Curious about how you handle metrics with ephemeral labels? We have a bunch of serverless workloads emitting a lot of short-lived metrics, and this causes big cardinality spikes because the labels are ephemeral.
Do you support storing such high cardinality metrics in disk alone and not in the in-memory index?
Our approach is that you need not worry about this. Oodle will be able to handle any scale including the ephemeral metrics.
Secondly, we only look at the unique time series in an hour when computing the billing. This should help us handle ephemeral metrics a lot better compared to many disk based data stores.
Finally, we do support high cardinality since the underlying datastore is columnar. Existing customers are sending on the order of few million cardinality per metric.
re: storing high cardinality metrics in disk vs in-memory index, vijay will comment shortly.
Curious about how you handle metrics with ephemeral labels? We have a bunch of serverless workloads emitting a lot of short-lived metrics, and this causes big cardinality spikes because the labels are ephemeral.
Do you support storing such high cardinality metrics in disk alone and not in the in-memory index?
Our approach is that you need not worry about this. Oodle will be able to handle any scale including the ephemeral metrics.
Secondly, we only look at the unique time series in an hour when computing the billing. This should help us handle ephemeral metrics a lot better compared to many disk based data stores.
Finally, we do support high cardinality since the underlying datastore is columnar. Existing customers are sending on the order of few million cardinality per metric.
re: storing high cardinality metrics in disk vs in-memory index, vijay will comment shortly.