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@Amira  Identify your exact index and sourcetypre for your data. Make sure your datamodel Cisco_SDWAN root event constraints have the same index and sourcetype. Are there events with the root even... See more...
@Amira  Identify your exact index and sourcetypre for your data. Make sure your datamodel Cisco_SDWAN root event constraints have the same index and sourcetype. Are there events with the root event constraint search? If not, your syslog data isn't being assigned the correct sourcetype/index that the app's data model expects. Also check Data Model Acceleration status Check the "Status" or "Acceleration" column. Is it enabled? Is it 100% built? - If not, Enable acceleration. If acceleration seems stuck, incomplete, or you suspect corruption - try to rebuild. Disk space summaries full? - Check your indexer disk space via the Monitoring Console (Settings > Monitoring Console > Indexing > Indexes and Volumes). If the volume holding the summaries is full, acceleration will fail. Regards, Prewin Splunk Enthusiast | Always happy to help! If this answer helped you, please consider marking it as the solution or giving a kudos/Karma. Thanks!
@Raj_Splunk_Ing  This is generally because API call normally defaults to UTC. So specify time zone in API call. If you are using Splunk python SDK, then try "tz": "America/Chicago" as search pa... See more...
@Raj_Splunk_Ing  This is generally because API call normally defaults to UTC. So specify time zone in API call. If you are using Splunk python SDK, then try "tz": "America/Chicago" as search parameter. By adding the tz parameter with your local time zone ("America/Chicago" for CST), you instruct Splunk to interpret earliest=-1d@d and latest=-0d@d relative to that timezone, making the API search behave identically to your UI search in terms of the time window. This should resolve the discrepancy in event counts Regards, Prewin Splunk Enthusiast | Always happy to help! If this answer helped you, please consider marking it as the solution or giving a kudos/Karma. Thanks!
Hello,  I am Looking for details of anyone that has successfully setup a enterprise search head cluster that is behind an AWS ALB using SAML with a Pingfederate IdP.  It seems this should be doable,... See more...
Hello,  I am Looking for details of anyone that has successfully setup a enterprise search head cluster that is behind an AWS ALB using SAML with a Pingfederate IdP.  It seems this should be doable, however there does not seem to be a lot of (or really any) details on this setup. 
Hi Rick, same user. i did use the earliest and latest in the search query itself as filters. API is using the services/export
Ah there's your problem. You assign the variable "extracted_ip_1" which then works fine within the function, but in the following phantom.save_run_data function call, it does not actually dump the va... See more...
Ah there's your problem. You assign the variable "extracted_ip_1" which then works fine within the function, but in the following phantom.save_run_data function call, it does not actually dump the value of the "extracted_ip_1" variable into the output, but rather the "code_3__extracted_ip_1" variable, which is previously set to None. You should change the phantom.save_run_data command to use the correct variable name in the value parameter: phantom.save_run_data(key="code_3:extracted_ip_1", value=json.dumps(extracted_ip_1)) Or, if you want to constrain all custom code between the "custom code" comment blocks, you can change the variable name: code_3__extracted_ip_1 = regex_extract_ipv4_3_data_extracted_ipv4[0]   Also you mentioned your data path on the input to the following block is "code_3:customer_function:extraced_ip_1", which has "customer_function" but it should have "custom_function". Not sure if this is just a typo in your post but if it exists also in your SOAR instance then it can also cause problems.
Archive/live links for conf files:   2016 talk by David Veuve: https://web.archive.org/web/20161205164708/http://conf.splunk.com/sessions/2016-sessions.html#search=David%20Veuve& Video recording:... See more...
Archive/live links for conf files:   2016 talk by David Veuve: https://web.archive.org/web/20161205164708/http://conf.splunk.com/sessions/2016-sessions.html#search=David%20Veuve& Video recording: https://conf.splunk.com/files/2016/recordings/how-to-scale-from-raw-to-tstats.mp4 Video recording archive: https://web.archive.org/web/20250601131324/https://conf.splunk.com/files/2016/recordings/how-to-scale-from-raw-to-tstats.mp4 Slides: https://conf.splunk.com/files/2016/slides/how-to-scale-from-raw-to-tstats.pdf Slides Archive: https://web.archive.org/web/20250601130416/https://conf.splunk.com/files/2016/slides/how-to-scale-from-raw-to-tstats.pdf   2017 talk again by David Veuve: https://web.archive.org/web/20171220012042/http://conf.splunk.com/sessions/2017-sessions.html#search=David%20Veuve& Video recording: https://conf.splunk.com/files/2017/recordings/searching-fast-how-to-start-using-tstats-and-other-acceleration-techniques.mp4 Video recording archive: https://web.archive.org/web/20171220012042/http://conf.splunk.com/files/2017/recordings/searching-fast-how-to-start-using-tstats-and-other-acceleration-techniques.mp4 Slides: https://conf.splunk.com/files/2017/slides/searching-fast-how-to-start-using-tstats-and-other-acceleration-techniques.pdf Slides archive: https://web.archive.org/web/20211202200036/http://conf.splunk.com/files/2017/slides/searching-fast-how-to-start-using-tstats-and-other-acceleration-techniques.pdf   2017 talk by Satoshi Kawasaki: https://web.archive.org/web/20171220012042/http://conf.splunk.com/sessions/2017-sessions.html#search=speed%20up& Recording: https://conf.splunk.com/files/2017/recordings/speed-up-your-searches.mp4 Recording archive: https://web.archive.org/web/20240122110515/https://conf.splunk.com/files/2017/recordings/speed-up-your-searches.mp4 Slides: https://conf.splunk.com/files/2017/slides/speed-up-your-searches.pdf Slides archive: https://web.archive.org/web/20250601130246/https://conf.splunk.com/files/2017/slides/speed-up-your-searches.pdf  
@Amira Have you verified this?  https://splunkbase.splunk.com/app/6657 
I'm experiencing an issue with the Cisco SD-WAN application in Splunk where the dashboards are not displaying the expected data. We have followed the official documentation step by step and are succ... See more...
I'm experiencing an issue with the Cisco SD-WAN application in Splunk where the dashboards are not displaying the expected data. We have followed the official documentation step by step and are successfully receiving both syslog and NetFlow data. However, it seems that the data model "Cisco_SDWAN" associated with the syslog data is not functioning correctly, which is likely causing the dashboards to fail. We've already performed extensive troubleshooting without success. Has anyone encountered a similar issue or can offer guidance on resolving the data model problem? Splunk Enterprise Security  Cisco Catalyst SD-WAN App for Splunk  and Cisco Catalyst SD-WAN Add-on for Splunk 
I don't think it is possible to constrain a dataset to "only intake 1 event containing each value of EventId and then exclude the rest of the events with the same EventId value." This would require t... See more...
I don't think it is possible to constrain a dataset to "only intake 1 event containing each value of EventId and then exclude the rest of the events with the same EventId value." This would require the dataset to check against a list of already-included EventId values for every new event it intakes. It would be better to do this in another way. Ideally you could change the events themselves so that they only have one event per EventID, but there are other tricks you could try, like making a search that makes summary-indexed events once per EventID while excluding all EventIDs that already exist in the destination index. Then you could set the datamodel+dataset to include events from the index of summary-indexed events.
If you suspect there's some time range discrepancy between those two searches, check their job logs. After the search is expanded as it's being dispatched to be executed, if I remember correctly it s... See more...
If you suspect there's some time range discrepancy between those two searches, check their job logs. After the search is expanded as it's being dispatched to be executed, if I remember correctly it should have the earliest and latest as epoch-based timestamps. Check if they differ. I assume you're spawning the searches from the same user, aren't you?
when i look at the _time which is pulled through API values look like below _time 2025-05-30 10:28:06.234 UTC 2025-05-30 04:48:45.178 UTC 2025-05-30 16:33:09.755 UTC 2025-05-30 14:20:23.054 UTC
when i look at the last row/record and look for _time the value it has is 2025-05-30 23:30:28.314 there is no record after this
Hi, I have this very simple splunk search query and i was able to run in splunk search portal or UI and I am using the same search query API (using the same query but in the form of encoded URL) - w... See more...
Hi, I have this very simple splunk search query and i was able to run in splunk search portal or UI and I am using the same search query API (using the same query but in the form of encoded URL) - what is the issue? I am getting total number of events as 164 in splunk portal but when i run the same query which is transted into encoded URL through python script i am getting 157 records/rows only... since this search is only for yesterday iam using earliest=-1d@d latest=-0d@d index=App001_logs sourcetype="App001_logs_st" earliest=-1d@d latest=-0d@d organization IN ("InternalApps","ExternalApps") AppclientId="ABC123" status_code=200 environment="UAT" | table _time, AppclientId,organization,environment,proxyBasePath,api_name the same exact query which is translated in encoded URL like https:// whole search query and when i run the python script in my desktop (my time zone is CST) i get only 157 records/rows I think there is something going on UTC and CST - this is what i see in splunk portal 164 events (5/30/25 12:00:00.000 AM to 5/31/25 12:00:00.000 AM) any guidance please?
{ "visualizations": { "viz_gsqlcpsd": { "type": "splunk.line", "dataSources": { "primary": "ds_xcdWhjuu" }, "title": "${sel... See more...
{ "visualizations": { "viz_gsqlcpsd": { "type": "splunk.line", "dataSources": { "primary": "ds_xcdWhjuu" }, "title": "${selected_server:-All Servers} - CPU Usage %" } }, "inputs": { "input_IAwTOhNf": { "options": { "items": [], "token": "selected_server", "defaultValue": "" }, "title": "Server Name", "type": "input.multiselect", "dataSources": { "primary": "ds_dIoNDOrf" }, "showProgressBar": true, "showLastUpdated": true, "context": {} }, "input_mj9iUMvw": { "options": { "defaultValue": "-15m,now", "token": "tr_hMOOrvcD" }, "title": "Time Range Input Title", "type": "input.timerange" } }, "layout": { "type": "grid", "globalInputs": [ "input_VtWuBSik", "input_mj9iUMvw" ], "options": { "backgroundColor": "transparent" }, "structure": [ { "item": "viz_gsqlcpsd", "type": "repeating", "repeatFor": { "input": "input_VtWuBSik" }, "position": { "x": 0, "y": 0, "w": 1200, "h": 400 } } ] }, "dataSources": { "ds_xcdWhjuu": { "type": "ds.search", "options": { "queryParameters": { "earliest": "-24h@h", "latest": "now" }, "query": "index=cto_epe_observability sourcetype=otel_host_metrics measurement=otel_system_cpu_time \r\n| search url IN($selected_server$) OR url=\"default_server\"\r\n| eval state_filter=if(match(state, \"^(idle|interrupt|nice|softirq|steal|system|user|wait)$\"), 1, 0)\r\n| where state_filter = 1\r\n| sort 0 _time url cpu state\r\n| streamstats current=f last(counter) as prev by url cpu state\r\n| eval delta = counter - prev\r\n| where delta >= 0\r\n| bin _time span=1m\r\n| eventstats sum(delta) as total by _time, url, cpu\r\n| eval percent = round((delta / total) * 100, 2)\r\n| eval url_state = url . \"_\" . state \r\n| timechart span=1m avg(percent) by url_state\r\n| foreach * [eval <<FIELD>> = round('<<FIELD>>', 2)]" }, "name": "CPU_Util_Search_1" } }, "ds_dIoNDOrf": { "type": "ds.search", "options": { "query": "index=server | dedup server|table server", "queryParameters": { "earliest": "$global_time.earliest$", "latest": "$global_time.latest$" } }, "name": "Server_Search_1" }, "title": "Test_Multi Line chart" } @kiran_panchavat Thanks for the quick response. Your understanding is right. I believe your code is static , but I want dynamic according to the query results in multi select. Here's my full code
@Sudhagar  Are you looking something like this? Attached image. I created using some dummy data with static values.  {     "title": "Static CPU Usage Charts per Host",     "visualizations": ... See more...
@Sudhagar  Are you looking something like this? Attached image. I created using some dummy data with static values.  {     "title": "Static CPU Usage Charts per Host",     "visualizations": {         "viz_host123": {             "dataSources": {                 "primary": "ds_host123"             },             "options": {                 "legendPlacement": "right",                 "xAxisTitle": "Time",                 "yAxisTitle": "CPU Usage (%)"             },             "title": "host123 - CPU Usage %",             "type": "splunk.line"         },         "viz_host456": {             "dataSources": {                 "primary": "ds_host456"             },             "options": {                 "legendPlacement": "right",                 "xAxisTitle": "Time",                 "yAxisTitle": "CPU Usage (%)"             },             "title": "host456 - CPU Usage %",             "type": "splunk.line"         },         "viz_host789": {             "dataSources": {                 "primary": "ds_host789"             },             "options": {                 "legendPlacement": "right",                 "xAxisTitle": "Time",                 "yAxisTitle": "CPU Usage (%)"             },             "title": "host789 - CPU Usage %",             "type": "splunk.line"         }     },     "dataSources": {         "ds_host123": {             "options": {                 "query": "| makeresults count=10\n| streamstats count as row\n| eval _time = relative_time(now(), \"-\" . (10 - row) . \"m\")\n| eval host=\"host123\"\n| eval state_list=split(\"user,system,idle\", \",\")\n| mvexpand state_list\n| eval state=state_list\n| eval percent=case(state==\"user\",20+random()%10,state==\"system\",10+random()%5,state==\"idle\",70+random()%10)\n| eval host_state=host.\"_\".state\n| timechart span=1m avg(percent) by host_state",                 "queryParameters": {                     "earliest": "-30m",                     "latest": "now"                 }             },             "type": "ds.search"         },         "ds_host456": {             "options": {                 "query": "| makeresults count=10\n| streamstats count as row\n| eval _time = relative_time(now(), \"-\" . (10 - row) . \"m\")\n| eval host=\"host456\"\n| eval state_list=split(\"user,system,idle\", \",\")\n| mvexpand state_list\n| eval state=state_list\n| eval percent=case(state==\"user\",20+random()%10,state==\"system\",10+random()%5,state==\"idle\",70+random()%10)\n| eval host_state=host.\"_\".state\n| timechart span=1m avg(percent) by host_state",                 "queryParameters": {                     "earliest": "-30m",                     "latest": "now"                 }             },             "type": "ds.search"         },         "ds_host789": {             "options": {                 "query": "| makeresults count=10\n| streamstats count as row\n| eval _time = relative_time(now(), \"-\" . (10 - row) . \"m\")\n| eval host=\"host789\"\n| eval state_list=split(\"user,system,idle\", \",\")\n| mvexpand state_list\n| eval state=state_list\n| eval percent=case(state==\"user\",20+random()%10,state==\"system\",10+random()%5,state==\"idle\",70+random()%10)\n| eval host_state=host.\"_\".state\n| timechart span=1m avg(percent) by host_state",                 "queryParameters": {                     "earliest": "-30m",                     "latest": "now"                 }             },             "type": "ds.search"         }     },     "layout": {         "layoutDefinitions": {             "layout_1": {                 "options": {                     "backgroundColor": "transparent"                 },                 "structure": [                     {                         "item": "viz_host123",                         "position": {                             "h": 400,                             "w": 1200,                             "x": 0,                             "y": 0                         },                         "type": "block"                     },                     {                         "item": "viz_host456",                         "position": {                             "h": 400,                             "w": 1200,                             "x": 0,                             "y": 400                         },                         "type": "block"                     },                     {                         "item": "viz_host789",                         "position": {                             "h": 400,                             "w": 1200,                             "x": 0,                             "y": 800                         },                         "type": "block"                     }                 ],                 "type": "grid"             }         },         "tabs": {             "items": [                 {                     "label": "New tab",                     "layoutId": "layout_1"                 }             ]         }     } }  
I am trying to repeat line chart for multiple host selection. Each line chart should display the cpu usage for each selected hosts separately. Here is my full source code in Dashboard studio. { ... See more...
I am trying to repeat line chart for multiple host selection. Each line chart should display the cpu usage for each selected hosts separately. Here is my full source code in Dashboard studio. { "visualizations": { "viz_gsqlcpsd": { "type": "splunk.line", "dataSources": { "primary": "ds_xcdWhjuu" }, "title": "${selected_server:-All Servers} - CPU Usage %" } }, "inputs": { "input_VtWuBSik": { "options": { "items": [ { "label": "All", "value": "*" }, { "label": "host123", "value": "host123" }, { "label": "host1234", "value": "host1234" } ], "defaultValue": [ "*" ], "token": "selected_server" }, "title": "server", "type": "input.multiselect" }, "input_mj9iUMvw": { "options": { "defaultValue": "-15m,now", "token": "tr_hMOOrvcD" }, "title": "Time Range Input Title", "type": "input.timerange" } }, "layout": { "type": "grid", "globalInputs": [ "input_VtWuBSik", "input_mj9iUMvw" ], "options": { "backgroundColor": "transparent" }, "structure": [ { "item": "viz_gsqlcpsd", "type": "repeating", "repeatFor": { "input": "input_VtWuBSik" }, "position": { "x": 0, "y": 0, "w": 1200, "h": 400 } } ] }, "dataSources": { "ds_xcdWhjuu": { "type": "ds.search", "options": { "queryParameters": { "earliest": "-24h@h", "latest": "now" }, "query": "index=host_metrics measurement=cpu_time \r\n| search url IN($selected_server$) OR url=\"default_server\"\r\n| eval state_filter=if(match(state, \"^(idle|interrupt|nice|softirq|steal|system|user|wait)$\"), 1, 0)\r\n| where state_filter = 1\r\n| sort 0 _time url cpu state\r\n| streamstats current=f last(counter) as prev by url cpu state\r\n| eval delta = counter - prev\r\n| where delta >= 0\r\n| bin _time span=1m\r\n| eventstats sum(delta) as total by _time, url, cpu\r\n| eval percent = round((delta / total) * 100, 2)\r\n| eval url_state = url . \"_\" . state \r\n| timechart span=1m avg(percent) by url_state\r\n| foreach * [eval <<FIELD>> = round('<<FIELD>>', 2)]" }, "name": "CPU_Util_Search_1" } }, "title": "Test_Multi Line chart" }  
Added a note to  the original post that indexers are having no IO issues and plenty of idle cpu. This post is for the scenario where  replication queue is full causing pipeline queues full as well b... See more...
Added a note to  the original post that indexers are having no IO issues and plenty of idle cpu. This post is for the scenario where  replication queue is full causing pipeline queues full as well but plenty of resources(cpu/IO) are still available. 
One question though - won't the parallelIngestionPipelines starve the searches of cpu cores?
Added a note to  the original post that indexers are having no IO issues and plenty of idle cpu.
@hrawat  Further Insights on the Suggestion Shared by @gcusello  It is recommended that indexers are provisioned with 12 to 48 CPU cores, each running at 2 GHz or higher, to ensure optimal perfor... See more...
@hrawat  Further Insights on the Suggestion Shared by @gcusello  It is recommended that indexers are provisioned with 12 to 48 CPU cores, each running at 2 GHz or higher, to ensure optimal performance. The disk subsystem should support at least 800 IOPS, ideally using SSDs for hot and warm buckets to handle the indexing workload efficiently. https://docs.splunk.com/Documentation/Splunk/latest/Capacity/Referencehardware  For environments still using traditional hard drives, prioritize models with higher rotational speeds, and lower average latency and seek times to maximize IOPS. For further insights, refer to this guide on Analyzing I/O Performance in Linux. Note that insufficient disk I/O is one of the most common performance bottlenecks in Splunk deployments. It is crucial to thoroughly review disk subsystem requirements during hardware planning. If the indexer's CPU resources exceed those of the standard reference architecture, it may be beneficial to tune parallelization settings to further enhance performance for specific workloads.