MABREK.GITHUB.IO

Anton Lebedevich's Blog

Raquo; My Top 10% Solution for Kaggle Rossman Store Sales Forecasting Competition. Raquo; Machine Learning is not BS in Monitoring. Raquo; Prototyping Long Term Time Series Storage with Kafka and Parquet. Raquo; Visual Exploration of Performance Monitoring Time Series with MDS and t-SNE. Raquo; Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA. Raquo; Spark vs. Cassandra (Cyanite) for Metric Timeseries. Raquo; Service Flapping and Load Oscillations. Mabrek (a) gmail.com.

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Anton Lebedevich's Blog | mabrek.github.io Reviews

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Raquo; My Top 10% Solution for Kaggle Rossman Store Sales Forecasting Competition. Raquo; Machine Learning is not BS in Monitoring. Raquo; Prototyping Long Term Time Series Storage with Kafka and Parquet. Raquo; Visual Exploration of Performance Monitoring Time Series with MDS and t-SNE. Raquo; Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA. Raquo; Spark vs. Cassandra (Cyanite) for Metric Timeseries. Raquo; Service Flapping and Load Oscillations. Mabrek (a) gmail.com.

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Anton Lebedevich's Blog | mabrek.github.io Reviews

https://mabrek.github.io

Raquo; My Top 10% Solution for Kaggle Rossman Store Sales Forecasting Competition. Raquo; Machine Learning is not BS in Monitoring. Raquo; Prototyping Long Term Time Series Storage with Kafka and Parquet. Raquo; Visual Exploration of Performance Monitoring Time Series with MDS and t-SNE. Raquo; Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA. Raquo; Spark vs. Cassandra (Cyanite) for Metric Timeseries. Raquo; Service Flapping and Load Oscillations. Mabrek (a) gmail.com.

INTERNAL PAGES

mabrek.github.io

1

Statistics for Monitoring: Load Testing (Tuning)

http://mabrek.github.io/blog/statistics-for-monitoring-load-testing

Statistics for Monitoring: Load Testing (Tuning). Shows how simple statistical methods can help clean obtained data and find bottlenecks for load testing. There are some important points to be aware of. First is a transient response. In practice transient response means that you need to wait until system metrics become stable after you apply load to the system. Second point is a sampling rate. The higher the sampling rate the better it is for load testing. Failure usually happens within seconds (mill...

2

Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA

http://mabrek.github.io/blog/multivariate-svd-pca

Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA. Most methods that were presented here so far are dealing with a single time series (performance metric) at a time. Now I’d like to make a quick overview of methods which allow to glance over a whole collection of time series at once. Data used here was gathered during a load test of an application which consists of several components: http server, messaging server ( RabbitMQ. In time series context SVD decomposes original set of ...

3

Prototyping Long Term Time Series Storage with Kafka and Parquet

http://mabrek.github.io/blog/kafka-parquet-timeseries

Prototyping Long Term Time Series Storage with Kafka and Parquet. Another attempt to find better storage for time series data, this time it looks quite promising. Last time I tried to switch from Graphite. Time series storage to Cyanite/Cassandra. But the attempt failed and I stayed with Whisper. Files After struggling with keeping disk IOPS sane while ingesting hi-resolution performance data I ended up putting Whisper. And do analysis. Large scale projects like Gorilla(pdf). But my values have type.

4

Spark vs. Cassandra (Cyanite) for Metric Timeseries

http://mabrek.github.io/blog/spark-cassandra-timeseries

Spark vs. Cassandra (Cyanite) for Metric Timeseries. An unsuccessful attempt to scale performance metrics processing by switching from Graphite/R to Spark/Cassandra/Cyanite. My typical workflow is to run load tests until the system fails (or discover production failure), extract data from Graphite. Files produced by Carbon. Import it into R. Graphite world’ ( Carbon. Has a great number of timeseries related libraries. Is said to be good. Is a simple app written in Clojure. Of metric names is the bottlene...

5

Machine Learning is not BS in Monitoring

http://mabrek.github.io/blog/machine-learning-is-not-bs

Machine Learning is not BS in Monitoring. Recently I came across provocatively titled Machine Learning in Monitoring is BS. And decided to reply but the response came out longer than typical comment so I posted it separately. Multiplicity of variables. It’s true that mass of metrics doesn’t make a lot of sense without context but adding context back and applying dimensionality reduction methods ( SVD. User experience. Not all machine learning methods are opaque. Deep learning. Visualization. Traditio...

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github.com

GitHub - rushter/data-science-blogs: A curated list of data science blogs

https://github.com/rushter/data-science-blogs

A curated list of data science blogs. Use Git or checkout with SVN using the web URL. Aug 3, 2016. Failed to load latest commit information. Aug 3, 2016. Jul 31, 2016. Add blog post frequency script. Aug 6, 2015. Aug 22, 2015. A curated list of data science blogs. A Blog From a Human-engineer-being http:/ www.erogol.com/. Aakash Japi http:/ aakashjapi.com/. Advanced Analytics and R http:/ advanceddataanalytics.net/. Adventures in Data Land http:/ blog.smola.org. Ahmed El Deeb https:/ medium.com/@D33B.

dieter.plaetinck.be

IT-Telemetry Google group. Trying to foster more collaboration around operational insights. · Dieter's blog

http://dieter.plaetinck.be/post/it-telemetry-google-group-collaboration-operational-insights

IT-Telemetry Google group. Trying to foster more collaboration around operational insights. December 6, 2014. The discipline of collecting infrastructure and application performance metrics, aggregation, storage, visualizations and alerting has many terms associated with it. Telemetry. Insights engineering. Operational visibility. I've seen a bunch of people present their work in advancing the state of the art in this domain:. From Anton Lebedevich's statistics for monitoring series. Or Etsy's Kale stack.

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mabrek.github.io

Anton Lebedevich's Blog

Raquo; My Top 10% Solution for Kaggle Rossman Store Sales Forecasting Competition. Raquo; Machine Learning is not BS in Monitoring. Raquo; Prototyping Long Term Time Series Storage with Kafka and Parquet. Raquo; Visual Exploration of Performance Monitoring Time Series with MDS and t-SNE. Raquo; Exploring Performance Monitoring Data with Multivariate Tools: SVD and PCA. Raquo; Spark vs. Cassandra (Cyanite) for Metric Timeseries. Raquo; Service Flapping and Load Oscillations. Mabrek (a) gmail.com.

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