Top five Elasticsearch use cases
Originally published on May 16, 2017, at ObjectRocket.com/blog.
Other than "you know, for searching," the uses of Elasticsearch® continue to grow and change over time. At ObjectRocket, we've offered hosted Elasticsearch on our platform for a while now. We see clear trends among our customers and how they're using the product. In this post, we share the top five ElasticSearch use cases that we see on the ObjectRocket platform.
#1 – Logging and log analysis
For anyone familiar with Elasticsearch, this one should be no surprise. The ecosystem built up around Elasticsearch has made it one of the most straightforward tools to implement and scale logging solutions. Many users on our platform have taken advantage of this functionality to add logging to their primary use case. From Beats to Logstash® to Ingest Nodes, Elasticsearch gives you plenty of options for grabbing data wherever it lives and indexing it. From there, tools like Kibana® give you the ability to create rich dashboards and analysis. Another tool, Curator, allows you to put the retention period on autopilot.
#2 – Scraping and combining public data
Like log data, the Elastic Stack has plenty of tools to grab and index remote data easily. Also, like most document stores, the lack of a strict schema gives Elasticsearch the flexibility to take in multiple different sources of data and still keep it all manageable and searchable. A cool example of this that you can check out is our Twitter connector. It allows you to set up hashtags to watch on Twitter and then grab all tweets with those hashtags and analyze them in Kibana. We built that product on core Elastic Stack components and added some additional pieces to help it scale.
#3 – Full-text search
It's also no surprise that full-text search, as the core capability of Elasticsearch, is high on this list. The surprising part is the applications of this among our customer set, which go well beyond traditional Enterprise search or E-commerce. From fraud detection and security to collaboration and beyond, our users find that the Elasticsearch search capabilities are powerful and flexible and include many tools to make searching easier. Elasticsearch has a query Domain Specific Language (DSL) and built-in capabilities for auto-complete "Did you mean" responses, and more.
#4 – Event data and metrics
Elasticsearch also operates really well on time-series data like metrics and application events. This feature is another area where the huge Beats ecosystem allows you to grab data for common applications easily. Whatever technologies you use, there's a good chance that Elasticsearch has the components to grab metrics and events out of the box—in the rare case that it can't, adding that capability is easy.
#5 – Visualizing data
With tons of charting options, a tile service for geo-data, and TimeLion for time-series data, Kibana is an amazingly powerful and easy to use visualization tool. Kibana has a visual component to handle every use case that we've shared in this post. After you're comfortable with the various data ingest tools, you'll find that Elasticsearch + Kibana is your go-to tool for visualizing data that you're trying to wrap your head around.
Though that's not every use case, those are the heavy-hitters we see on our service. Elasticsearch and the rest of the Elastic Stack have proven to be extremely versatile. As you can see, you can integrate Elasticsearch into what you're doing today and gain extra insight in a variety of ways. That, to me, is the coolest part of Elasticsearch—the ability to enhance the technologies you're already using rather than just add another database to store your data.
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