High Cardinality

Never compromise on high cardinality data again.

Get deep visibility and early warnings about cardinality explosion before it happens. Control data growth with streaming aggregations and slash the operational overhead of expensive queries.

high cardinality on levitate

High Cardinality Support is a Must

With the advent of cloud-native environments and micro-services-based applications, high cardinality metrics are a reality.

Monitoring tools have strict restrictions on querying high cardinality data, robbing users of accessing their own data.

These tools also have limits on ingesting data so high cardinality data starts getting dropped beyond a limit.

These defaults are fairly pedestrian for today’s modern cloud-native applications.

Levitate’s Superior Cardinality Defaults

1MCardinality per hour per metric
20MCardinality per day
per metric

Never Lose High Cardinality Data

Metrics with very high cardinality can overflow beyond the default limits.

But Levitate makes sure that they are never dropped. They are always available for inspection so that you can take informed decisions to control them instead of losing them blindly.

Decide to drop them at the source, reduce labels, or tame the cardinality explosion using streaming aggregations.

“Levitate stood the test of scale, accuracy, and real-time monitoring with our growing business. Using High Cardinality workflows from Levitate, we accurately measured customer SLAs across dimensions such as geo region and channels. We could extract knowledge about our systems and measure customer impact proactively in real time using Levitate.”

Ranjeet Walunj (Via G2 Review)Clevertap
streaming_aggregate.yaml
#Define streaming aggregate using plain YAML
-promql:'sum by (stack, le) (http_requests_duration_seconds_bucket {(service = "pushnotifs")} [2m])'
as: pushnotifs_http_requests_duration:2m

Streaming Aggregation FTW

Pre-aggregate the most expensive queries ahead of time and drastically reduce cardinality.

Streaming aggregation, unlike recording rules, aggregates the data in real-time as it arrives, massively reducing the query overhead at runtime.

Leverage the power of PromQL to create aggregations without the complexity of pipelines.