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Building a Scalable Database

Having a scalable database is essential for developers and businesses struggling with high data volumes, like time-series or time-series-like workloads. Learn what a scalable database is (in general) and how you can scale PostgreSQL (specifically).

What Is a Scalable Database?

A scalable database is a database system that can store more data and handle increasing requests without experiencing significant decreases in performance or availability. As the amount of data and users grows, a scalable database can accommodate both without compromising performance or reliability. It’s a vital tool for developers working on data-intensive applications. 

In business terms, a company is scaling when trying to increase the size, amount, or importance of something. In the developer world, database scalability also involves an element of dimension but requires a compromise between scalability, performance, and consistency. 

In this article, we’ll explore the concept of database scalability, why scalable databases are so important (especially for time-series workloads), and learn how we can make a relational database more scalable, in this case, PostgreSQL. 

Why Should You Have a Scalable Database?

Having a scalable database is essential for many reasons. First, they can accommodate your project’s growth. Expanding businesses generate massive amounts of data (such as time-series data, which is relentless) that needs to be managed, stored, and analyzed, preferably without slow queries or dashboards. A scalable database will help you avoid these performance bottlenecks.

Second, a scalable database ensures high availability and reliability, preventing crashes and downtime that can upset users. Scalable databases will be able to handle increasing requests, remaining highly available and performant so you can scale your business efficiently. 

So, Who Needs a Scalable Database?

If you identify with one of the following sentences, you probably need your database system to have extra scalability:

  • I can’t ingest my data fast enough.

  • I have high resource usage with peaks—what happens if we scale by 10x?

  • I have too many clients, concurrent queries, etc.

  • I pay too much for storage.

  • I need more storage.

Can you relate? So could we, which is why we came up with Timescale.

How Do You Design a Scalable Database?

Achieving a scalable database design will help your database be more resilient and efficiently handle, query, and store growing amounts of data.

Here are some principles you should keep in mind when designing a scalable database:

  • Use indexes: indexes help speed up queries by creating an index of frequently accessed data. This can significantly improve performance, particularly for large databases. Timescale indexes work just like PostgreSQL indexes, removing much of the guesswork when working with this powerful tool.

  • Partition your data: Partitioning involves dividing a large table into smaller, more manageable parts. This can improve performance by allowing the database to access data more quickly. Read how to optimize and test your data partitions’ size in Timescale.

  • Use buffer cache: In PostgreSQL, buffer caching involves storing frequently accessed data in memory, which can significantly improve performance. This is particularly useful for read-heavy workloads, and while it is always enabled in PostgreSQL, it can be tweaked for optimized performance.

  • Consider data distribution: In distributed databases, data distribution or sharding is an extension of partitioning, turning the database into smaller, more manageable partitions and then distributing (sharding) them across multiple cluster nodes. This can improve scalability by allowing the database to handle more data and traffic. However, sharding also requires more design work up-front to work correctly.

  • Or use a load balancer: Sharding and load balancing often rule each other out unless you use additional tooling. Load balancing involves distributing traffic across multiple servers to improve performance and scalability. This can be done using a load balancer that routes traffic to the appropriate server based on the workload—however, it will only work for read-only queries.

  • Optimize queries: Optimizing queries involves tuning them to improve performance and reduce the load on the database. This can include rewriting queries, creating indexes, and partitioning data.

What About Horizontal Database Scalability vs. Vertical Scalability?

Horizontal scalability or scaling out

Horizontal database scaling or scaling out generally involves adding more nodes to a database cluster to increase its processing power and storage capacity by distributing the load.

You can take the following steps to increase your database scalability horizontally:

  • Use a sharding (data distribution) strategy: you can partition a large database into distributed pieces called shards. By having each shard distributed to a different cluster node, your database will be able to accommodate more data and requests.

  • Load balancing: you use a load balancer to help distribute traffic across multiple database servers or clusters, which will also ensure the workload is evenly distributed across nodes in the cluster. Load balancing on read replicas can be an alternative, although it has issues like replication latency. Clusters, however, can be load balanceable or not.

  • Monitor and optimize: as your database cluster scales, it is vital that you monitor its performance and optimize its configuration—fine-tune settings such as buffer cache size, network settings, or database configurations. Read how you can optimize your ingest rate in Timescale.

Vertical scalability or scaling up

Vertical scaling or scaling up involves increasing the processing power and storage capacity of a single node in a database server. It’s a cost-effective (but not always simple) way to manage growing data volumes and can complement horizontal scaling.

Here’s how you vertically scale a database:

  • Upgrade hardware: upgrading the hardware of a database server is one way to improve its power and storage capacity. You can add more RAM, increase the number of CPU cores, or upgrade to a faster storage system. However, this may take months of planning and is not necessarily easy or fast to implement.

  • Use database partitioning: database partitioning involves dividing a large table into smaller, more manageable parts. This can improve performance by allowing the database to access data more quickly and reduce the amount of storage required by the database.

  • Use database indexing: indexing can help speed up database queries by creating an index of frequently accessed data. This can significantly improve performance, particularly for large databases.

💡Timescale Tip Use compression: compression can help reduce the amount of storage space, allowing Timescale to store more data on the same hardware while speeding up your queries. Want to learn more about compression from the developers who use it? Read how Ndustrial achieved 97 % compression.

Are Relational Databases Scalable?

Yes, there are several scalable relational database management systems (RDBMS) available.

These systems store data in tables with a well-defined schema and support a relational data management model. PostgreSQL and Timescale are two examples of RDBMS. 

PostgreSQL is an open-source and highly scalable RDBMS that can be horizontally or vertically scaled. It supports advanced features such as partitioning and indexing and is known for its high data integrity and reliability. Built on PostgreSQL, Timescale not only works like PostgreSQL; it is PostgreSQL under the hood and dramatically expands its possibilities.

Why Use Timescale to Scale PostgreSQL

Depending on your specific needs and requirements, there are several ways to scale PostgreSQL. You can scale it using the vertical and horizontal scalability approaches we mentioned, but the most straightforward way to do it is by using Timescale. Here’s how Timescale helps scale PostgreSQL:

  • Our automatic partitioning enables you to manage relentless time-series data.

  • Timescale can compress older partitions, reducing storage needs.

  • The data partitions (or chunks, as we call them) can be tiered, further reducing storage needs.

  • Timescale can optimize queries by extending the planning and execution of PostgreSQL queries.

  • Continuous aggregates (or automatically updated incremental materialized views) allow you to create always-consistent materialized views. Some queries may run more than a thousand times faster.

The fastest and most efficient way to scale your PostgreSQL database is by choosing Timescale. Sign up for a free 30-day trial and experience Timescale’s supercharged PostgreSQL++, no credit card required.

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