Scaling with a Sliding Time Window

Use when only recent data needs fast search -- social media posts, news articles, support tickets, logs, job listings. Old data either becomes irrelevant or can tolerate slower access.

Three strategies: shard rotation (recommended), collection rotation (when per-period config differs), and filter-and-delete (simplest, for continuous cleanup).

Shard Rotation (Recommended)

Use when: data has natural time boundaries (daily, weekly, monthly). Preferred because queries span all time periods in one request without application-level fan-out. User-defined sharding

Create one shard key per time period, write to the current one, and delete the oldest shard key once it leaves the retention window. The Time-Based Sharding tutorial walks through the setup.

• Deleting a shard key reclaims all resources instantly (no fragmentation, no optimizer overhead)
• Pre-create the next period's shard key before rotation to avoid write disruption
• Use shard_key_selector at query time to search only specific periods for efficiency
• Shard keys can be placed on specific nodes for hot/cold tiering

Collection Rotation (Alias Swap)

Use when: you need per-period collection configuration (e.g., different quantization or storage settings). Collection aliases

Create one collection per period behind a write alias, swap the alias to the new collection when a period starts Switch collection, and drop the oldest collection outside the window. Queries must fan out across all active collections in parallel and merge results client-side.

Trade-off vs shard rotation: allows per-collection config differences, but requires application-level fan-out and more operational overhead.

Filter-and-Delete

Use when: data arrives continuously without clear time boundaries, or you want the simplest setup.

1. Store the timestamp as a datetime payload on every point and index it Payload index
2. Filter to the desired window at query time with a datetime range condition Datetime range
3. Periodically delete expired points using delete-by-filter Delete points

• Run cleanup during off-peak hours in batches (10k-50k points) to avoid optimizer locks
• Deletes are not free: tombstoned points degrade search until optimizer compacts segments
• Does not reclaim disk instantly (compaction is asynchronous)

Hot/Cold Tiers

Use when: recent data needs fast in-RAM search, older data should remain searchable at lower performance.

• Shard rotation: place current shard key on fast-storage nodes, move older shard keys to cheaper nodes via shard placement. All queries still go through a single collection.
• Collection rotation: keep the current collection's quantized vectors in RAM (memory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older) and its dense vectors in page cache (memory: cached, the default; Qdrant rejects pinned for dense vectors), move older collections to mmap/on-disk vectors (memory: cold on 1.19 or newer, on_disk: true on 1.18 or older). Quantization

What NOT to Do

• Do not use filter-and-delete for high-volume time-series with millions of daily deletes (use rotation instead)
• Do not forget to index the timestamp field (range filters without an index cause full scans)
• Do not use collection rotation when shard rotation would suffice (unnecessary fan-out complexity)
• Do not drop a shard key or collection before verifying its period is fully outside the retention window
• Do not skip pre-creating the next period's shard key or collection (write failures during rotation are hard to recover)