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downsampling time series data algorithm in Go

Downsampling Algorthim

The Golang implemtation for downsampling time series data algorthim

Background

While monitoring the online system, there could be so many metrics' time series data will be stored into the Elasticsearch or NoSQL databaser for analysis. When the time passed, storing every piece of the histrical data is not very effective way, and ithose huge data could impact the analysis performance and the cost of storage.

One of solution just simply delete the aged histrical data(e.g. only keep the latest 6 months data), but there is a solution we can compressing those data to small size with good resolution.

Here is a demo shows how to downsamping the time series data from 7500 points to 500 points.

Acknowledgement

• All of the algorthims are based on Sveinn Steinarsson's 2013 paper Downsampling Time Series for Visual Representation

• This implmentation refers to Ján Jakub Naništa's implementation by Typescript

• The test data I borrow from one of python implmentation which is here

Usage

Sveinn Steinarsson's paper mentioned 3 types of algorithm:

• Largest triangle three buckets (LTTB)
• Largest triangle one bucket (LTOB)
• Largest triangle dynamic (LTD)

You can file all of the implmentation under src/downsampling directory.

Following the below instuction to compile and run this repo.

make vget 
make 
./build/bin/main

If everything goes fine, you will see the following message

2019/09/07 18:34:42 Reading the testing data...
2019/09/07 18:34:42 Downsampling the data from 7501 to 500...
2019/09/07 18:34:42 Downsampling data - LTOB algorithm done!
2019/09/07 18:34:42 Downsampling data - LTTB algorithm done!
2019/09/07 18:34:42 Downsampling data - LTD algorithm done!
2019/09/07 18:34:42 Creating the diagram file...
2019/09/07 18:34:43 Successfully created the diagram - ..../build/data/downsampling.chart.png

You can go to the ./build/data/ directory to check the diagram and the cvs files.

The diagram picture as below

• The first black chart at the top is the raw data with 7500 points
• The second, third, and fourth respectively are LTOB, LTTB and LTD downsampling data with 500 points
• The last one at the bottom just put all together.

Further Reading

• The Billion Data Point Challenge by Uber Engineering team
• Visualize Big Data on Mobile by dduraz
• Sampling large datasets in d3fc by William Ferguson
• Downsampling algorithms by Adrian S. Tam

Enjoy it!