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The following are the spec and example files for
Version 8.0.6 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 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 = <boolean> * 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 data model 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 search them with the 'tstats' command. * Default: false acceleration.earliest_time = <relative time string> * Specifies how far back in time the Splunk software keeps the column stores for an accelerated data model. * Also specifies when the Splunk software should create the column stores, when you do not have a setting for acceleration.backfill_time. * Specified by a relative time string. For example, "-7d" means "accelerate data within the last 7 days". * Default: empty string. * An empty string for this setting means "keep these stores for all time". acceleration.backfill_time = <relative time string> * ADVANCED: Specifies how far back in time the Splunk software creates its column stores. * ONLY set this parameter if you want to backfill less data than the retention period set by 'acceleration.earliest_time'. You might 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. * This setting MUST be set to a time that is more recent 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 can set 'acceleration.backfill_time' to "-20d" to create column stores that cover only the last 20 days. However, you should not set 'acceleration.backfill_time' to "-2y", because that setting goes farther back in time than the 'acceleration.earliest_time' setting of "-1y". * Default: empty string. * When 'acceleration.backfill_time' is unset, the Splunk software backfills fully to 'acceleration.earliest_time'. acceleration.max_time = <unsigned integer> * The maximum amount of time, in seconds, that the column store creation search can run. * NOTE: This is an approximate time. * An 'acceleration.max_time' setting of "0" indicates that there is no time limit. * Default: 3600 acceleration.poll_buckets_until_maxtime = <boolean> * In a distributed environment consisting of machines with varying amounts of free storage capacity and processing speed, summarizations might complete sooner on machines with less data and faster resources. After the summarization search is finished with all of the buckets, it is complete. The overall search runtime is determined by the slowest machine in the environment. * When this setting is set to "true", all of the machines run for "max_time" (approximately). The Splunk software repeatedly polls the buckets for new data to summarize. * Set 'poll_buckets_until_maxtime' to "true" if your data model is sensitive to summarization latency delays. * When 'poll_buckets_until_maxtime' is set to "true", the Splunk software counts the summarization search against the number of concurrent searches you can run until "max_time" is reached. * Default: false acceleration.cron_schedule = <cron-string> * This setting provides the cron schedule that the Splunk software follows when it probes or generates the column stores of this data model. * Default: */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 either of these conditions are present: * The data model search stored in its metadata no longer matches its current data model search. * The data model 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 data model search. * Default: false acceleration.max_concurrent = <unsigned integer> * The maximum number of concurrent acceleration instances for this data model that the scheduler is allowed to run. * Default: 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 <integer><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 the <integer> 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: 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. * Default: default * WARNING: Having too many searches with a non-default priority impedes 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 returns results only 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.source_guid = <string> * Use this setting to enable this data model to use a summary on a remote search head (SH) or search head cluster (SHC). You can save space and cut back on the work of building and maintaining summaries by accelerating the same data model once across multiple SC and SHC instances. * This setting specifies the GUID (globally unique identifier) of another SH or SHC. * If you are running a single instance you can find the GUID in etc/instance.cfg. * You can find the GUID for a SHC in the [shclustering] stanza in server.conf. * Set this for your data model only if you understand what you are doing! * After you set this setting: * Searches of this data model draw upon the summaries related to the provided GUID when possible. You cannot edit this data model in Splunk Web while a source GUID is specified for it. * The Splunk software ignores 'acceleration.enabled' and similar acceleration settings for your data model. * Summaries for this data model cease to be created on the indexers of the local deployment even if the model is accelerated. * All of the data models that use a particular summary should have definitions and acceleration time ranges that are very similar to each other, if not identical. * When you set this setting for this data model, its 'allow_old_summaries' setting defaults to 'true'. This happens because there may be a slight difference between the definitions of this data model and the data model at the remote SC or SHC, whose summary it will be using. * If the data model at the remote SC or SHC is changed, this data model could end up using mismatched data. * Default: not set 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 integer> * Applicable only to Hunk data models. Specifies the block size in bytes for the compression files. acceleration.hunk.file_format = [orc|parquet] * Applicable only to Hunk data models. 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 you have not specified a workload_pool, the Splunk software puts the search into a proper pool as specified by the workload rules defined in workload_rules.conf. If you have not defined a rule for this search, the Splunk software uses the default_pool defined in workload_pools.conf. ******** 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 search for the root data model dataset has been 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 = <integer> * The integer value used to calculate the sample ratio for the dataset diversity. The formula is 1 / <integer>. * 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 = <integer> * The limit of events to search over when previewing the dataset. * Default: 100000 dataset.display.currentCommand = <integer> * 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-string> * The earliest time used for the search that powers the datasummary view of the dataset. dataset.display.datasummary.latestTime = <time-string> * 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 you perform searches with this data model. * Default: empty (not set)
# Version 8.0.6 # # 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 [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 25 August, 2020
This documentation applies to the following versions of Splunk® Enterprise: 8.0.6
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