/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
$ npx -y skills add qdrant/skills --skill sliding-time-window --agent claude-codeHow it fires
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/sliding-time-window
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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.mdname: 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)
Read more
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)
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
Repo: qdrant/skills
Other skills on qdrant.
- /qdrant-clients-sdk
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Open skill - /qdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use
Open skill - /qdrant-monitoring
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Open skill - /debugging
Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades
Open skill

