Write Loki LogQL queries using log pipeline stages and metric queries to extract and aggregate structured fields from logs
domain: grafana.com · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Start with a stream selector: {app="api", namespace="production"} to select relevant log streams
Apply a line filter expression | |= "ERROR" or | != "healthcheck" to narrow lines before expensive parsing stages
Use a JSON or logfmt parser | json or | logfmt to extract structured fields, then apply label_filter | level="error" on extracted fields
Build a metric query using rate() or count_over_time() wrapping the filtered stream to produce time series: rate({app="api"} | json | level="error" [5m])
Use unwrap with avg_over_time or quantile_over_time to compute aggregations on numeric fields extracted from log lines
Known gotchas
Adding many extracted labels (high cardinality per-stream) creates label cardinality issues in Loki; use line_format or keep_labels to limit label proliferation
LogQL metric queries require a vector selector range; too short a range ([1m] on infrequent logs) produces sparse or zero-value data points
The logfmt parser silently skips malformed entries; a mixed log format (some JSON, some plain text) in the same stream causes incomplete field extraction
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