The Synergies Between GraphQL and Graph Databases

👉In this post, we’ll clear up any confusion you may have about GraphQL and graph databases by learning about what graph databases are, the main ideas behind them, when they’re most valuable, and how they differ from GraphQL.

⭐️You can see more at the link at the end of the article. Thank you for your interest in the blog, if you find it interesting, please give me a like, comment and share with everyone. Thanks! ❤️

#graphql #graph #database 

 

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Which Database Is Right For You?Graph Database vs. Relational Database

At the very beginning of most development endeavors lies an important question: What database do I choose? There is such an abundance of database technologies at this moment, it’s no wonder many developers don’t have the time or energy to research new ones. If you are one of those developers and you aren’t very familiar with graph databases in general, you’ve come to the right place!

In this article, you will learn about the main differences between a graph database and a relational database, what kind of use-cases are best suited for each database type, and what are their strengths and weaknesses.

How Does a Graph Database Differ from a Relational Database?

The Graph Data Model

The Relational Data Model

When to use a Graph Database?

When not to use a Graph Database

Is a Graph Database Worth it?

#graph-database #relational-database #graph-theory #graph-analysis #data-analytics #networks #data #database

Benchmarking the Mainstream Open Source Distributed Graph Databases

The deep learning and knowledge graph technologies have been developing rapidly in recent years. Compared with the “black box” of deep learning, knowledge graphs are highly interpretable, thus are widely adopted in such scenarios as search recommendations, intelligent customer support, and financial risk management.

Meituan has been digging deep in the connections buried in the huge amount of business data over the past few years and has gradually developed the knowledge graphs in nearly ten areas, including cuisine graphs, tourism graphs, and commodity graphs. The ultimate goal is to enhance the smart local life.

Compared with the traditional RDBMS, graph databases can store and query knowledge graphs more efficiently. It gains obvious performance advantage in multi-hop queries to select graph databases as the storage engine. Currently, there are dozens of graph database solutions out there on the market.

It is imperative for the Meituan team to select a graph database solution that can meet the business requirements and to use the solution as the basis of Meituan’s graph storage and graph learning platform. The team has outlined the basic requirements as below per our business status quo:

1. It should be an open-source project which is also business-friendly

By having control over the source code, the Meituan team can ensure data security and service availability.

1. It should support clustering and should be able to scale horizontally in terms of both storage and computation capabilities

The knowledge graph data size in Meituan can reach hundreds of billions of vertices and edges in total and the throughput can reach tens of thousands of QPS. With that being said, the single-node deployment cannot meet Meituan’s storage requirements.

1. It should work under OLTP scenarios with the capability of multi-hop queries at the millisecond level.

To ensure the best search experience for Meituan users, the team has strictly restricted the timeout value within all chains of paths. Therefore, it is unacceptable to respond to a query at the second level.

1. It should be able to import data in batch

The knowledge graph data is usually stored in data warehouses like Hive. The graph database should be equipped with the capability to quickly import data from such warehouses to the graph storage to ensure service effectiveness.

The Meituan team has tried the top 30 graph databases on DB-Engines and found that most well-known graph databases only support single-node deployment with their open-source edition, for example, Neo4j, ArangoDB, Virtuoso, TigerGraph, RedisGraph. This means that the storage service cannot scale horizontally and the requirement to store large-scale knowledge graph data cannot be met.

After thorough research and comparison, the team has selected the following graph databases for the final round: Nebula Graph (developed by a startup team who originally came from Alibaba), Dgraph (developed by a startup team who originally came from Google), and HugeGraph (developed by Baidu).

A Summary of The Testing Process

Hardware Configuration

1. Database instances: Docker containers running on different machines
2. Single instance resources: 32 Cores, 64 GB Memory, 1 TB SSD (Intel® Xeon® Gold 5218 CPU @ 2.30 GHz)
3. Number of instances: Three

#database #tutorial #graph database #database performance #nebula graph #dgraph #graph database adoption

How to Build a Pokedex React App with a Slash GraphQL Backend

Frontend developers want interactions with the backends of their web applications to be as painless as possible. Requesting data from the database or making updates to records stored in the database should be simple so that frontend developer can focus on what they do best: creating beautiful and intuitive user interfaces.

GraphQL makes working with databases easy. Rather than relying on backend developers to create specific API endpoints that return pre-selected data fields when querying the database, frontend developers can make simple requests to the backend and retrieve the exact data that they need—no more, no less. This level of flexibility is one reason why GraphQL is so appealing.

Even better, you can use a _hosted _GraphQL backend—Slash GraphQL (by Dgraph). This service is brand new and was publicly released on September 10, 2020. With Slash GraphQL, I can create a new backend endpoint, specify the schema I want for my graph database, and—voila!—be up and running in just a few steps.

The beauty of a hosted backend is that you don’t need to manage your own backend infrastructure, create and manage your own database, or create API endpoints. All of that is taken care of for you.

In this article, we’re going to walk through some of the basic setup for Slash GraphQL and then take a look at how I built a Pokémon Pokédex app with React and Slash GraphQL in just a few hours!

#development #web developement #databases #graph databases #reactjs #database design #database architecture #pokemon #graph databases in the cloud #dgraph

Data Migration From JanusGraph to Nebula Graph - Practice at 360 Finance

Speaking of graph data processing, we have had experience in using various graph databases. In the beginning, we used the stand-alone edition of AgensGraph. Later, due to its performance limitations, we switched to JanusGraph, a distributed graph database. I introduced details on how to migrate data in my article “Migrate tens of billions of graph data into JanusGraph (only in Chinese)”. As the data size and the number of business calls grew, a new problem appeared: Each query consumed too much time. In some business scenarios, a single query took up to 10 seconds, and with increase of the data size, a more complicated single query needed two or three seconds. These problems had seriously affected the performance of the entire business process and the development of related businesses.

The architecture design of JanusGraph determines that a single query is time-consuming. The core reason is that its storage depends on the external storage, and JanusGraph cannot control the external storage well. In our production environment, an HBase cluster is used, which makes it impossible for all queries to be pushed down to the storage layer for processing. Instead, data can only be queried from HBase to the JanusGraph Server memory and then filtered accordingly.

#database #tutorial #graph database #database performance #nebula graph #graph database adoption

The Synergies Between GraphQL and Graph Databases

👉In this post, we’ll clear up any confusion you may have about GraphQL and graph databases by learning about what graph databases are, the main ideas behind them, when they’re most valuable, and how they differ from GraphQL.

⭐️You can see more at the link at the end of the article. Thank you for your interest in the blog, if you find it interesting, please give me a like, comment and share with everyone. Thanks! ❤️

#graphql #graph #database