Community Spotlight: Mindhouse Achieves Monitoring Nirvana with Apache Druid
Sep 10, 2020
Matt Sarrel
Focus on the power of real-time user behavior and application performance monitoring
Mindhouse is a meditation and yoga app available for Android on Google play and for iPhone on the App Store. The app encourages mental wellness through a variety of guided meditation sessions and techniques, and yoga classes. Content is developed by a team of instructors and provided in an interactive format along with pre-recorded audio and video content. The app recommends content based on the user’s previous experience and selected goals such as better sleep, increased patience, sharper focus, relaxed mind, and relaxed body.
We recently discussed Mindhouse’s use of Apache Druid for clickstream data analysis and user behavior funnel analysis with Ankur Gupta, the company’s engineering technical lead. Ankur’s team relies on Druid to “segment users and understand how they are using our app”, and finds that “it’s especially helpful when we launch a new feature because we can understand the acceptance of the feature based on current user activity”.
Mindhouse streams data from Apache Kafka into Druid and runs dashboards as well as ad-hoc SQL queries to gain insights from user behavior and pinpoint potential issues in their meditation app. It takes about 30 to 40 seconds between the user activity taking place and when it appears in the dashboards. Mindhouse’s small team, consisting of one data engineer and one data analyst, is highly collaborative and productive, rapidly rolling out new dashboards and connecting new data sources. The data engineer is focused on the pipeline and data ingestion (ingestion specs, new data sources), and is responsible for the upkeep and maintenance of the overall system. The data analyst writes SQL queries and builds dashboards.
“I’ve used Druid for almost five years now. From an on-premises point of view, I found it easy to host and manage compared to other analytics tools. It really
doesn’t burn a hole in your pocket in terms of infrastructure cost.
Druid provides some of the key features of data warehouses and key features
of search tools. That hybrid nature is helpful enough to make Druid
Imply Lumi product update: what’s new and what’s coming
Since releasing Imply Lumi in September 2025 as a decoupled data layer for observability, the Imply R&D team has been hard at work to make it easier and more economical to retain, query, and analyze observability...
The Most-Read Imply Blogs of 2025 (and what they signal for 2026)
Before we take on 2026, let’s rewind. 2025 was the year observability teams stopped asking, “How do we reduce data?” and started asking the real question: “How do we build an architecture that can keep...
Observability is at a crossroads For years, observability has promised to give teams the visibility they need to keep digital services resilient. But as data volumes explode, many leaders are realizing the...