python-logreduce

Based on success logs, logreduce highlights useful text in failed logs.
The goal is to save time in finding a failure's root cause.
On average, learning run at 2000 lines per second, and
testing run at 1300 lines per seconds.
logreduce uses a model to learn successful logs and detect novelties in
failed logs:

Random words are manually removed using regular expression
Then lines are converted to a matrix of token occurrences
(using HashingVectorizer),
An unsupervised learner implements neighbor searches
(using NearestNeighbors).


          

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License: Apache-2.0
URL: https://logreduce.softwarefactory-project.io/