Thanks for the explanation. We&#x27;re currently using Elasticsearch for doing ad-hoc aggregations (e.g. count per month filter by a few dimensions) in real time. Indexes are some tens of million items per year per column; not much by big-data standards, but enough that we need a whole bunch of nodes, and small enough that even wide queries (like getting a total for the entire year) finish in a couple of seconds. A large part of ES&#x27;s speed comes from being able to perform the aggregation function locally on each node that has the shard, in parallel, rather than scanning the full data the data into the client first. Is this a kind of use case where Kudu would perform well? How does Kudu solve data locality? Would you actually do queries like these, or would you precompute periodic rollups?