51 lines
2.3 KiB
Markdown
51 lines
2.3 KiB
Markdown
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# Metrics
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Telegraf metrics are the internal representation used to model data during
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processing. Metrics are closely based on InfluxDB's data model and contain
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four main components:
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- **Measurement Name**: Description and namespace for the metric.
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- **Tags**: Key/Value string pairs and usually used to identify the
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metric.
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- **Fields**: Key/Value pairs that are typed and usually contain the
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metric data.
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- **Timestamp**: Date and time associated with the fields.
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This metric type exists only in memory and must be converted to a concrete
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representation in order to be transmitted or viewed. To achieve this we
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provide several [output data formats][] sometimes referred to as
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*serializers*. Our default serializer converts to [InfluxDB Line
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Protocol][line protocol] which provides a high performance and one-to-one
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direct mapping from Telegraf metrics.
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[output data formats]: /docs/DATA_FORMATS_OUTPUT.md
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[line protocol]: /plugins/serializers/influx
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## Tracking Metrics
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Tracking metrics are metrics that ensure that data is passed from the input and
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handed to an output before acknowledging the message back to the input. The
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use case for these types of metrics is to ensure that the message makes it to
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the destination before removing the metric from the input.
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For example, if a configuration is reading from MQTT, Kafka, or an AMQP source
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Telegraf will read the message and wait till the metric is handed to the output
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before telling the metric source that the message was read. If Telegraf were to
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stop or the system running Telegraf to crash, this allows the messages that
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were not completely delivered to an output to get re-read at a later date.
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Please note that this process applies only to internal plugins. For external
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plugins, the metrics are acknowledged regardless of the actual output.
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### Undelivered Messages
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When an input uses tracking metrics, an additional setting,
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`max_undelivered_messages`, is available in that plugin. This setting
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determines how many metrics should be read in before reading additional
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messages. In practice, this means that Telegraf may not read new messages from
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an input at every collection interval.
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Users need to use caution with this setting. Setting the value too high may
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mean that Telegraf pushes constant batches to an output, ignoring the flush
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interval.
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