Projecting other aggregates
You can project any numeric metric into the future by defining the number of days of past data to analyze along with the number of days into the future you want to predict. For example, you want to predict the next 30 days of an nth percentile calculation based on the last 60 days of data. This prediction returns a value for every day for the next 30 days. In order to accomplish this, a table (projection_config) is defined in the reporting database for the projection configuration of the future values.
Projections are performed using a polynomial function, which is beneficial when data follows a curved pattern toward a maximum or minimum value, such as percentage of disk storage used over time.
The projection_config table has the following form:
- name - Name to identify the projection; e.g., bangalore_60_30, austin_95th.
- description - Up to 255 characters to describe the projection.
- nth_percentile_config_id - ID of the row in the nth_percentile_config table on which to base a projection (only used for nth percentile projections.
- days_past - Integer indicating the number of past days to use in making the calculation.
- days_future - Integer indicating the number of days into the future to make projections.
- agg_column_name - Aggregation column name.
daily_<business_hours_config.name, if any>_<meta_metric.metric_name>The name of the table to which to write data is constructed as follows:
proj_<projection_config.name><business_hours_config.name, if any><meta_metric.metric_name>
Each projection row may have one or more associated job filters and metric filters. A job filter reduces the device sample set by allowing you to create a device group and assigning devices to that group. Several job filters may be defined for a given projection. Each filter is additive and increases the number of the devices included in the sample set. If no job filters are defined, then all devices are included in the sample set.
In addition to the group filters, you may also define and associate metric filters which restrict the metrics processed in the projection. If no metric filters are defined, all metrics are processed. Each metric processed is run in a separate thread. The projection calculations use data from the raw_v2 tables. To accommodate the maximum flexibility in which data can be used, the metric name in this context is the name of the daily table without the preceding "daily_". For normal aggregated metrics, this equates to the metric name (e.g., cpu__pct). For specially aggregated (computed) data, such as business hours or nth percentile, the metric name includes the computed name (such as austin_cpu__pct).