematics – is not the same as a chart, so don’t picture a bar or line chart. Rather, picture a network or mind map, like in the example to the right. Figure 2: A basic graph of a fraud ring sharing similar contact informa- tion. 5 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer A graph is composed of two elements: a node and a relationship. Each node represents an entity (a person, place, thing, category or other piece of data), and each relationship represents how two nodes are associated. For example, the two nodes “cake” and “dessert” would have the relationship “is a type of” pointing from “cake” to “des- sert.” This general-purpose structure allows you to model all kinds of scenarios – from a system of roads, to a network of devices, to a population’s medical history or anything else defined by relationships. What Is a Graph Database? A graph database is an online database management system with Create, Read, Update and Delete (CRUD) operations working on a graph data model. Graph databases are generally built for use with transactional (OLTP) systems. Accordingly, they are normally optimized Unlike other for transactional performance, and engineered with transactional integrity and operational availability in mind. databases, relationships take Unlike other databases, relationships take first priority in graph databases. This means your application doesn’t have to infer data connections using foreign keys or out-of-band process- first priority in ing, such as MapReduce. graph databases. By assembling the simple abstractions of nodes and relationships into connected structures, This means your graph databases enable us to build sophisticated models that map closely to our problem application doesn’t domain. have to infer data There are two important properties of graph database technologies: connections using Graph Storage foreign keys or out-of- band processing, such Some graph databases use native graph storage that is specifically designed to store and as MapReduce. manage graphs, while others use relational or object-oriented databases instead. Non-native storage is often much more latent, especially as data volume and query complexity grow. Graph Processing Engine Native graph processing (a.k.a. “index-free adjacency”) is the most efficient means of process - ing graph data because connected nodes physically “point” to each other in the database. Non-native graph processing uses other means to process CRUD operations that aren’t optimized for graphs, often involving an index lookup which results in reduced performance. What Are the Advantages of Using a Graph Database? A graph database is purpose-built to handle highly connected data, and the increase in the volume and connectedness of today’s data presents a tremendous opportunity for sustain- able competitive advantage. When it comes to applying a graph database to a real-world problem, with real-world tech- nical and business constraints, enterprise organizations choose graph databases for the following reasons: 6 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Minutes-to-Milliseconds Performance Query performance and responsiveness are at the top of many organizations’ concerns with regard to their data platforms. Online transactional systems – large web applications in particular – must respond to end users in milliseconds if they are to be successful. In the relational world, as an application’s dataset size grows, JOIN pains begin to manifest themselves, and performance deteriorates. Using index-free adjacency, a graph database turns complex JOINs into fast graph traversals – which are constant time operations – thereby maintaining millisecond performance irrespective of the overall size of the dataset. Drastically Accelerated Development Cycles The graph data model reduces the impedance mismatch that has plagued software development for decades, thereby reducing the development overhead of translating back and forth between an object model and a tabular relational model. More importantly, the graph model reduces the impedance mismatch between the technical and business domains. Subject matter experts, architects and developers can talk about and picture the core domain using a shared model that is then incorporated into the application itself. Extreme Business Responsiveness Successful applications rarely stay still. Changes in business conditions, user behaviors, and technical and operational infrastructures drive new requirements. In the past, this has required organizations to undertake careful and lengthy data migrations that involve modifying schemas, transforming data and maintaining redundant data to serve old and new features. Developing with graph databases aligns perfectly with today’s agile, test-driven development practices, allowing your graph database to evolve in step with the rest of the application and any changing business requirements. Rather than exhaustively modeling a do- main ahead of time, data teams can add to the existing graph structure without endangering current functionality. Enterprise Ready When employed in a mission-critical application, a data technology must be robust, scalable and – more often than not – transaction- al. Although some graph databases are fairly new and not yet fully mature, there are graph databases on the market that provide all the -ilities needed by large enterprises today: • ACID transactionality • High availability • Horizontal read scalability • Storage of billions of entities These characteristics have been an important factor leading to the adoption of graph databases by large organizations, not merely in modest offline or departmental capacities, but in ways that truly transform the business. What Are the Common Use Cases of Graph Databases? While graph databases first became popular with social applications for the consumer web (Facebook, LinkedIn, Twitter), their use cases extend far beyond the social space. Today’s enterprise organizations use graph database technology in a diversity of ways, including these six most common use cases: • Fraud detection • Real-time recommendation engines • Master data management (MDM) • Network and IT operations • Identity and access management (IAM) • Graph-based search For more information on graph technology use cases, see The Top 5 Use Cases of Graph Databases: Unlocking New Possibilities with Connected Data. 7 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Chapter 3: Data Modeling: Relational vs. Graph Models In some regards, graph databases are like the next generation of relational databases, but with first class support for “relationships,” or those implicit connections indicated via foreign keys in traditional relational databases. Each node (entity or attribute) in the graph database model directly and physically contains a list of relationship-records that represent its relationships to other nodes. These relationship records are organized by type and direction and may hold additional attributes. Graph databases like Neo4j provide a minutes-to- milliseconds performance Figure 3: A graph/JOIN table hybrid showing the foreign key data relationships advantage of several between the Persons and Departments tables in a relational database. orders of magnitude, Whenever you run the equivalent of a JOIN operation, the database just uses this list and has especially for JOIN- direct access to the connected nodes, eliminating the need for an expensive search-and- heavy queries. match computation. This ability to pre-materialize relationships into database structures allows graph databases like Neo4j to provide a minutes-to-milliseconds performance advantage of several orders of magnitude, especially for JOIN-heavy queries. The resulting data models are much simpler and at the same time more expressive than those produced using traditional relational or other NoSQL databases. Figure 4: A graph data model of our original Persons and Departments data. Nodes and relationships have replaced our tables, foreign keys and JOIN table. 8 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Graph databases support a very flexible and fine-grained data model that allows you to model and manage rich domains in an easy and intuitive way. You more or less keep the data as it is in the real world: small, normalized, yet richly connected entities. This allows you to query and view your data from any imaginable point of interest, supporting many different use cases (see Chapter 2 for more information). The fine-grained model also means that there is no fixed boundary around aggregates, so the scope of update operations is provided by the application during the read or write operation. Transactions group a set of node and relationship updates into an Atomic, Consistent, Isolated and Durable (ACID) operation. Graph databases like Neo4j fully support these transactional concepts, including write-ahead logs and recovery after abnormal termination, so you never lose your data that has been committed to the database. If you’re experienced in modeling with relational databases, think of the ease and beauty of a well-done, normalized entity-relationship diagram: a simple, easy-to-understand model you can quickly whiteboard with your colleagues and domain experts. A graph is exactly that: a clear model of the domain, focused on the use cases you want to efficiently support. Let’s take a model of the organizational domain and show how it would be modeled in a Graph databases relational database vs. the graph database. support a very flexible and fine-grained data First up, our relational database model: model that allows you to model and manage rich domains in an easy and intuitive way. Figure 5: A relational database model of a domain with Persons and Projects with- in an Organization with several Departments. 9 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer If we were to adapt this (above) relational database model into a graph database model, we would go through the following checklist to help with the transformation: • Each entity table is represented by a label on nodes • Each row in a entity table is a node • Columns on those tables become node properties. • Remove technical primary keys, but keep business primary keys • Add unique constraints for business primary keys, and add indexes for frequent lookup attributes • Replace foreign keys with relationships to the other table, remove them afterwards • Remove data with default values, no need to store those • Data in tables that is denormalized and duplicated might have to be pulled out into separate nodes to get a cleaner model • Indexed column names might indicate an array property (like email1, email2, email3) • JOIN tables are transformed into relationships, and columns on those tables become relationship properties Once we’ve taken these steps to simplify our relational database model, here’s what the graph data model would look like: Figure 6: A graph data model of the same domain with Persons and Projects within an Organization with several Departments. With the graph model, all of the initial JOIN tables have now become data relationships. This above example is just one simplified comparison of a relational and graph data model. Now it’s time to dive deeper into a more extended example taken from a real-world use case. Relational vs. Graph Data Modeling Case Study: A Data Center Management Domain To show you the true power of graph data modeling, we’re going to look at how we model a domain using both relational- and graph- based techniques. You’re probably already familiar with RDBMS data modeling techniques, so this comparison will highlight a few similarities – and many differences. In particular, we’ll uncover how easy it is to move from a conceptual graph model to a physical graph model, and how little the graph model distorts what we’re trying to represent versus the relational model. To facilitate this comparison, we’ll examine a simple data center management domain. In this domain, several data centers support many applications on behalf of many customers using different pieces of infrastructure, from virtual machines to physical load balancers. 10 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Here’s an example of a small data center domain: A graph is a clear model of the domain, focused on the use cases you want to efficiently support. Figure 7: A small domain of several application deployments within a data center. In this example above, we see a somewhat simplified view of several applications and the data center infrastructure necessary to support them. The applications, represented by nodes App 1, App 2 and App 3, depend on a cluster of databases labeled Database Server 1, 2, 3. While users logically depend on the availability of an application and its data, there is ad- ditional physical infrastructure between the users and the application; this infrastructure includes virtual machines (Virtual Machine 10, 11, 20, 30, 31), real servers (Server 1, 2, 3), racks for the servers (Rack 1, 2) and load balancers (Load Balancer 1, 2), which front the apps. Of course, between each of the components are many networking elements: cables, switch- es, patch panels, NICs (network interface controllers), power supplies, air conditioning and so on – all of which can fail at inconvenient times. To complete the picture we have a straw-man single user of Application 3, represented by User 3. 11 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer As the operators of such a data center domain, we have two primary concerns: • Ongoing provision of functionality to meet (or exceed) a service-level agreement, including the ability to perform forward-looking analyses to determine single points of failure, and retrospective analyses to rapidly determine the cause of any customer complaints regarding the availability of service. • Billing for resources consumed, including the cost of hardware, virtualization, network provisioning and even the costs of software development and operations (since these are simply logical extensions of the system we see here). If we are building a data center management solution, we’ll want to ensure that the underlying data model allows us to store and query data in a way that efficiently addresses these primary concerns. We’ll also want to be able to update the underlying model as the application portfolio changes, the physical layout of the data center evolves and virtual machine instances migrate. Given these needs and constraints, let’s see how the relational and graph models compare. Creating the Relational Model The first step in relational data The first step in relational data modeling is the same as with any other data modeling modeling is the same approach: to understand and agree on the entities in the domain, how they interrelate and as any other data the rules that govern their state transitions. modeling approach: This initial stage is often informal, with plenty of whiteboard sketches and discussions to understand and between subject matter experts and data architects. These talks then usually result in agree on the entities diagrams like Figure 7 above (which also happens to be a graph). in the domain. The next step is to convert this initial whiteboard sketch into a more rigorous entity- relationship (E-R) diagram (which is another graph). Transforming the conceptual model into a logical model using a stricter notation gives us a second chance to refine our domain vocabulary so that it can be shared with relational database specialists. (It’s worth noting that adept RDBMS developers often skip directly to table design and normalization without using an intermediate E-R diagram.) Here’s our sample E-R diagram: Figure 8: An entity-relationship (E-R) diagram for our data center domain. 12 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Now with a logical model complete, it’s time to map it into tables and relations, which are nor- malized to eliminate data redundancy. In many cases, this step can be as simple as transcrib- ing the E-R diagram into a tabular form and then loading those tables via SQL commands into the database. But even the simplest case serves to highlight the idiosyncrasies of the relational model. For example, in the figure below we see that a great deal of accidental complexity has crept into the model in the form of foreign key constraints (everything annotated FK), which support one-to-many relationships, and JOIN tables (e.g., AppDatabase), which support many-to-ma- ny relationships – and all this before we’ve added a single row of real user data. A Note on E-R Diagrams: Despite being graphs, E-R diagrams immediately show the shortcomings of the relational model. E-R diagrams allow only single, undirected relationships between entities. In this respect, the relational model is a poor fit for real- world domains where relationships between entities are both numerous and semantically rich. Figure 9: A full-fledged relational data model for our data center domain. These constraints and complexities are model-level metadata that exist simply so that we specify the relations between tables at query time. Yet the presence of this structural data is keenly felt, because it clutters and obscures the domain data with data that serves the database, not the user. The Problem of Relational Data Model Denormalization So far, we now have a normalized relational data model that is relatively faithful to the domain, but our design work is not yet complete. 13 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer One of the challenges of the relational paradigm is that normalized models generally aren’t fast enough for real-world needs. In theory, a normalized schema is fit for answering any kind of ad hoc query we pose to the domain, but in practice, the model must be further adapted for specific access patterns. In other words, to make relational databases perform well enough for regular application needs, we have to abandon any vestiges of true domain affinity and accept that we have to change the user’s data model to suit the database engine, not the user. This approach is called denormalization. Denormalization involves duplicating data (substantially in some cases) in order to gain query performance. For example, consider a batch of users and their contact details. A typical user often has several email addresses, which we would then usually store in a separate EMAIL table. However, to reduce the performance penalty of JOINing two tables, it’s quite common to add one or more columns within the USER table to store a user’s most important email addresses. Assuming every developer on the project understands the denormalized data model and how it maps to their domain-centric code (which is a big assumption), denormalization is not a trivial task. Often, development teams turn to an RDBMS expert to munge a normalized model into a denormalized one that aligns with the char- acteristics of the underlying RDBMS and physical storage tier. Doing all of this involves a substantial amount of data redundancy. The Cost of Rapid Change in the Relational Model It’s easy to think the design-normalize-denormalize process is acceptable because it’s only a one-off task. After all, the cost of this upfront work pays off across the lifetime of the system, right? Wrong. While this one-off, upfront idea is appealing, it doesn’t match the reality of today’s agile development process. Systems change fre - quently – not only during development, but also during their production lifetimes. Although the majority of systems spend most of their time in production environments, these environments are rarely stable. Busi- ness requirements change and regulatory requirements evolve, so our data models must too. Adapting our relational database model then requires a structural change known as a migration. Migrations provide a structured, step-wise approach to database refactorings so it can evolve to meet changing requirements. Unlike code refactorings – which typi- cally take a matter of minutes or seconds – database refactorings can take weeks or months to complete, with downtime for schema changes. The bottom-line problem with the denormalized relational model is its resistance to the rapid evolution that today’s business de- mands from applications. As we’ve seen in this data center example, the changes imposed on the initial whiteboard model from start to finish create a widening gulf between the conceptual world and the way the data is physically laid out. This conceptual-relational dissonance prevents business and other non-technical stakeholders from further collaborating on the evo- lution of the system. As a result, the evolution of the application lags significantly behind the evolution of the business. Now that we’ve thoroughly examined the relational data modeling process, let’s turn to the graph data modeling approach. Creating the Graph Data Model As we’ve seen, relational data modeling divorces an application’s storage model from the conceptual worldview of its stakeholders. Relational databases – with their rigid schemas and complex modeling characteristics – are not an especially good tool for supporting rapid change. What we need is a model that is closely aligned with the domain, but that doesn’t sacrifice performance, and that sup - ports evolution while maintaining the integrity of the data as it undergoes rapid change and growth. That model is the graph model. How, then, does the data modeling process differ? Let’s begin. 14 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer In the early stages of graph modeling, the work is similar to the relational approach: Using lo-fi methods like whiteboard sketches, we describe and agree upon the initial domain. After that, our data modeling methodologies diverge. Once again, here is our example data center domain modeled on the whiteboard: Instead of turning our model into tables, our next step is to simply enrich our already graph-like structure. No tables, no normalization, no denormalization. Once we have an accurate representation of our domain model, moving it into the database is trivial. Figure 10: Our example data center domain with several application deployments. Instead of turning our model into tables, our next step is to simply enrich our already graph- like structure. This enrichment aims to more accurately represent our application goals in the data model. In particular, we’ll capture relevant roles as labels, attributes as properties and connections to neighboring entities as relationships. By enriching this first-round domain model with additional properties and relationships, we produce a graph model attuned to our data needs; that is, we build our model to answer the kinds of questions our application will ask of its data. To polish off our developing graph data model, we just need to ensure correct semantic context. We do this by creating named and directed relationships between nodes to capture the structural aspects of our domain. Logically, that’s all we need to do. No tables, no normalization, no denormalization. Once we have an accurate representation of our domain model, moving it into the database is trivial. 15 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer The Catch So, what’s the catch? With a graph database, what you sketch on the whiteboard is what you store in the database. It’s that simple. No catch. After adding properties, labels and relationships, the resulting graph model for our data center scenario looks like this: With a graph database, what you sketch on the whiteboard is what you store in the database. It’s that simple. No catch. Figure 11: A full-fledged graph data model for our data center domain. Note that most of the nodes here have two labels: both a specific type label (such as Data- base, App or Server), and a more general-purpose Asset label. This allows us to target particular types of assets with some of our queries, and all assets – irrespective of type – with other queries. Compared to the finished relational database model (included again on the next page), ask yourself which of the two data models is easier to evolve, contains richer relationships and yet is still simple enough for business stakeholders to understand. We thought so. 16 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer While it might be easy to dismiss an upfront modeling headache with the idea that you won’t ever have to do it again, today’s agile development practices will have you back at the whiteboard (or worse, calling a migration Figure 12: A full-fledged relational data model for our data center domain. This data expert) sooner than model is significantly more complex – and less user friendly – than our graph model you think. on the preceding page. Conclusion Don’t forget: Data models are always changing. While it might be easy to dismiss an upfront modeling headache with the idea that you won’t ever have to do it again, today’s agile development practices will have you back at the whiteboard (or worse, calling a migration expert) sooner than you think. Even so, data modeling is only a part of the database development lifecycle. Being able to query your data easily and efficiently – which often means real time – is just as important as having a rich and flexible data model. In the next chapter, we’ll examine the differences between RDBMS and graph database query languages. 17 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Chapter 4: Query Languages: SQL vs. Cypher When it comes to a database query language, linguistic efficiency matters. Querying relational databases is easy with SQL. As a declarative query language, SQL allows both for easy ad hoc querying in a database tool as well as specifying use-case related queries in your code. Even object-relational mappers use SQL under the hood to talk to the database. But SQL runs up against major performance challenges when it tries to navigate connected data. For data-relationship questions, a single query in SQL can be many lines longer than the same query in a graph database query language like Cypher (more on Cypher below). Lengthy SQL queries not only take more time to run, but they are also more likely to include human coding mistakes because of their complexity. In addition, shorter queries increase the ease of understanding and maintenance across your team of developers. For example, imagine if an outside developer had to pick through a complicated SQL query and try to The efficiency of graph figure out the intent of the original developer – trouble would certainly ensue. queries means they run But what level of efficiency gains are we talking about between SQL queries and graph in real time, and in an queries? How much more efficient is one versus another? The answer: Fast enough to make a significant difference to your organization . economy that runs at the speed of a single The efficiency of graph queries means they run in real time, and in an economy that runs at the speed of a single tweet, that’s a bottom-line difference you can’t afford to ignore. tweet, that’s a bottom- line difference you can’t The Critical Relationship between Query Languages & Data Models afford to ignore. It’s worth noting that a query language isn’t just about asking (a.k.a. querying) the database for a particular set of results; it’s also about modeling that data in the first place. We know from the previous chapter that data modeling for a graph database is as easy as connecting circles and lines on a whiteboard. What you sketch on the whiteboard is what you store in the database. On its own, this ease of modeling has many business benefits, the most obvious of which is that you can understand what your database developers are actually creating. But there’s more to it: An intuitive model built with the right query language ensures there’s no mismatch between how you built the data model and how you analyze it. A query language represents its model closely. That’s why SQL is all about tables and JOINs while Cypher is about relationships between entities. As much as the graph model is more natural to work with, so is Cypher as it borrows from the pictorial representation of circles connected with arrows which any stakeholder (whether technical or non-technical) can understand. In a relational database, the data modeling process is so far abstracted from actual day-to- day SQL queries that there’s a major disparity between analysis and implementation. In other words, the process of building a relational database model isn’t fit for asking (and answering) questions efficiently from that same model. 18 neo4j.comThe Definitive Guide to Graph Databases for the RDBMS Developer Graph database models, on the other hand, not only communicate how your data is related, but they also help you clearly communi- cate the kinds of questions you want to ask of your data model. Graph models and graph queries are just two sides of the same coin. The right database query language helps us traverse both sides. An Introduction to Cypher, the Graph Query Language Just like SQL is the standard query language for relational databases, Cypher is an open, multi-vendor query language for graph tech- nologies. The advent of the openCypher project has expanded the reach of Cypher well beyond just Neo4j, its original sponsor. Cypher – also a declarative query language – is built on the basic concepts and clauses of SQL but with added graph-specific function - ality, making it simple to work with a rich graph model without being overly verbose. (Note: This introduction isn’t a reference document for Cypher but merely a high-level overview.) Cypher is designed to be easily read and understood by developers, database professionals and business stakeholders alike. It’s easy to use because it matches the way we intuitively describe graphs using diagrams. If you have ever tried to write a SQL statement with a large number of JOINs, you know that you quickly lose sight of what the query actually does, due to all the technical noise. In contrast, Cypher syntax stays clean and focused on domain concepts since queries are expressed visually. The basic notion of Cypher is that it allows you to ask the database to find data that matches a specific pattern. Colloquially, we might ask the database to “find things like this,” and the way we describe what “things like this” look like is to draw them using ASCII art. Consider the social graph below describing three mutual friends: Figure 13: A social graph describing the relationship between three friends. If we want to express the pattern of this basic graph in Cypher, we would write: (emil)-:KNOWS-(jim)-:KNOWS-(ian)-:KNOWS-(emil) This Cypher pattern describes a path which forms a triangle that connects a node we call jim to the two nodes we call ian and emil, and which also connects the ian node to the emil node. As you can see, Cypher naturally follows the way we draw graphs on the whiteboard. 19

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