OpenSearch Under the Hood: How It Actually Works

OpenSearch is a tool for efficiently searching and analyzing large datasets. It works by organizing data across a cluster of nodes, where each node contains multiple shards. An index is a logical grouping of related documents, similar to a table. Within an index, each document is a piece of data with fields, like name, region, and amount.

When you search, OpenSearch doesn't simply scan every document. Instead, it uses an inverted index to quickly locate documents containing specific words. For example, if you search for "OpenSearch," the inverted index tells you which documents contain that term. Doc values allow for efficient sorting and aggregations, like calculating the sum of amounts or finding top regions.

When you send a query, it's distributed across the relevant shards. Each shard processes the request on its local data and returns the results. A coordinating node then merges these results to provide the final answer. However, for aggregations, the process can be more complex. Since documents are distributed across shards, each shard performs local processing and sends results back to the coordinating node. The node then combines these results to get the final global output.

This method can sometimes give approximate results, especially when aggregating across multiple shards. The coordinating node combines individual shard results, but a region with a lower count on each shard might still have a high global count once all shards' results are merged. This is where distributed aggregation becomes tricky and sometimes requires approximations to ensure accuracy.