Qdrant Scaling Qps
QdrantOfficialPublished by Qdrant
- Skill
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- 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 technical guidance on configuring Qdrant for increased query throughput (QPS). It covers performance tuning for single nodes, strategies for minimizing the impact of write workloads, horizontal scaling via read replicas, and methods for addressing disk I/O bottlenecks.
What it does
- Explains how to tune segment configurations to favor throughput.
- Provides settings for optimizing memory usage and disk I/O.
- Outlines the use of batch search and gRPC to reduce request overhead.
- Describes how to use read replicas for horizontal scaling.
- Lists common pitfalls to avoid when configuring for high query volume.
- Offers diagnostic steps for identifying I/O bottlenecks.
When to use it
- When you need to increase the number of concurrent queries per second.
- When you are experiencing low throughput in your Qdrant deployment.
- When you need to scale read capacity horizontally.
- When you need to optimize search performance during heavy write operations.
Advantages
- Provides actionable configuration parameters for throughput optimization.
- Helps distinguish between throughput and latency tuning priorities.
- Offers clear strategies for managing disk-bound workloads.
- Includes guidance on avoiding common performance degradation patterns.
Usage examples
- “How can I increase the QPS for my Qdrant collection?”
- The agent will provide steps for segment optimization, enabling RAM-pinned quantization, and using the batch search API.
- “My Qdrant node is CPU saturated, how do I scale?”
- The agent will explain how to implement horizontal scaling using read replicas and load balancing.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-scaling-qps/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip qdrant-scaling-qps.zip -d .agents/skills
Documentation by Qdrant
Scaling for Query Throughput (QPS)
Throughput scaling means handling more parallel queries per second. This is different from latency. Segment count pulls throughput and latency in opposite directions, so pick a priority per collection.
High throughput favors fewer, larger segments so each query touches less overhead.
Performance Tuning for Higher RPS
Use fewer, larger segments (
default_segment_number: 2) Maximizing throughputEnable quantization pinned in RAM to reduce disk IO:
memory: pinnedon Qdrant 1.19 or newer,always_ram: trueon 1.18 or older QuantizationUse batch search API to amortize overhead Batch search
Prefer gRPC over REST for high-QPS workloads -- lower per-request overhead and connection multiplexing
Minimize impact of Update Workloads
Keep searches off unindexed data during heavy writes: the
indexed_onlysearch parameter, or the experimentalprevent_unoptimizedoptimizer setting (v1.17.1+) Query indexed data onlySet
optimizer_cpu_budgetto limit indexing CPUs (e.g.2on an 8-CPU node reserves 6 for queries)Delayed read fan-outs (v1.17+) cut tail latency by sending extra reads to other replicas, which adds read load; leave them off when throughput is the priority Delayed fan-outs
Horizontal Scaling for Throughput
If a single node is saturated on CPU after applying the tuning above, scale horizontally with read replicas.
Shard replicas serve queries from replicated shards, distributing read load across nodes
Each replica adds independent query capacity without re-sharding
Use
replication_factor: 2+and send requests through a load balancer across all nodes; if every request hits one node, its local replicas serve the query and the other replicas sit idle Deploy behind a load balancer
See also Horizontal Scaling for general horizontal scaling guidance.
Disk I/O Bottlenecks
If it is not possible to keep all vectors in RAM, disk I/O can become the bottleneck for throughput. In this case:
Upgrade to provisioned IOPS or local NVMe first. See impact of disk performance to vector search in Disk performance article
Enable async I/O:
storage.performance.io_uring: autoon Qdrant 1.19 or newer,async_scorer: trueon 1.18 or older. Requires Linux with a kernel that supportsio_uringAsync I/OOn 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. It needs a new collection or a new named vector, so existing data must be re-ingested turbo4In case of quantized vectors, prefer global rescoring over per-segment rescoring to reduce disk reads. Example in the tutorial
Raise
max_search_threadsto parallelize disk reads. The default (0, automatic) suits RAM-based search but may be too low for disk-based search. See configuration referenceUse the slow request log (v1.16+, always on, per node) to find which queries are slowest, then check
process_major_page_faults_totalin/metricsto confirm I/O is the bottleneck Slow request log Node metricsIf still saturated, scale out horizontally (each node adds independent IOPS)
What NOT to Do
Do not expect one segment configuration to maximize both throughput and latency: pick a priority per collection
Do not use many small segments for throughput workloads (increases per-query overhead)
Do not scale horizontally when IOPS-bound without also upgrading disk tier
Do not run at >90% RAM (OS cache eviction = severe performance degradation)
Files
- ATTRIBUTION.mdAdded by AutoGineer856 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md4.9 KiB
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Attribution and licence
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- qdrant/skills · skills/qdrant-scaling/scaling-qps · commit 9f55f6a
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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