Loki Label Analyzer

Loki Label Strategy Evaluator

This skill owns Grafana Loki label strategy work and is used to evaluate, audit, design, or improve a Loki label strategy. When asked to evaluate or redesign a label strategy, or when a user asks why their Loki queries are slow, use this guide to produce structured, actionable advice and concrete fixes.

For LogQL syntax, ingestion configuration, or general Loki questions outside label strategy, use the loki skill instead.

Core Concepts

Streams are the fundamental unit in Loki. Each unique combination of label key-value pairs creates a new stream. Too many streams = performance problems. Too few = broad, slow queries.

Cardinality = the number of unique values a label can have. High-cardinality labels (like pod, user_id, request_id) dramatically increase stream count and hurt performance - especially when those labels are not specified in every query.

The dual impact rule: High-cardinality labels hurt on both paths:

• Ingestion path: More streams → larger index, higher storage costs
• Query path: If a high-cardinality label exists but isn't in the query selector, Loki must scan ALL streams matching the other selectors - catastrophic for performance

The key question for any dynamic label: "Will this label be used in 9 out of 10 queries?" If no → it should NOT be a label.

Label Evaluation Framework

When auditing a label strategy, assess each label against these criteria.

Cardinality Scoring

Access Pattern Alignment

For each label, ask:

• Is this label used as a selector in most queries targeting these logs?
• Does this label logically segment data in the way users think about it?
• Would removing this label force users to scan dramatically more data?

Static vs. Dynamic Label Values

• Static labels (values don't change per log line, e.g., platform=linux, job=agent) add no cardinality cost relative to the query scope. Use freely for LBAC, exploration, and alert routing.
• Dynamic labels (values change per log line) must be bounded. Keep possible values in the single digits or low tens.

Consistency Check

• Are label names consistent across services? (case-sensitive - Level ≠ level)
• Are label values normalized? (INFO, info, Info should all become info)
• Is there a naming convention? (pick one: snake_case or camelCase - be consistent)

Evaluation Output Format

When auditing a label set, produce a report in this structure:

## Loki Label Strategy Audit

### Summary
[1-2 sentence overall assessment]

### Label Analysis
| Label | Cardinality | Used in Queries? | Verdict | Action |
|---|---|---|---|---|
| app | Low (tens) | Always | ✅ Keep | - |
| pod | Very High (transient)| Rarely | ❌ Remove | Move to structured metadata or embed in log line |

### Estimated Impact
- Stream count reduction: [X streams → Y streams]
- Query performance: [describe improvement]
- Storage impact: [if log line changes are involved]

### Recommended Label Set
[Final recommended labels]

### Migration Notes
Treat every label schema change as a change control event on the label allowlist, not a routine config tweak.
- Review and document changes to the allowed label names before applying them, including rollout and rollback notes.
- When renaming labels, do not cut over instantly. Dual-write both the old and new keys for a dashboard compatibility window so saved queries and dashboards keep working.
- During that window, validate the affected queries and dashboards against both keys before removing the old one.
- Write down the rollback path: how to revert the allowlist, the collector stages, and any updated query or dashboard references if regressions appear.

Recommended Common Labels

Every log source should consider these base labels - all low cardinality, high query value:

Kubernetes Pod Logs

Recommended Labels

Why workload beats app for K8s: Derived from {{controller_kind}}/{{controller_name}} - static values that never change like pod names do. Unlike app (which may aggregate multiple workload types), workload is precise and predictable. Users always know exactly what value to query.

Labels to AVOID in Kubernetes

pod label ❌

• Highly transient: pod names change on every restart/rollout
• Very high cardinality: 5 pods × 2 containers = 10 streams; add pod → 10 × N streams
• Users almost never query for a specific pod; they query for the workload
• Solution: Use workload as the label; store pod in structured metadata or embed in the log line

filename label (raw K8s path) ❌

• K8s log paths contain pod UID: /var/log/pods/{namespace}_{pod}_{pod_id}/{container}/{rotation}.log
• The pod_id component makes this unbounded
• Solution: Normalize to /var/log/pods/{namespace}/{controller_name}/{container}.log or drop entirely

// Normalize K8s filename to remove pod UID
stage.replace {
 source = "filename"
 expression = "/var/log/pods/([^/]+)_[^_]+_[^/]+/([^/]+)/\\d+\\.log"
 replace = "/var/log/pods/$1/$2/current.log"
}

Host / VM / Bare Metal Labels

In addition to common labels, add:

// Remove date suffixes from rotating log file names
// /var/log/myapp/logfile-20230927.txt → /var/log/myapp/logfile.txt
stage.replace {
 source = "filename"
 expression = "-\\d{8}(\\.log|\\.txt)$"
 replace = "$1"
}

Journal Logs

When collecting via loki.source.journal, many labels are auto-discovered under __journal__*: boot_id, cap_effective, cmdline, comm, exe, gid, hostname, machine_id, pid, stream_id, systemd_cgroup, systemd_invocation_id, systemd_slice, systemd_unit, transport, uid

Almost all are high-cardinality. Keep only:

• instance - hostname where journal logs were collected
• unit - the systemd_unit name (e.g., nginx.service)

Drop everything else:

loki.process "journal_labels" {
 forward_to = [...]
 stage.label_keep {
 values = ["instance", "unit", "env", "cluster"]
 }
}

Structured Metadata

Structured metadata attaches key-value pairs to log entries without making them index labels. The ideal home for high-cardinality values users occasionally need.

Requires: Loki 2.9+, Grafana Agent/Alloy. Enable via limits_config:

limits_config:
 allow_structured_metadata: true

Good candidates for structured metadata (not labels):

• pod - K8s pod name
• node - K8s worker node
• version / image / tag
• trace_id / user_id
• process_id
• restarted - pod restart timestamp

Query structured metadata at query time without a parser:

{app="payment-api"} | pod="payment-api-7f9d4b-xk2r9"

Embedding Metadata in Log Lines

When structured metadata isn't available, embed high-cardinality values into the log line rather than using them as labels.

Method 1: stage.template (append to log line)

loki.process "embed_pod" {
 forward_to = [...]

 // For JSON logs
 stage.match {
 selector = "{} |~ \"^\\s*\\{\""
 stage.replace {
 expression = "\\}$"
 replace = ""
 }
 stage.template {
 source = "log_line"
 template = "{{ .Entry }},\"_pod\":\"{{ .pod }}\"}"
 }
 }

 // For text logs
 stage.match {
 selector = "{} !~ \"^\\s*\\{\""
 stage.template {
 source = "log_line"
 template = "{{ .Entry }} _pod={{ .pod }}"
 }
 }

 stage.output { source = "log_line" }
}

Result: ts=... msg="..." _pod=agent-logs-cqhfk

Query by aggregate (normal use):

sum(count_over_time({workload="ReplicaSet/payment-api", level="error"}[1m]))

Query a specific pod (edge case debugging):

{workload="ReplicaSet/payment-api", level="error"} |= `_pod=payment-api-3`

Method 2: stage.pack (JSON envelope)

loki.process "pack_pod" {
 forward_to = [...]
 stage.pack {
 labels = ["pod"]
 ingest_timestamp = false
 }
}

Packed result: {"_entry": "original log line", "pod": "agent-logs-cqhfk"}

Unpack at query time:

{workload="ReplicaSet/payment-api", level="error"}
 |= `agent-logs-cqhfk`
 | unpack

Performance Bottleneck Diagnosis

When a user reports slow queries, identify where time is spent using Querier metrics.go logs.

Four Query Stages

Ideally, the majority of time is spent in Execution. If not, that indicates infrastructure or label design problems.

Checking Chunk Size

avg chunk size = total_bytes / cache_chunk_req

If the result is a few hundred bytes or kilobytes (instead of megabytes), chunks are too small. This means labels are over-splitting data into too many streams. Revisit and reduce label cardinality.

Common Label-Related Performance Problems

Problem: Query scans too many streams

• Cause: High-cardinality labels exist but aren't specified in the query selector
• Fix: Remove the label, or ensure queries always include it as a filter

Problem: High post_filter_lines discard ratio (post_filter_lines << total_lines)

• Cause: Insufficient label selectivity; query scans and discards most logs
• Fix: Add labels matching user access patterns (level, workload, container)

Problem: Small chunks

• Cause: Too many labels creating too many fine-grained streams
• Fix: Remove high-cardinality labels to consolidate streams

Query Optimization Quick Wins

1. Add container or workload to narrow scope before line filters
2. Add level label + always use it in queries (filters out 94%+ of logs when searching for errors)
3. Remove pod label → reduces stream count by ~5× in typical K8s deployments
4. Replace regex line filters (|~) with exact filters (|=) where possible

Alloy / Agent Configuration Patterns

Normalize Log Level

loki.process "normalize_level" {
 forward_to = [...]
 stage.replace { source = "level"; expression = "(?i)I(nfo)?"; replace = "info" }
 stage.replace { source = "level"; expression = "(?i)W(arn(ing)?)?"; replace = "warn" }
 stage.replace { source = "level"; expression = "(?i)E(rr(or)?)?"; replace = "error" }
 stage.replace { source = "level"; expression = "(?i)D(ebug?)?"; replace = "debug" }
 stage.labels { values = { level = "" } }
}

Conditional Meta-Label Extraction

// Only extract when the relevant field is present - avoids unnecessary cardinality
loki.process "conditional_extraction" {
 forward_to = [...]
 stage.match {
 selector = "{app=\"loki\"} |= \"component\""
 stage.logfmt { mapping = { "component" = "" } }
 stage.labels { values = { component = "" } }
 }
}

Enforce Approved Label Set (always use as final stage)

loki.process "enforce_labels" {
 forward_to = [loki.write.default.receiver]
 // ... other stages ...
 stage.label_keep {
 values = ["app", "env", "cluster", "level", "namespace", "workload", "container"]
 }
}

Soft Enforcement (inject "unknown" for missing labels)

stage.template {
 source = "team"
 template = "{{ if .Value }}{{ .Value }}{{ else }}unknown{{ end }}"
}
stage.labels { values = { team = "" } }

Log Line Optimization

Read references/log-line-optimization.md when reducing log storage cost or line size. It covers removing inline timestamps, ANSI color codes, duplicate level fields, and JSON null/placeholder values, with Alloy stages and measured savings.

Security & LBAC

Grafana Enterprise Logs (GEL) supports Label-Based Access Control (LBAC). Any label can serve as an access control selector.

Best labels for LBAC:

• classification - data sensitivity (public, restricted, confidential, top-secret)
• source - controls which teams can see which log origins
• team / squad - ownership-based access
• env - environment-level restrictions

Static aggregate labels like owner=sysadmins or category=database are particularly effective: one label value gates access to many log files, rather than requiring a long allowlist of filenames or streams.

When sharing an LBAC audit, keep evidence as redacted proof: show label names, policy shape, and example sensitivity classes, not sensitive log values.

The 80/20 Rule

The most impactful improvements almost always come from these four changes:

1. Remove pod as a label - biggest stream reduction in K8s environments
2. Add level as a label AND always specify it in queries - can eliminate 94%+ of scanned data when searching for errors
3. Normalize label values - eliminates phantom duplicate streams from inconsistent casing
4. Remove or normalize filename in K8s - highly variable paths inflate stream count significantly

Focus on these before anything else.

Labels to Avoid - Quick Reference