Victoriametrics Metrics

VictoriaMetrics Metrics Analysis

Query and analyze time-series metrics from VictoriaMetrics using MetricsQL (PromQL-compatible with extensions).

Authentication

IMPORTANT: Credentials are injected automatically by a proxy layer. Do NOT check for VICTORIAMETRICS_TOKEN in environment variables - they won't be visible to you. Just run the scripts directly; authentication is handled transparently.

MANDATORY: Context-Efficient Investigation

NEVER dump all series or run unbounded range queries. Always follow this pattern:

GET STATISTICS → INSTANT QUERY → RANGE QUERY (only if needed)

1. Statistics First — Know how many series exist, what metrics/jobs are active
2. Instant Query — Get current values (single point, compact output)
3. Range Query — Only when you need trend over time, and ALWAYS with topk() to cap output

Available Scripts

All scripts are in .claude/skills/metrics-victoriametrics/scripts/

get_statistics.py — ALWAYS START HERE

Discover what metrics exist and their cardinality.

python .claude/skills/metrics-victoriametrics/scripts/get_statistics.py --query '{job="api"}'
python .claude/skills/metrics-victoriametrics/scripts/get_statistics.py --query '{namespace="production"}' --time-range 120
python .claude/skills/metrics-victoriametrics/scripts/get_statistics.py --query '{}' --json

Output includes:

• Active series count
• Top 10 metric names by series count
• Top 5 jobs
• Compact summary (~20 lines)

query_metrics.py — Targeted Queries

Execute MetricsQL queries with output limits.

# Instant query (default - single value per series, compact)
python .claude/skills/metrics-victoriametrics/scripts/query_metrics.py --query 'up{job="api"}'
python .claude/skills/metrics-victoriametrics/scripts/query_metrics.py --query 'rate(http_requests_total{service="payment"}[5m])'

# Range query (use sparingly - shows latest value per series, not all datapoints)
python .claude/skills/metrics-victoriametrics/scripts/query_metrics.py --query 'rate(http_requests_total[5m])' --type range --time-range 60

# Limit output
python .claude/skills/metrics-victoriametrics/scripts/query_metrics.py --query 'topk(5, rate(http_requests_total[5m]))' --limit 10 --json

list_labels.py — Metadata Discovery

Discover available labels and values.

python .claude/skills/metrics-victoriametrics/scripts/list_labels.py
python .claude/skills/metrics-victoriametrics/scripts/list_labels.py --label job
python .claude/skills/metrics-victoriametrics/scripts/list_labels.py --label namespace --match '{job="api"}' --json

MetricsQL Quick Reference

MetricsQL is fully PromQL-compatible with additional extensions.

Basic Queries

# Instant vector
http_requests_total{service="api"}

# Rate of increase per second
rate(http_requests_total{service="api"}[5m])

# Histogram quantile
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

Aggregations

sum by (service) (rate(http_requests_total[5m]))
avg by (instance) (cpu_usage)
topk(5, sum by (service) (rate(http_requests_total[5m])))
bottomk(3, avg_over_time(cpu_usage[1h]))

MetricsQL Extensions (beyond PromQL)

WITH Templates — Reusable Filters

WITH (
  commonFilters = {job="api", env="prod"},
  errorRate(m) = rate(m{status=~"5.."}[5m]) / rate(m[5m])
)
errorRate(http_requests_total{commonFilters})

Rollup Functions

rollup(metric[5m])              # Returns min, max, avg in one query
rollup_rate(counter[5m])        # Rate with proper counter reset handling
rollup_increase(counter[5m])    # Increase with counter reset handling

Label Manipulation

label_set(metric, "env", "prod")       # Set label value
label_del(metric, "instance")          # Remove label
label_copy(metric, "pod", "instance")  # Copy label
label_move(metric, "old", "new")       # Rename label
label_join(metric, "dst", ",", "a", "b")  # Join labels

Range & Time Functions

range_median(metric[1h])         # Median over range
range_first(metric[1h])         # First value in range
range_last(metric[1h])          # Last value in range
running_avg(metric[1h])         # Running average

Label Matching

{status="500"}           # Exact match
{status=~"5.."}          # Regex match
{status!="200"}          # Not equal
{service="api", status=~"5.."}   # Multiple labels

Investigation Workflows

Error Rate Investigation

# Step 1: Get statistics
python get_statistics.py --query '{job="api"}'

# Step 2: Current error rate
python query_metrics.py --query 'sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))'

# Step 3: Error rate by service (top 5 only)
python query_metrics.py --query 'topk(5, sum by (service) (rate(http_requests_total{status=~"5.."}[5m])))'

Latency Investigation

python query_metrics.py --query 'histogram_quantile(0.95, sum by (le) (rate(http_request_duration_seconds_bucket{service="api"}[5m])))'

Resource Investigation

python query_metrics.py --query 'topk(5, rate(container_cpu_usage_seconds_total{namespace="prod"}[5m]))'
python query_metrics.py --query 'topk(5, container_memory_usage_bytes{namespace="prod"} / container_spec_memory_limit_bytes{namespace="prod"})'

Anti-Patterns to Avoid

1. NEVER run unbounded query_range — Always use topk() or filter by specific labels
2. NEVER skip get_statistics.py — Know your cardinality before querying
3. NEVER use rate() without range vector — Always include [5m] or similar
4. NEVER compare counters directly — Use rate() or increase() first
5. Avoid short steps in range queries — Use step >= 2x scrape interval