Promql Generator

PromQL Query Generator

Overview

This skill provides a comprehensive, interactive workflow for generating production-ready PromQL queries with best practices built-in. Generate queries for monitoring dashboards, alerting rules, and ad-hoc analysis with an emphasis on user collaboration and planning before code generation.

When to Use This Skill

Invoke this skill when:

• Creating new PromQL queries from scratch
• Building monitoring dashboards (Grafana, Prometheus UI, etc.)
• Implementing alerting rules for Prometheus Alertmanager
• Analyzing metrics for troubleshooting or capacity planning
• Converting monitoring requirements into PromQL expressions
• Learning PromQL or teaching others
• The user asks to "create", "generate", "build", or "write" PromQL queries
• Working with Prometheus metrics (counters, gauges, histograms, summaries)
• Implementing RED (Rate, Errors, Duration) or USE (Utilization, Saturation, Errors) metrics

Interactive Query Planning Workflow

CRITICAL: This skill emphasizes interactive planning before query generation. Always engage the user in a collaborative planning process to ensure the generated query matches their exact intentions.

Follow this workflow when generating PromQL queries:

Stage 1: Understand the Monitoring Goal

Start by understanding what the user wants to monitor or measure. Ask clarifying questions to gather requirements:

1. Primary Goal: What are you trying to monitor or measure?

   • Request rate (requests per second)
   • Error rate (percentage of failed requests)
   • Latency/duration (response times, percentiles)
   • Resource usage (CPU, memory, disk, network)
   • Availability/uptime
   • Queue depth, saturation, throughput
   • Custom business metrics

2. Use Case: What will this query be used for?

   • Dashboard visualization (Grafana, Prometheus UI)
   • Alerting rule (firing when threshold exceeded)
   • Ad-hoc troubleshooting/analysis
   • Recording rule (pre-computed aggregation)
   • Capacity planning or SLO tracking

3. Context: Any additional context?

   • Service/application name
   • Team or project
   • Priority level
   • Existing metrics or naming conventions

Use the AskUserQuestion tool to gather this information if not provided.

When to Ask vs. Infer: If the user's initial request already clearly specifies the goal, use case, and context (e.g., "Create an alert for P95 latency > 500ms for payment-service"), you may acknowledge these details in your response instead of re-asking. Only ask clarifying questions for information that is missing or ambiguous.

Stage 2: Identify Available Metrics

Determine which metrics are available and relevant:

1. Metric Discovery: What metrics are available?

   • Ask the user for metric names
   • If uncertain, suggest common naming patterns
   • Check for metric type indicators in the name:
     • _total suffix → Counter
     • _bucket, _sum, _count suffix → Histogram
     • No suffix → Likely Gauge
     • _created suffix → Counter creation timestamp

2. Metric Type Identification: Confirm the metric type(s)

   • Counter: Cumulative metric that only increases (or resets to zero)
     • Examples: http_requests_total, errors_total, bytes_sent_total
     • Use with: rate(), irate(), increase()
   • Gauge: Point-in-time value that can go up or down
     • Examples: memory_usage_bytes, cpu_temperature_celsius, queue_length
     • Use with: avg_over_time(), min_over_time(), max_over_time(), or directly
   • Histogram: Buckets of observations with cumulative counts
     • Examples: http_request_duration_seconds_bucket, response_size_bytes_bucket
     • Use with: histogram_quantile(), rate()
   • Summary: Pre-calculated quantiles with count and sum
     • Examples: rpc_duration_seconds{quantile="0.95"}
     • Use _sum and _count for averages; don't average quantiles

3. Label Discovery: What labels are available on these metrics?

   • Common labels: job, instance, environment, service, endpoint, status_code, method
   • Ask which labels are important for filtering or grouping

Use the AskUserQuestion tool to confirm metric names, types, and available labels.

Stage 3: Determine Query Parameters

Gather specific requirements for the query.

Pre-confirmation for User-Provided Parameters

IMPORTANT: When the user has already specified parameters in their initial request (e.g., "5-minute window", "500ms threshold", "> 5% error rate"), you MUST:

1. Acknowledge the provided values explicitly in your response
2. Present them as pre-filled defaults in AskUserQuestion with the first option being "Use specified values"
3. Allow quick confirmation rather than re-asking for information already given

Example: If user says "alert when P95 latency exceeds 500ms", use:

AskUserQuestion:
- Question: "Confirm the alert threshold?"
- Options:
  1. "500ms (as specified)" - Use the threshold from your request
  2. "Different threshold" - Let me specify a different value

This respects the user's input and speeds up the workflow while still allowing modifications.

1. Time Range: What time window should the query cover?

   • Instant value (current)
   • Rate over time ([5m], [1h], [1d])
   • For rate calculations: typically [1m] to [5m] for real-time, [1h] to [1d] for trends
   • Rule of thumb: Rate range should be at least 4x the scrape interval

2. Label Filtering: Which labels should filter the data?

   • Exact matches: job="api-server", status_code="200"
   • Negative matches: status_code!="200"
   • Regex matches: instance=~"prod-.*"
   • Multiple conditions: {job="api", environment="production"}

3. Aggregation: Should the data be aggregated?

   • No aggregation: Return all time series as-is
   • Aggregate by labels: sum by (job, endpoint), avg by (instance)
   • Aggregate without labels: sum without (instance, pod), avg without (job)
   • Common aggregations: sum, avg, max, min, count, topk, bottomk

4. Thresholds or Conditions: Are there specific conditions?

   • For alerting: threshold values (e.g., error rate > 5%)
   • For filtering: only show series above/below a value
   • For comparison: compare against historical data (offset)

Use the AskUserQuestion tool to gather or confirm these parameters. When the user has already provided values (e.g., "5-minute window", "> 5%"), present them as the default option for confirmation.

Stage 4: Present the Query Plan

BEFORE GENERATING ANY CODE, present a plain-English query plan and ask for user confirmation:

## PromQL Query Plan

Based on your requirements, here's what the query will do:

**Goal**: [Describe the monitoring goal in plain English]

**Query Structure**:
1. Start with metric: `[metric_name]`
2. Filter by labels: `{label1="value1", label2="value2"}`
3. Apply function: `[function_name]([metric][time_range])`
4. Aggregate: `[aggregation] by ([label_list])`
5. Additional operations: [any calculations, ratios, or transformations]

**Expected Output**:
- Data type: [instant vector/scalar]
- Labels in result: [list of labels]
- Value represents: [what the number means]
- Typical range: [expected value range]

**Example Interpretation**:
If the query returns `0.05`, it means: [plain English explanation]

**Does this match your intentions?**
- If yes, I'll generate the query and validate it
- If no, let me know what needs to change

Use the AskUserQuestion tool to confirm the plan with options:

• "Yes, generate this query"
• "Modify [specific aspect]"
• "Show me alternative approaches"

When the user chooses:

• "Modify [specific aspect]": ask one focused follow-up question about what to change (metric, labels, function, time range, threshold, or output shape), then present an updated plan before generating.
• "Show me alternative approaches": provide at least two valid query plans with trade-offs (accuracy, cost, cardinality, readability), then ask the user to choose one before generating.

Stage 5: Generate the PromQL Query

Once the user confirms the plan, generate the actual PromQL query following best practices.

IMPORTANT: Consult Reference Files Before Generating

Before writing any query code, you MUST:

1. Identify the query category first (histogram, RED, USE, function-specific, optimization, etc.).

2. Read only the relevant reference section(s) using the Read tool:

   • For histogram queries → Read references/metric_types.md (Histogram section)
   • For error/latency patterns → Read references/promql_patterns.md (RED method section)
   • For resource monitoring → Read references/promql_patterns.md (USE method section)
   • For optimization questions → Read references/best_practices.md
   • For specific functions → Read references/promql_functions.md
   • Re-read a section only if requirements changed or you have not consulted it yet in the current thread.

3. If a needed reference cannot be read, state the issue and continue with best-effort generation using the most applicable documented pattern you already have.

4. Cite the applicable pattern or best practice in your response:

   As documented in references/promql_patterns.md (Pattern 3: Latency Percentile):
   # 95th percentile latency
   histogram_quantile(0.95, sum by (le) (rate(...)))

5. Reference example files when generating similar queries:

   Based on examples/red_method.promql (lines 64-82):
   # P95 latency with proper histogram_quantile usage

This keeps generated queries aligned with documented patterns while avoiding unnecessary full-file rereads on iterative follow-ups.

Best Practices for Query Generation

1. Always Use Label Filters

   # Good: Specific filtering reduces cardinality
   rate(http_requests_total{job="api-server", environment="prod"}[5m])
   
   # Bad: Matches all time series, high cardinality
   rate(http_requests_total[5m])

2. Use Appropriate Functions for Metric Types

   # Counter: Use rate() or increase()
   rate(http_requests_total[5m])
   
   # Gauge: Use directly or with *_over_time()
   memory_usage_bytes
   avg_over_time(memory_usage_bytes[5m])
   
   # Histogram: Use histogram_quantile()
   histogram_quantile(0.95,
     sum by (le) (rate(http_request_duration_seconds_bucket[5m]))
   )

3. Apply Aggregations with by() or without()

   # Aggregate by specific labels (keeps only these labels)
   sum by (job, endpoint) (rate(http_requests_total[5m]))
   
   # Aggregate without specific labels (removes these labels)
   sum without (instance, pod) (rate(http_requests_total[5m]))

4. Use Exact Matches Over Regex When Possible

   # Good: Faster exact match
   http_requests_total{status_code="200"}
   
   # Bad: Slower regex match when not needed
   http_requests_total{status_code=~"200"}

5. Calculate Ratios Properly

   # Error rate: errors / total requests
   sum(rate(http_requests_total{status_code=~"5.."}[5m]))
   /
   sum(rate(http_requests_total[5m]))

6. Use Recording Rules for Complex Queries

   • If a query is used frequently or is computationally expensive
   • Pre-aggregate data to reduce query load
   • Follow naming convention: level:metric:operations

7. Format for Readability

   # Good: Multi-line for complex queries
   histogram_quantile(0.95,
     sum by (le, job) (
       rate(http_request_duration_seconds_bucket{job="api-server"}[5m])
     )
   )

Common Query Patterns

Pattern 1: Request Rate

# Requests per second
rate(http_requests_total{job="api-server"}[5m])

# Total requests per second across all instances
sum(rate(http_requests_total{job="api-server"}[5m]))

Pattern 2: Error Rate

# Error ratio (0 to 1)
sum(rate(http_requests_total{job="api-server", status_code=~"5.."}[5m]))
/
sum(rate(http_requests_total{job="api-server"}[5m]))

# Error percentage (0 to 100)
(
  sum(rate(http_requests_total{job="api-server", status_code=~"5.."}[5m]))
  /
  sum(rate(http_requests_total{job="api-server"}[5m]))
) * 100

Pattern 3: Latency Percentile (Histogram)

# 95th percentile latency
histogram_quantile(0.95,
  sum by (le) (
    rate(http_request_duration_seconds_bucket{job="api-server"}[5m])
  )
)

Pattern 4: Resource Usage

# Current memory usage
process_resident_memory_bytes{job="api-server"}

# Average CPU usage over 5 minutes
avg_over_time(process_cpu_seconds_total{job="api-server"}[5m])

Pattern 5: Availability

# Percentage of up instances
(
  count(up{job="api-server"} == 1)
  /
  count(up{job="api-server"})
) * 100

Pattern 6: Saturation/Queue Depth

# Average queue length
avg_over_time(queue_depth{job="worker"}[5m])

# Maximum queue depth in the last hour
max_over_time(queue_depth{job="worker"}[1h])

Stage 6: Validate the Generated Query

ALWAYS attempt to validate the generated query first using the devops-skills:promql-validator skill:

After generating the query, automatically invoke:
Skill(devops-skills:promql-validator)

The devops-skills:promql-validator skill will:
1. Check syntax correctness
2. Validate semantic logic (correct functions for metric types)
3. Identify anti-patterns and inefficiencies
4. Suggest optimizations
5. Explain what the query does
6. Verify it matches user intent

Validation checklist:

• Syntax is correct (balanced brackets, valid operators)
• Metric type matches function usage
• Label filters are specific enough
• Aggregation is appropriate
• Time ranges are reasonable
• No known anti-patterns
• Query is optimized for performance

If validation fails, fix issues and re-validate until all checks pass.

If the validator skill is unavailable, fails to run, or cannot complete after two fix/re-validate cycles:

• Report the validator failure briefly (tool unavailable, timeout, parsing error, etc.).
• Run a manual fallback check (syntax shape, metric/function compatibility, label filtering, aggregation, time range sanity).
• Mark any unchecked areas as UNVERIFIED and ask the user whether to proceed with best-effort output or provide more context for another validation attempt.

IMPORTANT: Display Validation Results to User

After running validation, you MUST display the structured results to the user in this format:

## PromQL Validation Results

### Syntax Check
- Status: ✅ VALID / ⚠️ WARNING / ❌ ERROR / ⚠️ UNVERIFIED
- Issues: [list any syntax errors]

### Best Practices Check
- Status: ✅ OPTIMIZED / ⚠️ CAN BE IMPROVED / ❌ HAS ISSUES / ⚠️ UNVERIFIED
- Issues: [list any problems found]
- Suggestions: [list optimization opportunities]

### Validation Coverage
- Validator tool run: [successful / failed / unavailable]
- Checks completed: [syntax, semantics, anti-patterns, performance, intent-match]
- Checks skipped: [list any skipped checks, or "None"]

### Query Explanation
- **What it measures**: [plain English description]
- **Output labels**: [list labels in result, or "None (scalar)"]
- **Expected result structure**: [instant vector / scalar / etc.]

This transparency helps users understand the validation process and any recommendations.

Stage 7: Provide Usage Instructions

After generation and validation (or manual fallback validation), provide the user with:

1. The Final Query:

   [Generated and validated PromQL query]

2. Query Explanation:

   • What the query measures
   • How to interpret the results
   • Expected value range
   • Labels in the output

3. How to Use It:

   • For Dashboards: Copy into Grafana/Prometheus UI panel query
   • For Alerts: Integrate into Alertmanager rule with threshold
   • For Recording Rules: Add to Prometheus recording rule config
   • For Ad-hoc: Run directly in Prometheus expression browser

4. Customization Notes:

   • Time ranges that might need adjustment
   • Labels to modify for different environments
   • Threshold values to tune
   • Alternative functions if requirements change

5. Related Queries:

   • Suggest complementary queries
   • Mention recording rule opportunities
   • Recommend dashboard panels

Native Histograms (Prometheus 3.x+)

Native histograms are now stable in Prometheus 3.0+ (released November 2024). They offer significant advantages over classic histograms:

• Sparse bucket representation with near-zero cost for empty buckets
• No configuration of bucket boundaries during instrumentation
• Coverage of the full float64 range
• Efficient mergeability across histograms
• Simpler query syntax

Important: Starting with Prometheus v3.8.0, native histograms are fully stable. However, scraping native histograms still requires explicit activation via the scrape_native_histograms configuration setting. Starting with v3.9, no feature flag is needed but scrape_native_histograms must be set explicitly.

Native vs Classic Histogram Syntax

# Classic histogram (requires _bucket suffix and le label)
histogram_quantile(0.95,
  sum by (job, le) (rate(http_request_duration_seconds_bucket[5m]))
)

# Native histogram (simpler - no _bucket suffix, no le label needed)
histogram_quantile(0.95,
  sum by (job) (rate(http_request_duration_seconds[5m]))
)

Native Histogram Functions

# Get observation count rate from native histogram
histogram_count(rate(http_request_duration_seconds[5m]))

# Get sum of observations from native histogram
histogram_sum(rate(http_request_duration_seconds[5m]))

# Calculate fraction of observations between two values
histogram_fraction(0, 0.1, rate(http_request_duration_seconds[5m]))

# Average request duration from native histogram
histogram_sum(rate(http_request_duration_seconds[5m]))
/
histogram_count(rate(http_request_duration_seconds[5m]))

Detecting Native vs Classic Histograms

Native histograms are identified by:

• No _bucket suffix on the metric name
• No le label in the time series
• The metric stores histogram data directly (not separate bucket counters)

When querying, check if your Prometheus instance has native histograms enabled:

# prometheus.yml - Enable native histogram scraping
scrape_configs:
  - job_name: 'my-app'
    scrape_native_histogram: true  # Prometheus 3.x+

Custom Bucket Native Histograms (NHCB)

Prometheus 3.4+ supports custom bucket native histograms (schema -53), allowing classic histogram to native histogram conversion. This is a key migration path for users with existing classic histograms.

Benefits of NHCB:

• Keep existing instrumentation (no code changes needed)
• Store classic histograms as native histograms for lower costs
• Query with native histogram syntax
• Improved reliability and compression

Configuration (Prometheus 3.4+):

# prometheus.yml - Convert classic histograms to NHCB on scrape
global:
  scrape_configs:
    - job_name: 'my-app'
      convert_classic_histograms_to_nhcb: true  # Prometheus 3.4+

Querying NHCB:

# Query NHCB metrics the same way as native histograms
histogram_quantile(0.95, sum by (job) (rate(http_request_duration_seconds[5m])))

# histogram_fraction also works with NHCB (Prometheus 3.4+)
histogram_fraction(0, 0.2, rate(http_request_duration_seconds[5m]))

Note: Schema -53 indicates custom bucket boundaries. These histograms with different custom bucket boundaries are generally not mergeable with each other.

SLO, Error Budget, and Burn Rate Patterns

Service Level Objectives (SLOs) are critical for modern SRE practices. These patterns help implement SLO-based monitoring and alerting.

Error Budget Calculation

# Error budget remaining (for 99.9% SLO over 30 days)
# Returns value between 0 and 1 (1 = full budget, 0 = exhausted)
1 - (
  sum(rate(http_requests_total{job="api", status_code=~"5.."}[30d]))
  /
  sum(rate(http_requests_total{job="api"}[30d]))
) / 0.001  # 0.001 = 1 - 0.999 (allowed error rate)

# Simplified: Availability over 30 days
sum(rate(http_requests_total{job="api", status_code!~"5.."}[30d]))
/
sum(rate(http_requests_total{job="api"}[30d]))

Burn Rate Calculation

Burn rate measures how fast you're consuming error budget. A burn rate of 1 means you'll exhaust the budget exactly at the end of the SLO window.

# Current burn rate (1 hour window, 99.9% SLO)
# Burn rate = (current error rate) / (allowed error rate)
(
  sum(rate(http_requests_total{job="api", status_code=~"5.."}[1h]))
  /
  sum(rate(http_requests_total{job="api"}[1h]))
) / 0.001  # 0.001 = allowed error rate for 99.9% SLO

# Burn rate > 1 means consuming budget faster than allowed
# Burn rate of 14.4 consumes 2% of monthly budget in 1 hour

Multi-Window, Multi-Burn-Rate Alerts (Google SRE Standard)

The recommended approach for SLO alerting uses multiple windows to balance detection speed and precision:

# Page-level alert: 2% budget in 1 hour (burn rate 14.4)
# Long window (1h) AND short window (5m) must both exceed threshold
(
  (
    sum(rate(http_requests_total{job="api", status_code=~"5.."}[1h]))
    /
    sum(rate(http_requests_total{job="api"}[1h]))
  ) > 14.4 * 0.001
)
and
(
  (
    sum(rate(http_requests_total{job="api", status_code=~"5.."}[5m]))
    /
    sum(rate(http_requests_total{job="api"}[5m]))
  ) > 14.4 * 0.001
)

# Ticket-level alert: 5% budget in 6 hours (burn rate 6)
(
  (
    sum(rate(http_requests_total{job="api", status_code=~"5.."}[6h]))
    /
    sum(rate(http_requests_total{job="api"}[6h]))
  ) > 6 * 0.001
)
and
(
  (
    sum(rate(http_requests_total{job="api", status_code=~"5.."}[30m]))
    /
    sum(rate(http_requests_total{job="api"}[30m]))
  ) > 6 * 0.001
)

SLO Recording Rules

Pre-compute SLO metrics for efficient alerting:

# Recording rules for SLO calculations
groups:
  - name: slo_recording_rules
    interval: 30s
    rules:
      # Error ratio over different windows
      - record: job:slo_errors_per_request:ratio_rate1h
        expr: |
          sum by (job) (rate(http_requests_total{status_code=~"5.."}[1h]))
          /
          sum by (job) (rate(http_requests_total[1h]))

      - record: job:slo_errors_per_request:ratio_rate5m
        expr: |
          sum by (job) (rate(http_requests_total{status_code=~"5.."}[5m]))
          /
          sum by (job) (rate(http_requests_total[5m]))

      # Availability (success ratio)
      - record: job:slo_availability:ratio_rate1h
        expr: |
          1 - job:slo_errors_per_request:ratio_rate1h

Latency SLO Queries

# Percentage of requests faster than SLO target (200ms)
(
  sum(rate(http_request_duration_seconds_bucket{le="0.2", job="api"}[5m]))
  /
  sum(rate(http_request_duration_seconds_count{job="api"}[5m]))
) * 100

# Requests violating latency SLO (slower than 500ms)
(
  sum(rate(http_request_duration_seconds_count{job="api"}[5m]))
  -
  sum(rate(http_request_duration_seconds_bucket{le="0.5", job="api"}[5m]))
)
/
sum(rate(http_request_duration_seconds_count{job="api"}[5m]))

Burn Rate Reference Table