Skip to main content

Why do companies need so many different database technologies?

· 7 min read
Ovais Tariq

In our inaugural blog post Hello world, we talked about the problem of data infrastructure sprawl that, over the years, has complicated modern application development, while putting a lot of burden on the operations team who must manage, maintain and secure many different databases and technologies.

In this blog post, we'll explain what we mean when we say "data infrastructure sprawl" by walking through a typical example, and then we'll explain why it doesn't have to be this way.

The standard evolution story of a modern application​

Let's start by taking a look at what an application looks like at the beginning of the journey. The application is simple, it talks to a database, typically a traditional OLTP database such as Postgres, or MySQL.

Application and Database

To scale the application further and increase reliability, some of the application logic is moved to background processing. This requires introducing a message queue such as RabbitMQ or ActiveMQ. Now the application architecture looks something like this:

Application, Database and Message Queue

Over time, the application grows in popularity and more features are added. The development team starts to feel the pain of working with a relational database. They want to be able to scale reads and writes independently, so they introduce read replicas and update the application logic to split the read and write requests. This exposes the development team to infrastructure-related concerns. At the same time, it also increases the application complexity as developers now need to decide when it's safe to read from the read replicas and when they need to query the primaries instead.

Application, Database with replicas, Message Queue

At some point, the business analysts may need to analyze the data to guide business decisions. They could do it for a while using the primary database, but eventually these requests will have to be isolated from production. One easy solution that companies adopt involves running a nightly job that exports the data out of production into a data warehouse like Redshift or BigQuery.

Application, Database with replicas, Message Queue, Data Warehouse

As the business continues to grow, so does the complexity of the application infrastructure required to support it. The developers continue to expand the application's functionality by adding full text search capabilities. They decide that introducing Elasticsearch or Solr would be best since the primary OLTP database does not have a capable search engine. The search functionality is introduced via a new microservice so that the team can iterate quickly and experiment with new technology without disrupting the existing business. The new search microservice needs to know when certain operations are performed on the primary OLTP database, so a message bus, such as Kafka, is introduced as well. Now the application architecture looks something like this:

Application, Database with replicas, Message Queue, Data Warehouse, Search

Does this look familiar? The application isn’t doing anything crazy from a technological perspective. All the team has built is a backend that can:

  1. Power CRUD operations.
  2. Provide search functionality.
  3. Exposes key business data to our analysts in a data warehouse.
  4. Allows experimentation and quick iteration in a “safe” manner that doesn’t put the entire business in jeopardy.

The backend though has become a complex distributed system with many different moving components. Each component must be deployed, configured, secured, monitored, and maintained. The team has to think about things like data replication, data consistency, availability, and scalability for each of the individual components as well.

Over time, the team starts spending less time building the functionality required to grow the business, and more time managing incidental infrastructure.

Fundamental reasons for the data infrastructure sprawl​

This type of “data infrastructure sprawl” is the norm in the industry today, and justifiably so. At every point in the story above, engineering leadership made well-founded and logical architectural decisions, but the end result was high amounts of incidental complexity that made even straightforward feature development time-consuming. But we don't think it has to be this complicated. Users are forced into this situation because the open source marketplace is filled with rich and powerful building blocks, but it's lacking in holistic solutions.

Most open source databases today take a narrow view of the world:

  • They have rich query functionality, but they can't scale beyond a single node. Or, they can scale horizontally, but push all the hard correctness and consistency problems to the application developers.
  • They can function as your primary data store, or they support rich full text search functionality, but rarely both.
  • Multi-tenancy and isolation between workloads are an afterthought or not present at all. Applications can't be logically isolated while sharing the same underlying “database platform” in a safe manner. Often the database itself is “unsafe by default” and won't provide so much as a warning before happily performing a full table scan every time someone navigates to your home page.

What should a modern database look like?​

We believe that, “Correctness, reliability, and user experience over raw performance” is a good guiding principle that, if followed, would lead to the development of a modern data platform that could keep the data infrastructure sprawl at bay.

This of course sounds great on paper, but prioritizing user experience over micro-benchmarks is not how most modern database companies try to differentiate themselves.

Database Comparisons Today

What most database comparisons look like today

This may seem obvious, but building a holistic data platform that can keep data architectural sprawl at bay means that the developers of this new system must take a principled stand to always do right by the user instead of the benchmarks. This is a very difficult thing to do in the current competitive climate that is dominated by benchmark-obsessed marketing material. The recent controversy between Snowflake and Databricks is a great example of this. It's much easier to quantify rows/s/core than it is to quantify (a) sleepless nights, (b) apologies issued to your customers, and (c) developer productivity.

Business Value Created

Product Success

What most database comparisons should look like

So what would a database that helps customers maximize business value instead of micro-benchmarks look like? We believe it boils down to a system with the following characteristics:

  1. A flexible data model that enables developers to model data in a way that best suits their applications' needs. The data model should also be easy to evolve, and schema changes should be as simple and painless as regular feature development.
  2. Simple and intuitive APIs that allow developers to quickly insert and retrieve data while continuing to use their preferred programming language - no new database query language to learn.
  3. Strictly serializable isolation by default. This ensures that developers never need to think about how transactions work or what their limitations are, nor do they need to configure things like read and write concerns or transaction isolation levels.
  4. “Distributed by default” with no painful transition point when the needs of the application begin to exceed the capabilities of a single node. Sharding should also be transparent, and the system should ensure that the database scales seamlessly as the application traffic increases, while core operations such as transactions do not have any restrictions in terms of the records or shards that can participate.
  5. Multi-tenant by default so that learning how to deploy, operate, and secure a single database technology is enough for the majority of the applications.
  6. An integrated search engine that provides developers with real-time search functionality eliminating the need to run a separate search platform and synchronize data.
  7. Built-in low latency replication to cloud data lakes for OLAP workloads that eliminates the need for developers to configure, track, and maintain separate ETL processes.