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Practical Systems Programming in Go

Practical Systems Programming in Go

By : Mihalis Tsoukalos
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Practical Systems Programming in Go

Practical Systems Programming in Go

By: Mihalis Tsoukalos

Overview of this book

Go has become a leading language for Systems Programming thanks to its simplicity, strong concurrency model, and excellent performance. Practical Systems Programming in Go shows how to use Go beyond application development by building software that interacts with the operating system, networks, and data at scale. The book starts with a focused introduction to Go, covering the language features and standard packages most relevant to systems programming, including those handling concurrency, interfaces, testing, and runtime internals. It then moves into essential system-level topics such as file handling and I/O, UNIX signals, file systems, structured data formats, databases, and TCP/IP networking. Readers build practical command-line tools, web services, and network clients and servers along the way. In the final section, the book explores advanced applications, including writing an interpreter for a small programming language, processing time series data, building data-mining tools, and creating efficient indexes for UNIX file systems. By the end, readers will be able to design and implement robust systems software in Go for real-world environments.
Table of Contents (21 chapters)
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1
Part 1: A quick introduction to Go
6
Part 2: Systems Programming Essentials
12
Part 3: Applications
20
Index

Clustering

Time Series Clustering is an unsupervised learning technique where the objective is to partition data (time series in our case) into distinct groups or "clusters" such that sequences within the same group are highly similar to each other while being significantly different from those in other groups. Unlike classification, there are no pre-defined labels; the algorithm must discover the underlying structure of the data on its own, making it invaluable for exploratory data analysis — such as identifying common usage patterns in server logs or categorizing stock movements.

Clustering methodology

The K-Means Clustering utility organizes your time series into k distinct groups by iteratively refining the center point, or centroid, of each cluster. The process begins by randomly selecting k files to serve as the initial centroids. In the assignment phase, the program uses parallel processing to compare every time series against these centroids, assigning each file...

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Tech Concepts
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Programming languages
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Practical Systems Programming in Go
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