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Chronosphere uses collectors to ingest data, and utilizes push and pull models of ingestion, depending on the telemetry data collected and the method of ingestion. Chronosphere provides the following control concepts to help manage your data and keep only the data that’s most important to your organization.

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Consumption concepts

In a consumption model, you create partitions, which are slices of your data that have distinct owners. Partitions provide a consistent structure for attributing usage and costs to the appropriate owners in your organization so they can isolate and control independent parts of the business. Partitions let you apply budgets, which are optional shaping policies you can assign to a partition to safeguard against runaway usage and overspending. Budgets provide flexibility to enforce accountability at the correct level of ownership. Budgets include thresholds that define which actions to take when a threshold is exceeded. To learn more about these control mechanisms, see Manage consumption.

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Capacity concepts

The capacity model is being replaced by the consumption model.
In a capacity model, metrics use quotas to assign specific percentages of your total persisted writes limit to pools of metrics. Each pool might represent teams or other logical groupings within your organization. Use metric quotas to give each pool a specific quota of your total persisted writes license, expressed either as a percentage or a value in data points per second (DPPS). To learn more about these control mechanisms, see quotas and pools.

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Trace control concepts

In both the consumption and capacity models, you can use datasets as a proactive control mechanism to organize your data and map it to named groups relevant to your organization. Datasets can either overlap or stand alone, and can be assigned to only a single business unit or service within your organization. Each dataset entity lets you define a filter to assign specific chunks of data to a particular dataset. A default, system-defined dataset exists for budgeting. You can assign the default dataset to data that isn’t explicitly assigned to any other dataset. To change the sampling rates of one or more datasets and to more effectively control your persisted data, use behaviors. This shaping mechanism lets you change sampling rates of one or more datasets without needing to write fine-grained sampling or shaping rules. To learn more about these control mechanisms, see sample your traces.