Walk through what happens internally, at a high level, when a client sends a filtered vector search request to a distributed Qdrant cluster.

The request first lands on whichever node the client is connected to. That node acts as the coordinator for this request. It parses the query, validates the filter, and determines which shards are involved. If the collection uses custom sharding and the request specifies a shard key, the coordinator routes to that one shard. Otherwise it fans out to all shards of the collection. Each shard then runs the query locally: it consults its payload indexes to plan the filter, prunes segments that cannot contain matching points, and runs a filter-aware HNSW traversal on the remaining segments. The filter is applied during the traversal, not after it, so the search expands its candidate list until it has enough points that pass the filter. Each segment returns its local top-k, each shard merges its segments' results, and the coordinator collects the per-shard results and merges them into a global top-k, which it returns to the client.

The most important internal detail is the interaction between the filter and the HNSW traversal. If Qdrant applied the filter after the ANN search, it could return fewer than k results when the filter is selective, and it would waste work on points that do not match. Instead, the filter is evaluated during graph traversal: when the search visits a candidate, it checks the filter, and only matching points are added to the result set. But a filter that rejects most candidates means the traversal has to explore more of the graph to find k matches, so the effective search breadth must increase. This is why filter-aware search can be slower and can drop recall if ef is not raised. Qdrant uses payload indexes to help: for a filter on an indexed field, the planner can identify a candidate set or a bitmap of matching points and use it to guide the traversal. For a filter on an unindexed field, the traversal has to check the payload for every visited point, which is more expensive. The second important detail is segment pruning: if a segment's payload index shows that no point in it can match the filter, the coordinator skips it entirely, which is a significant saving when the filter is selective.