Splunk Enterprise version 7.2 is no longer supported as of April 30, 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. Click here for the latest version.Download topic as PDF
The following are the spec and example files for
Version 7.2.10 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 http://docs.splunk.com/Documentation/Splunk/latest/Admin/Aboutconfigurationfiles
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 data. * 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 limit. 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 environment. * 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 on. * 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" ******** 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 sample. * 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. 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 7.2.10 # # 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 [mymodel] 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 19 February, 2020
This documentation applies to the following versions of Splunk® Enterprise: 7.2.10
Feedback submitted, thanks!