I spent the last 3 years building production observability stacks for Kubernetes clusters (50+ nodes), and I noticed a pattern: every team I worked with spent 2-4 weeks reinventing log parsing.
The problem is concrete. You deploy a service, logs hit your aggregator (Elasticsearch, Loki, Datadog), and suddenly you're writing regex patterns for:
- CRI-O container timestamps:
2024-01-15T10:23:41.123456789Z(most people miss the nanosecond precision and drop 30-40% of lines) - Multiline stacktraces that merge incorrectly (the
[FP]flag in CRI-O that nobody documents) - IPv6 addresses in access logs (
[2001:db8::1]breaks naive regex) - Structured JSON with nested exceptions
- Application-specific formats (Spring Boot, Go, Node.js each log differently)
This pack contains 50+ production-tested regex patterns and ready-to-use configurations for the entire stack:
- Log collectors: Fluent Bit, Vector, Filebeat, Logstash (complete configs with performance tuning)
- Aggregators: Elasticsearch, Loki, Datadog (grok patterns, parser pipelines)
- Specific parsers: CRI-O/containerd, Kubernetes kubelet, Nginx/Apache, PostgreSQL, MySQL
- Gotchas I learned the hard way: multiline handling, timezone normalisation, cardinality explosion
Example: The CRI-O pattern that works:
^(?P<time>\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d+Z)\s+(?P<stream>stdout|stderr)\s+(?P<flags>[FP])\s+(?P<log>.*)$
Most teams use a simpler pattern, lose the [FP] flag info, and can't reconstruct partial lines correctly.
The pack is £9.50 as a downloadable reference guide (not a subscription). It's practical—copy the configs, adjust for your stack, done.
https://riverbend36.gumroad.com/l/dyrpv
Feedback welcome—I'm iterating on this based on what DevOps teams actually need.
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