Next Vector converts the logs it can change messages discard them or combine several messages into one. It then forwards them to a storage system or queue Vector architecture diagram collecting logs converting them and sending them VRL language examples Vector comes with the powerful Vector Remap Language VRL a language that allows you to perform all sorts of transformations. Lets take a quick look at VRL and start with log filtering. The VRL filter below cuts off logs with severityequal to info transforms.filter_severity type filter inputs logs condition .severity info When Vector collects pod logs it also augments the log lines with additional and labels.
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However pod labels may include labels that are use only by Kubernetes controllers and therefore do not provide any value to the user. To avoid wasting resources on processing them we recommend deleting the following labels a newline character to the message field. In addition Netherlands WhatsApp Number List the section ends_whenuses a VRL expression to check whether a line ends with a backslash similar to how multiline comments are concatenate in Bash. Log collection topologies Now lets talk about log collection topologies that can be use with Vector. Distribute topology. In this case Vector agents are deploye on all nodes of the Kubernetes cluster.
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Then they collect transform and directly send logs to the storage Scheme of operation of a distribute log collection topology Centralize topology. Vector agents also run on all nodes but do not perform complex transformations aggregators do this. The advantage of this type is the Austria Phone Number List preictability of the load. You can deploy deicate nodes for aggregators and if necessary scale them optimizing Vectors resource consumption on cluster nodes Scheme of operation of the centralize log collection topology Stream topology. In it Kubernetes pods get rid of logs as quickly as possible. But writing logs directly to Elasticsearch is slower because it spends time processing them when writing.
