#TRANSFORMS CONF IN SPLUNK LICENSE#

Of course, depending on your system and number of sourcetypes, and indexes and sizes of events, your mileage in performance and data ingestion reductions will vary. – The amount of space each event takes up

The addition of the punct field to a sourcetype is a default configuration within Splunk and it is sometimes useful for highlighting events with outlying patterns within searches (and therefore useful for seeing unusual/dangerous/illegal activity) however, it is rarely used and can be generated at search time in searches that may need it.Īs generating the punct field is an extra piece of processing that Splunk will not need to do and as the field is added to the index of your data which you may never need, if you switch it off, then you will reduce: There is another optimization that Splunk taught me about the “punct” field. We can reduce the amount of work that Splunk needs to do drastically and therefore give it more time for other tasks, such as searching, reporting, alerting and dashboarding by applying the Splunk Best Practices below. So the case for configuring our timestamps is pretty strong.

#TRANSFORMS CONF IN SPLUNK LICENSE#

– All of this extra work can clog up a Heavy Forwarder or Indexer in the Aggregator Pipeline (and cost more on a Workload Processing License Model). – Wrong timestamps can also cause Splunk to create unduly-small index buckets, making searches inefficient. – Incorrect timestamps can result in data being aged out prematurely or retained for too long. – The time zone may be wrong (and therefore the event time may be recorded incorrectly). – Running over 30 different REGEX pattern searches FOR EACH EVENT is a LOT of work! – The date may be in a different format than Splunk thinks that it is (such as the Month/Day being switched), although there are some clever checks it does before validating this.

– The first timestamp that is in the data may not be the one that is needed. – The timestamp might not be in the first 128 characters of the event. There are a few issues with this behaviour: – Splunk uses over 30 different REGEX patterns to search the event for a suitable timestamp that it can use. – Splunk uses the first timestamp that it finds in the event. – Splunk looks at the first 128 characters in an event for the timestamp. Several things of note about this generic process are that: Splunk is pretty clever about finding time stamps for its events (because, well, without a timestamp, Splunk cannot organise the data) and so, when it has separated the data into events, it searches each event to find a timestamp. You have a recurring multiline event where a different field/value pair sits on a separate line, and each pair is separated by a colon followed by a tab space.The other great performance boost that we can apply to Splunk data onboarding is telling Splunk about how the timestamps are to be found. You can use the DELIMS attribute in field transforms to configure field extractions for events where field values or field/value pairs are separated by delimiters such as commas, colons, tab spaces, and more. This ensures that these new regular expressions are not overridden by automatic field extraction, and it also helps increase your search performance.įor more information on automatic key-value field extraction, see Automatic key-value field extraction for search-time data.Ĭonfigure delimiter-based field extractions This disables automatic key-value field extraction for the identified source type while letting your manually defined extractions continue. Besides using multiple field transforms, the field extraction stanza also sets KV_MODE=none.

Transforms conf in splunk