Drt Analyze

DRT Health Analyzer

Analyze a DRT CockroachDB cluster's health over a time range. Produces a tiered health report with evidence-backed findings, then supports interactive drill-down.

Prerequisites

• Datadog auth: roachdev datadog auth login (verify: roachdev datadog auth status)
• If auth fails during analysis, stop and ask the user to re-authenticate.

Invocation

/drt-analyze cluster:<name> from:<time> to:<time>

• cluster (required): DRT cluster name (e.g., drt-scale-300)
• from (required): now-4h, now-12h, or UTC absolute 2026-03-18T06:00:00Z
• to (optional): defaults to now (relative) or from + 4h (absolute)

Time Range Guardrail

If the window exceeds 24 hours, reject it — Datadog averages data into multi-hour bins at that scale, reducing spike detection accuracy and truncating the operations timeline. Suggest narrowing to 4-24h or running multiple analyses. Best granularity (~1 min data points) comes from 4h windows.

Analysis Workflow

Step 1: Verify Cluster

Quick sanity check that the cluster exists:

roachdev datadog metrics query \
  "avg:cockroachdb.sys.uptime{cluster:<name>}" \
  --from "<from>" --to "<to>"

If no data, try host:*<name>*. Confirm with the user before proceeding.

Step 2: Launch 3 Parallel Agents

Launch all three as general-purpose subagents simultaneously.

Agent 1: Operations Timeline

You are analyzing DRT operations for cluster "<name>" from <from> to <to>.

Query Datadog events to reconstruct what operations ran. Run all 4 queries
in parallel, then parse results with the bundled script.

1. roachdev datadog mcp call search_datadog_events \
     --arg query="cluster:<name> phase:run" \
     --arg from="<from>" --arg to="<to>" \
     --arg sort="timestamp" --arg max_tokens=15000

2. roachdev datadog mcp call search_datadog_events \
     --arg query="cluster:<name> phase:run result:(failed OR panicked)" \
     --arg from="<from>" --arg to="<to>" \
     --arg sort="timestamp" --arg max_tokens=10000

3. roachdev datadog mcp call search_datadog_events \
     --arg query="cluster:<name> phase:cleanup" \
     --arg from="<from>" --arg to="<to>" \
     --arg sort="timestamp" --arg max_tokens=10000

4. roachdev datadog mcp call search_datadog_events \
     --arg query="cluster:<name> phase:dependency-check" \
     --arg from="<from>" --arg to="<to>" \
     --arg sort="timestamp" --arg max_tokens=5000

Save each query's JSON output to a temp file, then run:
  python3 <skill-dir>/scripts/parse_events.py run.json cleanup.json failed.json depcheck.json

If the script can't parse the response format, fall back to manual Python
parsing. The key outputs are:
- Summary: total ops, success/fail/panic counts, success rate
- Timeline: chronological list with timestamps, names, workers, results
- Failures: error details for each failed/panicked operation
- DISRUPTIVE_WINDOW lines: <op-name> | <start> | <cleanup_end> | <recovery_end>

The cleanup_end MUST come from the actual cleanup event timestamp (query 3),
not the run event. The run event only shows when disruption started; cleanup
shows when the cluster was restored. If no cleanup event exists, estimate
cleanup_end as run_start + 5 min. recovery_end = cleanup_end + 10 min.

You may run Python/bash scripts autonomously — do not ask the user.

Agent 2: Metrics Health Check

You are checking CockroachDB cluster health metrics for cluster "<name>"
from <from> to <to>. Use roachdev datadog CLI. Cluster tag: cluster:<name>.

Batch multiple metrics per query — target ~10 queries total, not one per metric.
Run exactly these batches. You may run Python/bash scripts autonomously.

REPORTING RULES:
- For per-node metrics (queried "by {host}"), always report the per-node
  breakdown, not just aggregates. Include node name and value for each.
- When a metric has multiple separated spikes (returns to baseline between
  them), report each spike as a SEPARATE FINDING with its own timestamp.
  This matters for accurate correlation with operations.

## Batch 1: Cluster-wide availability

roachdev datadog metrics query \
  "max:cockroachdb.ranges.unavailable{cluster:<name>}" \
  "max:cockroachdb.ranges.underreplicated{cluster:<name>}" \
  "max:cockroachdb.kv.replica_circuit_breaker.num_tripped_replicas{cluster:<name>}" \
  "max:cockroachdb.requests.slow.raft{cluster:<name>}" \
  "max:cockroachdb.intentcount{cluster:<name>}" \
  "avg:cockroachdb.livenodes{cluster:<name>}" \
  "avg:cockroachdb.ranges{cluster:<name>}" \
  "max:cockroachdb.kv.closed_timestamp.max_behind_nanos{cluster:<name>}" \
  --from "<from>" --to "<to>"

Thresholds:
- ranges.unavailable: CRITICAL if > 0 for 10+ min
- ranges.underreplicated: WARNING if > 0 for 1+ hour
- circuit_breaker: WARNING if sudden increase (stable non-zero may be baseline from leftover merged-range replicas)
- requests.slow.raft: WARNING if > 0 for 10+ min
- livenodes: WARNING if drops below (total−1) for > 2 min, CRITICAL ≤ total/2
- intentcount: WARNING > 10M for 2+ min
- ranges: WARNING if linear growth >10% without workload change (broken MVCC GC)
- closed_timestamp.max_behind_nanos: CRITICAL if spike (node crash precursor)

## Batch 2: Per-node availability & network

roachdev datadog metrics query \
  "avg:cockroachdb.liveness.heartbeatlatency{cluster:<name>} by {host}" \
  "max:cockroachdb.liveness.heartbeatfailures{cluster:<name>} by {host}" \
  "avg:cockroachdb.sys.uptime{cluster:<name>} by {host}" \
  "max:cockroachdb.rpc.connection.unhealthy{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

Thresholds: heartbeatlatency WARNING > 500ms / CRITICAL > 3s; heartbeatfailures
WARNING if increasing; sys.uptime CRITICAL if reset; rpc.unhealthy WARNING > 0

## Batch 3: Performance + anomaly detection

roachdev datadog metrics query \
  "avg:cockroachdb.sql.failure{cluster:<name>}" \
  "avg:cockroachdb.txn.restarts{cluster:<name>}" \
  "avg:cockroachdb.sql.bytesout{cluster:<name>} by {host}" \
  "avg:cockroachdb.rpc.method.get.recv{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

roachdev datadog mcp call get_datadog_metric \
  --arg 'queries=["avg:cockroachdb.sql.service.latency{cluster:<name>} by {host}"]' \
  --arg 'formulas=["anomalies(query0, \"basic\", 2)"]' \
  --arg from="<from>" --arg to="<to>"

Thresholds: sql.failure/txn.restarts WARNING if spike above baseline;
bytesout INFO if host > 20% growth; rpc.get.recv WARNING if host > 2× median

## Batch 4: Per-node resources

roachdev datadog metrics query \
  "avg:cockroachdb.sys.cpu.combined.percent.normalized{cluster:<name>} by {host}" \
  "avg:cockroachdb.sys.rss{cluster:<name>} by {host}" \
  "avg:cockroachdb.sys.host.disk.iopsinprogress{cluster:<name>} by {host}" \
  "avg:cockroachdb.sys.cgo.allocbytes{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

Thresholds: CPU WARNING > 0.8 (4h) / CRITICAL > 0.9 (1h); hot node if max CPU
exceeds median by 30+ pts for 2h; disk.iops WARNING > 10 / CRITICAL > 20;
rss WARNING if monotonic increase (memory leak); cgo WARNING if rapid growth (OOM risk from rangefeed catchup scans)

## Batch 5: Disk capacity

roachdev datadog metrics query \
  "avg:cockroachdb.capacity.available{cluster:<name>} by {host}" \
  "avg:cockroachdb.capacity{cluster:<name>} by {host}" \
  "avg:cockroachdb.capacity.used{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

Thresholds: available/capacity WARNING < 30% / CRITICAL < 10%; divergence where
used flat but available decreasing = invisible disk usage (temp dirs, SST leaks)

## Batch 6: Storage health

roachdev datadog metrics query \
  "avg:cockroachdb.rocksdb.read.amplification{cluster:<name>} by {host}" \
  "max:cockroachdb.storage.write.stalls{cluster:<name>} by {host}" \
  "max:cockroachdb.storage.wal.fsync.latency{cluster:<name>} by {host}" \
  "max:cockroachdb.admission.io.overload{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

Thresholds: read.amp WARNING > 50 (1h) / CRITICAL > 150 (15m); write.stalls
WARNING ≥ 1/min / CRITICAL ≥ 1/sec; wal.fsync WARNING > 100ms;
io.overload WARNING > 0.5 / CRITICAL > 1.0

## Batch 7: Changefeed health

roachdev datadog metrics query \
  "max:cockroachdb.changefeed.failures{cluster:<name>}" \
  "max:cockroachdb.changefeed.error.retries{cluster:<name>}" \
  "max:cockroachdb.changefeed.commit.latency{cluster:<name>}" \
  "max:cockroachdb.changefeed.max.behind.nanos{cluster:<name>}" \
  "max:cockroachdb.jobs.changefeed.currently_running{cluster:<name>}" \
  "max:cockroachdb.jobs.changefeed.currently_paused{cluster:<name>}" \
  "max:cockroachdb.changefeed.backfill_count{cluster:<name>}" \
  --from "<from>" --to "<to>"

Thresholds: failures CRITICAL > 0; retries WARNING > 50/15m; commit.latency
WARNING > 10m / CRITICAL > 15m; max.behind WARNING if growing; running WARNING
if drops to 0; paused WARNING > 0 for 15m; backfill INFO (correlate cgo growth)

## Batch 8: Job health

roachdev datadog metrics query \
  "max:cockroachdb.jobs.backup.currently_running{cluster:<name>}" \
  "max:cockroachdb.jobs.restore.currently_running{cluster:<name>}" \
  "max:cockroachdb.jobs.restore.currently_paused{cluster:<name>}" \
  "max:cockroachdb.jobs.changefeed.protected_age_sec{cluster:<name>}" \
  "max:cockroachdb.schedules.backup.failed{cluster:<name>}" \
  "max:cockroachdb.jobs.schema_change.currently_running{cluster:<name>}" \
  "max:cockroachdb.jobs.schema_change.currently_paused{cluster:<name>}" \
  "max:cockroachdb.kv.protectedts.reconciliation.oldest_record_age{cluster:<name>}" \
  "max:cockroachdb.queue.range_merge.process.success{cluster:<name>}" \
  --from "<from>" --to "<to>"

Thresholds: backup.running WARNING if unexpected 0; restore.running INFO (correlate
disk/WAL/AmbiguousResult); restore.paused WARNING > 30m; protected_age
WARNING if growing; backup.failed WARNING > 0; schema.paused WARNING > 30m (may be stuck reverting);
protectedts.oldest CRITICAL > 24h (stuck job blocking GC); merge.success WARNING if ~0 with range growth

## Batch 9: Goroutine & Admission Control

roachdev datadog metrics query \
  "avg:cockroachdb.sys.goroutines{cluster:<name>} by {host}" \
  "avg:cockroachdb.sys.runnable.goroutines.per_cpu{cluster:<name>} by {host}" \
  "avg:cockroachdb.admission.granter.slots_exhausted_duration{cluster:<name>,name:kv} by {host}" \
  "avg:cockroachdb.admission.granter.io_tokens_exhausted_duration{cluster:<name>} by {host}" \
  "avg:cockroachdb.admission.wait_durations.kv{cluster:<name>} by {host}" \
  --from "<from>" --to "<to>"

Goroutine outlier rule: compute median across hosts. > 2× median → WARNING.
> 3× with runnable_per_cpu < 5 → CRITICAL (AC death spiral — requests time out
inside AC queues before the leaseholder check). runnable_per_cpu WARNING > 32.
slots_exhausted WARNING > 500ms/s / CRITICAL > 1s/s sustained.
io_tokens_exhausted WARNING > 500ms/s (search logs for io_load_listener).

## Batch 10: LSM & KV Prober

roachdev datadog metrics query \
  "max:cockroachdb.storage.l0-sublevels{cluster:<name>} by {host}" \
  "max:cockroachdb.kv.prober.write.failures{cluster:<name>}" \
  "max:cockroachdb.kv.prober.read.failures{cluster:<name>}" \
  "avg:cockroachdb.kv.prober.write.latency{cluster:<name>}" \
  "avg:cockroachdb.kv.prober.read.latency{cluster:<name>}" \
  --from "<from>" --to "<to>"

l0-sublevels WARNING > 10 / CRITICAL > 20 sustained (4-10 normal for elastic work).
prober failures CRITICAL > 0 for 3+ min (confirms SQL unavailability, baseline ~185ms).
prober latency WARNING > 500ms / CRITICAL > 3s.

## Output Format

For each finding:
FINDING: <severity> | <metric> | <timestamp_range> | <value> | <host_or_cluster> | <description>

Severity: CRITICAL, WARNING, or INFO.
End with: NO_DATA: <comma-separated metrics with no data>

Agent 3: Logs Analysis

You are searching CockroachDB logs for cluster "<name>" from <from> to <to>.
Use roachdev datadog CLI. All logs are in Flex storage tier.
Cluster filter: cluster:<name> (NOT host:*<name>* — hosts use AWS instance IDs).

You may run Python/bash scripts autonomously — do not ask the user.

## Phase 1: Pattern Discovery (run all 3 in parallel)

Discover what's actually in the logs before looking for specific errors.

1a. Error patterns — let Datadog cluster all error logs into patterns:
   roachdev datadog mcp call search_datadog_logs \
     --arg query="cluster:<name> status:error" --arg storage_tier=flex \
     --arg use_log_patterns=true --arg from="<from>" --arg to="<to>" --arg max_tokens=10000

1b. Warning patterns — many issues surface as warnings before errors:
   roachdev datadog mcp call search_datadog_logs \
     --arg query="cluster:<name> status:warn" --arg storage_tier=flex \
     --arg use_log_patterns=true --arg from="<from>" --arg to="<to>" --arg max_tokens=10000

1c. Per-host error volume — find when/where errors cluster:
   roachdev datadog logs search "cluster:<name> status:error" \
     --from "<from>" --to "<to>" --storage flex --group-by host

## Phase 2: Critical Signal Checks (run all in parallel)

These are must-not-miss signals that warrant dedicated searches regardless
of what patterns found. Run all in parallel.

2a. Panics:           cluster:<name> panic
2b. OOM:              cluster:<name> (oom OR "out of memory" OR oom_kill)
2c. Node restarts:    cluster:<name> "CockroachDB node starting"
2d. Fatal errors:     cluster:<name> severity:fatal
2e. Disk/WAL stalls:  cluster:<name> ("disk stall" OR "disk slowness detected" OR "syncdata" OR "store liveness withdrawal")
2f. Closed TS regression: cluster:<name> "closed timestamp regression"
2g. CDC violations:   cluster:<name> "cdc ux violation"

Use: roachdev datadog logs search "<query>" --from "<from>" --to "<to>" --storage flex

## Phase 3: Anomaly Drill-Down

After Phase 1 and 2 complete, analyze the discovered patterns:

3a. Classify each pattern from Phase 1 into known or novel:
   KNOWN categories: panic, oom, node_restart, disk_stall, wal_sync_stall,
     cdc_violation, fatal, closed_ts_regression, ac_overload, schema_change_failure,
     overload_error, sst_mismatch, slow_consumer, restore_failure, job_failure

3b. For NOVEL patterns (high-count patterns that don't match known categories),
   fetch sample log lines to understand them:
   roachdev datadog logs search "cluster:<name> <pattern-signature>" \
     --from "<from>" --to "<to>" --storage flex
   Include the pattern count and a representative sample in findings.

3c. For known patterns that Phase 2 did NOT cover (e.g., AC overload,
   schema change failures, ambiguous results, SST mismatch, slow consumer,
   restore failures, job failures), only drill in if Phase 1 patterns
   showed significant volume. This avoids wasted queries for absent issues.

## Severity Classification

CRITICAL: panics, fatals, OOM, closed_ts_regression, disk_stall, wal_stall,
  restore_failure, novel patterns with > 100 occurrences in the window
WARNING: error patterns (known), schema_change, overload, slow_consumer,
  job_failure, ac_overload, novel patterns with 10-100 occurrences
INFO: node restarts, novel patterns with < 10 occurrences

## Output Format

For each finding:
LOG_FINDING: <severity> | <category> | <timestamp> | <host> | <summary>
<representative log excerpt>

For novel patterns use category: novel_pattern
Include the pattern template and occurrence count in the summary.

Step 3: Correlate and Synthesize

After all 3 agents complete, synthesize their results yourself (not a subagent).

3a. Temporal Correlation

For each finding, check if it falls within a disruptive operation window.

Read references/operation-impacts.md for the operation-to-expected-impact mapping.

Matching rules:

• A finding is attributed to an operation only if its START time falls within [op_start, recovery_end]. Findings that began before op_start are never attributed.
• If multiple operations overlap, pick the one whose expected impact set best matches the finding's metric.

Classification:

• START in window AND metric in expected set AND recovered → expected
• START in window AND metric in expected set AND NOT recovered → CRITICAL ("cluster didn't recover after operation completed")
• START in window but metric NOT in expected set → not attributed, classify on severity independently
• START before op_start → never attributed

3b. Root Cause Grouping

Group findings within a 5-minute window into a single incident. Pick the most fundamental cause: node restart > OOM > panic > disk stall > network partition > unavailable ranges > under-replicated > latency spike > error rate

3c. Generate Report

Keep it concise and scannable — summary first, drill-down on request.

## <cluster-name> Health: HEALTHY | DEGRADED | UNHEALTHY
**Period:** <from> — <to> UTC | **Ops:** X ran (Y ok, Z failed, W panicked) | **Success rate:** N%

### Findings

| # | Severity | What | Time (UTC) | Related Op |
|---|----------|------|------------|------------|
| 1 | CRITICAL | <one-line title> | HH:MM–HH:MM | <op name> or — |

### Failed Operations

| Operation | Time | Worker | Error |
|-----------|------|--------|-------|
| <op-name> | HH:MM | N | <one-line error> |

Rules:

• Each finding = one table row. No multi-paragraph evidence blocks.
• Severity: CRITICAL (bugs), WARNING (monitor), expected (explained by op)
• Do NOT include: full ops timeline, no-data metrics, suggested actions, evidence blocks. These are available on drill-down.
• HEALTHY = no CRITICAL/WARNING; DEGRADED = WARNING only; UNHEALTHY = any CRITICAL

Step 4: Interactive Drill-Down

After the summary, tell the user:

Ask me to drill down:
- "details on #N" — launches investigation agent with root cause analysis
- "ops timeline" — full chronological list of operations
- "logs <host> <time range>" — raw logs from a specific node
- "metrics that had no data" — list of unqueryable metrics

For "details on #N": read references/investigation-protocols.md, construct the investigation brief from the protocol table, and launch a general-purpose subagent. Do NOT investigate in the main conversation.

For "ops timeline", "logs", "no data" — handle directly with targeted queries.

Datadog CLI Reference

# Events
roachdev datadog mcp call search_datadog_events \
  --arg query="<q>" --arg from="<t>" --arg to="<t>" --arg sort="timestamp" --arg max_tokens=10000

# Metrics (batch multiple in one call)
roachdev datadog metrics query "<m1>" "<m2>" --from "<t>" --to "<t>"

# Anomaly detection
roachdev datadog mcp call get_datadog_metric \
  --arg 'queries=["<q>"]' --arg 'formulas=["anomalies(query0, \"basic\", 2)"]' \
  --arg from="<t>" --arg to="<t>"

# Logs (flex storage)
roachdev datadog logs search "<q>" --from "<t>" --to "<t>" --storage flex

# Log patterns
roachdev datadog mcp call search_datadog_logs \
  --arg query="<q>" --arg storage_tier=flex --arg use_log_patterns=true \
  --arg from="<t>" --arg to="<t>"

Datadog UI Links

Logs: https://us5.datadoghq.com/logs?query=<url-encoded-query>&from_ts=<epoch_ms>&to_ts=<epoch_ms>&live=false&storage=flex

Compute epoch ms: date -juf '%Y-%m-%dT%H:%M:%S' '<timestamp>' '+%s' (multiply by 1000)