The course teaches how to refine systems data without losing fidelity by implementing strategies such as reducing, reusing, and recycling data, structuring data effectively, sampling data, and using statistics for accurate insights. The teaching method includes discussing complex systems management, user experiences, and practical strategies for managing data effectively. The course is intended for engineers and professionals looking to optimize data collection and analysis in observability infrastructures.
Overview
Syllabus
Intro
Complex systems are hard to manage.
User experiences.
User experiences marbles.
without breaking the bank?
Three strategies for taming the spew.
Reduce. Reuse. Recycle.
Store less data.
Stop writing read-never data.
First, structure your data.
One event per transaction.
Often, trimming isn't enough.
Sample your data.
Statistics to the rescue!
Count 1/N events.
Count traces together.
Don't be afraid of sample rates.
Don't believe me? Ask a data scientist.
Aggregate data.
Aggregation destroys cardinality.
Temporal correlation is weak.
Math on quantiles is misleading.
Aggregation is a last resort.
How can sampling be cheap enough?
Systems scale with load.
Reconcile using the sample rate.
How can we save the relevant events?
Normalize per-key.
Different key, different probability.
Retain errors & slow queries.
Metrics and events can be friends!
Taught by
USENIX
