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Configuring aging properties of systems and business drivers

Use the Aging configuration page to view the parameters that impact the efficiency and the disk space needed by the data warehouse. You can dismiss and purge old and unused data that is associated with systems and business drivers. For more information, see Enabling-automatic-dismissal-and-purging-of-systems-and-business-drivers.

To access the Aging configuration page, under the Administration tab, click Data Warehouse and select Aging configuration. 

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The aging parameter defines the number of days that the specific metric value will be kept in the data table using its timestamp as starting date. It is important to properly tune this parameter, as it can strongly influence the space occupied in each tablespace and the availability of the data for analysis purposes. The aging configuration contains the following types of tables:

  • *_CONF_STAGE: These tables are used to temporarily load the ETL data until the data warehouse copies it to the DETAIL tables
  • *_DATA_DETAIL: These tables are used to store more detailed resolutions
  • *_DATA_DETAIL_SPLIT: These are internal tables used by near real time warehouse and are not used in analyses, models, etc.
  • *_DATA_DH: These tables are used to store data at hour resolution
  • *_DATA_D: These tables are used to store data at day resolution
  • *_DATA_MDCH: These tables are used to store data for month at day resolution
  • *_DATA_STAT: These tables are used to store system statistics

For example, consider the table SYS_DATA_DETAIL. It holds the system metrics collected at the maximum detail available, given that the duration of the samples is greater or equal to 5 minutes. According to your needs, you can decide to reduce or increase the aging parameter value for this table:

  • A small value will save space because the detailed data will be deleted after a short period of time, but will prevent you from performing detailed analyses
  • A high value will let you perform detailed analyses over more data, but the tradeoff will be a high space consumption and slower performances

The Aging parameter also regulates the data retention period of BMC Helix Capacity Optimization. As data is automatically aggregated at different time levels, when the highest level data (usually, WKLD_DATA_MDCH and SYS_DATA_MDCH) becomes old (for instance, its aging period expires) it is deleted and all information about that period of time is lost.

Moreover, the Aging parameter directly affects the maximum age of the data that can be imported in BMC Helix Capacity Optimization, that is, the latency. The latency is specific to the entity and is automatically set equal to the Aging parameter values of the SYS_DATA_DETAIL and WKLD_DATA_DETAIL tables.

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The latency setting limits the maximum age of data imported by an ETL task: if it is older than the aging policies of the SYS_DATA_DETAIL and WKLD_DATA_DETAIL tables it will be discarded by the warehousing engine and thus become useless.

For example, if the latency is set to six months, the warehousing process only takes into account the data present in a stage table whose timestamp is not older than six months. If data loaded by an ETL task refers to metrics collected before this date, they will be ignored by the warehousing engine and will not be reported.

These settings are necessary because the warehousing process manages a great volume of summarizations and establishing limits prevents the process from becoming unmanageable.

 

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BMC Helix Capacity Optimization 20.02