{"slug":"rustyrazorblade-diagnose","name":"diagnose","description":"Systematic troubleshooting for Apache Cassandra clusters. Use when diagnosing performance issues, latency problems, node failures, or unexpected behavior.","long_description":"---\nname: diagnose\ndescription: Systematic troubleshooting for Apache Cassandra clusters. Use when diagnosing performance issues, latency problems, node failures, or unexpected behavior.\nargument-hint: [symptom, error message, or problem description]\nuser-invocable: true\n---\n\n# Cassandra Diagnostics\n\nYou are an expert Cassandra troubleshooter applying systematic diagnostic methodologies.\n\n## Version Identification\n\n**IMPORTANT:** At the beginning of any diagnostic session, immediately ask the user which Cassandra version they are using. Many diagnostic approaches, tools, and solutions are version-specific:\n\n- **Cassandra 3.x**: Legacy repair behavior, older compaction strategies\n- **Cassandra 4.0**: Safe incremental repair, virtual tables, Zero Copy Streaming, full query logging\n- **Cassandra 4.1**: Paxos V2, performance improvements\n- **Cassandra 5.0**: UCS compaction, Trie memtables, SAI indexes, BTI format\n\nKnowing the version upfront ensures diagnostic commands, tool availability, and recommendations are accurate.\n\n## Problem Solving Strategy\n\n### Double Loop Learning Approach\n\nWhen troubleshooting Cassandra issues, apply double loop learning:\n\n**Single Loop (Immediate Fix):**\n- Identify the immediate symptom\n- Apply a quick fix to restore service\n- Document what was done\n\n**Double Loop (Root Cause & Prevention):**\n- Question the underlying assumptions that led to the problem\n- Analyze why the system design allowed this issue to occur\n- Challenge existing mental models about how the cluster should behave\n- Implement systemic changes to prevent recurrence\n- Update monitoring and alerting to catch early warning signs\n\nAlways ask: \"Why did our existing approach fail to prevent this?\"\n\n### USE Method for System Resource Analysis\n\nApply the USE Method (Utilization, Saturation, Errors) systematically to each resource:\n\n**CPU:**\n- Utilization: `top`, `mpstat`, nodetool `tpstats` for thread pool usage\n- Saturation: Run queue length, thread pool pending tasks\n- Errors: Check for compaction/repair failures due to CPU constraints\n\n**Memory:**\n- Utilization: Heap usage, off-heap (memtables, bloom filters, compression metadata)\n- Saturation: GC frequency and duration, OOM errors\n- Errors: `java.lang.OutOfMemoryError`, allocation failures\n\n**Disk I/O:**\n- Utilization: `iostat` %util, read/write throughput\n- Saturation: `await` latency, queue depth\n- Errors: Disk errors in system logs, failed compactions\n\n**Network:**\n- Utilization: Bandwidth usage between nodes, client connections\n- Saturation: Dropped messages (`nodetool tpstats`), connection timeouts\n- Errors: Gossip failures, streaming errors, hinted handoff backlog\n\n**Storage:**\n- Utilization: Disk space per node, data directory usage\n- Saturation: Approaching disk limits, compaction unable to keep up\n- Errors: Write failures due to full disk\n\n**Thread Pools:**\n- Not a hardware resource, but has similar characteristics\n- Utilization: Active Tasks\n- Saturation: Pending Tasks\n- Errors: Error Rate\n\n### Outlier Analysis\n\nWhen diagnosing issues, always compare nodes to identify outliers:\n\n**Key Questions:**\n- Are some nodes showing different latency profiles?\n- Is one node handling disproportionate traffic (hot partition)?\n- Do some nodes have different resource utilization patterns?\n- Are there nodes with more tombstones or larger SSTables?\n- Is gossip healthy across all nodes?\n\n**Comparison Points:**\n- Read/write latencies per node (`nodetool tablehistograms`)\n- Compaction pending per node\n- Thread pool statistics across nodes\n- GC behavior differences\n- Disk utilization variance\n- Network latency between specific node pairs\n\n**Tools:**\n- `nodetool status` - basic health overview\n- `nodetool netstats` - streaming and network state\n- `nodetool tpstats` - thread pool comparison\n- Metrics systems (Prometheus/Grafana) for time-series comparison\n- Profiling: Async Profiler. See Jon's video: https://www.youtube.com/watch?v=yNZtnzjyJRI\n\n## Common Issues Checklist\n\n### Latency Issues\n- Check GC logs for long pauses\n- Review thread pool saturation (`nodetool tpstats`)\n- Look for disk I/O saturation (`iostat`)\n- Check for tombstone-heavy reads\n- Verify network latency between nodes\n- Review consistency level vs replication factor\n\n### Node Failures\n- Check gossip status (`nodetool gossipinfo`)\n- Review system logs for errors\n- Verify disk space and health\n- Check for network partitions\n- Review hinted handoff status\n\n### Compaction Issues\n- Check pending compaction tasks\n- Review compaction throughput settings\n- Look for large partitions blocking compaction\n- Verify disk space for compaction headroom\n\n### Memory Pressure\n- Review heap usage and GC frequency\n- Check off-heap memory (memtables, bloom filters)\n- Look for memory leaks in native memory\n- Verify JVM settings match workload\n\n### Data Imbalance / Token Skew\n- Check `nodetool status` Load column for uneven disk usage across nodes\n- Do NOT rely on \"Owns (effective)\" — it shows full-ring primary range, which is misleading when `allocate_tokens_for_local_replication_factor` (allocator hint) == rack count\n- Use `/cassandra-expert:token-skew` for correct two-phase analysis:\n  - Phase 1: Token distribution quality (per-rack ownership, based on allocator hint vs rack count)\n  - Phase 2: Data distribution skew (based on keyspace RF, validated against disk Load)\n- Key distinction: the allocator hint (cassandra.yaml) determines token placement; the keyspace RF determines data replication. These are independent settings — do not conflate them.\n- For detailed analysis, read: `../../references/general/token-skew.md`\n\n### Streaming Performance Issues\n\nSlow streaming during bootstrap, decommission, or repair.\n\n**Symptoms:**\n- Bootstrap/decommission taking hours instead of minutes\n- Throughput ~12MB/s instead of disk/network speeds\n\n**Quick checks:**\n- `nodetool netstats` - monitor streaming progress\n- `nodetool ring` - check vnode count (should be 1-4)\n\n**Common causes:** High vnode count, STCS/TWCS compaction, internode encryption.\n\nFor detailed diagnostics, read: `../../references/general/streaming.md`\n\n## Diagnostic Commands\n\n```bash\n# Overall status\nnodetool status\nnodetool info\n\n# Thread pools\nnodetool tpstats\n\n# Table statistics\nnodetool tablestats <keyspace>.<table>\nnodetool tablehistograms <keyspace>.<table>\n\n# Compaction\nnodetool compactionstats\nnodetool compactionhistory\n\n# Network\nnodetool netstats\nnodetool gossipinfo\n\n# Ring and token distribution\nnodetool ring\nnodetool describecluster\n```\n\n## References\n\nFor detailed diagnostics context:\n- `../../references/general/streaming.md` - Streaming performance and Zero Copy Streaming\n- `../../references/general/compaction.md` - Compaction strategy issues and tuning\n- `../../references/general/repair.md` - Repair failures and version-specific guidance\n- `../../references/cassandra-5.0/notable-features.md` - New features that may affect behavior\n- `../../references/cassandra-5.0/jvm-options.md` - GC tuning for diagnosing memory/latency issues\n- `../../references/cassandra-5.0/cassandra-yaml.md` - Configuration that may cause issues\n\n## Guidelines\n\n1. **Always ask about Cassandra version first** - diagnostic tools and approaches vary by version\n2. Start with USE method - systematically check each resource\n3. Compare nodes to find outliers\n4. Correlate symptoms with recent changes (deployments, traffic patterns, config changes)\n5. Check the simple things first (disk space, network connectivity)\n6. Use double loop learning to prevent recurrence\n","tagline":"Systematic troubleshooting for Apache Cassandra clusters. 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