Are you dealing with expanding graph datasets, but your database can’t keep up with the growth? As the amount of data a graph database stores and manages grows, it becomes increasingly important for the database to be scalable and able to handle the additional load. Therefore, scalability is a critical requirement for any scalable graph database. However, many challenges must be addressed to ensure efficient scalability. In this article, we will discuss the dimensions of scalability in graph databases and explore the challenges and solutions to them. We focus on Objectivity/DB customers and their experience with different scaling architectures.
Graph databases have become increasingly popular in recent times, given their ability to handle complex relationships between data points. They are ideal for modern use cases such as social networking platforms or enhancing recommendation engines. Graph databases can manage and process vast amounts of data in real-time, ranging from small datasets to enormous datasets that span tens of billions to even trillions of nodes. However, scaling up graph databases is not an easy task, and it is crucial to understand the challenges involved in managing growing datasets.
The two main problems with scaling graph databases are the supernode problem and the network hop problem. The supernode problem arises when a node has too many edges or connections, and the graph database struggles to handle such connectivity. The network hop problem occurs when querying a distributed database architecture that takes too many hops to fetch data from various nodes, causing a delay in record retrieval. To ensure optimal performance, the scalability of graph databases must be carefully evaluated along several dimensions.##Dimensions of Scalability in Graph Databases
The ability to scale graphs can be examined along different dimensions, including vertical scalability, horizontal scalability, replication, distribution, and ingest scalability. Each of these dimensions has a different impact on capabilities, performance, and cost and should be assessed when considering scaling strategies.
- Vertical Scalability: This type of scaling involves adding more resources to a single machine, such as adding more memory, CPU, or storage. It is an excellent option for smaller datasets that don’t require a distributed architecture. Vertical scalability is also beneficial for optimizing performance. However, it has limitations as machines can only handle a certain amount of data before performance begins to degrade.
- Horizontal Scalability: Also known as scaling out, horizontal scalability involves adding more machines to a distributed database architecture. This approach is ideal for larger graph datasets that require a distributed design to handle the increase in workload. Adding more machines also ensures that there is a high availability of data, making it an excellent option for mission-critical apps.
- Replication: Database replication allows for the creation of copies of a database instance, which can be distributed across multiple machines. Replication is often used to ensure disaster recovery, high availability and improved query performance. It is the process of creating multiple copies of data and distributing them over several machines or cluster nodes. Replication ensures that data is available despite hardware failures or outages.
- Distribution: Unlike replication, data distribution ensures that the dataset is partitioned into different nodes, reducing the time it takes to run queries. Data distribution also ensures that the dataset is evenly spread across several machines, which reduces the amount of data each machine has to process.
- Ingest Scalability: This is the ability of the database to handle an increasing number of ingestions while maintaining query performance. Ingest scalability is essential for applications that require constant data ingestion.
Challenges in Scaling Graph Databases
Scaling graph databases comes with significant challenges, such as handling supernodes and network hop problems.
Supernodes: A supernode is a node in the graph that has too many edges or connections, making it difficult for the database to handle such connectivity. This problem often affects vertices that are highly followed, such as celebrities in a social network. One way to solve the supernode problem is to use sharding techniques, which involve breaking down supernodes into smaller ones that are easier to manage.
Network Hop Problem: This occurs when querying a distributed database architecture takes too many hops to fetch data from various nodes. This problem causes latency in record retrieval, which negatively impacts query performance. One solution to this problem is to use vertex-centric indexes, which allow data to be stored in one place and retrieved more efficiently. Another solution is to use SmartGraphs from ArangoDB, which minimizes network hops during query execution.
We will explore potential solutions for these challenges as well as others in the next section.