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The database itself could help us get universal timeseries metrics by counting encrypted (to exclude plaintext meta events like subscriptions etc.) log messages posted to stream patterns like:
Countable thing
Stream pattern
Pattern meaning
User logins
/+/users/+/logins
/<tenant>/users/<userid>/logins
Sensors observations
/+/sensors/+
/<tenant>/sensors/<sensorId>
Comments posted
/+/comments/+
/<tenant>/comments/<threadid>
SSL certificates renewed
/+/tls-certificates/+
/<tenant>/tls-certificates/<managedCertificateId>
Also we can get:
all in aggregate (over all tenants)
breakdowns by tenant
This can be done somewhat securely without needing access to encryption keys if we only use metadata (log entry # per stream), assuming streams are granular enough.
The text was updated successfully, but these errors were encountered:
The database itself could help us get universal timeseries metrics by counting encrypted (to exclude plaintext meta events like subscriptions etc.) log messages posted to stream patterns like:
/+/users/+/logins
/<tenant>/users/<userid>/logins
/+/sensors/+
/<tenant>/sensors/<sensorId>
/+/comments/+
/<tenant>/comments/<threadid>
/+/tls-certificates/+
/<tenant>/tls-certificates/<managedCertificateId>
Also we can get:
This can be done somewhat securely without needing access to encryption keys if we only use metadata (log entry # per stream), assuming streams are granular enough.
The text was updated successfully, but these errors were encountered: