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Extract internal monitoring data from application logs for collection in a time-series database. mtail is a tool for extracting metrics from application logs to be exported into a timeseries database or timeseries calculator for alerting and dashboarding. It fills a monitoring niche by being the glue between applications that do not export their own internal state (other than via logs) and existing monitoring systems, such that system operators do not need to patch those applications to...

Logrus is a structured, pluggable logger for Go (golang) that is completely API compatible with the standard library logger. It encourages careful, structured logging through much more discoverable logging fields rather than long, unparseable error messages. This produces much more useful logging messages. Logrus is currently in maintenance mode, which means that new features will no longer be introduced. This does not mean however, that it is dead. It continues to be maintained for...

The Logging operator solves your logging-related problems in Kubernetes environments by automating the deployment and configuration of a Kubernetes logging pipeline. The Logging operator manages the log collectors and log forwarders of your logging infrastructure, and the routing rules that specify where you want to send your different log messages. You can filter and process the incoming log messages using the flow custom resource of the log forwarder to route them to the appropriate...

Beats is a platform of lightweight, single-use data shippers written in Go. It captures all sorts of operational data from hundreds or thousands of machines and systems and sends them to Logstash or Elasticsearch. Beats is lightweight in that it has a small installation footprint, uses limited system resources and has no runtime dependencies. It consists of: Auditbeat (for audit data), Filebeat (for log files), Functionbeat (serverless shipper), Heartbeat (for uptime monitoring),...

Kafka/distributed commit log service in Go. Protocol compatible with Kafka so Kafka clients and services work with Jocko. Use Serf for discovery, Raft for consensus (and remove the need to run ZooKeeper). Able to use percentages of disk space for retention policies rather than only bytes and time kept. Handling size configs when you change the number of partitions or add topics. API versioning [more API versions to implement]. Replication [first draft done - testing heavily now]. Partition...