log-analysis

name: log-analysis description: Analyze, filter, transform, and convert log files using Kelora. Use for parsing logs, extracting patterns, investigating incidents, calculating metrics, or converting formats. metadata: version: "2.0"

Log Analysis with Kelora

Kelora is a streaming log processor with embedded Rhai scripting. It auto-detects formats (JSON, logfmt, syslog, Apache/combined, CSV, and built-in application-log formats), transparently decompresses .gz/.zst input, and ships 150+ built-in functions.

Getting Help

Kelora ships its own reference on these dedicated screens — consult them before asking the user:

• kelora -h - One-screen quick reference (cheat sheet)
• kelora --help [KEYWORD] - Full CLI reference (searchable, e.g. --help since)
• kelora --help-examples - Practical end-to-end patterns
• kelora --help-functions [KEYWORD] - All 150+ functions (KEYWORD filters, e.g. --help-functions ip)
• kelora --help-rhai - Rhai scripting guide and stage semantics
• kelora --help-formats - Input/output format reference
• kelora --help-time - Timestamp formats (--since/--until, parsing)
• kelora --help-multiline - Multiline event strategies (-M)
• kelora --help-regex - Regex parsing guide (-f regex:...)

Start Here: Explore Unknown Logs

When you don't know what's in a file, profile it first — no flags or regex needed:

kelora --discover app.log          # Field names, types, cardinality, samples (-d)
kelora --drain -k msg app.log      # Cluster near-duplicate lines into templates

--discover reports the detected format and how every field maps. --drain collapses noisy, near-identical messages into a handful of templates so you see what's actually happening.

Core Patterns

Filter:

kelora -l ERROR,WARN app.log                          # By level
kelora --filter 'e.status >= 500' api.log             # By expression
kelora --since "1 hour ago" --until now app.log       # By time

Transform:

kelora -e 'e.duration_sec = e.duration_ms / 1000' api.log
kelora -e 'e.absorb_json("data")' events.log          # Parse embedded JSON

Convert formats:

kelora -f combined -J access.log > access.jsonl       # Apache to JSON
kelora -j -F logfmt events.jsonl                      # JSON to logfmt
kelora -f syslog -F csv syslog.log                    # Syslog to CSV

Mixed formats in one file (cascade): try each parser per line, first success wins; the winner is tagged in _format.

kelora -f json,line mixed.log --filter 'e._format == "json"'

Metrics (one-flag aggregations, all imply -q):

kelora --freq level app.log                           # Count per distinct value
kelora --describe duration_ms app.log                 # count/min/max/avg/p50/p95/p99
kelora --card user.id app.log                         # Approx distinct count (HyperLogLog)
kelora -s app.log                                     # Summary stats
kelora -m -e 'track_freq("by_level", e.level)' app.log  # Custom metrics via Rhai

--freq/--describe/--card are repeatable and accept dotted paths (e.g. user.id). Output as --metrics=short|full|tsv|json; tsv is auto-selected when piped, so --freq url | head gives the top-N.

Context around matches:

kelora -C 5 --filter 'e.level == "ERROR"' app.log     # 5 lines before/after

Field Access

e.level              // Direct
e["@timestamp"]      // Special chars
e.get_path("a.b.c")  // Safe nested (returns () if missing)
e.has("field")       // Check exists

Key Options

Tips

1. Use -f auto (default) — Kelora detects JSON, logfmt, syslog, combined, CSV, and named app-log formats. Use a cascade (-f json,line) for files that mix formats.
2. Profile first with --discover / --drain; preview with -n 10 or --head 100 before processing large files.
3. Reach for --freq / --describe / --card before hand-writing track_*() calls — they're the common aggregations in one flag.
4. Run kelora --help-functions KEYWORD to find a function (e.g. --help-functions ip); --help KEYWORD searches the CLI reference.