Graph Query Language: What You Should Know
Organizations worldwide are facing a huge problem due to the exponential growth of data. According to the Institute of Electrical and Electronics Engineers, about 168 zettabytes of data will have been created by 2025. This is massive, and it’s not yet peak level. What levels of data are we looking at in a decade and beyond?
These are mind boggling figures. But there is one more trend that is also worrying: Traditional databases are not very good at handling the overwhelming data volume . A study published on ResearchGate reveals just how limited relational databases are, particularly when dealing with big data.
Because of this growing frustration with traditional databases in the midst of enormous data, organizations are turning to modern database solutions. And with this need, graph databases have now come of age.
The unique thing, which also happens to be the greatest advantage of graph databases, is that they are designed to store and manage large sets of interconnected data points, such as social networks, supply chains, and financial transactions. As we have previously indicated when breaking down the concept of a graph database, these databases are particularly useful for applications that require real-time processing and analysis of data, such as fraud detection, recommendation engines, and social network analysis tools.
Now, like any other database out there, graph databases too need a way (language) to communicate with their clients (users). For graph databases, these languages are known as graph query languages. Whether you are working with an open source graph database or a commercial one, you’ll need a super good graph query language by your side.
Let’s break it all down.
What is a graph query language?
So what exactly is a graph query language?
A graph query language is a programming language designed to work with graph databases. It allows users to specify the structure of the graph they want to query, as well as the rules for traversing the graph and retrieving the relevant data.
Graph query languages are typically declarative, meaning that users specify what they want to retrieve, rather than how to retrieve it. In other words the language instructs the database on what to do, which is what unlocks the numerous benefits that they offer.
Furthermore, graph query languages can recognize and relate the current data to other data sets in real-time.
How does a graph query language work?
Graph query languages work by allowing users to specify patterns in the graph that they want to query. Users can specify the nodes and edges that they are interested in, as well as any conditions that must be met for the query to return results. The language then processes the query and returns the results in a format that can be easily consumed by the user.
For instance, let's say a financial institution wants to detect fraudulent activity related to credit card transactions. The institution can store all transactions in a graph database, where each transaction is a node and edges connect transactions to their associated accounts, customers, and merchants.
Using a graph query language, a developer can write queries to identify patterns that may indicate fraud, such as transactions that involve multiple cards, transactions from new or suspicious merchants, and transactions that occur outside of a customer's usual geographic region.
The output of this query could be further analyzed to identify potentially fraudulent transactions involving multiple cards and merchants, which could then be flagged for further investigation.
For example, a simple query might look for all nodes in the graph that are labeled "person" and return their names and ages. The query might also specify that the results should only include nodes that are connected to other nodes labeled "organization." The query language would then process the query and return a list of all the matching nodes and their associated information.
Popular graph query languages
You’ll notice that most of the graph query languages are developed by players in the graph database market, which is only natural. A graph database provider would be best placed to develop a query language that works best for their solution.
Below is a look at some of the top ones that your business can consider:
nGQL is NebulaGraph’s query language. It is a declarative language that allows users to query the graph database using various patterns and criteria. It’s quite friendly for both developers and professionals on the operations side of things.
nGQL provides a flexible and expressive syntax for querying graph data. It supports a wide range of operations, such as pattern match, traversal, graph mutation, aggregation, access control, filtering, composite queries , and index.
The beauty about this query language is that it’s constantly being improved to accommodate new features and support more environments. It’s compatible with openCypher 9, but not completely - does not support mutations and controls syntax.
Gremlin is run by Apache TinkerPop. It allows users to specify complex graph traversals using a simple and intuitive syntax, enabling powerful queries that would be difficult or impossible to express using the traditional SQL.
One of the key strengths of Gremlin is its ability to traverse graphs of any size, complexity, and shape, making it well-suited for analyzing highly connected data. Gremlin is also highly extensible, with a large and active community of developers contributing to its development and supporting a wide range of graph databases and tools.
SPARQL is designed specifically for querying RDF (Resource Description Framework) data. As you may already know, RDF is the model used to represent and link data on the web, and SPARQL allows users to query this data in a standardized and expressive way. It provides a flexible and powerful way to extract information from RDF datasets, allowing users to retrieve data based on complex patterns and relationships.
SPARQL was developed by the RDF Data Access Working Group of the World Wide Web Consortium (W3C), a global community that develops web standards and guidelines. The language was first introduced in 2008 and has since been updated to include additional features and functionality.
The W3C continues to manage and maintain the SPARQL standard, and the language is widely used in academic, research, and industry applications for querying and analyzing RDF data. Several open-source and commercial SPARQL query engines are available, providing users with a range of options for working with RDF data.
Cypher was developed by Neo4j and is maintained as an open-source project under the openCypher initiative. openCypher is a community-driven project that aims to promote the adoption and standardization of the Cypher query language across different graph database platforms.
The openCypher project is managed by a team of developers and contributors from various organizations, including Neo4j.
AQL (ArangoDB Query Language) is used for querying and manipulating data in the ArangoDB multi-model database. It is also declarative in nature, in which you express requests based on the data you want – as opposed to how to retrieve it.
The language is designed to be simple and easy to learn, with a SQL-like syntax that makes it accessible to a wide range of users. AQL supports a variety of data manipulation operations, including filtering, sorting, grouping, and aggregating data, as well as complex graph traversals and joins between different collections and data models.
GraphQL is specifically tailored for API-based data. With GraphQL, you can send a query to your API and retrieve only the data you need, making data retrieval simpler and more efficient.
One of the key benefits of using GraphQL is that it allows for highly predictable queries, which makes for faster and more reliable apps.
Rather than relying on the server to control the data, GraphQL puts the control in your hands, allowing you to optimize data retrieval for your specific needs.
What makes graph query languages crucial for enterprises?
How can the above graph query languages, and all the others we have not mentioned, help your business? Of course the developers within the company have to select the right query language for the job, then implement it appropriately depending on the intended outcomes.
Below is a look at some of the top benefits of graph query languages for businesses:
1. Simplified data queries
Graph query languages allow you to retrieve data much more quickly and correlate it to other data sets in the database.
The data is stored as nodes (data entities) and edges (data relationships) instead of tables, rows, and columns. The nodes and edges are easy to correlate, which helps simplify data queries, even when dealing with large data sets.
2. Better Data integrity
Graph database query languages help guarantee data accuracy, completeness, and consistency. They allow you to retrieve data from multiple disparate sources and represent it in an easy-to-follow visual pattern.
This means you are able to see how your data is connected and how changes to one set can affect other data sets.
3. Enhanced engagement efficiency
With a graph query language, you can see how your requested data relates to other data in the database - eliminating the need for manual data exploration.
Additionally, these languages allow for precise data querying without over-fetching, which is a common issue with relational databases. According to an MDPI study, graph databases outperformed relational databases when dealing with complex data queries.
4. Better data modeling
Graph databases represent data much more naturally in easy-to-follow graphs when compared to a relational database. When using a graph query language, you can be able to model your data much better and identify relationships and patterns. It’s so easy to query and analyze data in a way that is intuitive and closer to the way humans think. For example, it's easier to model social networks, supply chain relationships, and other complex systems.
Graph query languages also make it easy to query data that is constantly changing, which is difficult to do with traditional query languages. As new nodes and edges are added or deleted, a graph database query language efficiently query the data and gibe returns that reflect these changes.
You can even automate the data queries and analysis process, leading to faster predictions.
5. Complex pathfinding
Pathfinding queries, such as finding the shortest path between two nodes or all the paths between two nodes that meet certain criteria, are essential in applications such as transportation planning, logistics, and network optimization.
Graph query languages make it possible to perform these complex queries pathfinding with ease.
Choosing the right graph database query language
How do you choose between the many graph query languages we have covered here? Consider the factors below:
1. The type of graph database and data you are dealing with
As we went about describing the various query languages, you may have noticed that some of them are tailored for specific functions or environments.
Therefore, your choice of language may be heavily influenced by the choice of graph database you are using. Of course the choice of database will be influenced by what you want to achieve, which could be operational or user/customer centric.
Consider these items:
A key reason why graph query languages and databases have gained popularity is due to their ability to enhance data querying efficiency.
As a result, it's crucial that you select a language that provides speedy returns on queries, particularly when dealing with large data sets requiring real-time data analysis.
3. Ease of use
Maybe this is quite obvious but it's important that we mention it. A good graph query language should be easy to use.
For example, declarative languages allow you to request data without indicating how the data will be retrieved – the language's engine does this for you. Such a feature makes it easy to use the language without writing complex algorithms.
4. Check out the security and privacy
We are right in the middle of data security and privacy concerns - all over the world. Thus, you need to be aware of any potential security risks that come with a specific language.
For example, according to DevOps, GraphQL comes with potential security concerns, which you need to be aware of and prepare appropriate mitigation strategies.
A study published on Science Direct shows how graph queries were used to uncover hidden corruption of various individuals named in the Panama Papers. If you can take a moment to imagine the huge role that graph query language played in this groundbreaking investigation, then you immediately appreciate the power of these languages.
Going forward, developers working with graph query languages should keep in mind the importance of data quality and consistency. As graphs become more complex, ensuring data accuracy and completeness will be critical to producing reliable outcomes.
One area where graph query languages may see significant growth is in the integration with AI and machine learning techniques, allowing for more sophisticated querying and analysis of graph data.