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/sliding-time-window

Guides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire old vectors', 'time-based data rotation', 'delete old data efficiently', 'social media feed search', 'news search', 'log search with retention', or 'how to keep only

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Install
$ npx -y skills add qdrant/skills --skill sliding-time-window --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/sliding-time-window

Context preview

The summary Claude sees to decide when to auto-load this skill.

Guides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire old vectors', 'time-based data rotation', 'delete old data efficiently', 'social media feed search', 'news search', 'log search with retention', or 'how to keep only

SKILL.md

sliding-time-window.SKILL.md
name: qdrant-sliding-time-window
description: "Guides sliding time window scaling in Qdrant. Use when someone asks 'only recent data matters', 'how to expire old vectors', 'time-based data rotation', 'delete old data efficiently', 'social media feed search', 'news search', 'log search with retention', or 'how to keep only last N months of data'."

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](https://skills.qdrant.tech/md/documentation/scaling/distributed_deployment/?s=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](https://skills.qdrant.tech/md/documentation/manage-data/collections/?s=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](https://skills.qdrant.tech/md/documentation/manage-data/collections/?s=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](https://skills.qdrant.tech/md/documentation/manage-data/indexing/?s=payload-index) 2. Filter to the desired window at query time using `range` condition [Range filter](https://skills.qdrant.tech/md/documentation/search/filtering/?s=range) 3. Periodically delete expired points using delete-by-filter [Delete points](https://skills.qdrant.tech/md/documentation/manage-data/points/?s=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 (`memory: pinned` on Qdrant 1.19 or newer, `always_ram: true` on 1.18 or older), move older collections to mmap/on-disk vectors (`memory: cold` on 1.19 or newer, `on_disk: true` on 1.18 or older). [Quantization](https://skills.qdrant.tech/md/documentation/manage-data/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)
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Agent skills for building with Qdrant vector search Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model

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