The Aggregation Rules page is a centralized location in Chronosphere Observability
Platform to review all of your rollup and
mapping rules.In the navigation menu, click Go to Admin and
then select Control > Aggregation Rules.
The Table Settings button enables you to change the displayed columns. Select a
checkbox to add a column, clear the checkbox to remove it. Clear the checkbox for a
group to remove all columns in the group.The following columns are available for display:
Name: (Default) The rule name and aggregation rule type (rollup or mapping).
Slug Name: The unique slug name of the rule.
Rule Configuration: This group contains details about the rule.
Filter: (Default) Specifies the names of the metrics or labels the rule
matches. Filters can include both Prometheus and Graphite metrics.
Label Policy: (Default) Indicates whether a rollup rule is discarding
specific label names (exclude by) as indicated by the Discard label, or
explicitly keeping specific label names (group by) as indicated by the Keep
label.
Drop Raw: (Default) Displays true when dropping raw values, or false when
retaining them.
Metric Name: (Default) The output metric’s name.
Interval: Amount of time between aggregated data points. For example, 15s
indicates a 15 second pause between each data storage attempt.
Use shaping impact data to manage your traffic shape, by highlighting the benefits
of the Control Plane and providing more transparency around the individual impact of
existing rules.The following columns explain the Shaping Impact of a rule:
Matched Writes: The number of writes per second matched or ingested
into the Observability Platform aggregation tier.
Rule Efficiency: A measure of how effective the rule is in aggregating incoming
data points, expressed as a percentage reduction in data points per seconds from
incoming to aggregated data.
Persisted Impact: The change in total persisted data points per second as a
result of the rule.
Matched Impact: The percentage of the matched writes license used by the rule.
Output Metric Utility Score: An aggregate number that indicates the relative
usefulness of the output metric, determined by the number of References,
Executions, and unique users. A higher score means users include the output
metric in their workflows. To learn more, refer to
Telemetry Usage Analyzer.
Some ways you can use shaping impact are:
Reduce persisted dataTo reduce persisted data in your system, look for rules that have low utility, but
add persisted data. Use the Least Valuable (by Persisted Write) sort to find
rules to change or remove.
Clean up ineffective rulesTo optimize rules, review rules that aren’t aggregating data effectively or don’t
match incoming data points. Sort by Least Effective to find rules to change or
remove.
Next to the search box is the sorting menu. These sort options let you view your
least or most valuable rules, determined using the underlying shaping impact metrics
and your unique needs. By default, rules sort by Least Effective.
Least Valuable (by Matched Impact): Find rules that have high matched writes,
but low output metric utility.
Most Valuable (by Matched Impact): Find rules that have low matched writes
and high output metric utility.
Least Valuable (by Persisted Write): Find rules that add to the persisted
data in the system and have low output metric utility.
Most Valuable (by Persisted Write): Find rules that conserve persisted
data in the system and have high output metric utility.
Least Effective: Find rules that either aren’t aggregating data effectively or
don’t match any incoming data points.
Most Effective: Find rules that are aggregating data effectively.
Manual: Displays when the user clicks a specific shaping impact column
to sort by.
The Shaping impact tab displays historical graphs that show how your rules
performed during the selected time frame:
Matched Writes: The raw number of incoming data points per second (DPPS) matching
the rule.
Rule Efficiency: The effectiveness of the rule in aggregating incoming data
points. This graph displays the percentage reduction in retained data for the
selected rule, and a raw number of DPPS conserved.
System Persisted Impact: The change in total persisted data points per
second as a result of the rule. This graph displays the percentage reduction in
retained overall data for the selected rule, and a raw number of DPPS
added or DPPS conserved.
Matched Impact: The percentage of the matched writes license used by the rule.
Hold the pointer over a graph to display the specific data at point in the graph.
Click and drag in the graph to narrow your search to a specific time range.Use the time selector menu to change the period displayed on the graph. Time ranges
from the Last 5 minutes up to the Last 14 days, or you can select a
Custom time range.To select a custom time range:
Select a specific date and time from the picker, or manually enter a
Start Time and End Time in the format YYYY-MM-DD HH:MM:SS.
Click Apply to update the graph, or Cancel to close the picker.
Click Refresh to reload the graphs with the most
recent data.
Click the Configuration tab to display the definition used to create the rule.
Select API, Chronoctl, or Terraform to set the type of definition
displayed.You can copy the definition to your clipboard for use with creating or updating a
rule using normal creation methods.
You can analyze the incoming data for a specific metric. Select a rule from the
list, and then click View Incoming Metrics. The
Live Telemetry Analyzer displays,
actively profiling
the selected metric or value.
Click Edit to display the
rollup rules creation dialog.
The dialog is pre-populated with the selected rule’s details. You can make changes,
and then copy the new configuration file and apply it to your system to update the
rule using Terraform or Chronoctl.
