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fluent-plugin-elasticsearch-timestamp-check
Advanced tools
Fluent plugin to ensure @timestamp is in correct format for elasticsearch
gem install fluent-plugin-elasticsearch-timestamp-check
The purpose of this filter is to make sure the @timestamp field exists in the record which is necessary for the record to be indexed properly by elasticsearch.
If @timestamp
field already exists, it will ensure the format is correct
by parse and convert to format '%Y-%m-%dT%H:%M:%S%z'. As of version 0.2.4, it
will support epoch second / epoch millis format as a valid timestamp value. If
such value is detected, it will be converted to iso8601 format for easier
consumption of elasticsearch when dynamic mapping is used.
By default, it will check whether fields named timestamp
, time
, or
syslog_timestamp
exists, if so it will parse that field and conver it to
format '%Y-%m-%dT%H:%M:%S.%L%z' then store it in @timestamp
field. In
addition, a field fluent_converted_timestamp
is added to the object with
the same value.
(>=0.3.0) the list of fields can be overriden by setting the
timestamp_fields
parameter. It accepts a list of strings, the default is set
to: ['@timestamp', 'timestamp', 'time', 'syslog_timestamp']
If none of the above field exists, it will insert current event time in
'%Y-%m-%dT%H:%M:%S.%L%z' format as the @timestamp
field. A field
fluent_added_timestamp
is added to the object with same value.
subsecond_precision
controls the subsecond precision during the conversion.
Default value is set to 3
(millisecond).
Other subsecond_precision
sample values are:
6
(microsecond)9
(nanosecond)12
(picosecond)and more high precision is also supported.
<filter **>
type elasticsearch_timestamp_check
subsecond_precision 3
</filter>
FAQs
Unknown package
We found that fluent-plugin-elasticsearch-timestamp-check demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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Socket MCP brings real-time security checks to AI-generated code, helping developers catch risky dependencies before they enter the codebase.
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