I can concur. Most of Uber's traffic for real-time analytics was served by Uber's Elasticsearch clusters with customized querying layer. My team was also a user

I can concur. Most of Uber's traffic for real-time analytics was served by Uber's Elasticsearch clusters with customized querying layer. My team was also a user of the ES service. The service was sometimes unstable, but in general was fast enough for us, and scalability was never a problem. My team got paged only once from the ES team because we drove a huge spike to the team, and they ended up scanning more than 500 million records per second, for which they had to scramble to scale out. But even at that time, there was no visible degradation on our side, let alone outage. The owning team also presented their pain points, and none of the pains had to do with CPU not being fast enough. By the way, Uber already had two real-time analytics systems before AresDB. One is the aforementioned ES-based service, and the other is Pinot, which was owned by a Pinot contributor. I was in one of those so-called alignment meetings about using AresDB. Engineers from both the ES service and Pinot were there. It was a disaster. The engineers simply asked what AresDB was supposed to solve, and presented charts over charts to show that computation or lack of join operator was never a problem (because for analytics, data streams can be pre-joined), while efficient IO was. The AresDB team simply repeated that join was important, and parallel computation was critical. I left the company soon after, so I'm not sure how many critical use cases AresDB has been serving since then. Hopefully they do find some sweet spot to justify the cost of developing such system.