In 2017, the Elastic stack hit 100 million downloads. A year later and Elastic has filed for an initial public offering on the stock market, with a potential valuation of up to $3 billion. This powerful stack has grown significantly in a relatively short period of time, and Elasticsearch in particular is going through some interesting changes.
The Evolution of Elasticsearch
Elasticsearch started out as a technology that’s very focused on text search and making this functionality easy. However, Elastic is starting to look beyond search to build an ecosystem that has many different directions for Elasticsearch and how organizations can use this product as it becomes mature. Elasticsearch already exhibits many characteristics of product maturity. The user base growth has started to level out, as many users that needed it for text search are already aware of it and are using it. The product has a healthy amount of competition on the market and it has differentiation from those solutions.
Elasticsearch also has an active development and third-party support community, with many managed database service providers offering hosted Elasticsearch databases and other support solutions to help organizations get the most out of their deployments.
The question is, how will Elasticsearch move forward from here?
Different Data Models for Elasticsearch Growth
Elasticsearch has two different ways that it can evolve: super deep or super wide. If it takes the super deep approach, then the developers would focus entirely on the search capabilities and look for innovative ways to make it even better at this core functionality. They would seek to own text search and become the go-to choice for organizations. A deep dive into all the potential text search use cases and what organizations need the most will present new opportunities under this growth model. Whenever someone thinks about text search, Elasticsearch will be the first option that they turn towards.
If Elasticsearch goes super wide, then it starts migrating away from search. Instead of spending the majority of their time and resources on improving this capability, Elastic will start moving horizontally and implementing new functionality that is outside of its core focus. Not only does that bring new features to Elasticsearch, it also improves the Elastic stack as a whole. The biggest benefit to Elasticsearch taking the super wide approach is that it brings in more potential users through use cases that aren’t currently addressed by Elasticsearch or the Elastic stack as a whole.
What is Elasticsearch doing now?
Elasticsearch is disruptive in several interesting ways in database areas. Since it’s not intended to be a main database like MySQL or MongoDB for applications, it has more flexibility to expand in ways that a typical database product can’t. It appears that Elastic is taking the “go wide” growth model to expand its user base and innovate the Elastic stack. They create other use cases where Elasticsearch is valuable, and start to nibble away at the market share of other database technologies. They sweep up all the areas around the edges that MongoDB, MySQL, and other solutions fail to focus on.
For example, an Elasticsearch add-on for Hadoop makes it possible to have great search in this solution. A plugin or connector makes it possible to create indexes in Elasticsearch so that organizations can improve Hadoop search functionalities, which have typically been poor.
Visualization is another area where it excels. The Elastic stack includes a tool called Kibana, which makes graphs, charts, and other valuable visualizations from data. Mongo lacks a quality open source tool for doing visualizations, and the same goes for MySQL. Most of the solutions available are prohibitively expensive and the Elastic stack is free. It’s easy to use these tools to pump data from those data stores into Elasticsearch and then create visualizations with Kibana.
Elasticsearch is on an exciting growth journey and the super wide approach appears to suit their long-term strategy well. While they won’t abandon the functionality that make Elasticsearch a big name in the database world, they will spend more resources on looking for the edge cases that primary database solutions fail to address.