An Introduction to the Graph Database Query Languages


Nowadays, it’s hard to grow a successful business without relying on data. Companies use various insights to assess their competition, find market gaps, and improve other operational procedures.

Because of the overwhelming quantity of data that needs to be processed, brands have started shifting away from traditional databases. They have adopted modern, scalable graph databases, such as NebulaGraph, to improve customer relationships, fraud detection, supply chains, recommendation engines, and other processes.

Behind every efficient graph database lies a graph query language, a programming language that allows developers to do their magic. In this article, we explain what the term refers to, after which we’ll go through some of the best languages you can use.

What are graph query languages?

As mentioned, these languages are tailor-made for graph databases. By using them, developers can create a specific graph structure and introduce rules that would allow data retrieval and traversing. It’s worth mentioning that these languages are declarative in nature, meaning they help create instruction for the data that needs to be retrieved, not how the data should be retrieved.

How do these languages work?

By relying on these languages, IT specialists can specify patterns they want to discover. They can focus on particular edges and nodes while also setting the rules that the program needs to abide by before retrieving information. After providing instructions, a language will execute the query and retrieve the appropriate data that is most suitable for the user.

With a graph query language, an expert can introduce queries that would help track suspicious financial behavior, recommend products based on previous purchases, or suggest content best suited for a user. Given the vast number of interesting features, graph databases are among the most popular business solutions in the world, allowing companies to streamline numerous internal processes.

5 Best database query languages

The good news is that there are lots of fantastic query languages your IT team can implement. Here are the four best ones:

1.    GraphQL

GraphQL is one of the more flexible graph query languages and is among the better solutions for improving client-server interactions. It allows users to be very specific with their requests, which makes it much easier to retrieve just the right data.

Pros

  • Ideal for merging several microservices in one schema
  • It can use data from your existing company systems and repackage them for GraphQL
  • Fantastic solution for ensuring your documentation is in accordance with the current API
  • GraphQL allows you to fetch required data with one request

Cons

  • Several well-known performance issues during querying might force users to turn to REST API for complex data processing
  • Even if you use the same entity to process queries, you might still have trouble implementing a simple cache due to query diversity
  • You won’t get the file uploading function as it is not included in its specifications

2.    nGQL

nGQL is a declarative language that was specifically made to be used for NebulaGraph. The great thing about it is that it can be effectively used by operations professionals and developers. The graph query language is rather easy to use, and it receives continuous updates.

Pros

  • It can perform all sorts of tasks 
  • One of the easier graph query languages to use
  • It gets significantly better over time with constant updates

Cons

  • It doesn’t support Gremlin, GraphQL, or Cypher 9
  • While easy to learn, it does have some unique features that might cause issues

3.    Cypher

Cypher is a popular graph database query language that can simplify data retrieval from graph structure. The best thing about it is that it’s rather easy to get the hang of, as it carries numerous similarities with other languages

Experts classify Cypher as a Neo4j query language. The thing that makes it unique is its heavy reliance on patterns. Furthermore, with Cypher, you can visually match different patterns and relationships.

Pros

  • Perhaps the easiest language to learn within this category
  • Allows users to create expressive and intuitive queries for streamlining fast data retrieval
  • A highly secure solution that can be used for any application development
  • It can easily establish patterns between nodes and relationships while providing a visual representation of data

Cons

  • It is not the best solution for simple data models unless the model requires a lot of aggregation and joining
  • It isn’t best for scale writes
  • If you’re going with Neo4j Cypher, don’t expect internal support for date data type

4.    Gremlin

Gremlin is a brain-child of the Apache TinkerPop company, and it can be used in all sorts of ways. It allows the chaining of traversal operators to form paths, which makes it ideal for graph databases but also other systems. With it, you can easily retrieve data from graphs, modify the data inside of them, and express complex mutations.

You can create queries for Gremlin in numerous programming languages, including Javascript, Java, Python, Groovy, and Scala.

Pros

  • A great solution if you’re looking to craft descriptive and procedural traversals in graphs
  • These traversals can be made in languages that support function nesting and composition

Cons

  • Many developers will struggle to read this query language
  • Creating complex queries can be annoying, especially if we’re talking about queries that need pattern-matching
  • Certain complex queries are not suited for Gremlin

5.    AQL

AQL stands for ArangoDB Query Language. As you can presume, this language is used by people who want to access and modify data that can be found within the ArangoDB graph database.

The great thing about AQL is that it’s well-suited for all sorts of users as it relies on English keywords. Like any other declarative query language, it focuses on results but not necessarily the process of achieving those results.

Pros

  • AQL is a fantastic solution when dealing with robust applications
  • It allows users to natively map the data to the database
  • AQL is a great choice if you need to tackle complex computation, as it supports numerous advanced functions that can help you out
  • Great for scaling as it allows seamless modifications

Cons

  • It’s limited to 1,000 result registers
  • It’s limited to 4,000 execution nodes in an initial execution plan
  • AQL doesn’t support certain operations, limiting its flexibility

Conclusion

Graph database query languages are significantly different in terms of their features and flexibility. As such, you need to be very careful when choosing the right one for your developers. However, as long as you follow the tips in this guide, you’ll have a much easier time selecting a suitable option. 











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