Bucket Count by State over Index

This search counts the amount of buckets per state for each index. To learn more about | dbinspect go to: http://docs.splunk.com/Documentation/Splunk/7.1.2/SearchReference/Dbinspect |dbinspect index=* | eval state=case(state==”warm” OR state==”hot”,”hot/warm”,1=1, state) | chart dc(bucketId) over index by state

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Memory Usage (MB) per Splunk Process Class

Use the following search with a column chart visualisation. It will give you a good overview of what Splunk processes use the most memory: index=_introspection sourcetype=splunk_resource_usage component=PerProcess host=* | eval process = ‘data.process’ | eval args = ‘data.args’ | eval sid = ‘data.search_props.sid’ | eval elapsed = ‘data.elapsed’ | eval mem_used = ‘data.mem_used’ | eval mem […]

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Universal Forwarder Throughput Limit Hit Count

This search counts the amount of times the UF’s throughput limit is hit. I also threw in a sparkline: index=_internal sourcetype=splunkd “current data throughput” | rex “Current data throughput \((?<kb>\S+)” | eval rate=case(kb < 500, “256”, kb > 499 AND kb < 520, “512”, kb > 520 AND kb < 770 ,”768″, kb>771 AND kb<1210, […]

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Top 10 Accessed Dashboards

Where “host=”your_sh_host”” you could specify a host, or put a wildcard * in place. index=”_internal” source=*access.log user!=”-” */app/* (host=”your_sh_host”) | rex field=referer “/en-US/app/(?<app>[^/]+)/(?<dashboard>[^?/\s]+)” | top 10 dashboard

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Universal Forwarder Throughput Statistics

This search creates a table to list all  Universal Forwarders. There is also an eval in there that classifies hosts based on their average Kbps. You can modify this as needed. index=_internal source=*metrics.log group=tcpin_connections splunk_server=* | eval host=if(isnull(hostname), sourceHost,hostname) | search (host=*) AND (host!=”(ALL)”) | eval connectionType=case(fwdType==”uf”,”univ fwder”, fwdType==”lwf”, “lightwt fwder”,fwdType==”full”, “heavy fwder”, connectionType==”cooked” or […]

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Search for all errors in splunkd

index=_internal sourcetype=”splunkd” log_level=”ERROR” | stats sparkline count dc(host) as hosts last(_raw) as last_raw_msg values(sourcetype) as sourcetype last(_time) as last_msg_time first(_time) as first_msg_time values(index) as index by punct | eval delta=round((first_msg_time-last_msg_time),2) | eval msg_per_sec=round((count/delta),2) | convert ctime(last_msg_time) ctime(first_msg_time) | table last_raw_msg count hosts sparkline msg_per_sec sourcetype index first_msg_time last_msg_time delta | sort -count

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