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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

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Claude Code

$ npx -y skills add qdrant/skills --skill debugging --agent claude-code

SKILL.md

debugging.SKILL.md
name: qdrant-monitoring-debugging
description: "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 without obvious config changes."

How to Debug Qdrant with Metrics

First check optimizer status. Most production issues trace back to active optimizations competing for resources. If optimizer is clean, check memory, then request metrics.

Optimizer Stuck or Too Slow

Use when: optimizer running for hours, not finishing, or showing errors.

  • Use the `/collections/{collection_name}/optimizations` endpoint (v1.17+) to see queued and running optimizations and their progress; add `?with=queued,completed,idle_segments` for the full lists [Optimization monitoring](https://skills.qdrant.tech/md/documentation/ops-optimization/optimizer/?s=optimization-monitoring)
  • Web UI has an Optimizations tab with timeline view and per-task duration metrics [Web UI](https://skills.qdrant.tech/md/documentation/ops-optimization/optimizer/?s=web-ui)
  • If `optimizer_status` shows an error in collection info, check logs for disk full or corrupted segments
  • Large merges and HNSW rebuilds legitimately take hours on big datasets. Check progress before assuming it's stuck.

Memory Seems Too High

Use when: memory exceeds expectations, node crashes with OOM, or memory keeps growing.

  • Watch the process memory metrics on `/metrics`: `memory_resident_bytes`, `memory_allocated_bytes`, and `process_major_page_faults_total` [Node metrics](https://skills.qdrant.tech/md/documentation/ops-monitoring/monitoring/?s=node-metrics-metrics)
  • Qdrant uses two types of RAM: resident memory (data structures, quantized vectors) and OS page cache (cached disk reads). Page cache filling available RAM is normal. [Memory article](https://skills.qdrant.tech/md/articles/memory-consumption/)
  • If resident memory (RSSAnon) exceeds 80% of total RAM, investigate
  • Check `/telemetry` for per-collection point counts and vector configurations. For a per-component breakdown of RAM, page cache, and disk use, call `GET /collections/{collection_name}/memory` or open the Web UI's Memory tab (v1.18+) [Memory usage](https://skills.qdrant.tech/md/documentation/ops-monitoring/memory-usage/)
  • Estimate expected memory per component (vectors, HNSW index, ID tracker, payload indexes) for the structures kept in RAM, plus about 20% headroom, and compare it with actual usage [Capacity planning](https://skills.qdrant.tech/md/documentation/capacity-planning/?s=calculating-ram-and-disk-size)
  • Common causes of unexpected growth: quantized vectors pinned in RAM (`memory: pinned` on Qdrant 1.19 or newer, `always_ram: true` on 1.18 or older), too many payload indexes, large `max_segment_size` during optimization

Queries Are Slow

Use when: queries slower than expected and you need to identify the cause.

  • Slow request log (v1.16+, always on): surfaces individual slow queries with timing breakdown. It's per node and resets on restart, so query every node in a cluster by using `GET /profiler/slow_requests` [Slow request log](https://skills.qdrant.tech/md/documentation/ops-monitoring/slow-request-log/)
  • Use the `rest_responses_duration_seconds` histogram (v1.8+; `grpc_responses_duration_seconds` for gRPC) for latency percentiles, rather than the avg and max gauges. Add `?per_collection=true` to `/metrics` to break them down by collection [Per-collection API metrics](https://skills.qdrant.tech/md/documentation/ops-monitoring/monitoring/?s=per-collection-api-metrics)
  • Check optimizer status first. Active optimizations compete for CPU and I/O, degrading search latency.
  • Check segment count via collection info. Too many unmerged segments after bulk upload causes slower search.
  • Compare filtered vs unfiltered query times. Large gap means missing payload index. [Payload index](https://skills.qdrant.tech/md/documentation/manage-data/indexing/?s=payload-index)

What NOT to Do

  • Ignore optimizer status when debugging slow queries (most common root cause)
  • Assume memory leak when page cache fills RAM (normal OS behavior)
  • Make config changes while optimizer is running (causes cascading re-optimizations)
  • Blame Qdrant before checking if bulk upload just finished (unmerged segments)
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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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Questions about debugging.

Which coding agents can use debugging?
debugging installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.