Splunk® Enterprise

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Splunk Enterprise version 7.3 is no longer supported as of October 22, 2021. See the Splunk Software Support Policy for details. For information about upgrading to a supported version, see How to upgrade Splunk Enterprise.
This documentation does not apply to the most recent version of Splunk® Enterprise. For documentation on the most recent version, go to the latest release.
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The following are the spec and example files for datamodels.conf.


   Version 7.3.6

 This file contains possible attribute/value pairs for configuring
 data models.  To configure a datamodel for an app, put your custom
 datamodels.conf in $SPLUNK_HOME/etc/apps/MY_APP/local/

 For examples, see datamodels.conf.example.  You must restart Splunk to
 enable configurations.

 To learn more about configuration files (including precedence) please see
 the documentation located at


 Use the [default] stanza to define any global settings.
   * You can also define global settings outside of any stanza, at the top
     of the file.
   * Each conf file should have at most one default stanza. If there are
     multiple default stanzas, attributes are combined. In the case of
     multiple definitions of the same attribute, the last definition in the
     file wins.
   * If an attribute is defined at both the global level and in a specific
     stanza, the value in the specific stanza takes precedence.


* Each stanza represents a data model. The data model name is the stanza name.

acceleration = <bool>
* Set acceleration to true to enable automatic acceleration of this data model.
* Automatic acceleration creates auxiliary column stores for the fields
  and values in the events for this datamodel on a per-bucket basis.
* These column stores take additional space on disk, so be sure you have the
  proper amount of disk space. Additional space required depends on the
  number of events, fields, and distinct field values in the data.
* The Splunk software creates and maintains these column stores on a schedule 
  you can specify with 'acceleration.cron_schedule.' You can query
  them with the 'tstats' command.

acceleration.earliest_time = <relative-time-str>
* Specifies how far back in time the Splunk software should keep these column 
  stores (and create if acceleration.backfill_time is not set).
* Specified by a relative time string. For example, '-7d' means 'accelerate 
  data within the last 7 days.'
* Defaults to an empty string, meaning 'keep these stores for all time.'

acceleration.backfill_time = <relative-time-str>
* ADVANCED: Specifies how far back in time the Splunk software should create 
  its column stores.
* ONLY set this parameter if you want to backfill less data than the
  retention period set by 'acceleration.earliest_time'. You may want to use
  this parameter to limit your time window for column store creation in a large 
  environment where initial creation of a large set of column stores is an 
  expensive operation.
* WARNING: Do not set 'acceleration.backfill_time' to a 
  narrow time window. If one of your indexers is down for a period longer 
  than this backfill time, you may miss accelerating a window of your incoming 
* MUST be set to a more recent time than 'acceleration.earliest_time'. For
  example, if you set 'acceleration.earliest_time' to '-1y' to retain your  
  column stores for a one year window, you could set 'acceleration.backfill_time' 
  to '-20d' to create column stores that only cover the last 20 days. However, 
  you cannot set 'acceleration.backfill_time' to '-2y', because that goes 
  farther back in time than the 'acceleration.earliest_time' setting of '-1y'.
* Defaults to empty string (unset). When 'acceleration.backfill_time' is unset, 
  the Splunk software always backfills fully to 'acceleration.earliest_time.'

acceleration.max_time = <unsigned int>
* The maximum amount of time that the column store creation search is
  allowed to run (in seconds).
* Note that this is an approximate time.
* Defaults to: 3600
* An 'acceleration.max_time' setting of '0' indicates that there is no time 

acceleration.poll_buckets_until_maxtime = <bool>
* In a distributed environment that consist of heterogenous machines, summarizations might complete sooner
  on machines with less data and faster resources. After the summarization search is finished with all of 
  the buckets, the search ends. However, the overall search runtime is determined by the slowest machine in the 
* When set to "true": All of the machines run for "max_time" (approximately). 
  The buckets are polled repeatedly for new data to summarize
* Set this to true if your data model is sensitive to summarization latency delays.
* When this setting is enabled, the summarization search is counted against the 
  number of concurrent searches you can run until "max_time" is reached.
* Default: false

acceleration.cron_schedule = <cron-string>
* Cron schedule to be used to probe/generate the column stores for this
  data model.
* Defaults to: */5 * * * *

acceleration.manual_rebuilds = <bool>
* ADVANCED: When set to 'true,' this setting prevents outdated summaries from 
  being rebuilt by the 'summarize' command.
* Normally, during the creation phase, the 'summarize' command automatically 
  rebuilds summaries that are considered to be out-of-date, such as when the 
  configuration backing the data model changes.
* The Splunk software considers a summary to be outdated when:
	* The data model search stored in its metadata no longer matches its current 
	  data model search.
	* The search stored in its metadata cannot be parsed.
* NOTE: If the Splunk software finds a partial summary be outdated, it always 
  rebuilds that summary so that a bucket summary only has results corresponding to
  one datamodel search.
* Defaults to: false

acceleration.max_concurrent = <unsigned int>
* The maximum number of concurrent acceleration instances for this data
  model that the scheduler is allowed to run.
* Defaults to: 3

acceleration.allow_skew = <percentage>|<duration-specifier>
* Allows the search scheduler to randomly distribute scheduled searches more
  evenly over their periods.
* When set to non-zero for searches with the following cron_schedule values,
  the search scheduler randomly "skews" the second, minute, and hour that the
  search actually runs on:
    * * * * *     Every minute.
    */M * * * *   Every M minutes (M > 0).
    0 * * * *     Every hour.
    0 */H * * *   Every H hours (H > 0).
    0 0 * * *     Every day (at midnight).
* When set to non-zero for a search that has any other cron_schedule setting,
  the search scheduler can only randomly "skew" the second that the search runs
* The amount of skew for a specific search remains constant between edits of
  the search.
* An integer value followed by '%' (percent) specifies the maximum amount of
  time to skew as a percentage of the scheduled search period.
* Otherwise, use <int><unit> to specify a maximum duration.  Relevant units
  are: m, min, minute, mins, minutes, h, hr, hour, hrs, hours, d, day, days.
  (The <unit> may be omitted only when <int> is 0.)
* Examples:
    100% (for an every-5-minute search) = 5 minutes maximum
    50% (for an every-minute search) = 30 seconds maximum
    5m = 5 minutes maximum
    1h = 1 hour maximum
* A value of 0 disallows skew.
* Default is 0.

acceleration.schedule_priority = default | higher | highest
* Raises the scheduling priority of a search:
  + "default": No scheduling priority increase.
  + "higher": Scheduling priority is higher than other data model searches.
  + "highest": Scheduling priority is higher than other searches regardless of
    scheduling tier except real-time-scheduled searches with priority = highest
    always have priority over all other searches.
  + Hence, the high-to-low order (where RTSS = real-time-scheduled search, CSS
    = continuous-scheduled search, DMAS = data-model-accelerated search, d =
    default, h = higher, H = highest) is:
      RTSS(H) > DMAS(H) > CSS(H)
      > RTSS(h) > RTSS(d) > CSS(h) > CSS(d)
      > DMAS(h) > DMAS(d)
* The scheduler honors a non-default priority only when the search owner has
  the 'edit_search_schedule_priority' capability.
* Defaults to: default
* WARNING: Having too many searches with a non-default priority will impede the
  ability of the scheduler to minimize search starvation.  Use this setting
  only for mission-critical searches.

acceleration.allow_old_summaries = <bool>
* Sets the default value of 'allow_old_summaries' for this data model.
* Only applies to accelerated data models.
* When you use commands like 'datamodel', 'from', or 'tstats' to run a search 
  on this data model, allow_old_summaries=false causes the Splunk software to
  verify that the data model search in each bucket's summary metadata matches 
  the scheduled search that currently populates the data model summary.
  Summaries that fail this check are considered "out of date" and are not used 
  to deliver results for your events search.
* This setting helps with situations where the definition of an accelerated
  data model has changed, but the Splunk software has not yet updated its
  summaries to reflect this change. When allow_old_summaries=false for a data
  model, an event search of that data model only returns results from bucket
  summaries that match the current definition of the data model.
* If you set allow_old_summaries=true, your search can deliver results from
  bucket summaries that are out of date with the current data model definition.
* Default: false

acceleration.hunk.compression_codec = <string>
* Applicable only to Hunk Data models. Specifies the compression codec to
  be used for the accelerated orc/parquet files.

acceleration.hunk.dfs_block_size = <unsigned int>
* Applicable only to Hunk data models. Specifies the block size in bytes for
  the compression files.

acceleration.hunk.file_format = <string>
* Applicable only to Hunk data models. Valid options are "orc" and "parquet"

acceleration.workload_pool = <name of workload pool>¬
* Optional.
* Sets the workload pool to be used by this search.
* There are multiple workload pools defined in workload_pools.conf.
  Each workload pool has resource limits associated with it. For example,
  CPU, Memory, etc.
* The specific workload_pool to use is defined in workload_pools.conf.
* The search process for this search runs in the specified workload_pool.
* If workload management is enabled and a workload_pool is not specified,
  the search is put into a proper pool as specified by the workload rules defined
  in workload_rules.conf. If there is no rule defined for this search, the
  default_pool defined in workload_pools.conf is used.

******** Dataset Related Attributes ******
 These attributes affect your interactions with datasets in Splunk Web and should
 not be changed under normal conditions. Do not modify them unless you are sure you
 know what you are doing.

dataset.description = <string>
* User-entered description of the dataset entity.

dataset.type = [datamodel|table]
* The type of dataset:
  + "datamodel": An individual data model dataset.
  + "table": A special root data model dataset with a search where the dataset is 
    defined by the dataset.commands attribute.
* Default: datamodel

dataset.commands = [<object>(, <object>)*]
* When the dataset.type = "table" this stringified JSON payload is created by the 
  table editor and defines the dataset.

dataset.fields = [<string>(, <string>)*]
* Automatically generated JSON payload when dataset.type = "table" and the root 
  data model dataset's search is updated.

dataset.display.diversity = [latest|random|diverse|rare]
* The user-selected diversity for previewing events contained by the dataset:
  + "latest": search a subset of the latest events
  + "random": search a random sampling of events
  + "diverse": search a diverse sampling of events
  + "rare": search a rare sampling of events based on clustering
* Default: latest

dataset.display.sample_ratio = <int>
* The integer value used to calculate the sample ratio for the dataset diversity. 
  The formula is 1 / <int>.
* The sample ratio specifies the likelihood of any event being included in the 
* For example, if sample_ratio = 500 each event has a 1/500 chance of being 
  included in the sample result set.
* Default: 1

dataset.display.limiting = <int>
* The limit of events to search over when previewing the dataset.
* Default: 100000

dataset.display.currentCommand = <int>
* The currently selected command the user is on while editing the dataset.

dataset.display.mode = [table|datasummary]
* The type of preview to use when editing the dataset:
  + "table": show individual events/results as rows.
  + "datasummary": show field values as columns.
* Default: table

dataset.display.datasummary.earliestTime = <time-str>
* The earliest time used for the search that powers the datasummary view of 
  the dataset.

dataset.display.datasummary.latestTime = <time-str>
* The latest time used for the search that powers the datasummary view of 
  the dataset.

strict_fields = <boolean>
* Sets the default value for the 'strict_fields' argument when you use
  '| datamodel' in a search.
  * When you set 'strict_fields' to 'true', the search returns only the fields
    specified in the constraints for the data model.
  * When you set 'strict_fields' to 'false', the search returns all fields,
    including fields inherited from parent datasets and fields derived through
    search-time processes such as field extraction, eval-based field
    calculation, and lookup matching.
* You can override this setting by specifying the 'strict_fields' argument for
  a '| datamodel' search.
* This setting also applies to the 'from' command. When you use '| from' to 
  search a data model that has 'strict_fields=true', the search returns only 
  those fields that are defined in the constraints for the data model.
* Default: true

tags_whitelist = <list-of-tags>
* A comma-separated list of tag fields that the data model requires 
  for its search result sets.
* This is a search performance setting. Apply it only to data models 
  that use a significant number of tag field attributes in their 
  definitions. Data models without tag fields cannot use this setting. 
  This setting does not recognize tags used in constraint searches.
* Only the tag fields identified by tag_whitelist (and the event types 
  tagged by them) are loaded when searches are performed with this 
  data model.
* When you update tags_whitelist for an accelerated data model, 
  the Splunk software rebuilds the data model unless you have 
  enabled accleration.manual_rebuild for it.
* If tags_whitelist is empty, the Splunk software attempts to optimize 
  out unnecessary tag fields when searches are performed with this 
  data model.
* Defaults to empty.


#   Version 7.3.6
# Configuration for example datamodels

# An example of accelerating data for the 'mymodel' datamodel for the
# past five days, generating and checking the column stores every 10 minutes
acceleration = true
acceleration.earliest_time = -5d
acceleration.poll_buckets_until_maxtime = true
acceleration.cron_schedule = */10 * * * *
acceleration.hunk.compression_codec = snappy
acceleration.hunk.dfs_block_size = 134217728
acceleration.hunk.file_format = orc

Last modified on 29 May, 2020

This documentation applies to the following versions of Splunk® Enterprise: 7.3.6

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