Qdrant Sliding Time Window

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

1. Create a collection with user-defined sharding enabled
2. Create one shard key per time period (e.g., 2025-01, 2025-02, ..., 2025-06)
3. Ingest data into the current period's shard key
4. When a new period starts, create a new shard key and redirect writes
5. Delete the oldest shard key outside the retention window

• 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

1. Create one collection per time period, point a write alias at the newest
2. Query across all active collections in parallel, merge results client-side
3. When a new period starts, create the new collection and swap the write alias Switch collection
4. Drop the oldest collection outside the window

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 a timestamp payload on every point, create a payload index on it Payload index
2. Filter to the desired window at query time using range condition Range filter
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 current collection in RAM (always_ram: true), move older collections to mmap/on-disk vectors. 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)

Source: github/awesome-copilot → skills/qdrant-scaling/scaling-data-volume/sliding-time-window/SKILL.md