Graph database

Key takeaways

Graph database belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Graph database to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Graph database from memory before moving on to harder problems.

Reference excerpt

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data. Graph databases are commonly referred to as a NoSQL database. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction and lack easy traversal over a chain of edges. The underlying storage mechanism of graph databases can vary. Relationships are first-class citizens in a graph database and can be labeled, directed, and given properties. Some depend on a relational engine and store the graph data in a table (although a table is a logical element, therefore this approach imposes a level of abstraction between the graph database management system and physical storage devices). Others use a key–value store or document-oriented database for storage, making them inherently NoSQL structures. As of 2021, no graph query language has been universally adopted in the same way as SQL was for relational databases. There is a wide variety of systems used, many of which are tightly tied to one product. Early standardization efforts led to multi-vendor query languages like Gremlin, SPARQL, and Cypher. In September 2019 a proposal for a project to create a new standard graph query language (ISO/IEC 39075 Information Technology — Database Languages — GQL) was approved by members of ISO/IEC Joint Technical Committee 1(ISO/IEC JTC 1). GQL is intended to be a declarative database query language, like SQL. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs). Graph databases differ from graph compute engines. Graph databases are technologies that are translations of the relational online transaction processing (OLTP) databases. On the other hand, graph compute engines are used in online analytical processing (OLAP) for bulk analysis. Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations using proprietary graph databases, along with the introduction of open-source graph databases. One study concluded that an RDBMS was "comparable" in performance to existing graph analysis engines at executing graph queries.

History In the mid-1960s, navigational databases such as IBM's IMS supported tree-like structures in its hierarchical model, but the strict tree structure could be circumvented with virtual records. Graph structures could be represented in network model databases from the late 1960s. CODASYL, which had defined COBOL in 1959, defined the Network Database Language in 1969. Labeled graphs could be represented in graph databases from the mid-1980s, such as the Logical Data Model. Commercial object databases (ODBMSs) emerged in the early 1990s. In 2000, the Object Data Management Group published a standard language for defining object and relationship (graph) structures in their ODMG'93 publication. In the mid-to-late 2000s, commercial graph databases with ACID guarantees such as Neo4j and Oracle Spatial and Graph became available. In the 2010s, commercial ACID graph databases that could be scaled horizontally became available. Further, SAP HANA brought in-memory and columnar technologies to graph databases. Also in the 2010s, multi-model databases that supported graph models (and other models such as relational database or document-oriented database) became available, such as OrientDB, ArangoDB, and MarkLogic (starting with its 7.0 version). During this time, graph databases of various types have become especially popular with social network analysis with the advent of social media companies. Also during the decade, cloud-based graph databases such as Amazon Neptune and Neo4j AuraDB became available.

Background Based on graph theory, graph databases store data in a way that reflects how it is conceptually understood, using nodes to represent entities and edges to represent the relationships between them. It consists of a set of objects, such as a node or an edge.

Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked. They are roughly the equivalent of a record, relation, or row in a relational database, or a document in a document-store database. Edges, also termed graphs or relationships, are the lines that connect nodes to other nodes; representing the relationship between them. Meaningful patterns emerge when examining the connections and interconnections of nodes, properties and edges. The edges can either be directed or undirected. In an undirected graph, an edge connecting two nodes has a single meaning. In a directed graph, the edges connecting two different nodes have different meanings, depending on their direction. Edges are the key concept in graph databases, representing an abstraction that is not directly implemented in a relational model or a document-store model. Properties are information associated to nodes. For example, if Wikipedia were one of the nodes, it might be tied to properties such as website, reference material, or words that starts with the letter w, depending on which aspects of Wikipedia are germane to a given database.

Graph models

Labeled-property graph

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Graph database

Start with the simplest possible case. Write down what Graph database claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Graph database before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Graph database ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Graph database

In research: Graph database appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Graph database in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Graph database is common in secondary-school and first-year university syllabi. It links to neighbouring topics Database models, Graph databases, so understanding it makes those chapters shorter.
In everyday life: Look for Graph database outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Graph database in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Graph database means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Graph database out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Graph database in simple terms?

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph (or edge or relationship).

Why does Graph database matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Graph database?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Graph database.