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@9637e1a official
by githubgithub/awesome-copilot40k stars
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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 without obvious config changes.

  • 1 file
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  • Updated 6 months ago
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Use this Skill: https://skilld.dev/gh/github/awesome-copilot/debugging

This session only. Nothing lands on disk.

SKILL.md

≈80 tokens always: the name and description. ≈784 when used: this file.

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 /collections/{collection_name}/optimizations endpoint (v1.17+) to check status Optimization monitoring
  • Query with optional detail flags: ?with=queued,completed,idle_segments
  • Returns: queued optimizations count, active optimizer type, involved segments, progress tracking
  • Web UI has an Optimizations tab with timeline view and per-task duration metrics 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.

  • Process memory metrics available via /metrics (RSS, allocated bytes, page faults)
  • 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
  • If resident memory (RSSAnon) exceeds 80% of total RAM, investigate
  • Check /telemetry for per-collection breakdown of point counts and vector configurations
  • Estimate expected memory: num_vectors * dimensions * 4 bytes * 1.5 for vectors, plus payload and index overhead Capacity planning
  • Common causes of unexpected growth: quantized vectors with always_ram=true, 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.

  • Track rest_responses_avg_duration_seconds and rest_responses_max_duration_seconds per endpoint
  • Use histogram metric rest_responses_duration_seconds (v1.8+) for percentile analysis in Grafana
  • Equivalent gRPC metrics with grpc_responses_ prefix
  • 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

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)

Source: SKILL.md on GitHub

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Last checked against GitHub 18 hours ago.

Activeupdated 6 months ago

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Diagnoses Qdrant production issues by checking optimizer status, memory consumption, and request latency metrics. Targets problems like stuck optimizations, OOM crashes, slow queries, and unexplained latency spikes using Qdrant's `/metrics` and `/optimizations` endpoints.

Generated from the current SKILL.md.

Does this skill work with Qdrant Cloud or only self-hosted?
The skill focuses on self-hosted Qdrant where you have direct access to metrics endpoints and logs. Qdrant Cloud exposes some metrics but the debugging workflow assumes access to `/metrics`, `/telemetry`, and collection optimization endpoints.
What Qdrant version do I need?
The skill targets v1.8+ for histogram metrics and v1.17+ for the `/collections/{collection_name}/optimizations` endpoint. Older versions have limited observability for the optimizer debugging workflows described.
Can this help with connection timeouts or network errors?
No. The skill addresses production performance issues (slow queries, high memory, stuck optimizations) and does not cover network connectivity, authentication, or gRPC/REST client errors.
Do I need to install monitoring tools like Prometheus or Grafana?
The skill assumes you can query Qdrant's built-in `/metrics` and `/telemetry` endpoints directly. Grafana is mentioned for percentile analysis but not required; you can interpret metrics without it.

Generated from the current SKILL.md. These answers refresh after source changes.