Module 4What Changes as the Collection Grows
This three-article collection needs no tuning. At millions of points, both the indexing and storage layers need attention.
Index Time Against Search Quality
Module 2 introduced m and ef_construct, which control how much work goes into building the HNSW graph. Higher values make the graph more accurate, but they also make indexing slower and use more memory. The defaults suit most collections. See Optimize Performance when you have measured a gap you need to close.
Memory Usage
Qdrant keeps every vector in memory by default, which is fast and expensive. Quantization is the lever to try first, because it cuts memory for a small loss of precision that you can measure. On-disk vectors go further and trade latency for capacity, which fits a collection much larger than the memory you want to pay for.
Indexing Lag
Points become searchable as soon as they are stored. The HNSW graph may finish indexing them later. get_collection reports both numbers, and their difference is the backlog:
info = client.get_collection("news")
print(info.points_count, info.indexed_vectors_count)
# Expected output:
# 3 3
On a collection this small, the two numbers match. For the first big upload, use the batching approach in Bulk Upload, then watch both counts as it runs. If the gap keeps growing, points are arriving faster than Qdrant’s optimizer can index them. See Optimizer for what to do next.