Metric shaping rules

Observability Platform includes various shaping rules to manipulate the data you send to Observability Platform. Use metric shaping rules to reduce the metrics you store in Observability Platform, optimize query performance, and create aliases for expressions.

Observability Platform applies shaping rules at different phases along the data journey. Drop rules are applied at ingestion time. After metrics are sent but before they’re stored, rollup rules and mapping rules are applied. Derived metrics and recording rules are then applied at query time.

Reduce stored metrics

Chronosphere Observability Platform ingests metrics from many sources. These sources can supply multiple metric types, which are generally ingested as raw data.

Although raw data might provide detailed information about a specific point in time, that data grows rapidly and is expensive to store. Use the following shaping rules to control costs by aggregating, downsampling, or dropping unneeded metric data:

• Drop rules reduce incoming data or cardinality to pare down your stored metrics across both time and labels. Use drop rules to omit incoming metrics based on labels, and only retain the metrics you need. These rules help reduce persisted data, and you can implement them quickly.

• Rollup rules downsample and aggregate metrics after they’re sent by the client but before they’re stored. Use rollup rules to reduce cardinality, downsample data, and perform basic aggregations across both time and labels. These rules are scalable, can drop raw data, and handle late-arriving data.

• Mapping rules downsample in-memory metric data on the streaming ingest path and then store any results in the database, which happens before metric aggregation. Use mapping rules to reduce stored metrics across time only.

• Recommendations identify metrics and labels with no usage or utility over the past 30 days. Apply the suggested recommendations to reduce the impact on persisted writes and persisted cardinality.

Review your metrics to determine if they’re useful because they have references, or because some of their labels have high utility (dimensions).

See Reducing cardinality for more information about this process.

Optimize query performance

Although Observability Platform provides recording rules to optimize query performance, Chronosphere recommends using rollup rules in most cases because they can reduce both cardinality and the volume of the data you persist.

Recording rules primarily improve query performance. You can execute any PromQL query and add labels, which make queries more efficient. However, recording rules impact your persisted data. If you want to change how data is presented to make it easier for users to query, consider using alias expressions, which don’t impact persisted data.

If you experience issues with late-arriving data, consider using rollup rules instead, since recording rules might impact short-term query results.

You can also use rollup rules and drop rules to fix slow queries by reducing the number of time series those queries return.

Alias expressions

Derived metrics and derived labels provide frequently used aliases for queries without impacting persisted data limits.

Derived metrics help reduce alert and dashboard complexity, replace recording rules, and provide frequently used aliases for queries.

Derived labels are non-destructive, and are specifically designed for efficient operations on time series at scale. Unlike Prometheus relabel rules, which overwrite existing data, derived labels standardize your label names without overwriting them permanently.