Bayesian Modeling Of Hardware Failure Rates

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates:

This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds almost identically.

Of course, on the first day this streaming analysis is rolled out, it starts from nothing. Even after two quarters of data here, there’s still significant uncertainty about failure rates, because failures are rare.

An organization that’s transitioning this kind of offline data science to an online streaming context probably does have plenty of historical data. This is just the kind of prior belief about failure rates that can be injected as a prior distribution on failure rates!

Bayesian approaches work really well with streaming data if you think of the streams as sampling events used to update your priors to a new posterior distribution.

Related Posts

MRAppMaster Errors Running MapReduce Jobs

I have a post looking at potential causes when PolyBase MapReduce jobs are unable to find the MRAppMaster class: Let me tell you about one of my least favorite things I like to see in PolyBase: Error: Could not find or load main class org.apache.hadoop.mapreduce.v2.app.MRAppMaster This error is not limited to PolyBase but is instead […]

Read More

Database-First or Kafka-First for Event Streaming

Gwen Shapiro takes us through a scenario where database-first writes for event streaming makes the most sense: Note that the DB does quite a lot for you: it enforces serializability, locks, your logical constraints, etc. If the DB is distributed (Vitesse, Cockroach, Spanner, Yugabyte), it does even more. If you were to go Kafka-first… well, […]

Read More

Categories

February 2019
MTWTFSS
« Jan Mar »
 123
45678910
11121314151617
18192021222324
25262728