Qdrant Search Speed Optimization
QdrantOfficialPublished by Qdrant
- Skill
- Review and audit
- Data and AI
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to diagnosing and resolving performance issues in Qdrant, such as high latency, low throughput, or slow filtered searches. It guides users through identifying root causes—including memory pressure, unoptimized indexes, or background optimizer contention—and provides actionable configuration adjustments and architectural best practices to restore search speed.
What it does
- Diagnoses high latency in individual search queries.
- Identifies bottlenecks in filtered search operations.
- Provides strategies to improve system-wide query throughput (QPS).
- Offers guidance on resolving read-write contention caused by background optimizers.
- Lists common anti-patterns to avoid during performance tuning.
- Suggests specific configuration changes like quantization, payload indexing, and HNSW tuning.
When to use it
- When search latency is higher than expected for individual queries.
- When the system fails to meet required queries-per-second (QPS) targets.
- When filtered searches are significantly slower than unfiltered searches.
- When performance degrades following data growth or configuration changes.
- When background processes like optimizers are suspected of impacting search performance.
Advantages
- Systematic approach to isolating performance bottlenecks.
- Covers both latency-sensitive and throughput-heavy scenarios.
- Includes specific guidance for complex filter optimization.
- Helps prevent common configuration mistakes that lead to disk thrashing or memory waste.
Usage examples
- “My Qdrant search latency is too high, how do I diagnose it?”
- The agent will guide you to check the slow request log, test queries with payload/vector retrieval disabled, and evaluate memory pressure.
- “Filtered searches in Qdrant are slow, what should I do?”
- The agent will suggest creating payload indexes, using tenant-specific indexing, or switching to the ACORN algorithm for complex filter conditions.
- “How can I improve QPS in my Qdrant cluster?”
- The agent will recommend batching search requests, enabling quantization, and adjusting segment counts or adding replicas.
- “My search performance drops when I perform bulk updates.”
- The agent will help you tune the optimizer CPU budget and use the prevent_unoptimized setting to manage read-write contention.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-search-speed-optimization/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip qdrant-search-speed-optimization.zip -d .agents/skills
Documentation by Qdrant
Diagnose a problem
There are multiple possible reasons for search performance degradation. The most common ones are:
Memory pressure: if the working set exceeds available RAM
Complex requests (e.g. high
hnsw_ef, complex filters without payload index)Competing background processes (e.g. optimizer still running after bulk upload)
Problem with the cluster (e.g. network issues, hardware degradation)
Single Query Too Slow (Latency)
Use when: individual queries take too long regardless of load.
Diagnostic steps:
Read the slow request log (v1.16+, always on, per node) to find the slowest unique operations over 50 ms Slow request log
Check if second run of the same request is significantly faster (indicates memory pressure)
Try the same query with
with_payload: falseandwith_vectors: falseto see if payload retrieval is the bottleneckIf request uses filters, try to remove them one by one to identify if a specific filter condition is the bottleneck
Common fixes:
Tune HNSW parameters: Fine-tuning search
Enable in-memory quantization: Scalar quantization
On v1.19+, store vectors as
datatype: turbo4(4-bit, about 8x smaller) to cut disk reads. Lossy: the originals are no longer full precision, so check recall. Set it when creating the collection or adding a new named vector, so existing data must be re-ingested turbo4Reduce Vector Dimensionality with Matryoshka Models: Matryoshka Models
Use oversampling + rescore for high-dimensional vectors Search with quantization
Enable io_uring for disk-heavy workloads on Linux io_uring
Can't Handle Enough QPS (Throughput)
Use when: system can't serve enough queries per second under load.
Reduce segment count (
default_segment_numberto 2) Maximizing throughputUse batch search API instead of single queries Batch search
Enable quantization to reduce CPU cost Scalar quantization
Add replicas to distribute read load Replication
Filtered Search Is Slow
Use when: filtered search is significantly slower than unfiltered. Most common SA complaint after memory.
Create payload index on the filtered field Payload index
Use
is_tenant=truefor primary filtering condition: Tenant indexTry ACORN algorithm for complex filters: ACORN
nestedfilter conditions check each remaining candidate's payload element by element, which is slow when many candidates remain. On Qdrant 1.20 or newer, create per-element indexes (thenestedparameter of the payload index) on every field the nested filter uses, so Qdrant answers it from the indexes without reading payload Per-element index.Substring match (
matchwithsubstring, 1.20+) is expensive: it checks every distinct value of the field, and Qdrant rejects it whenever strict mode is enabled (the Qdrant Cloud default). Prefer prefix match (keyword index withprefix: true) or full-text match where the use case allows. A keyword index, especially withprefix, speeds it up; a full-text index doesn't help Substring matchIf payload index was added after HNSW build, trigger re-index to create filterable subgraph links
Optimize search performance with parallel updates
Diagnostic steps
Try to run the same query with
indexed_only=trueparameter, if the query is significantly faster, it means that the optimizer is still running and has not yet indexed all segments.If CPU or IO usage is high even with no queries, it also indicates that the optimizer is still running.
Recommended configuration changes
reduce
optimizer_cpu_budgetto reserve more CPU for queriesUse
prevent_unoptimized=trueto prevent creating segments with a large amount of unindexed data for searches. Instead, once a segment reaches the so called indexing_threshold, all additional points will be added in ‘deferred state’.
Learn more in the read-write contention guide
What NOT to Do
Set quantization to not stay in RAM (disk thrashing on every search): avoid
memory: cold/cachedon Qdrant 1.19 or newer; on 1.18 or older, avoid leavingalways_ramfalse or unset while the original vectors areon_disk: truePut HNSW on disk for latency-sensitive production (only for cold storage)
Increase segment count for throughput (opposite: fewer = better)
Create payload indexes on every field (wastes memory)
Blame Qdrant before checking optimizer status
Files
- ATTRIBUTION.mdAdded by AutoGineer901 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Node.js
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Attribution and licence
- Published by
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- Qdrant (Official)
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- qdrant/skills · skills/qdrant-performance-optimization/search-speed-optimization · commit 9f55f6a
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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