12/30/2023 0 Comments Splunk subsearch tutorials![]() Perform calculations on the smallest amount of data.Here’s a real-life example of how impactful using the fields command can be. Whenever possible, try using the fields command right after the first pipe of your SPL as shown below. While this does cut down on the number of events (vertical) that are retrieved, you should also focus on cutting down the number of fields (horizontal) that are retrieved.īy using the fields streaming command early on within your SPL, you not only lower the amount of data being pulled from the indexers, but also the amount that has to be transferred to and processed by the search head. To lower the amount of data coming back from the indexers, many articles recommend filtering your data early on. Minimize the amount of data coming back from the indexers.This technique can also be used in place of the append, dedup, and table commands. | stats count(eval(sourcetype="splunkd")) AS metric_count count(eval(sourcetype="audittrail")) AS audit_count BY host (index=_internal sourcetype=splunkd component=Metrics) OR (index=_audit sourcetype=audittrail) [search index=_audit sourcetype=audittrail Index=_internal sourcetype=splunkd component=Metrics These factors lead to a truncation of results, which often goes unnoticed and leads to incorrect answers.Ĭombine your subsearch with your primary search and accomplish the join with a stats command instead. By default, they have a timeout of 60 seconds and a limitation of 50,000 events (see subsearch_maxtime and subsearch_maxout in nf for Splunk Enterprise or Splunk Cloud Platform). With each subsearch comes additional trips to the indexers, which increase the level of communication and overhead that might need to be involved. This is because both commands make use of a subsearch (the content between the square brackets). Although these commands are widely used, they’re not the most efficient. One of the best ways to minimize the number of trips to the indexers is to avoid using the join and append commands. Minimize the number of trips to the indexers.see those extra rows from the 1st dataset are not showing because it’s not present in both datasets. As we discussed earlier, it is fetching only common data from both the datasets. It will only show those results which are common in both the result-set depending on the movie_id field. ![]() ![]() If you look carefully then you can notice that in the sub-search we renamed the id field as movie_id because in the main search it’s named as movie_id. In the above figure, we have added two result-sets using join command and we took movie_id as our matching field. Inner join: In case of inner join it will bring only the common field values from the two data-sets (by default it takes Inner join) index="movie_details" | table movie_id,language,movie_name,country | join type=inner movie_id Let’s take an example: we have two different datasets.ġst Dataset: with four fields – movie_id, language, movie_name, countryĢnd Dataset: with two fields – id,director Now what are these two things take a look into the below figure it will be the search query of dataset 2īasically, with join command, there are two joins is possible 1) Inner 2) Left or outer It is the common field that is present in both of theĭata-set. Max etc we will discuss only about type in this blog. Syntax: | join - It will be the search query of your dataset 1 - There are many join-options like type, overwrite, It is a very important command of Splunk, which is basically used for combining the result of sub search with the main search and importantly one or more fields should be common in both the result-sets. ![]()
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