Best for
- Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'.
Source profileQuality 74/100
github/awesome-copilot/skills/qdrant-performance-optimization/indexing-performance-optimization/SKILL.md
qdrant-indexing-performance-optimization
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.
- Source repository stars
- 37,126
- Declared platforms
- 0
- Static risk flags
- 1
- Last source update
- 2026-07-28
- Source checked
- 2026-07-28
Decision brief
What it does—and where it fits
Qdrant does NOT build HNSW indexes immediately. Small segments use brute-force until they exceed indexingthresholdkb (default: 20 MB). Search during this window is slower by design, not a bug.
Not for
- Tasks that require unconfirmed production actions or broad system permissions.
- Environments where the pinned source and install steps cannot be inspected.
Compatibility matrix
Platform support, with evidence labels
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
Inspect first. Install second.
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
Source-detected install commandSource
npx skills add https://github.com/github/awesome-copilot --skill "skills/qdrant-performance-optimization/indexing-performance-optimization"Safe inspection promptEditorial
Inspect the Agent Skill "qdrant-indexing-performance-optimization" from https://github.com/github/awesome-copilot/blob/9933dcad5be5caeb288cebcd370eeeb2fc2f1685/skills/qdrant-performance-optimization/indexing-performance-optimization/SKILL.md at commit 9933dcad5be5caeb288cebcd370eeeb2fc2f1685. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
What the source asks the agent to do
- 01
Uploads/Ingestion Too Slow
Use when: upload or upsert API calls are slow. Identify bottleneck: client-side (network, batching) vs server-side (CPU, disk I/O)
Use batch upserts (64-256 points per request) Points APIUse 2-4 parallel upload streamsCreate more shards (3-12), each shard has an independent update worker Sharding - 02
Optimizer Stuck or Taking Too Long
Use when: optimizer running for hours, not finishing.
Check actual progress via optimizations endpoint (v1.17+) Optimization monitoringLarge merges and HNSW rebuilds legitimately take hours on big datasetsCheck CPU and disk I/O (HNSW is CPU-bound, merging is I/O-bound, HDD is not viable) - 03
HNSW Build Time Too High
Use when: HNSW index build dominates total indexing time.
Reduce m (default 16, good for most cases, 32+ rarely needed) HNSW paramsReduce efconstruct (100-200 sufficient) HNSW configKeep maxindexingthreads proportional to CPU cores Configuration - 04
HNSW index for multi-tenant collections
If you have a multi-tenant use case where all data is split by some payload field (e.g. tenantid), you can avoid building a global HNSW index and instead rely on payloadm to build HNSW index only for subsets of data. Skipping global HNSW index can significantly reduce indexing t…
If you have a multi-tenant use case where all data is split by some payload field (e.g. tenantid), you can avoid building a global HNSW index and instead rely on payloadm to build HNSW index only for subsets of data. Sk…See Multi-tenant collections for details. - 05
Additional Payload Indexes Are Too Slow
Qdrant builds extra HNSW links for all payload indexes to ensure that quality of filtered vector search does not degrade. Some payload indexes (e.g. text fields with long texts) can have a very high number of unique values per point, which can lead to long HNSW build time.
Qdrant builds extra HNSW links for all payload indexes to ensure that quality of filtered vector search does not degrade. Some payload indexes (e.g. text fields with long texts) can have a very high number of unique val…You can disable building extra HNSW links for specific payload index and instead rely on slightly slower query-time strategies like ACORN.Read more about disabling extra HNSW links in documentation
Permission review
Static risk signals and limitations
Network access
The documentation includes network, browsing, or remote request actions.
Use batch upserts (64-256 points per request) [Points API](https://search.qdrant.tech/md/documentation/manage-data/points/?s=upload-points)Evidence record
Why each signal appears
EvidenceSourceComputedTestedEditorial
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 74/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 37,126 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Provenance and original SKILL.md
- Repository
- github/awesome-copilot
- Skill path
- skills/qdrant-performance-optimization/indexing-performance-optimization/SKILL.md
- Commit
- 9933dcad5be5caeb288cebcd370eeeb2fc2f1685
- License
- MIT
- Collected
- 2026-07-28
- Default branch
- main
View the original SKILL.md
What to Do When Qdrant Indexing Is Too Slow
Qdrant does NOT build HNSW indexes immediately. Small segments use brute-force until they exceed indexing_threshold_kb (default: 20 MB). Search during this window is slower by design, not a bug.
- Understand the indexing optimizer Indexing optimizer
Uploads/Ingestion Too Slow
Use when: upload or upsert API calls are slow. Identify bottleneck: client-side (network, batching) vs server-side (CPU, disk I/O)
For client-side, optimize batching and parallelism:
- Use batch upserts (64-256 points per request) Points API
- Use 2-4 parallel upload streams
For server-side, optimize Qdrant configuration and indexing strategy:
- Create more shards (3-12), each shard has an independent update worker Sharding
- Create payload indexes before HNSW builds (needed for filterable vector index) Payload index
Suitable for initial bulk load of large datasets:
- Disable HNSW during bulk load (set
indexing_threshold_kbvery high, restore after) Collection params - Setting
m=0to disable HNSW is legacy, use highindexing_threshold_kbinstead
Careful, fast unindexed upload might temporarily use more RAM and degrade search performance until optimizer catches up.
See https://search.qdrant.tech/md/documentation/tutorials-develop/bulk-upload/
Optimizer Stuck or Taking Too Long
Use when: optimizer running for hours, not finishing.
- Check actual progress via optimizations endpoint (v1.17+) Optimization monitoring
- Large merges and HNSW rebuilds legitimately take hours on big datasets
- Check CPU and disk I/O (HNSW is CPU-bound, merging is I/O-bound, HDD is not viable)
- If
optimizer_statusshows an error, check logs for disk full or corrupted segments
HNSW Build Time Too High
Use when: HNSW index build dominates total indexing time.
- Reduce
m(default 16, good for most cases, 32+ rarely needed) HNSW params - Reduce
ef_construct(100-200 sufficient) HNSW config - Keep
max_indexing_threadsproportional to CPU cores Configuration - Use GPU for indexing GPU indexing
HNSW index for multi-tenant collections
If you have a multi-tenant use case where all data is split by some payload field (e.g. tenant_id), you can avoid building a global HNSW index and instead rely on payload_m to build HNSW index only for subsets of data.
Skipping global HNSW index can significantly reduce indexing time.
See Multi-tenant collections for details.
Additional Payload Indexes Are Too Slow
Qdrant builds extra HNSW links for all payload indexes to ensure that quality of filtered vector search does not degrade.
Some payload indexes (e.g. text fields with long texts) can have a very high number of unique values per point, which can lead to long HNSW build time.
You can disable building extra HNSW links for specific payload index and instead rely on slightly slower query-time strategies like ACORN.
Read more about disabling extra HNSW links in documentation
Read more about ACORN in documentation
What NOT to Do
- Do not create payload indexes AFTER HNSW is built (breaks filterable vector index)
- Do not use
m=0for bulk uploads into an existing collection, it might drop the existing HNSW and cause long reindexing - Do not upload one point at a time (per-request overhead dominates)